Category: Higher Ed

Making sense of noise: introducing students to stochastic processes in order to better understand biological behaviors (and even free will).

 Biological systems are characterized by the ubiquitous roles of weak, that is, non-covalent molecular interactions, small, often very small, numbers of specific molecules per cell, and Brownian motion. These combine to produce stochastic behaviors at all levels from the molecular and cellular to the behavioral. That said, students are rarely introduced to the ubiquitous role of stochastic processes in biological systems, and how they produce unpredictable behaviors. Here I present the case that they need to be and provide some suggestions as to how it might be approached.  

Background: Three recent events combined to spur this reflection on stochasticity in biological systems, how it is taught, and why it matters. The first was an article describing an approach to introducing students to homeostatic processes in the context of the bacterial lac operon (Booth et al., 2022), an adaptive gene regulatory system controlled in part by stochastic events. The second were in-class student responses to the question, why do interacting molecules “come back apart” (dissociate).  Finally, there is the increasing attention paid to what are presented as deterministic genetic factors, as illustrated by talk by Kathryn Harden, author of the “The Genetic Lottery: Why DNA matters for social equality” (Harden, 2021).  Previous work has suggested that students, and perhaps some instructors, find the ubiquity, functional roles, and implications of stochastic, that is inherently unpredictable processes, difficult to recognize and apply. Given their practical and philosophical implications, it seems essential to introduce students to stochasticity early in their educational journey.

added 7 March 2023; Should have cited:  You & Leu (2020).

What is stochasticity and why is it important for understanding biological systems? Stochasticity results when intrinsically unpredictable events, e.g. molecular collisions, impact the behavior of a system. There are a number of drivers of stochastic behaviors. Perhaps the most obvious, and certainly the most ubiquitous in biological systems is thermal motion. The many molecules within a solution (or a cell) are moving, they have kinetic energy – the energy of motion and mass. The exact momentum of each molecule cannot, however, be accurately and completely characterized without perturbing the system (echos of Heisenberg). Given the impossibility of completely characterizing the system, we are left uncertain as to the state of the system’s components, who is bound to whom, going forward. 

Through collisions energy is exchanged between molecules.  A number of chemical processes are driven by the energy delivered through such collisions. Think about a typical chemical reaction. In the course of the reaction, atoms are rearranged – bonds are broken (a process that requires energy) and bonds are formed (a process that releases energy). Many (most) of the chemical reactions that occur in biological systems require catalysts to bring their required activation energies into the range available within the cell.   [1]  

What makes the impact of thermal motion even more critical for biological systems is that many (most) regulatory interactions and macromolecular complexes, the molecular machines discussed by Alberts (1998) are based on relatively weak, non-covalent surface-surface interactions between or within molecules. Such interactions are central to most regulatory processes, from the activation of signaling pathways to the control of gene expression. The specificity and stability of these non-covalent interactions, which include those involved in determining the three-dimensional structure of macromolecules, are directly impacted by thermal motion, and so by temperature – one reason controlling body temperature is important.  

So why are these interactions stochastic and why does it matter?  A signature property of a stochastic process is that while it may be predictable when large numbers of atoms, molecules, or interactions are involved, the behaviors of individual atoms, molecules, and interactions are not. A classic example, arising from factors intrinsic to the atom, is the decay of radioactive isotopes. While the half-life of a large enough population of a radioactive isotope is well defined, when any particular atom will decay is, in current theory, unknowable, a concept difficult for students (see Hull and Hopf, 2020). This is the reason we cannot accurately predict whether Schrȍdinger’s cat is alive or dead. The same behavior applies to the binding of a regulatory protein to a specific site on a DNA molecule and its subsequent dissociation: predictable in large populations, not-predictable for individual molecules. The situation is exacerbated by the fact that biological systems are composed of cells and cells are, typically, small, and so contain relatively few molecules of each type (Milo and Phillips, 2015). There are typically one or two copies of each gene in a cell, and these may be different from one another (when heterozygous). The expression of any one gene depends upon the binding of specific proteins, transcription factors, that act to activate or repress gene expression. In contrast to a number of other cellular proteins, “as a rule of thumb, the concentrations of such transcription factors are in the nM range, corresponding to only 1-1000 copies per cell in bacteria or 103-106 in mammalian cells” (Milo and Phillips, 2015). Moreover, while DNA binding proteins bind to specific DNA sequences with high affinity, they also bind to DNA “non-specifically” in a largely sequence independent manner with low affinity. Given that there are many more non-specific (non-functional) binding sites in the DNA than functional ones, the effective concentration of a particular transcription factor can be significantly lower than its total cellular concentration would suggest. For example, in the case of the lac repressor of the bacterium Escherichia coli (discussed further below), there are estimated to be ~10 molecules of the tetrameric lac repressor per cell, but “non-specific affinity to the DNA causes >90% of LacI copies to be bound to the DNA at locations that are not the cognate promoter site” (Milo and Phillips, 2015); at most only a few molecules are free in the cytoplasm and available to bind to specific regulatory sites.  Such low affinity binding to DNA allows proteins to undergo one-dimensional diffusion, a process that can greatly speed up the time it takes for a DNA binding protein to “find” high affinity binding sites (Stanford et al., 2000; von Hippel and Berg, 1989). Most transcription factors bind in a functionally significant manner to hundreds to thousands of gene regulatory sites per cell, often with distinct binding affinities. The effective binding affinity can also be influenced by positive and negative interactions with other transcription and accessory factors, chromatin structure, and DNA modifications. Functional complexes can take time to assemble, and once assembled can initiate multiple rounds of polymerase binding and activation, leading to a stochastic phenomena known as transcriptional bursting. An analogous process occurs with RNA-dependent polypeptide synthesis (translation). The result, particularly for genes expressed at lower levels, is that stochastic (unpredictable) bursts of transcription/translation can lead to functionally significant changes in protein levels (Raj et al., 2010; Raj and van Oudenaarden, 2008).

Figure adapted from Elowitz et al 2002

There are many examples of stochastic behaviors in biological systems. Originally noted by Novick and Weiner (1957) in their studies of the lac operon, it was clear that gene expression occurred in an all or none manner. This effect was revealed in a particularly compelling manner by Elowitz et al (2002) who used lac operon promoter elements to drive expression of transgenes encoding cyan and yellow fluorescent proteins (on a single plasmid) in E. coli.  The observed behaviors were dramatic; genetically identical cells were found to express, stochastically, one, the other, both, or neither transgenes. The stochastic expression of genes and downstream effects appear to be the source of much of the variance found in organisms with the same genotype in the same environmental conditions (Honegger and de Bivort, 2018).

Beyond gene expression, the unpredictable effects of stochastic processes can be seen at all levels of biological organization, from the biased random walk behaviors that underlie various forms of chemotaxis (e.g. Spudich and Koshland, 1976) and the search behaviors in C. elegans (Roberts et al., 2016) and other animals (Smouse et al., 2010), the noisiness in the opening of individual neuronal voltage-gated ion channels (Braun, 2021; Neher and Sakmann, 1976), and various processes within the immune system (Hodgkin et al., 2014), to variations in the behavior of individual organisms (e.g. the leafhopper example cited by Honegger and de Bivort, 2018). Stochastic events are involved in a range of “social” processes in bacteria (Bassler and Losick, 2006). Their impact serves as a form of “bet-hedging” in populations that generate phenotypic variation in a homogeneous environment (see Symmons and Raj, 2016). Stochastic events can regulate the efficiency of replication-associated error-prone mutation repair (Uphoff et al., 2016) leading to increased variation in a population, particularly in response to environmental stresses. Stochastic “choices” made by cells can be seen as questions asked of the environment, the system’s response provides information that informs subsequent regulatory decisions (see Lyon, 2015) and the selective pressures on individuals in a population (Jablonka and Lamb, 2005). Together stochastic processes introduce a non-deterministic (i.e. unpredictable) element into higher order behaviors (Murakami et al., 2017; Roberts et al., 2016).

Controlling stochasticity: While stochasticity can be useful, it also needs to be controlled. Not surprisingly then there are a number of strategies for “noise-suppression”, ranging from altering regulatory factor concentrations, the formation of covalent disulfide bonds between or within polypeptides, and regulating the activity of repair systems associated with DNA replication, polypeptide folding, and protein assembly via molecular chaperones and targeted degradation. For example, the identification of “cellular competition” effects has revealed that “eccentric cells” (sometimes, and perhaps unfortunately referred to as of “losers”) can be induced to undergo apoptosis (die) or migration in response to their “normal” neighbors (Akieda et al., 2019; Di Gregorio et al., 2016; Ellis et al., 2019; Hashimoto and Sasaki, 2020; Lima et al., 2021).

Student understanding of stochastic processes: There is ample evidence that students (and perhaps some instructors as well) are confused by or uncertain about the role of thermal motion, that is the transfer of kinetic energy via collisions, and the resulting stochastic behaviors in biological systems. As an example, Champagne-Queloz et al (2016; 2017) found that few students, even after instruction through molecular biology courses, recognize that collisions with other molecules were  responsible for the disassembly of molecular complexes. In fact, many adopt a more “deterministic” model for molecular disassembly after instruction (see part A panel figure on next page). In earlier studies, we found evidence for a similar confusion among instructors (part B of figure on the next page)(Klymkowsky et al., 2010). 

Introducing stochasticity to students: Given that understanding stochastic (random) processes can be difficult for many (e.g. Garvin-Doxas and Klymkowsky, 2008; Taleb, 2005), the question facing course designers and instructors is when and how best to help students develop an appreciation for the ubiquity, specific roles, and implications of stochasticity-dependent processes at all levels in biological systems. I would suggest that  introducing students to the dynamics of non-covalent molecular interactions, prevalent in biological systems in the context of stochastic interactions (i.e. kinetic theory) rather than a ∆G-based approach may be useful. We can use the probability of garnering the energy needed to disrupt an interaction to present concepts of binding specificity (selectivity) and stability. Developing an understanding of the formation and  disassembly of molecular interactions builds on the same logic that Albert Einstein and Ludwig Böltzman used to demonstrate the existence of atoms and molecules and the reversibility of molecular reactions (Bernstein, 2006). Moreover, as noted by Samoilov et al (2006) “stochastic mechanisms open novel classes of regulatory, signaling, and organizational choices that can serve as efficient and effective biological solutions to problems that are more complex, less robust, or otherwise suboptimal to deal with in the context of purely deterministic systems.”

The selectivity (specificity) and stability of molecular interactions can be understood from an energetic perspective – comparing the enthalpic and entropic differences between bound and unbound states. What is often missing from such discussions, aside from the fact of their inherent complexity, particularly in terms of calculating changes in entropy and exactly what is meant by energy (Cooper and Klymkowsky, 2013) is that many students enter biology classes without a robust understanding of enthalpy, entropy, or free energy (Carson and Watson, 2002).  Presenting students with a molecular  collision, kinetic theory-based mechanism for the dissociation of molecular interactions, may help them better understand (and apply) both the dynamics and specificity of molecular interactions. We can gage the strength of an interaction (the sum of the forces stabilizing an interaction) based on the amount of energy (derived from collisions with other molecules) needed to disrupt it.  The implication of student responses to relevant Biology Concepts Instrument (BCI) questions and beSocratic activities (data not shown), as well as a number of studies in chemistry, is that few students consider the kinetic/vibrational energy delivered through collisions with other molecules (a function of temperature), as key to explaining why interactions break (see Carson and Watson, 2002 and references therein).  Although this paper is 20 years old, there is little or no evidence that the situation has improved. Moreover, there is evidence that the conventional focus on mathematics-centered, free energy calculations in the absence of conceptual understanding may serve as an unnecessary barrier to the inclusion of a more socioeconomically diverse, and under-served populations of students (Ralph et al., 2022; Stowe and Cooper, 2019). 

The lac operon as a context for introducing stochasticity: Studies of the E. coli  lac operon hold an iconic place in the history of molecular biology and are often found in introductory courses, although typically presented in a deterministic context. The mutational analysis of the lac operon helped define key elements involved in gene regulation (Jacob and Monod, 1961; Monod et al., 1963). Booth et al (2022) used the lac operon as the context for their “modeling and simulation lesson”, Advanced Concepts in Regulation of the Lac Operon. Given its inherently stochastic regulation (Choi et al., 2008; Elowitz et al., 2002; Novick and Weiner, 1957; Vilar et al., 2003), the lac operon is a good place to start introducing students to stochastic processes. In this light, it is worth noting that Booth et al describes the behavior of the lac operon as “leaky”, which would seem to imply a low, but continuous level of expression, much as a leaky faucet continues to drip. As this is a peer-reviewed lesson, it seems likely that it reflects widely held mis-understandings of how stochastic processes are introduced to, and understood by students and instructors.

E. coli cells respond to the presence of lactose in growth media in a biphasic manner, termed diauxie, due to “the inhibitory action of certain sugars, such as glucose, on adaptive enzymes (meaning an enzyme that appears only in the presence of its substrate)” (Blaiseau and Holmes, 2021). When these (preferred) sugars are depleted from the media, growth slows. If lactose is present, however, growth will resume following a delay associated with the expression of the proteins encoded by the operon that enables the cell to import and metabolize lactose. Although the term homeostatic is used repeatedly by Booth et al, the lac operon is part of an adaptive, rather than a homeostatic, system. In the absence of glucose, cyclic AMP (cAMP) levels in the cell rise. cAMP binds to and activates the catabolite activator protein (CAP), encoded for by the crp gene. Activation of CAP leads to the altered expression of a number of target genes, whose products are involved in adaption to the stress associated with the absence of common and preferred metabolites. cAMP-activated CAP acts as both a transcriptional repressor and activator, “and has been shown to regulate hundreds of genes in the E. coli genome, earning it the status of “global” or “master” regulator” (Frendorf et al., 2019). It is involved in the adaptation to environmental factors, rather than maintaining the cell in a particular state (homeostasis). 

The lac operon is a classic polycistronic bacterial gene, encoding three distinct polypeptides: lacZ (β-galactosidase), lacY (β-galactoside permease), and lacA (galactoside acetyltransferase). When glucose or other preferred energy sources are present, expression of the lac operon is blocked by the inactivity of CAP. The CAP protein is a homodimer and its binding to DNA is regulated by the binding of the allosteric effector cAMP.  cAMP is generated from ATP by the enzyme adenylate cyclase, encoded by the cya gene. In the absence of glucose the enyzme encoded by the crr gene is phosphorylated and acts to activate adenylate cyclase (Krin et al., 2002).  As cAMP levels increase, cAMP binds to the CAP protein, leading to a dramatic change in its structure (↑), such that the protein’s  DNA binding domain becomes available to interact with promoter sequences (figure from Sharma et al., 2009).

Binding of activated (cAMP-bound) CAP is not, by itself sufficient to activate expression of the lac operon because of the presence of the constitutively expressed lac repressor protein, encoded for by the lacI gene. The active repressor is a tetramer, present at very low levels (~10 molecules) per cell. The lac operon contains three repressor (“operator”) binding sites; the tetrameric repressor can bind two operator sites simultaneously (upper figure → from Palanthandalam-Madapusi and Goyal, 2011). In the absence of lactose, but in the presence of cAMP-activated CAP, the operon is expressed in discrete “bursts” (Novick and Weiner, 1957; Vilar et al., 2003). Choi et al (2008) found that these burst come in two types, short and long, with the size of the burst referring to the number of mRNA molecules synthesized (bottm figure adapted from Choi et al ↑). The difference between burst sizes arises from the length of time that the operon’s repressor binding sites are unoccupied by repressor. As noted above, the tetravalent repressor protein can bind to two operator sites at the same time. When released from one site, polymerase binding and initiation produces a small number of mRNA molecules. Persistent binding to the second site means that the repressor concentration remains locally high, favoring rapid rebinding to the operator and the cessation of transcription (RNA synthesis). When the repressor releases from both operator sites, a rarer event, it is free to diffuse away and interact (non-specifically, i.e. with low affinity) with other DNA sites in the cell, leaving the lac operator sites unoccupied for a longer period of time. The number of such non-specific binding sites greatly exceeds the number (three) of specific binding sites in the operon. The result is the synthesis of a larger “burst” (number) of mRNA molecules. The average length of time that the operator  sites remain unoccupied is a function of the small number of repressor molecules present and the repressor’s low but measurable non-sequence specific binding to DNA. 

The expression of the lac operon leads to the appearance of β-galactosidase and β-galactoside permease. An integral membrane protein, β-galactoside permease enables extracellular lactose to enter the cell while cytoplasmic β-galactosidase catalyzes its breakdown and the generation of allolactone, which binds to the lac repressor protein, inhibiting its binding to operator sites, and so removing repression of transcription. In the absence of lactose, there are few if any of the proteins (β-galactosidase and β-galactoside permease) needed to activate the expression of the lac operon, so the obvious question is how, when lactose does appear in the extracellular media, does the lac operon turn on? Booth et al and the Wikipedia entry on the lac operon (accessed 29 June 2022) describe the turn on of the lac operon as “leaky” (see above). The molecular modeling studies of Vilar et al and Choi et al (which, together with Novick and Weiner, are not cited by Booth et al) indicate that the system displays distinct threshold and maintenance concentrations of lactose needed for stable lac gene expression. The term “threshold” does not occur in the Booth et al article. More importantly, when cultures are examined at the single cell level, what is observed is not a uniform increase in lac expression in all cells, as might be expected in the context of leaky expression, but more sporadic (noisy) behaviors. Increasing numbers of cells are “full on” in terms of lac operon expression over time when cultured in lactose concentrations above the operon’s activation threshold. This illustrates the distinctly different implications of a leaky versus a stochastic process in terms of their impacts on gene expression. While a leak is a macroscopic metaphor that produces a continuous, dependable, regular flow (drips), the occurrence of “bursts” of gene expression implies a stochastic (unpredictable) process ( figure from Vilar et al ↓). 

As the ubiquity and functionally significant roles of stochastic processes in biological systems becomes increasingly apparent, e.g. in the prediction of phenotypes from genotypes (Karavani et al., 2019; Mostafavi et al., 2020), helping students appreciate and understand the un-predictable, that is stochastic, aspects of biological systems becomes increasingly important. As an example, revealed dramatically through the application of single cell RNA sequencing studies, variations in gene expression between cells of the same “type” impacts organismic development and a range of behaviors. For example, in diploid eukaryotic cells is now apparent that in many cells, and for many genes, only one of the two alleles present is expressed; such “monoallelic” expression can impact a range of processes (Gendrel et al., 2014). Given that stochastic processes are often not well conveyed through conventional chemistry courses (Williams et al., 2015) or effectively integrated into, and built upon in molecular (and other) biology curricula; presenting them explicitly in introductory biology courses seems necessary and appropriate.

It may also help make sense of discussions of whether humans (and other organisms) have “free will”.  Clearly the situation is complex. From a scientific perspective we are analyzing systems without recourse to non-natural processes. At the same time, “Humans typically experience freely selecting between alternative courses of action” (Maoz et al., 2019)(Maoz et al., 2019a; see also Maoz et al., 2019b).  It seems possible that recognizing the intrinsically unpredictable nature of many biological processes (including those of the central nervous system) may lead us to conclude that whether or not free will exists is in fact a non-scientific, unanswerable (and perhaps largely meaningless) question. 

footnotes

[1]  For this discussion I will ignore entropy, a factor that figures in whether a particular reaction in favorable or unfavorable, that is whether, and the extent to which it occurs.  

Acknowledgements: Thanks to Melanie Cooper and Nick Galati for taking a look and Chhavinder Singh for getting it started. Updated 6 January 2023.

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Williams, L. C., Underwood, S. M., Klymkowsky, M. W. and Cooper, M. M. (2015). Are Noncovalent Interactions an Achilles Heel in Chemistry Education? A Comparison of Instructional Approaches. Journal of Chemical Education 92, 1979–1987.

 

Higher Education Malpractice: curving grades

If there is one thing that university faculty and administrators could do today to demonstrate their commitment to inclusion, not to mention teaching and learning over sorting and status, it would be to ban curve-based, norm-referenced grading. Many obstacles exist to the effective inclusion and success of students from underrepresented (and underserved) groups in science and related programs.  Students and faculty often, and often correctly, perceive large introductory classes as “weed out” courses preferentially impacting underrepresented students. In the life sciences, many of these courses are “out-of-major” requirements, in which students find themselves taught with relatively little regard to the course’s relevance to bio-medical careers and interests. Often such out-of-major requirements spring not from a thoughtful decision by faculty as to their necessity, but because they are prerequisites for post-graduation admission to medical or graduate school. “In-major” instructors may not even explicitly incorporate or depend upon the materials taught in these out-0f-major courses – rare is the undergraduate molecular biology degree program that actually calls on students to use calculus or a working knowledge of physics, despite the fact that such skills may be relevant in certain biological contexts – see Magnetofiction – A Reader’s Guide.  At the same time, those teaching “out of major” courses may overlook the fact that many (and sometimes most) of their students are non-chemistry, non-physics, and/or non-math majors.  The result is that those teaching such classes fail to offer a doorway into the subject matter to any but those already comfortable with it. But reconsidering the design and relevance of these courses is no simple matter.  Banning grading on a curve, on the other  hand, can be implemented overnight (and by fiat if necessary). 

 So why ban grading on a curve?  First and foremost, it would put faculty and institutions on record as valuing student learning outcomes (perhaps the best measure of effective teaching) over the sorting of students into easy-to-judge groups.  Second, there simply is no pedagogical justification for curved grading, with the possible exception of providing a kludgy fix to correct for poorly designed examinations and courses. There are more than enough opportunities to sort students based on their motivation, talent, ambition, “grit,” and through the opportunities they seek after and successfully embraced (e.g., through volunteerism, internships, and independent study projects). 

The negative impact of curving can be seen in a recent paper by Harris et al,  (Reducing achievement gaps in undergraduate general chemistry …), who report a significant difference in overall student inclusion and subsequent success based on a small grade difference between a C, which allows a student to proceed with their studies (generally as successfully as those with higher grades) and a C-minus, which requires them to retake the course before proceeding (often driving them out of the major).  Because Harris et al., analyzed curved courses, a subset of students cannot escape these effects.  And poor grades disproportionately impact underrepresented and underserved groups – they say explicitly “you do not belong” rather than “how can I help you learn”.   

Often naysayers disparage efforts to improve course design as “dumbing down” the course, rather than improving it.  In many ways this is a situation analogous to blaming patients for getting sick or not responding to treatment, rather than conducting an objective analysis of the efficacy of the treatment.  If medical practitioners had maintained this attitude, we would still be bleeding patients and accepting that more than a third are fated to die, rather than seeking effective treatments tailored to patients’ actual diseases – the basis of evidence-based medicine.  We would have failed to develop antibiotics and vaccines – indeed, we would never have sought them out. Curving grades implies that course design and delivery are already optimal, and the fate of students is predetermined because only a percentage can possibly learn the material.  It is, in an important sense, complacent quackery.

Banning grading on a curve, and labelling it for what it is – educational malpractice – would also change the dynamics of the classroom and might even foster an appreciation that a good teacher is one with the highest percentage of successful students, e.g. those who are retained in a degree program and graduate in a timely manner (hopefully within four years). Of course, such an alternative evaluation of teaching would reflect a department’s commitment to construct and deliver the most engaging, relevant, and effective educational program. Institutional resources might even be used to help departments generate more objective, instructor-independent evaluations of learning outcomes, in part to replace the current practice of student-based opinion surveys, which are often little more than measures of popularity.  We might even see a revolution in which departments compete with one another to maximize student inclusion, retention, and outcomes (perhaps even to the extent of applying pressure on the design and delivery of “out of major” required courses offered by other departments).  

“All a pipe dream” you might say, but the available data demonstrates that resources spent on rethinking course design, including engagement and relevance, can have significant effects on grades, retention, time to degree, and graduation rates.  At the risk of being labeled as self-promoting, I offer the following to illustrate the possibilities: working with Melanie Cooper at Michigan State University, we have built such courses in general and organic chemistry and documented their impact, see Evaluating the extent of a large-scale transformation in gateway science courses.

Perhaps we should be encouraging students to seek out legal representation to hold institutions (and instructors) accountable for detrimental practices, such as grading on a curve.  There might even come a time when professors and departments would find it prudent to purchase malpractice insurance if they insist on retaining and charging students for ineffective educational strategies.(1)  

Acknowledgements: Thanks to daughter Rebecca who provided edits and legal references and Melanie Cooper who inspired the idea. Educate! image from the Dorian De Long Arts & Music Scholarship site.

(1) One cannot help but wonder if such conduct could ever rise to the level of fraud. See, e.g., Bristol Bay Productions, LLC vs. Lampack, 312 P.3d 1155, 1160 (Colo. 2013) (“We have typically stated that a plaintiff seeking to prevail on a fraud claim must establish five elements: (1) that the defendant made a false representation of a material fact; (2) that the one making the representation knew it was false; (3) that the person to whom the representation was made was ignorant of the falsity; (4) that the representation was made with the intention that it be acted upon; and (5) that the reliance resulted in damage to the plaintiff.”).

Going virtual without a net

Is the coronavirus-based transition from face to face to on-line instruction yet another step to down-grading instructional quality?

transitioning education overnight

It is certainly a strange time in the world of higher education. In response to the current corona virus pandemic, many institutions have quickly, sometimes within hours and primarily by fiat, transitioned from face to face to distance (web-based) instruction. After a little confusion, it appears that laboratory courses are included as well, which certainly makes sense. While virtual laboratories can be built (see our own virtual laboratories in biology)  they typically fail to capture the social setting of a real laboratory.  More to the point, I know of no published studies that have measured the efficacy of such on-line experiences in terms of the ideas and skills students master.

Many instructors (including this one) are being called upon to carry out a radical transformation of instructional practice “on the fly.” Advice is being offered from all sides, from University administrators and technical advisors (see as an example Making Online Teaching a Success).  It is worth noting that much (all?) of this advice falls into the category of “personal empiricism”, suggestions based on various experiences but unsupported  by objective measures of educational outcomes – outcomes that include the extent of student engagement as well as clear descriptions of i) what students are expected to have mastered, ii) what they are expected to be able to do with their knowledge, and iii) what they can actually do. Again, to my knowledge there have been few if any careful comparative studies on learning outcomes achieved via face to face versus virtual teaching experiences. Part of the issue is that many studies on teaching strategies (including recent work on what has been termed “active learning” approaches) have failed to clearly define what exactly is to be learned, a necessary first step in evaluating their efficacy.  Are we talking memorization and recognition, or the ability to identify and apply core and discipline-specific ideas appropriately in novel and complex situations?

At the same time, instructors have not had practical training in using available tools (zoom, in my case) and little in the way of effective support. Even more importantly, there are few published and verified studies to inform what works best in terms of student engagement and learning outcomes. Even if there were clear “rules of thumb” in place to guide the instructor or course designer, there has not been the time or resources needed to implement these changes. The situation is not surprising given that the quality of university level educational programs rarely attracts critical analysis, or the necessary encouragement, support, and recognition needed to make it a departmental priority (see Making education matter in higher education).  It seems to me that the current situation is not unlike attempting to perform a complicated surgery after being told to watch a 3 minute youtube video. Unsurprisingly patient (student learning) outcomes may not be pretty.     

Much of what is missing from on-line instructional scenarios is the human connection, the ability of an instructor to pay attention to how students respond to the ideas presented. Typically this involves reading the facial expressions and body language of students, and through asking challenging (Socratic) questions – questions that address how the information presented can be used to generate plausible explanations or to predict the behavior of a system. These are interactions that are difficult, if not impossible to capture in an on-line setting.

While there is much to be said for active engagement/active learning strategies (see Hake 1998, Freeman et al 2014 and Theobald et al 2020), one can easily argue that all effective learning scenarios involve an instructor who is aware and responsive to students’ pre-existing knowledge. It is also important that the instructor has the willingness (and freedom) to entertain their questions, confusions, and the need for clarification (saying it a different way), or when it may be necessary to revisit important, foundational, ideas and skills – a situation that can necessitate discarding planned materials and “coaching up” students on core concepts and their application. The ability of the instructor to customize instruction “on the fly” is one of the justifications for hiring disciplinary experts in instructional positions, they (presumably) understand the conceptual foundations of the materials they are called upon to present. In its best (Socratic) form, the dialog between student and instructor drives students (and instructors) to develop a more sophisticated and metacognitive understanding of the web of ideas involved in most scientific explanations.

In the absence of an explicit appreciation of the importance of the human interactions between instructor and student, interactions already strained in the context of large enrollment courses, we are likely to find an increase in the forces driving instruction to become more and more about rote knowledge, rather than the higher order skills associated with the ability to juggle ideas, identifying those needed and those irrelevant to a specific situation.  While I have been trying to be less cynical (not a particularly easy task in the modern world), I suspect that the flurry of advice on how to carry out distance learning is more about avoiding the need to refund student fees than about improving students’ educational outcomes (see Colleges Sent Students Home. Now Will They Refund Tuition?)

A short post-script (17 April 2020): Over the last few weeks I have put together the tools to make the on-line MCDB 4650 Developmental Biology course somewhat smoother for me (and hopefully the students). I use Keynote (rather than Powerpoint) for slides; since the iPad is connected wirelessly to the project, this enables me to wander around the class room. The iOS version of Keynote enables me, and students, to draw on slides. Now that I am tethered, I rely more on pre-class beSocratic activities and the Mirroring360 application to connect my iPad to my laptop for Zoom sessions. I am back to being more interactive with the materials presented. I am also starting to pick students at random to answer questions & provide explanations (since they are quiet otherwise) – hopefully that works. Below (↓) is my set up, including a good microphone, laptop, iPad, and the newly arrived volume on Active Learning.

Gradients & Molecular Switches: a biofundamentalist perspective

Embryogenesis is based on a framework of social (cell-cell) interactions, initial and early asymmetries, and cascading cell-cell signaling and gene regulatory networks (DEVO posts one, two, & three). The result is the generation of embryonic axes, germ layers (ectoderm, mesoderm, endoderm), various organs and tissues (brains, limbs, kidneys, hearts, and such) and their characteristic cell types, their patterning, and their coordination into a functioning organism. It is well established that all animals share a common ancestor (hundreds of millions of years ago) and that a number of molecular  modules were already present in that common ancestor.  

At the same time evolutionary processes are, and need to be, flexible enough to generate the great diversity of organisms, with their various adaptations to particular life-styles. The extent of both conservation and flexibility (new genes, new mechanisms) in developmental systems is, however, surprising. Perhaps the most striking evidence for the depth of this conservation was supplied by the discovery of the organization of the Hox gene cluster in the fruit fly Drosophila and in the mouse (and other vertebrates). In both, the Hox genes are arranged and expressed in a common genomic and expression patterns. But as noted by Denis Duboule (2007) Hox gene organization is often presented in textbooks in a distorted manner (↓).

hox gene cluster variation

The Hox gene clusters of vertebrates are compact, but are split, disorganized, and even “atomized” in other types of organisms. Similarly, processes that might appear foundational, such as the role of the Bicoid gradient in the early fruit fly embryo (a standard topic in developmental biology textbooks), is in fact restricted to a small subset of flies (Stauber et al., 1999). New genes can be generated through well defined processes, such as gene duplication and divergence, or they can arise de novo out of sequence noise (Carvunis et al., 2012; Zhao et al., 2014 – see Van Oss & Carvunis 2019. De novo gene birth). Comparative genomic analyses can reveal the origins of specific adaptations (see Stauber et al., 1999).  The result is that organisms as closely related to each other as the great apes (including humans) have significant species-specific genetic differences (see Florio et al., 2018; McLean et al., 2011; Sassa, 2013 and references therein) as well as common molecular and cellular mechanisms.

A universal (?) feature of developing systems – gradients and non-linear responses: There is a predilection to find (and even more to teach) simple mechanisms that attempt to explain everything (witness the distortion of the Hox cluster, above) – a form of physics “theory of everything” envy.  But the historic nature, evolutionary plasticity, and need for regulatory robustness generally lead to complex and idiosyncratic responses in biological systems.  Biological systems are not “intelligently designed” but rather cobbled together over time through noise (mutation) and selection (Jacob, 1977)(see blog post). 
That said, a  common (universal?) developmental process appears to be the transformation of asymmetries into unambiguous cell fate decisions. Such responses are based on threshold events controlled by a range of molecular behaviors, leading to discrete gene expression states. We can approach the question of how such decisions are made from both an abstract and a concrete perspective. Here I outline my initial approach – I plan to introduce organism specific details as needed.  I start with the response to a signaling gradient, such as that found in many developmental systems, including the vertebrate spinal cord (top image Briscoe and Small, 2015) and the early Drosophila embryo (Lipshitz, 2009)(↓). gradients-decisions

bicoid gradient - lipschitz

We begin with a gradient in the concentration of a “regulatory molecule” (the regulator).  The shape of the gradient depends upon the sites and rates of synthesis, transport away from these sites, and turnover (degradation and/or inactivation). We assume, for simplicity’s sake, that the regulator directly controls the expression of target gene(s). Such a molecule binds in a sequence specific manner to regulatory sites, there could be a few or hundreds, and lead to the activation (or inhibition) of the DNA-dependent, RNA polymerase (polymerase), which generates RNA molecules complementary to one strand of the DNA. Both the binding of the regulator and the polymerase are stochastic processes, driven by diffusion, molecular collisions, and binding interactions.(1) 

Let us now consider the response of target gene(s) as a function of cell position within the gradient.  We might (naively) expect that the rate of target gene expression would be a simple function of regulator concentration. For an activator, where the gradient is high, target gene expression would be high, where the gradient concentration is low, target gene expression would be low – in between, target gene expression would be proportional to regulator concentration.  But generally we find something different, we find that the expression of target genes is non-uniform, that is there are thresholds in the gradient: on one side of the threshold concentration the target gene is completely off (not expressed), while on the other side of the threshold concentration, the target gene is fully on (maximally expressed).  The target gene responds as if it is controlled by an on-off switch. How do we understand the molecular basis for this behavior? 

Distinct mechanisms are used in different systems, but we will consider a system from the gastrointestinal bacteria E. coli that students may already be familiar with; these are the genes that enable E. coli to digest the mammalian milk sugar lactose.  They encode a protein needed to import  lactose into a bacterial cell and an enzyme needed to break lactose down so that it can be metabolized.  Given the energetic cost to synthesize these proteins, it is in the bacterium’s adaptive self interest to synthesize them only when lactose is present at sufficient concentrations in their environment.  The response is functionally similar to that associated with quorum sensing, which is also governed by threshold effects. Similarly cells respond to the concentration of regulator molecules (in a gradient) by turning on specific genes in specific domains, rather than uniformly. 

Now let us look in a little more detail at the behavior of the lactose utilization system in E. coli following an analysis by Vilar et al (2003)(2).  At an extracellular lactose concentration below the threshold, the system is off.  If we increase the extracellular lactose concentration above threshold the system turns on, the lactose permease and β-galactosidase proteins are made and lactose can enter the cell and be broken down to produce metabolizable sugars.  By looking at individual cells, we find that they transition, apparently stochastically from off to on (→), but whether they stay on depends upon the extracellular lactose concentration. We can define a concentration, the maintenance concentration, below the threshold, at which “on” cells will remain on, while “off” cells will remain off.  

The circuitry of the lactose system is well defined  (Jacob and Monod, 1961; Lewis, 2013; Monod et al., 1963)(↓).  The lacI gene encodes the lactose operon repressor protein and it is expressed constituately at a low level; it binds to sequences in the lac operon and inhibits transcription.  The lac operon itself contains three genes whose expression is regulated by a constituatively active promoter.  LacY encodes the permease while the lacZ encodes β-galactosidase.  β-galactosidase has two functions: it catalyzes the reaction that transforms lactose into allolactone and it cleaves lactose into the metabolically useful sugars glucose and galactose. Allolactone is an allosteric modulator of the Lac repressor protein; if allolactone is present, it binds to lac epressor proteins and inactivates them, allowing lac operon expression.  

The cell normally contains only ~10 lactose repressor proteins. Periodically (stochastically), even in the absence of lactose, and so its derivative allolactone, the lac operon promoter region is free of repressor proteins, and a lactose operon is briefly expressed – a few LacY and LacZ  polypeptides are synthesized (↓).  This noisy leakiness in the regulation of the lac operon allows the cell to respond if lactose happens to be present – some lactose molecules enter the cell through the permease, are converted to allolactone by β-galactosidase.  Allolactone is an allosteric effector of the lac repressor; when present it binds to and inactivates the lac repressor protein so that it no longer binds to its target sequences (the operator or “O” sites).  In the absence of repressor binding, the lac operon is expressed.  If lactose is not present, the lac operon is inhibited and lacY and LacZ disappear from the cell by turnover or growth associated dilution.     

The question of how the threshold concentration for various signal-regulated decisions is set often involves homeostatic processes that oppose the signaling response. The binding and activation of regulators can involve cooperative interactions between molecular components and both positive and negative feedback effects. 

In the case of patterning a tissue, in terms of regional responses to a signaling gradient, there can be multiple regulatory thresholds for different genes, as well as indirect effects, where the initiation of gene expression of one set of target genes impacts the sensitive expression of subsequent sets of genes.  One widely noted mechanism, known as reaction-diffusion, was suggested by the English mathematician Alan Turing (see Kondo and Miura, 2010) – it postulates a two component system. One component is an activator of gene expression, which in addition to its own various targets, positively regulates its own expression. The second component is a repressor of the first.  Both of these two regulator molecules are released by the signaling cell or cells; the repressor diffuses away from the source faster than the activator does.  The result can be a domain of target gene expression (where the concentration of activator is sufficient to escape repression), surrounded by a zone in which expression is inhibited (where repressor concentration is sufficient to inhibit the activator).  Depending upon the geometry of the system, this can result in discrete regions (dots or stripes) of primary target gene expression  (see Sheth et al., 2012).  In real systems there are often multiple gradients present; their relative orientations can produce a range of patterns.   

The point of all of this, is that when we approach a particular system – we need to consider the mechanisms involved.  Typically they are selected to produce desired phenotypes, but also to be robust in the sense that they need to produce the same patterns even if the system in which they occur is subject to perturbations, such as embryo/tissue size (due to differences in cell division / growth rates) and temperature and other environmental variables. 

note: figures returned – updated 13 November 2020.  

Footnotes:

  1. While stochastic (random) these processes can still be predictable.  A classic example involves the decay of an unstable isotope (atom), which is predictable at the population level, but unpredictable at the level of an individual atom.  Similarly, in biological systems, the binding and unbinding of molecules to one another, such as a protein transcription regulator to its target DNA sequence is stochastic but can be predictable in a large enough population.   
  2. and presented in biofundamentals ( pages 216-218). 

literature cited: 

Briscoe & Small (2015). Morphogen rules: design principles of gradient-mediated embryo patterning. Development 142, 3996-4009.

Carvunis et al  (2012). Proto-genes and de novo gene birth. Nature 487, 370.

Duboule (2007). The rise and fall of Hox gene clusters. Development 134, 2549-2560.

Florio et al (2018). Evolution and cell-type specificity of human-specific genes preferentially expressed in progenitors of fetal neocortex. eLife 7.

Jacob  (1977). Evolution and tinkering. Science 196, 1161-1166.

Jacob & Monod (1961). Genetic regulatory mechanisms in the synthesis of proteins. Journal of Molecular Biology 3, 318-356.

Kondo & Miura (2010). Reaction-diffusion model as a framework for understanding biological pattern formation. Science 329, 1616-1620.

Lewis (2013). Allostery and the lac Operon. Journal of Molecular Biology 425, 2309-2316.

Lipshitz (2009). Follow the mRNA: a new model for Bicoid gradient formation. Nature Reviews Molecular Cell Biology 10, 509.

McLean et al  (2011). Human-specific loss of regulatory DNA and the evolution of human-specific traits. Nature 471, 216-219.

Monod Changeux & Jacob (1963). Allosteric proteins and cellular control systems. Journal of Molecular Biology 6, 306-329.

Sassa (2013). The role of human-specific gene duplications during brain development and evolution. Journal of Neurogenetics 27, 86-96.

Sheth et al (2012). Hox genes regulate digit patterning by controlling the wavelength of a Turing-type mechanism. Science 338, 1476-1480.

Stauber et al (1999). The anterior determinant bicoid of Drosophila is a derived Hox class 3 gene. Proceedings of the National Academy of Sciences 96, 3786-3789.

Vilar et al (2003). Modeling network dynamics: the lac operon, a case study. J Cell Biol 161, 471-476.

Zhao et al (2014). Origin and Spread of de Novo Genes in Drosophila melanogaster Populations. Science. 343, 769-772

Aggregative & clonal metazoans: a biofundamentalist perspective

21st Century DEVO-2  In the first post in this series [link], I introduced the observation that single celled organisms can change their behaviors, often in response to social signals.  They can respond to changing environments and can differentiate from one cellular state to the another. Differentiation involves changes in which sets of genes are expressed, which polypeptides and proteins are made [previous post], where the proteins end up within the cell, and which behaviors are displayed by the organism. Differentiation enables individuals to adapt to hostile conditions and to exploit various opportunities. 

The ability of individuals to cooperate with one another, through processes such as quorum sensing, enables them to tune their responses so that they are appropriate and useful. Social interactions also makes it possible for them to produce behaviors that would be difficult or impossible for isolated individuals.  Once individual organisms learn, evolutionarily, how to cooperate, new opportunities and challenges (cheaters) emerge. There are strategies that can enable an organism to adapt to a wider range of environments, or to become highly specialized to a specific environment,  through the production of increasingly complex behaviors.  As described previously, many of these cooperative strategies can be adopted by single celled organisms, but others require a level of multicellularity.  Multicellularity can be transient – a pragmatic response to specific conditions, or it can be (if we ignore the short time that gametes exist as single cells) permanent, allowing the organism to develop the range of specialized cells types needed to build large, macroscopic organisms with complex and coordinated behaviors. In appears that various forms of multicellularity have arisen independently in a range of lineages (Bonner, 1998; Knoll, 2011). We can divide multicellularity into two distinct types, aggregative and clonal – which we will discuss in turn (1).  Aggregative (transient) multicellularity:  Once organisms had developed quorum sensing, they can monitor the density of related organisms in their environment and turn or (or off) specific genes (or sets of genes, necessary to produce a specific behavior.  While there are many variants, one model for such  a behavior is  a genetic toggle switch, in which a particular gene (or genes) can be switched on or off in response to environmental signals acting as allosteric regulators of transcription factor proteins (see Gardner et al., 2000).  Here is an example of an activity (↓) that we will consider in class to assess our understanding of the molecular processes involved.

One outcome of such a signaling system is to provoke the directional migration of amoeba and their aggregation to form the transient multicellular “slug”.  Such behaviors has been observed  in a range of normally unicellular organisms (see Hillmann et al., 2018)(↓). The classic example is  the cellular slime mold Dictyostelium discoideum (Loomis, 2014).  Under normal conditions, these

unicellular amoeboid eukaryotes migrate, eating bacteria and such. In this state, the range of an individual’s movement is restricted to short distances.  However when conditions turn hostile, specifically a lack of necessary nitrogen compounds, there is a compelling reason to abandon one environment and migrate to another, more distant that a single-celled organism could reach. This is a behavior that depends upon the presence of a sufficient density (cells/unit volume) of cells that enables them to: 1) recognize one another’s presence (through quorum sensing), 2) find each other through directed (chemotactic) migration, and 3) form a multicellular slug that can go on to differentiate. Upon differentiation about 20% of the cells differentiate (and die), forming a stalk that lifts the other ~80% of the cells into the air.  These non-stalk cells (the survivors) differentiate into spore (resistant to drying out) cells that are released into the air where they can be carried to new locations, establishing new populations.  

The process of cellular differentiation in D. discoideum has been worked out in molecular detail and involves two distinct signaling systems: the secreted pre-starvation factor (PSF) protein and cyclic AMP (cAMP).  PSF is a quorum signaling protein that also serves to activate the cell aggregation and differentiation program (FIG. ↓)

If bacteria, that is food, are present, the activity of PSF is inhibited and  cells remain in their single cell state. The key regulator of downstream aggregation and differentiation is the cAMP-dependent protein kinase PKA.  In the unicellular state, PKA activity is inhibited by PufA.  As PSF increases, while food levels decrease, YakA activity increases, inactivating PufA, leading to increased PKA activity.  Active PKA induces the synthesis of two downstream proteins, adenylate cyclase (ACA) and the cAMP receptor (CAR1). ACA catalyzes cAMP synthesis, much of which is secreted from the cell as a signaling molecule. The membrane-bound CAR1 protein acts as a receptor for autocrine (on the cAMP secreting cell) and paracrine (on neighboring cells) signaling.  The binding of cAMP to CAR1 leads to further activation of PKA, increasing cAMP synthesis and secretion – a positive feed-back loop. As cAMP levels increase, downstream genes are activated (and inhibited) leading cells to migrate toward one another, their adhesion to form a slug.  Once the slug forms and migrates to an appropriate site, the process of differentiation (and death) leading to stalk and spore formation begins. The fates of the aggregated cells is determined stochastically, but social cheaters can arise. Mutations can lead to individuals that avoid becoming stalk cells.  In the long run, if all individuals were to become cheaters, it would be impossible to form a stalk, so the purpose of social cooperation would be impossible to achieve.  In the face of environmental variation, populations invaded by cheaters are more likely to become extinct.  For our purposes the various defenses against cheaters are best left to other courses (see here if interested Strassmann et al., 2000).  

Clonal (permanent) multicellularity:  The type of multicellularity that most developmental biology courses focus on is what is termed clonal multicellularity – the organism is a clone of an original cell, the zygote, a diploid cell produced by the fusion of sperm and egg, haploid cells formed through the process of meiosis (2).  It is during meiosis that most basic genetic processes occur, that is the recombination between maternal and paternal chromosomes leading to the shuffling of alleles along a chromosome, and the independent segregation of chromosomes to form haploid gametes, gametes that are genetically distinct from those present in either parent. Once the zygote forms, subsequent cell divisions involve mitosis, with only a subset of differentiated cells, the cells of the germ line, capable of entering meiosis.  

Non-germ line, that is somatic cells, grow and divide. They interact with one another directly and through various signaling processes to produce cells with distinct patterns of gene expression, and so differentiated behaviors.  A key difference from a unicellular organism, is that the cells will (largely) stay attached to one another, or to extracellular matrix materials secreted by themselves and their neighbors.  The result is ensembles of cells displaying different specializations and behaviors.  As such cellular colonies get larger, they face a number of physical constraints – for example, cells are open non-equilibrium systems, to maintain themselves and to grow and reproduce, they need to import matter and energy from the external world. Cells also produce a range of, often toxic, waste products that need to be removed.  As the cluster of zygote-derived cells grows larger, and includes more and more cells, some cells will become internal and so cut off from necessary resources. While diffusive processes are often adequate when a cell is bathed in an aqueous solution, they are inadequate for a cell in the interior of a large cell aggregate (3).  The limits of diffusive processes necessitate other strategies for resource delivery and waste removal; this includes the formation of tubular vascular systems (such as capillaries, arteries, veins) and contractile systems (hearts and such) to pump fluids through these vessels, as well as cells specialized to process and transport a range of nutrients (such as blood cells).  As organisms get larger, their movements require contractile machines (muscle, cartilage, tendons, bones, etc) driving tails, fins, legs, wings, etc. The coordination of such motile systems involves neurons, ganglia, and brains. There is also a need to establish barriers between the insides of an organism and the outside world (skin, pulmonary, and gastrointestinal linings) and the need to protect the interior environment from invading pathogens (the immune system).  The process of developing these various systems depends upon controlling patterns of cell growth, division, and specialization (consider the formation of an arm), as well as the controlled elimination of cells (apoptosis), important in morphogenesis (forming fingers from paddle-shaped appendages), the maturation of the immune system (eliminating cells that react against self), and the wiring up, and adaptation of the nervous system. Such changes are analogous to those involved in aggregative multicellularity.      

Origins of multicellularity:  While aggregative multicellularity involves an extension of quorum sensing and social cooperation between genetically distinct, but related individuals, we can wonder whether similar drivers are responsible for clonal multicellularity.  There are a number of imaginable adaptive (evolutionary) drivers but two spring to mind: a way to avoid predators by getting bigger than the predators and as a way to produce varied structures needed to exploit various ecological niches and life styles. An example of the first type of driver of multicellularity is offered by the studies of Boraas et al  (1998). They cultured the unicellular green alga Chlorella vulgaris, together with a unicellular predator, the phagotrophic flagellated protist Ochromonas vallescia. After less than 100 generations (cell divisions), they observed the appearance of multicellular, and presumable inedible (or at least less easily edible), forms. Once selected, this trait appears to be stable, such that “colonies retained the eight-celled form indefinitely in continuous culture”.  To my knowledge, the genetic basis for this multicellularity remains to be determined.  

Cell Differentiation:  One feature of simple colonial organisms is that when dissociated into individual cells, each cell is capable of regenerating a new organism. The presence of multiple (closely related) cells in a single colony opens up the possibility of social interactions; this is distinct from the case in aggregative multicellularity, where social cooperation came first. Social cooperation within a clonal metazoan means that most cells “give up” their ability to reproduce a new organism (a process involving meiosis). Such irreversible social interactions mark the transition from a colonial organism to a true multicellular organism. As social integration increases, cells can differentiate so as to perform increasingly specialized functions, functions incompatible with cell division. Think for a moment about a human neuron or skeletal muscle cell – in both cases, cell division is no longer possible (apparently). Nevertheless, the normal functioning of such cells enhances the reproductive success of the organism as a whole – a classic example of inclusive fitness (remember heterocysts?)  Modern techniques of single cell sequencing and data analysis have now been employed to map this process of cellular differentiation in increasingly great detail, observations that will inform our later discussions (see Briggs et al., 2018 and future posts). In contrast, the unregulated growth of a cancer cell is an example of an asocial behavior, an asocial behavior that is ultimately futile, except in those rare cases (four known at this point) in which a cancer cell can move from one organism to another (Ujvari et al., 2016).  

Unicellular affordances for multicellularity:  When considering the design of a developmental biology course, we are faced with the diversity of living organisms – the basic observation that Darwin, Wallace, their progenitors and disciplinary descendants set out to solve. After all there are many millions of different types of organisms; among the multicellular eukaryotes, there are six major group : the ascomycetes and basidiomycetes fungi, the florideophyte red algae, laminarialean brown algae, embryophytic land plants and animals

(Knoll, 2011 ↑).  Our focus will be on animals. “All members of Animalia are multicellular, and all are heterotrophs (i.e., they rely directly or indirectly on other organisms for their nourishment). Most ingest food and digest it in an internal cavity.” [Mayer link].  From a macroscopic perspective, most animals have (or had at one time during their development) an anterior to posterior, that is head to tail, axis. Those that can crawl, swim, walk, or fly typically have a dorsal-ventral or back to belly axis, and some have a left-right axis as well.  

But to be clear, a discussion of the various types of animals is well beyond the scope of any introductory course in developmental biology, in part because there are 35 (assuming no more are discovered) different “types” (phyla) of animals – nicely illustrated at this website [BBC: 35 types of animals, most of whom are really weird)].  So again, our primary focus will be on one group, the vertebrates – humans are members of this group.  We will also consider experimental insights derived from studies of various “model” systems, including organisms from another metazoan group, the  ecdysozoa (organisms that shed their outer layer as they grow bigger), a group that includes fruit flies and nematode worms. 

My goal will be to ignore most of the specialized terminology found in the scholarly literature, which can rapidly turn a biology course into a vocabulary lesson and that add little to understanding of basic processes relevant to a general understanding of developmental processes (and relevant to human biology, medicine, and biotechnology). This approach is made possible by the discovery that the basic processes associated with animal (and metazoan) development are conserved. In this light, no observation has been more impactful than the discovery that the nature and organization of the genes involved in specifying the head to tail axes of the fruit fly and vertebrates (such as the mouse and human) is extremely similar in terms of genomic organization and function (Lappin et al., 2006 ↓), an observation that we will return to repeatedly.  Such molecular similarities extend to cell-cell and cell-matrix adhesion systems, systems that release and respond to various signaling molecules, controlling cell behavior and gene expression, and reflects the evolutionary conservation and the common ancestry of all animals (Brunet and King, 2017; Knoll, 2011). 

What can we know about the common ancestor of the animals?  Early on in the history of comparative cellular anatomy, the striking structural similarities between  the feeding system of choanoflagellate protozoans, a motile (microtubule-based) flagellum a surrounded by a “collar”of microfilament-based microvilli) and a structurally similar organelle in a range of multicellular organisms led to the suggestion that choanoflagellates and animals shared a common ancestor.  The advent of genomic sequencing and analysis has only strengthened this hypothesis, namely that choanoflagellates and animals form a unified evolutionary clade, the ‘Choanozoa’  (see tree↑ above)(Brunet and King, 2017).  Moreover, “many genes required for animal multicellularity (e.g., tyrosine kinases, cadherins, integrins, and extracellular matrix domains) evolved before animal origins”.  The implications is that the Choanozoan ancestor was predisposed to exploit some of the early opportunities offered by clonal multicellularity. These pre-existing affordances, together with newly arising genes and proteins (Long et al., 2013) were exploited in multiple lineages in the generation of multicellular organisms (see Knoll, 2011).

Basically to understand what happened next, some ~600 million years ago or so, we will approach the various processes involved in the shaping of animal development.  Because all types of developmental processes, including the unicellular to colonial transition, involve changes in gene expression, we will begin with the factors involved in the regulation of gene expression.  


Footnotes:
1). Please excuse the inclusive plural, but it seems appropriate in the context of what I hope will be a highly interactive course.
2). I will explicitly ignore variants as (largely) distractions, better suited for more highly specialized courses.
3). We will return to this problem when (late in the course, I think) we will discuss the properties of induced pluripotent stem cell (iPSC) derived organoids.

Literature cited:
Bonner, J. T. (1998). The origins of multicellularity. Integrative Biology: Issues, News, and Reviews: Published in Association with The Society for Integrative and Comparative Biology 1, 27-36.

Boraas, M. E., Seale, D. B. and Boxhorn, J. E. (1998). Phagotrophy by a flagellate selects for colonial prey: a possible origin of multicellularity. Evolutionary Ecology 12, 153-164.

Briggs, J. A., Weinreb, C., Wagner, D. E., Megason, S., Peshkin, L., Kirschner, M. W. and Klein, A. M. (2018). The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360, eaar5780.

Brunet, T. and King, N. (2017). The origin of animal multicellularity and cell differentiation. Developmental cell 43, 124-140.

Gardner, T. S., Cantor, C. R. and Collins, J. J. (2000). Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339-342.

Hillmann, F., Forbes, G., Novohradská, S., Ferling, I., Riege, K., Groth, M., Westermann, M., Marz, M., Spaller, T. and Winckler, T. (2018). Multiple roots of fruiting body formation in Amoebozoa. Genome biology and evolution 10, 591-606.

Knoll, A. H. (2011). The multiple origins of complex multicellularity. Annual Review of Earth and Planetary Sciences 39, 217-239.

Lappin, T. R., Grier, D. G., Thompson, A. and Halliday, H. L. (2006). HOX genes: seductive science, mysterious mechanisms. The Ulster medical journal 75, 23.

Long, M., VanKuren, N. W., Chen, S. and Vibranovski, M. D. (2013). New gene evolution: little did we know. Annual review of genetics 47, 307-333.

Loomis, W. F. (2014). Cell signaling during development of Dictyostelium. Developmental biology 391, 1-16.

Strassmann, J. E., Zhu, Y. and Queller, D. C. (2000). Altruism and social cheating in the social amoeba Dictyostelium discoideum. Nature 408, 965-967.

Ujvari, B., Gatenby, R. A. and Thomas, F. (2016). Transmissible cancers, are they more common than thought? Evolutionary applications 9, 633-634.

On teaching developmental biology in the 21st century: a biofundamentalist perspective

On teaching developmental biology and trying to decide where to start: differentiation

Having considered the content of courses in chemistry [1] and  biology [2, 3], and preparing to teach developmental biology for the first time, I find myself reflecting on how such courses might be better organized.  In my department, developmental biology (DEVO) has returned after a hiatus as the final capstone course in our required course sequence, and so offers an opportunity within which to examine what students have mastered as they head into their more specialized (personal) educational choices.  Rather than describe the design of the course that I will be teaching, since at this point I am not completely sure what will emerge, what I intend to do (in a series of posts) is to describe, topic by topic, the progression of key concepts, the observations upon which they are based, and the logic behind their inclusion.

Modern developmental biology emerged during the mid-1800s from comparative embryology [4] and was shaped by the new cell theory (the continuity of life and the fact that all organisms are composed of cells and their products) and the ability of cells to differentiate, that is, to adopt different structures and behaviors [5].  Evolutionary theory was also key.  The role of genetic variation based on mutations and selection, in the generation of divergent species from common ancestors, explained why a single, inter-connected Linnaean (hierarchical) classification system (the phylogenic tree of life →) of organisms was possible and suggested that developmental mechanisms were related to similar processes found in their various ancestors. 

So then, what exactly are the primary concepts behind developmental biology and how do they emerge from evolutionary, cell, and molecular biology?  The concept of “development” applies to any process characterized by directional changes over time.  The simplest such process would involve the progress from the end of one cell division event to the beginning of the next; cell division events provide a convenient benchmark.  In asexual species, the process is clonal, a single parent gives rise to a genetically identical (except for the occurrence of new mutations) offspring. Often there is little distinction between parent and offspring.  In sexual species, a dramatic and unambiguous benchmark involves the generation of a new and genetically distinct organism.  This “birth” event is marked by the fusion of two gametes (fertilization) to form a new diploid organism.  Typically gametes are produced by a complex cellular differentiation process (gametogenesis), ending with meiosis and the formation of haploid cells.  In multicellular organisms, it is often the case that a specific lineage of cells (which reproduce asexually), known as the germ line, produce the gametes.  The rest of the organism, the cells that do not produce gametes, is known as the soma, composed of somatic cells.   Cellular continuity remains, however, since gametes are living (albeit haploid) cells.  

It is common for the gametes that fuse to be of two different types, termed oocyte and sperm.  The larger, and generally immotile gamete type is called an oocyte and an individual that produces oocytes is termed female. The smaller, and generally motile gamete type is called a sperm; individuals that produces sperm are termed male. Where a single organism can produce both oocytes and sperm, either at the same time or sequentially, they are referred to as hermaphrodites (named after Greek Gods, the male Hermes and the female Aphrodite). Oocytes and sperm are specialized cells; their formation involves the differential expression of genes and the specific molecular mechanisms that generate the features characteristic of the two cell types.  The fusion of gametes, fertilization,  leads to a zygote, a diploid cell that (usually) develops into a new, sexually mature organism.    

An important feature of the process of fertilization is that it requires a level of social interaction, the two fusing cells (gametes) must recognize and fuse with one another.  The organisms that produce these gametes must cooperate; they need to produce gametes at the appropriate time and deliver them in such a way that they can find and recognize each other and avoid “inappropriate” interactions”.  The specificity of such interactions underlie the reproductive isolation that distinguishes one species from another.  The development of reproductive isolation emerges as an ancestral population of organisms diverges to form one or more new species.  As we will see, social interactions, and subsequent evolutionary effects, are common in the biological world.  

The cellular and molecular aspects of development involve the processes by which cells grow, replicate their genetic material (DNA replication), divide to form distinct parent-offspring or similar sibling cells, and may alter their morphology (shape), internal organization, motility, and other behaviors, such as the synthesis and secretion of various molecules, and how these cells respond to molecules released by other cells.  Developmental processes involve the expression and the control of all of these processes.

Essentially all changes in cellular behavior are associated with changes in the activities of biological molecules and the expression of genes, initiated in response to various external signaling events – fertilization itself is such a signal.  These signals set off a cascade of regulatory interactions, often leading to multiple “cell types”, specialized for specific functions (such as muscle contraction, neural and/or hormonal signaling, nutrient transport, processing, and synthesis,  etc.).  For specific parts of the organism, external or internal signals can result in a short term “adaptive” response (such as sweating or panting in response to increased internal body temperature), after which the system returns to its original state, or in the case of developing systems, to new states, characterized by stable changes in gene expression, cellular morphology, and behavior.    

Development in bacteria (and other unicellular organisms):  In most unicellular organisms, the cell division process is reasonably uneventful, the cells produced are similar to the original cell – but not always.  A well studied example is the bacterium Caulobacter crescentus (and related species) [link][link].  In cases such as this, the process of growth  leads to phenotypically different daughters.  While it makes no sense to talk about a beginning (given the continuity of life after the appearance of the last universal common ancestor or LUCA), we can start with a “swarmer” cell, characterized by the presence of a motile flagellum (a molecular machine driven by coupled chemical reactions – see past blogpost] that drives motility [figure modified from 6 ↓]. 

A swarmer will eventually settle down, loose the flagellum, and replace it with a specialized structure (a holdfast) designed to anchor the cell to a solid substrate.  As the organism grows, the holdfast develops a stalk that lifts the cell away from the substrate.  As growth continues, the end of the cell opposite the holdfast begins to differentiate (becomes different) from the holdfast end of the cell – it begins the process leading to the assembly of a new flagellar apparatus.  When reproduction (cell growth, DNA replication, and cell division) occurs, a swarmer cell is released and can swim away and colonize another area, or settle nearby.  The holdfast-anchored cell continues to grow, producing new swarmers.  This process is based on the inherent asymmetry of the system – the holdfast end of the cell is molecularly distinct from the flagellar end [see 7].

The process of swarmer cell formation in Caulobacter is an example of what we will term deterministic phenotypic switching.  Cells can also exploit molecular level noise (stochastic processes) that influence gene expression to generate phenotypic heterogeneity, different behaviors expressed by genetically identical cells within the same environment [see 8, 9].  Molecular noise arises from the random nature of molecular movements and the rather small (compared to macroscopic systems) numbers of most molecules within a cell.  Most cells contain one or two copies of any particular gene, and a similarly small number of molecular sequences involved in their regulation [10].  Which molecules are bound to which regulatory sequence, and for how long, is governed by inter-molecular surface interactions and thermally driven collisions, and is inherently noisy.  There are strategies that can suppress but not eliminate such noise [see 11].  As dramatically illustrated by Elowitz  and colleagues [8](↑), molecular level noise can produce cells with different phenotypes.  Similar processes are active in eukaryotes (including humans), and can lead to the expression of one of the two copies of a gene (mono-allelic expression) present in a diploid organism.  This can lead to effects such as haploinsufficiency and selective (evolutionary) lineage effects if the two alleles are not identical [12, 13]. Such phenotypic heterogeneity among what are often genetically identical cells is a topic that is rarely discussed (as far as I can discern) in introductory cell, molecular, or developmental biology courses [past blogpost].

The ability to switch phenotypes can be a valuable trait if an organism’s environment is subject to significant changes.  As an example, when the environment gets hostile, some bacterial cells transition from a rapidly dividing to a slow or non-dividing state.  Such “spores” can differentiate so as to render them highly resistant to dehydration and other stresses.  If changes in environment are very rapid, a population can protect itself by continually having some cells (stochastically) differentiating into spores, while others continue to divide rapidly. Only a few individuals (spores) need to survive a catastrophic environmental change to quickly re-establish the population.

Dying for others – social interactions between “unicellular” organisms:  Many students might not predict that one bacterial cell would “sacrifice” itself for the well being of others, but in fact there are a number of examples of this type of self-sacrificing behavior, known as programmed cell death, which is often a stochastic process.  An interesting example is provided by cellular specialization for photosynthesis or nitrogen fixation in cyanobacteria [see 9].  These two functions require mutually exclusive cellular environments to occur, in particular the molecular oxygen (O2) released by photosynthesis inhibits the process of nitrogen fixation.  Nevertheless, both are required for optimal growth.  The solution?  some cells differentiate into what are known as heterocysts, cells committed to nitrogen fixation (← a heterocyst in Anabaena spiroides, adapted from link), while most ”vegetative” cells continue with photosynthesis.  Heterocysts cannot divide, and eventually die – they sacrifice themselves for the benefit of their neighbors, the vegetative cells, cells that can reproduce.

The process by which the death of an individual can contribute resources that can be used to insure or enhance the survival and reproduction of surrounding individuals is an inherently social process, and is subject of social evolutionary mechanisms [14, 15][past blogpost].  Social behaviors can be selected for because the organism’s neighbors, the beneficiaries of their self-sacrifice are likely to be closely (clonally) related to themselves.  One result of the social behavior is, at the population level, an increase in one aspect of evolutionary fitness,  termed “inclusive fitness.”  

Such social behaviors can enable a subset of the population to survive various forms of environmental stress (see spore formation above).  An obvious environmental stress involves the impact of viral infection.  Recall that viruses are completely dependent upon the metabolic machinery of the infected cell to replicate. While there are a number of viral strategies, a common one is bacterial lysis – the virus replicates explosively, kills the infected cells, leading to the release of virus into the environment to infect others.  But, what if the infected cell kills itself BEFORE the virus replicates – the dying (self-sacrificing, altruistic) cell “kills” the virus (although viruses are not really alive) and stops the spread of the infection.  Typically such genetically programmed cell death responses are based on a simple two-part system, involving a long lived toxin and a short-lived anti-toxin.  When the cell is stressed, for example early during viral infection, the level of the anti-toxin can fall, leading to the activation of  the toxin. 

Other types of social behavior and community coordination (quorum effects):  Some types of behaviors only make sense when the density of organisms rises above a certain critical level.  For example,  it would make no sense for an Anabaena cell  to differentiate into a heterocyst (see above) if there are no vegetative cells nearby.  Similarly, there are processes in which a behavior of a single bacterial cell, such as the synthesis and secretion of a specific enzyme, a specific import or export machine,  or the construction of a complex, such as a DNA uptake machine, makes no sense in isolation – the secreted molecule will just diffuse away, and so be ineffective, the molecule to be imported (e.g. lactose) or exported (an antibiotic) may not be present, or there may be no free DNA to import.  However, as the concentration (organisms per volume) of bacteria increases, these behaviors can begin to make biological sense – there is DNA to eat or incorporate and the concentration of secreted enzyme can be high enough to degrade the target molecules (so they are inactivated or can be imported as food).   

So how does a bacterium determine whether it has neighbors or whether it wants to join a community of similar organisms?  After all, it does not have eyes to see. The process used is known as quorum sensing.  Each individual synthesizes and secretes a signaling molecule and a receptor protein whose activity is regulated by the binding of the signaling molecule.  Species specificity in signaling molecules and receptors insures that organisms of the same kind are talking to one another and not to other, distinct types of organisms that may be in the environment.   At low signaling molecule concentrations, such as those produced by a single bacterium in isolation, the receptor is not activated and the cell’s behavior remains unchanged.  However, as the concentration of bacteria increases, the concentration of the signal increases, leading to receptor activation.  Activation of the receptor can have a number of effects, including increased synthesis of the signal and other changes, such as movement in response to signals through regulation of flagellar and other motility systems, such a system can lead to the directed migration (aggregation) of cells [see 16].   

In addition to driving the synthesis of a common good (such as a useful extracellular molecule), social interactions can control processes such as  programmed cell death.  When the concentration of related neighbors is high, the programmed death of an individual can be beneficial, it can  lead to release of nutrients (common goods, including DNA molecules) that can be used by neighbors (relatives)[17, 18] – an increase in the probability of cell death in response to a quorum can increased in a way that increases inclusive fitness.  On the other hand,  if there are few related individuals in the neighborhood, programmed cell death “wastes” these resources, and so is likely to be suppressed (you might be able to generate a plausible mechanism that could control the probability of programmed cell death).     

As we mentioned previously with respect to spore formation, the generation of a certain percentage of “persisters” – individuals that withdraw from active growth and cell division, can enable a population to survive stressful situations, such as the presence of an antibiotic.  On the other hand, generating too many persisters may place the population at a reproductive disadvantage.  Once the antibiotic is gone, the persisters can return into active division. The ability of bacteria to generate persisters is a serious problem in treating people with infections, particularly those who stop taking their antibiotics too early [19].  

Of course, as in any social system, the presumption of cooperation (expending energy to synthesize the signal, sacrificing oneself for others) can open the system to cheaters [blogpost].  All such “altruistic” behaviors are vulnerable to cheaters.*  For example, a cheater that avoids programmed cell death (for example due to an inactivating mutation that effects the toxin molecule involved) will come to take over the population.  The downside, for the population, is that if cheaters take over,  the population is less likely to survive the environmental events that the social behavior was evolve to address.  In response to the realities of cheating, social organisms adopt various social-validation and policing systems [see 20 as an example]; we see this pattern of social cooperation, cheating, and social defense mechanism throughout the biological world. 

Follow-on posts:

footnotes:

* Such as people who fail to pay their taxes or disclose their tax returns.

literature cited: 

1. Cooper, M.M. and M.W. Klymkowsky, Chemistry, life, the universe, and everything: a new approach to general chemistry, and a model for curriculum reform. J. Chem. Educ. 2013. 90: 1116-1122 & Cooper, M. M., R. Stowe, O. Crandell and M. W. Klymkowsky. Organic Chemistry, Life, the Universe and Everything (OCLUE): A Transformed Organic Chemistry Curriculum. J. Chem. Educ. 2019. 96: 1858-1872.

2. Klymkowsky, M.W., Teaching without a textbook: strategies to focus learning on fundamental concepts and scientific process. CBE Life Sci Educ, 2007. 6: 190-3.

3. Klymkowsky, M.W., J.D. Rentsch, E. Begovic, and M.M. Cooper, The design and transformation of Biofundamentals: a non-survey introductory evolutionary and molecular biology course. LSE Cell Biol Edu, 2016. pii: ar70.

4. Arthur, W., The emerging conceptual framework of evolutionary developmental biology. Nature, 2002. 415:  757.

5. Wilson, E.B., The cell in development and heredity. 1940.

6. Jacobs‐Wagner, C., Regulatory proteins with a sense of direction: cell cycle signalling network in Caulobacter. Molecular microbiology, 2004. 51:7-13.

7. Hughes, V., C. Jiang, and Y. Brun, Caulobacter crescentus. Current biology: CB, 2012. 22:R507.

8. Elowitz, M.B., A.J. Levine, E.D. Siggia, and P.S. Swain, Stochastic gene expression in a single cell. Science, 2002. 297:1183-6.

9. Balázsi, G., A. van Oudenaarden, and J.J. Collins, Cellular decision making and biological noise: from microbes to mammals. Cell, 2011. 144: 910-925.

10. Fedoroff, N. and W. Fontana, Small numbers of big molecules. Science, 2002. 297:1129-1131.

11. Lestas, I., G. Vinnicombe, and J. Paulsson, Fundamental limits on the suppression of molecular fluctuations. Nature, 2010. 467:174-178.

12. Zakharova, I.S., A.I. Shevchenko, and S.M. Zakian, Monoallelic gene expression in mammals. Chromosoma, 2009. 118:279-290.

13. Deng, Q., D. Ramsköld, B. Reinius, and R. Sandberg, Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science, 2014. 343: 193-196.

14. West, S.A., A.S. Griffin, A. Gardner, and S.P. Diggle, Social evolution theory for microorganisms. Nature reviews microbiology, 2006. 4:597.

15. Bourke, A.F.G., Principles of Social Evolution. Oxford series in ecology and evolution. 2011, Oxford: Oxford University Press.

16. Park, S., P.M. Wolanin, E.A. Yuzbashyan, P. Silberzan, J.B. Stock, and R.H. Austin, Motion to form a quorum. Science, 2003. 301:188-188.

17. West, S.A., S.P. Diggle, A. Buckling, A. Gardner, and A.S. Griffin, The social lives of microbes. Annual Review of Ecology, Evolution, and Systematics, 2007: 53-77.

18. Durand, P.M. and G. Ramsey, The Nature of Programmed Cell Death. Biological Theory, 2018:  1-12.

19. Fisher, R.A., B. Gollan, and S. Helaine, Persistent bacterial infections and persister cells. Nature Reviews Microbiology, 2017. 15:453.

20. Queller, D.C., E. Ponte, S. Bozzaro, and J.E. Strassmann, Single-gene greenbeard effects in the social amoeba Dictyostelium discoideum. Science, 2003. 299: 105-106.

On teaching genetics, social evolution and understanding the origins of racism

Links between genetics and race crop up periodically in the popular press (link; link), but the real, substantive question, and the topic of a number of recent essays (see Saletan. 2018a. Stop Talking About Race and IQ) is whether the idea of “race” as commonly understood, and used by governments to categorize people (link), makes scientific sense.  More to the point, do biology educators have an unmet responsibility to modify and extend their materials and pedagogical approaches to address the non-scientific, often racist, implications of racial characterizations.  Such questions are complicated by a social geneticssecond factor, independent of whether the term race has any useful scientific purpose, namely to help students understand the biological (evolutionary) origins of racism itself, together with the stressors that lead to its periodic re-emergence as a socio-political factor. In times of social stress, reactions to strangers (others) identified by variations in skin color or overt religious or cultural signs (dress), can provoke hostility against those perceived to be members of a different social group.  As far as I can tell, few in the biology education community, which includes those involved in generating textbooks, organizing courses and curricula, or the design, delivery, and funding of various public science programs, including PBS’s NOVA, the science education efforts of HHMI and other private foundations, and programs such as Science Friday on public radio, directly address the roots of racism, roots associated with biological processes such as the origins and maintenance of multicellularity and other forms of social organization among organisms, involved in coordinating their activities and establishing defenses against social cheaters and processes such as cancer, in an organismic context (1).  These established defense mechanisms can, if not recognized and understood, morph into reflexive and unjustified intolerance, hostility toward, and persecution of various “distinguishable others.”  I will consider both questions, albeit briefly, here. 


Two factors have influenced my thinking about these questions.  The first involves the design of the biofundamentals text/course and its extension to include topics in genetics (2).  This involved thinking about what is commonly taught in genetics, what is critical for students to know going forward (and by implication what is not), and where materials on genetic processes best fit into a molecular biology curriculum (3).  While engaged in such navel gazing there came an email from Malcolm Campbell describing student responses to the introduction of a chapter section on race and racism in his textbook Integrating Concepts in Biology.  The various ideas of race, the origins of racism, and the periodic appearance of anti-immigrant, anti-religious and racist groups raise important questions – how best to clarify what is an undeniable observation, that different, isolated, sub-populations of a species can be distinguished from one another (see quote from Ernst Mayr’s 1994 “Typological versus Population thinking” →), from the deeper biological reality, that at the level of the individual these differences are meaningless. In what I think is an interesting way, the idea that people can be meaningfully categorized as different types of various platonic ideals (for example, as members of one race or the other) based on anatomical / linguistic differences between once distinct sub-populations of humans is similar to the dichotomy between common wisdom (e.g. that has influenced people’s working understanding of the motion of objects) and the counter-intuitive nature of empirically established scientific ideas (e.g. Newton’s laws and the implications of Einstein’s theory of general relativity).  What appears on the surface to be true but in fact is not.  In this specific case, there is a pressure toward what Mayr terms “typological” thinking, in which we class people into idealized (platonic) types or races (↓).   

As pointed out most dramatically, and repeatedly, by Mayr (1985; 1994; 2000), and supported by the underlying commonality of molecular biological mechanisms and the continuity of life, stretching back to the last universal common ancestor, there are only individuals who are members of various populations that have experienced various degrees of separation from one another.  In many cases, these populations have diverged and, through geographic, behavioral, and structure adaptations driven by natural, social, and sexual selection together with the effects of various events, some non-adaptive, such as bottlenecks, founder effects, and genetic drift, may eventually become reproductively isolated from one another, forming new species.  An understanding of evolutionary principles and molecular mechanisms transforms biology from a study of non-existent types to a study of populations with their origins in common, sharing a single root – the last universal common ancestor (LUCA).   Over the last ~200,000 years the movement of humans first within Africa and then across the planet  has been impressive (→).  These movements have been accompanied by the fragmentation of human populations. Campbell and Tishkoff (2008) identified 13 distinct ancestral African populations while Busby et al (2016) recognized 48 sub-saharan population groups.  The fragmentation of the human population is being reversed (or rather rendered increasingly less informative) by the effects of migration and extensive intermingling (↓).   

    Ideas, such as race (and in a sense species), try to make sense of the diversity of the many different types of organisms we observe. They are based on a form of essentialist or typological thinking – thinking that different species and populations are completely different “kinds” of objects, rather than individuals in a population connected historically to all other living things. Race is a more pernicious version of this illusion, a pseudo-scientific, political and ideological idea that postulates that humans come  in distinct, non-overlapping types (quote ↓ again, from Mayr).  Such a weird idea underlies various illogical and often contradictory legal “rules” by which a person’s “race” is determined.  

Given the reality of the individual and the unreality of race, racial profiling (see Satel,
2002) can lead to serious medical mistakes, as made clear in the essays by Acquaviva & Mintz (2010) “Are We Teaching Racial Profiling?”,  Yudell et al  (2016) “Taking Race out of Human Genetics”, and Donovan (2014) “The impact of the hidden curriculum”. 

The idea of race as a type fails to recognize the dynamics of the genome over time.  If possible (sadly not) a comparative analysis of the genome of a “living fossil”, such as modern day coelacanths and their ancestors (living more than 80 million years ago) would likely reveal dramatic changes in genomic DNA sequence.  In this light the fact that between 100 to 200 new mutations are introduced into the human genome per generation (see Dolgin 2009 Human mutation rate revealed) seems like a useful number to be widely appreciated by students, not to mention the general public. Similarly, the genomic/genetic differences between humans, our primate relatives, and other mammals and the mechanisms behind them (Levchenko et al., 2017)(blog link) would seem worth considering and explicitly incorporating into curricula on genetics and human evolution.  

While race may be meaningless, racism is not.  How to understand racism?  Is it some kind of political artifact, or does it arise from biological factors.  Here, I believe, we find a important omission in many biology courses, textbooks, and curricula – namely an introduction and meaningful discussion of social evolutionary mechanisms. Many is the molecular/cell biology curriculum that completely ignores such evolutionary processes. Yet, the organisms that are the primary focus of biological research (and who pay for such research, e.g. humans) are social organisms at two levels.  In multicellular organisms somatic cells, which specialize to form muscular, neural, circulatory and immune systems, bone and connective tissues, sacrifice their own inter-generational reproductive future to assist their germ line (sperm and/or eggs) relatives, the cells that give rise to the next generation of organisms, a form of inclusive fitness (Dugatkin, 2007).  Moreover, humans are social organisms, often sacrificing themselves, sharing their resources, and showing kindness to other members of their group. This social cooperation is threatened by cheaters of various types (POST LINK).  Unless these social cheaters are suppressed, by a range of mechanisms, and through processes of kin/group selection, multicellular organisms die and socially dysfunctional social populations are likely to die out.  Without the willingness to cooperate, and when necessary, self-sacrifice, social organization is impossible – no bee hives, no civilizations.  Imagine a human population composed solely of people who behave in a completely selfish manner, not honoring their promises or social obligations.  

A key to social interactions involves recognizing those who are, and who are not part of your social group.  A range of traits can serve as markers for social inclusion.  A plausible hypothesis is that the explicit importance of group membership and defined social interactions becomes more critical when a society, or a part of society, is under stress.  Within the context of social stratification, those in the less privileged groups may feel that the social contract has been broken or made a mockery of.  The feeling (apparent reality) that members of “elite” or excessively privileged sub-groups are not willing to make sacrifices for others serves as evidence that social bonds are being broken (4). Times of economic and social disruption (migrations and conquests) can lead to increased explicit recognition of both group and non-group identification.  The idea that outsiders (non-group members) threaten the group can feed racism, a justification for why non-group members should be treated differently from group members.  From this position it is a small (conceptual) jump to the conclusion that non-group members are somehow less worthy, less smart, less trustworthy, less human – different in type from members of the group – many of these same points are made in an op-ed piece by Judis. 2018. What the Left Misses About Nationalism.

That economic or climatic stresses can foster the growth of racist ideas is no new idea; consider the unequal effects of various disruptions likely to be associated with the spread of automation (quote from George Will ↓) and the impact of climate change on migrations of groups within and between countries (see Saletan 2018b: Why Immigration Opponents Should Worry About Climate Change) are likely to spur various forms of social unrest, whether revolution or racism, or both – responses that could be difficult to avoid or control.   

So back to the question of biology education – in this context understanding the ingrained responses of social creatures associated with social cohesion and integrity need to be explicitly presented. Similarly, variants of such mechanisms occur within multicellular organisms and how they work is critical to understanding how diseases such as cancer, one of the clearest forms of a cheater phenotype, are suppressed.  Social evolutionary mechanisms provide the basis for understanding a range of phenomena, and the ingrained effects of social selection may be seen as one of the roots of racism, or at the very least a contributing factor worth acknowledging explicitly.  

Thanks to Melanie Cooper and Paul Strode for comments. Minor edits 4 May 2019.

Footnotes:

  1. It is an interesting possibility whether the 1%, or rather the super 0.1% represent their own unique form of social parasite, leading periodically to various revolutions – although sadly, new social parasites appear to re-emerge quite quickly.
  2. A part of the CoreBIO-biofundamentals project 
  3. At this point it is worth noting that biofundamentals itself includes sections on social evolution, kin/group and sexual selection (see Klymkowsky et al., 2016; LibreText link). 
  4. One might be forgiven for thinking that rich and privileged folk who escape paying what is seen as their fair share of taxes, might be cast as social cheaters (parasites) who, rather than encouraging racism might lead to revolutionary thoughts and actions. 

Literature cited: 

Acquaviva & Mintz. (2010). Perspective: Are we teaching racial profiling? The dangers of subjective determinations of race and ethnicity in case presentations. Academic Medicine 85, 702-705.

Busby et  al. (2016). Admixture into and within sub-Saharan Africa. Elife 5, e15266.

Campbell & Tishkoff. (2008). African genetic diversity: implications for human demographic history, modern human origins, and complex disease mapping. Annu. Rev. Genomics Hum. Genet. 9, 403-433.

Donovan, B.M. (2014). Playing with fire? The impact of the hidden curriculum in school genetics on essentialist conceptions of race. Journal of Research in Science Teaching 51: 462-496.

Dugatkin, L. A. (2007). Inclusive fitness theory from Darwin to Hamilton. Genetics 176, 1375-1380.

Klymkowsky et al., (2016). The design and transformation of Biofundamentals: a non-survey introductory evolutionary and molecular biology course..” LSE Cell Biol Edu pii: ar70.

Levchenko et al., (2017). Human accelerated regions and other human-specific sequence variations in the context of evolution and their relevance for brain development. Genome biology and evolution 10, 166-188.

Mayr, E. (1985). The Growth of Biological Thought: Diversity, Evolution, and Inheritance. Cambridge, MA: Belknap Press of Harvard University Press.

Mayr, E. (1994). Typological versus population thinking. Conceptual issues in evolutionary biology, 157-160.

—- (2000). Darwin’s influence on modern thought. Scientific American 283, 78-83.

Satel, S. (2002). I am a racially profiling doctor. New York Times 5, 56-58.

Yudell et al., (2016). Taking race out of human genetics. Science 351, 564-565.

Genes – way weirder than you thought

Pretty much everyone, at least in societies with access to public education or exposure to media in its various forms, has been introduced to the idea of the gene, but “exposure does not equate to understanding” (see Lanie et al., 2004).  Here I will argue that part of the problem is that instruction in genetics (or in more modern terms, the molecular biology of the gene and its role in biological processes) has not kept up with the advances in our understanding of the molecular mechanisms underlying biological processes (Gayon, 2016). spacer bar

Let us reflect (for a moment) on the development of the concept of a gene: Over the course of human history, those who have been paying attention to such things have noticed that organisms appear to come in “types”, what biologists refer to as species. At the same time, individual organisms of the same type are not identical to one  another, they vary in various ways. Moreover, these differences can be passed from generation to generation, and by controlling  which organisms were bred together; some of the resulting offspring often displayed more extreme versions of the “selected” traits.  By strictly controlling which individuals were breddogs
together, over a number of generations, people were able to select for the specific traits they desired (→).  As an interesting aside, as people domesticated animals, such as cows and goats, the availability of associated resources (e.g. milk) led to reciprocal effects – resulting in traits such as adult lactose tolerance (see Evolution of (adult) lactose tolerance & Gerbault et al., 2011).  Overall, the process of plant and animal breeding is generally rather harsh (something that the fanciers of strange breeds who object to GMOs might reflect upon), in that individuals that did not display the desired trait(s) were generally destroyed (or at best, not allowed to breed). spacer bar

Charles Darwin took inspiration from this process, substituting “natural” for artificial (human-determined) selection to shape populations, eventually generating new species (Darwin, 1859).  Underlying such evolutionary processes was the presumption that traits, and their variation, was “encoded” in some type of “factors”, eventually known as genes and their variants, alleles.  Genes influenced the organism’s molecular, cellular, and developmental systems, but the nature of these inheritable factors and the molecular trait building machines active in living systems was more or less completely obscure. 

Through his studies on peas, Gregor Mendel was the first to clearly identify some of the rules for the behavior of these inheritable factors using highly stereotyped, and essentially discontinuous traits – a pea was either yellow or green, wrinkled or smooth.  Such traits, while they exist in other organisms, are in fact rare – an example of how the scientific exploration of exceptional situations can help understand general processes, but the downside is the promulgation of the idea that genes and traits are somehow discontinuous – that a trait is yes/no, displayed by an organism or not – in contrast to the realities that the link between the two is complex, a reality rarely directly addressed (apparently) in most introductory genetics courses.  Understanding such processes is critical to appreciating the fact that genetics is often not destiny, but rather alterations in probabilities (see Cooper et al., 2013).  Without such an more nuanced and realistic understanding, it can be difficult to make sense of genetic information.     spacer bar

A gene is part of a molecular machine:  A number of observations transformed the abstraction of Darwin’s and Mendel’s hereditary factors into physical entities and molecular mechanisms (1).  In 1928 Fred Griffith demonstrated that a genetic trait could be transferred from dead to living organisms – implying a degree of physical / chemical stability; subsequent observations implied that the genetic information transferred involved DNA molecules. The determination of the structure of double-stranded DNA immediately suggested how information could be stored in DNA (in variations of bases along the length of the molecule) and how this information could be duplicated (based on the specificity of base pairing).  Mutations could be understood as changes in the sequence of bases along a DNA molecule (introduced by chemicals, radiation, mistakes during replication, or molecular reorganizations associated with DNA repair mechanisms and selfish genetic elements.  

But on their own, DNA molecules are inert – they have functions only within the context of a living organism (or highly artificial, that is man made, experimental systems).  The next critical step was to understand how a gene works within a biological system, that is, within an organism.  This involve appreciating the molecular mechanisms (primarily proteins) involved in identifying which stretches of a particular DNA molecule were used as templates for the synthesis of RNA molecules, which in turn could be used to direct the synthesis of polypeptides (see previous post on polypeptides and proteins).  In the context of the introductory biology courses I am familiar with (please let me know if I am wrong), these processes are based on a rather deterministic context; a gene is either on or off in a particular cell type, leading to the presence or absence of a trait. Such a deterministic presentation ignores the stochastic nature of molecular level processes (see past post: Biology education in the light of single cell/molecule studies) and the dynamic interaction networks that underlie cellular behaviors.  spacer bar

But our level of resolution is changing rapidly (2).  For a number of practical reasons, when the human genome was first sequence, the identification of polypeptide-encoding genes was based on recognizing “open-reading frames” (ORFs) encoding polypeptides of > 100 amino acids in length (> 300 base long coding sequence).  The increasing sensitivity of mass spectrometry-based proteomic studies reveals that smaller ORFs (smORFs) are present and can lead to the synthesis of short (< 50 amino acid long) polypeptides (Chugunova et al., 2017; Couso, 2015).  Typically an ORF was considered a single entity – basically one gene one ORF one polypeptide (3).  A recent, rather surprising discovery is what are known as “alternative ORFs” or altORFs; these RNA molecules that use alternative reading frames to encode small polypeptides.  Such altORFs can be located upstream, downstream, or within the previously identified conventional ORFalternative orfs
(figure →)(see Samandi et al., 2017).  The implication, particularly for the analysis of how variations in genes link to traits, is that a change, a mutation or even the  experimental  deletion of a gene, a common approach in a range of experimental studies, can do much more than previously presumed – not only is the targeted ORF effected, but various altORFs can also be modified.  

The situation is further complicated when the established rules of using RNAs to direct polypeptide synthesis via the process of translation, are violated, as occurs in what is known as “repeat-associated non-ATG (RAN)” polypeptide synthesis (see Cleary and Ranum, 2017).  In this situation, the normal signal for the start of RNA-directed polypeptide synthesis, an AUG codon, is subverted – other RNA synthesis start sites are used leading to underlying or imbedded gene expression.  This process has been found associated with a class of human genetic diseases, such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) characterized by the expansion of simple (repeated) DNA sequences  (see Pattamatta et al., 2018).  Once they exceed a certain length, such“repeat” regions have been found to be associated with the (apparently) inappropriarepeat region RAN process
te transcription of RNA in both directions, that is using both DNA strands as templates (← A: normal situation, B: upon expansion of the repeat domain).  These abnormal repeat region RNAs are translated via the RAN process to generate six different types of toxic polypeptides. spacer bar

So what are the molecular factors that control the various types of altORF transcription and translation?  In the case of ALS and FTD, it appears that other genes, and the polypeptides and proteins they encode, are involved in regulating the expression of repeat associated RNAs (Kramer et al., 2016)(Cheng et al., 2018).  Similar or distinct mechanisms may be involved in other  neurodegenerative diseases  (Cavallieri et al., 2017).  

So how should all of these molecular details (and it is likely that there are more to be discovered) influence how genes are presented to students?  I would argue that DNA should be presented as a substrate upon which various molecular mechanisms occur; these include transcription in its various forms (directed and noisy), as well as DNA synthesis, modification, and repair mechanisms occur.   Genes are not static objects, but key parts of dynamic systems.  This may be one reason that classical genetics, that is genes presented within a simple Mendelian (gene to trait) framework, should be moved deeper into the curriculum, where students have the background in molecular mechanisms needed to appreciate its complexities, complexities that arise from the multiple molecular machines acting to access, modify, and use the information captured in DNA (through evolutionary processes), thereby placing the gene in a more realistic cellular perspective (4). 

Footnotes:

1. Described greater detail in biofundamentals™

2. For this discussion, I am completely ignoring the roles of genes that encode RNAs that, as far as is currently know, do not encode polypeptides.  That said, as we go on, you will see that it is possible that some such non-coding RNA may encode small polypeptides.  

3. I am ignoring the complexities associated with alternative promoter elements, introns, and the alternative and often cell-type specific regulated splicing of RNAs, to create multiple ORFs from a single gene.  

4. With respects to Norm Pace – assuming that I have the handedness of the DNA molecules wrong or have exchanged Z for A or B. 

literature cited: 

  • Cavallieri et al, 2017. C9ORF72 and parkinsonism: Weak link, innocent bystander, or central player in neurodegeneration? Journal of the neurological sciences 378, 49.
  • Cheng et al, 2018. C9ORF72 GGGGCC repeat-associated non-AUG translation is upregulated by stress through eIF2α phosphorylation. Nature communications 9, 51.
  • Chugunova et al, 2017. Mining for small translated ORFs. Journal of proteome research 17, 1-11.
  • Cleary & Ranum, 2017. New developments in RAN translation: insights from multiple diseases. Current opinion in genetics & development 44, 125-134.
  • Cooper et al, 2013. Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease. Human genetics 132, 1077-1130.
  • Couso, 2015. Finding smORFs: getting closer. Genome biology 16, 189.
  • Darwin, 1859. On the origin of species. London: John Murray.
  • Gayon, 2016. From Mendel to epigenetics: History of genetics. Comptes rendus biologies 339, 225-230.
  • Gerbault et al, 2011. Evolution of lactase persistence: an example of human niche construction. Philosophical Transactions of the Royal Society of London B: Biological Sciences 366, 863-877.
  • Kramer et al, 2016. Spt4 selectively regulates the expression of C9orf72 sense and antisense mutant transcripts. Science 353, 708-712.
  • Lanie et al, 2004. Exploring the public understanding of basic genetic concepts. Journal of genetic counseling 13, 305-320.
  • Pattamatta et al, 2018. All in the Family: Repeats and ALS/FTD. Trends in neurosciences 41, 247-250.
  • Samandi et al, 2017. Deep transcriptome annotation enables the discovery and functional characterization of cryptic small proteins. Elife 6.

Molecular machines and the place of physics in the biology curriculum

The other day, through no fault of my own, I found myself looking at the courses required by our molecular biology undergraduate degree program. I discovered a requirement for a 5 credit hour physics course, and a recommendation that this course be taken in the students’ senior year – a point in their studies when most have already completed their required biology courses.  Befuddlement struck me, what was the point of requiring an introductory physics course in the context of a molecular biology major?  Was this an example of time-travel (via wormholes or some other esoteric imagining) in which a physics course in the future impacts a students’ understanding of molecular biology in the past?  I was also struck by the possibility that requiring such a course in the students’ senior year would measurably impact their time to degree. 

In a search for clarity and possible enlightenment, I reflected back on my own experiences in an undergraduate biophysics degree program – as a practicing cell and molecular  biologist, I was somewhat confused. I could not put my finger on the purpose of our physics requirement, except perhaps the admirable goal of supporting physics graduate students. But then, after feverish reflections on the responsibilities of faculty in the design of the courses and curricula they prescribe for their students and the more general concepts of instructional (best) practice and malpractice, my mind calmed, perhaps because I was distracted by an article on Oxford Nanopore’s MinION (↓), a “portable real-time device for DNA and RNA sequencing”,a device that plugs into the USB port on one’s laptop!

Distracted from the potentially quixotic problem of how to achieve effective educational reform at the undergraduate level, I found myself driven on by an insatiable curiosity (or a deep-seated insecurity) to insure that I actually understood how this latest generation of DNA sequencers worked. This led me to a paper by Meni Wanunu (2012. Nanopores: A journey towards DNA sequencing)[1].  On reading the paper, I found myself returning to my original belief, yes, understanding physics is critical to developing a molecular-level understanding of how biological systems work, BUT it was just not the physics normally inflicted upon (required of) students [2]. Certainly this was no new idea.  Bruce Alberts had written on this topic a number of times, most dramatically in his 1989 paper “The cell as a collection of molecular machines” [3].  Rather sadly, and not withstanding much handwringing about the importance of expanding student interest in, and understanding of, STEM disciplines, not much of substance in this area has occurred. While (some minority of) physics courses may have adopted active engagement pedagogies (in the meaning of Hake [4]) most insist on teaching macroscopic physics, rather than to focus on, or even to consider, the molecular level physics relevant to biological systems, explicitly the physics of protein machines in a cellular (biological) context. Why sadly, because conventional, that is non-biologically relevant introductory physics and chemistry courses, all to often serve the role of a hazing ritual, driving many students out of biology-based careers [5], in part I suspect, because they often seem irrelevant to students’ interests in the workings of biological systems. (footnote 1)  

Nanopore’s sequencer and Wanunu’s article (footnote 2) got me thinking again about biological machines, of which there are a great number, ranging from pumps, propellers, and oars to  various types of transporters, molecular truckers that move chromosomes, membrane vesicles, and parts of cells with respect to one another, to DNA detanglers, protein unfolders, and molecular recyclers (↓). 

Nanopore’s sequencer works based on the fact that as a single strand of DNA (or RNA) moves through a narrow pore, the different bases (A,C,T,G) occlude the pore to different extents, allowing different numbers of ions, different amounts of current, to pass through the pore. These current differences can be detected, and allows for a nucleotide sequence to be “read” as the nucleic acid strand moves through the pore. Understanding the process involves understanding how molecules move, that is the physics of molecular collisions and energy transfer, how proteins and membranes allow and restrict ion movement, and the impact of chemical gradients and electrical fields across a membrane on molecular movements  – all physical concepts of widespread significance in biological systems (here is an example of where a better understanding of physics could be useful to biologists).  Such ideas can be extended to the more general questions of how molecules move within the cell, and the effects of molecular size and inter-molecular interactions within a concentrated solution of proteins, protein polymers, lipid membranes, and nucleic acids, such as described in Oliverira et al., (2016 Increased cytoplasmic viscosity hampers aggregate polar segregation in Escherichia coli)[6].  At the molecular level, the processes, while biased by electric fields (potentials) and concentration gradients, are stochastic (noisy). Understanding of stochastic processes is difficult for students [7], but critical to developing an appreciation of how such processes can lead to phenotypic  differences between cells with the same genotypes (previous post) and how such noisy processes are managed by the cell and within a multicellular organism.   

As path leads on to path, I found myself considering the (←) spear-chucking protein machine present in the pathogenic bacteria Vibrio cholerae; this molecular machine is used to inject toxins into neighbors that the bacterium happens to bump into (see Joshi et al., 2017. Rules of Engagement: The Type VI Secretion System in Vibrio cholerae)[8].  The system is complex and acts much like a spring-loaded and rather “inhumane” mouse trap.  This is one of a number of bacterial  type VI systems, and “has structural and functional homology to the T4 bacteriophage tail spike and tube” – the molecular machine that injects bacterial cells with the virus’s genetic material, its DNA.

Building the bacterium’s spear-based injection system is control by a social (quorum sensing) system, a way that unicellular organisms can monitor whether they are alone or living in an environment crowded with other organisms. During the process of assembly, potential energy, derived from various chemically coupled, thermodynamically favorable reactions, is stored in both type VI “spears” and the contractile (nucleic acid injecting) tails of the bacterial viruses (phage). Understanding the energetics of this process, exactly how coupling thermodynamically favorable chemical reactions, such as ATP hydrolysis, or physico-chemical reactions, such as the diffusion of ions down an electrochemical gradient, can be used to set these “mouse traps”, and where the energy goes when the traps are sprung is central to students’ understanding of these and a wide range of other molecular machines. 

Energy stored in such molecular machines during their assembly can be used to move the cell. As an example, another bacterial system generates contractile (type IV pili) filaments; the contraction of such a filament can allow “the bacterium to move 10,000 times its own body weight, which results in rapid movement” (see Berry & Belicic 2015. Exceptionally widespread nanomachines composed of type IV pilins: the prokaryotic Swiss Army knives)[9].  The contraction of such a filament has been found to be used to import DNA into the cell, an early step in the process of  horizontal gene transfer.  In other situations (other molecular machines) such protein filaments access thermodynamically favorable processes to rotate, acting like a propeller, driving cellular movement. 

During my biased random walk through the literature, I came across another, but molecularly distinct, machine used to import DNA into Vibrio (see Matthey & Blokesch 2016. The DNA-Uptake Process of Naturally Competent Vibrio cholerae)[10].

This molecular machine enables the bacterium to import DNA from the environment, released, perhaps, from a neighbor killed by its spear.  In this system (←), the double stranded DNA molecule is first transported through the bacterium’s outer membrane; the DNA’s two strands are then separated, and one strand passes through a channel protein through the inner (plasma) membrane, and into the cytoplasm, where it can interact with the bacterium’s  genomic DNA.

The value of introducing students to the idea of molecular machines is that it helps to demystify how biological systems work, how such machines carry out specific functions, whether moving the cell or recognizing and repairing damaged DNA.  If physics matters in biological curriculum, it matters for this reason – it establishes a core premise of biology, namely that organisms are not driven by “vital” forces, but by prosaic physiochemical ones.  At the same time, the molecular mechanisms behind evolution, such as mutation, gene duplication,  and genomic reorganization provide the means by which new structures emerge from pre-existing ones, yet many is the molecular biology degree program that does not include an introduction to evolutionary mechanisms in its required course sequence – imagine that, requiring physics but not evolution? (see [11]).

One final point regarding requiring students to take a biologically relevant physics course early in their degree program is that it can be used to reinforce what I think is a critical and often misunderstood point. While biological systems rely on molecular machines, we (and by we I mean all organisms) are NOT machines, no matter what physicists might postulate -see We Are All Machines That Think.  We are something different and distinct. Our behaviors and our feelings, whether ultimately understandable or not, emerge from the interaction of genetically encoded, stochastically driven non-equilibrium systems, modified through evolutionary, environmental, social, and a range of unpredictable events occurring in an uninterrupted, and basically undirected fashion for ~3.5 billion years.  While we are constrained, we are more, in some weird and probably ultimately incomprehensible way.

Footnotes:

[1]  A discussion with Melanie Cooper on what chemistry is relevant to a life science major was a critical driver in our collaboration to develop the chemistry, life, the universe, and everything (CLUE) chemistry curriculum.  

[2]  Together with my own efforts in designing the biofundamentals introductory biology curriculum. 

literature cited

1. Wanunu, M., Nanopores: A journey towards DNA sequencing. Physics of life reviews, 2012. 9(2): p. 125-158.

2. Klymkowsky, M.W. Physics for (molecular) biology students. 2014  [cited 2014; Available from: http://www.aps.org/units/fed/newsletters/fall2014/molecular.cfm.

3. Alberts, B., The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell, 1998. 92(3): p. 291-294.

4. Hake, R.R., Interactive-engagement versus traditional methods: a six-thousand-student survey of mechanics test data for introductory physics courses. Am. J. Physics, 1998. 66: p. 64-74.

5. Mervis, J., Weed-out courses hamper diversity. Science, 2011. 334(6061): p. 1333-1333.

6. Oliveira, S., R. Neeli‐Venkata, N.S. Goncalves, J.A. Santinha, L. Martins, H. Tran, J. Mäkelä, A. Gupta, M. Barandas, and A. Häkkinen, Increased cytoplasm viscosity hampers aggregate polar segregation in Escherichia coli. Molecular microbiology, 2016. 99(4): p. 686-699.

7. Garvin-Doxas, K. and M.W. Klymkowsky, Understanding Randomness and its impact on Student Learning: Lessons from the Biology Concept Inventory (BCI). Life Science Education, 2008. 7: p. 227-233.

8. Joshi, A., B. Kostiuk, A. Rogers, J. Teschler, S. Pukatzki, and F.H. Yildiz, Rules of engagement: the type VI secretion system in Vibrio cholerae. Trends in microbiology, 2017. 25(4): p. 267-279.

9. Berry, J.-L. and V. Pelicic, Exceptionally widespread nanomachines composed of type IV pilins: the prokaryotic Swiss Army knives. FEMS microbiology reviews, 2014. 39(1): p. 134-154.

10. Matthey, N. and M. Blokesch, The DNA-uptake process of naturally competent Vibrio cholerae. Trends in microbiology, 2016. 24(2): p. 98-110.

11. Pallen, M.J. and N.J. Matzke, From The Origin of Species to the origin of bacterial flagella. Nat Rev Microbiol, 2006. 4(10): p. 784-90.

Making education matter in higher education


It may seem self-evident that providing an effective education, the type of educational experiences that lead to a useful bachelors degree and serve as the foundation for life-long learning and growth, should be a prime aspirational driver of Colleges and Universities (1).  We might even expect that various academic departments would compete with one another to excel in the quality and effectiveness of their educational outcomes; they certainly compete to enhance their research reputations, a competition that is, at least in part, responsible for the retention of faculty, even those who stray from an ethical path. Institutions compete to lure research stars away from one another, often offering substantial pay raises and research support (“Recruiting or academic poaching?”).  Yet, my own experience is that a department’s performance in undergraduate educational outcomes never figures when departments compete for institutional resources, such as supporting students, hiring new faculty, or obtaining necessary technical resources (2).

 I know of no example (and would be glad to hear of any) of a University hiring a professor based primarily on their effectiveness as an instructor (3).

In my last post, I suggested that increasing the emphasis on measures of departments’ educational effectiveness could help rebalance the importance of educational and research reputations, and perhaps incentivize institutions to be more consistent in enforcing ethical rules involving research malpractice and the abuse of students, both sexual and professional. Imagine if administrators (Deans and Provosts and such) were to withhold resources from departments that are performing below acceptable and competitive norms in terms of undergraduate educational outcomes?

Outsourced teaching: motives, means and impacts

Sadly, as it is, and particularly in many science departments, undergraduate educational outcomes have little if any impact on the perceived status of a department, as articulated by campus administrators. The result is that faculty are not incentivized to, and so rarely seriously consider the effectiveness of their department’s course requirements, a discussion that would of necessity include evaluating whether a course’s learning goals are coherent and realistic, whether the course is delivered effectively, whether it engages students (or is deemed irrelevant), and whether students’ achieve the desired learning outcomes, in terms of knowledge and skills achieved, including the ability to apply that knowledge effectively to new situations.  Departments, particularly research focussed (dependent) departments, often have faculty with low teaching loads, a situation that incentivizes the “outsourcing” of key aspects of their educational responsibilities.  Such outsourcing comes in two distinct forms, the first is requiring majors to take courses offered by other departments, even if such courses are not well designed, delivered, or (in the worst cases) relevant to the major.  A classic example is to require molecular biology students to take macroscopic physics or conventional calculus courses, without regard to whether the materials presented in these courses is ever used within the major or the discipline.  Expecting a student majoring in the life sciences to embrace a course that (often rightly) seems irrelevant to their discipline can alienate a student, and poses an unnecessary obstacle to student success, rather than providing students with needed knowledge and skills.  Generally, the incentives necessary to generate a relevant course, for example, a molecular level physics course that would engage molecular biology students, are simply not there.  A version of this situation is to require courses that are poorly designed or delivered (general chemistry is often used as the poster child for such a course). These are courses that have high failure rates, sometimes justified in terms of “necessary rigor” when in fact better course design could (and has) resulted in lower failure rates and improved learning outcomes.  In addition, there are perverse incentives associated with requiring “weed out” courses offered by other departments, as they reduce the number of courses a department’s faculty needs to teach, and can lead to fewer students proceeding into upper division courses.

The second type of outsourcing involves excusing tenure track faculty from teaching introductory courses, and having them replaced by lower paid instructors or lecturers.  Independently of whether instructors, lecturers, or tenure track professors make for better teaching, replacing faculty with instructors sends an implicit message to students.  At the same time, the freedom of instructors/lecturers to adopt an effective (socratic) approach to teaching is often severely constrained; common exams can force classes to move in lock step, independently of whether that pace is optimal for student engagement and learning. Generally, instructors/lecturers do not have the freedom to adjust what they teach, to modify the emphasis and time they spend on specific topics in response to their students’ needs. How an instructor instructs their students suffers when teachers do not have the freedom to customize their interactions with students in response to where they are intellectually.  This is particularly detrimental in the case of underrepresented or underprepared students. Generally, a flexible and adaptive approach to instruction (including ancillary classes on how to cope with college: see An alternative to remedial college classes gets results) can address many issues, and bring the majority of students to a level of competence, whereas tracking students into remedial classes can succeed in driving them out of a major or college (see Colleges Reinvent Classes to Keep More Students in Science and Redesigning a Large-Enrollment Introductory Biology Course and Does Remediation Work for All Students? )

How to address this imbalance, how can we reset the pecking order so that effective educational efforts actually matter to a department? 

My (modest) suggestion is to base departmental rewards on objective measures of educational effectiveness.   And by rewards I mean both at the level of individuals (salary and status) as well as support for graduate students, faculty positions, start up funds, etc.  What if, for example, faculty in departments that excel at educating their students received a teaching bonus, or if the number of graduate students within a department supported by the institution was determined not by the number of classes these graduate students taught (courses that might not be particularly effective or engaging) but rather by a departments’ undergraduate educational effectiveness, as measured by retention, time to degree, and learning outcomes (see below)?  The result could well be a drive within a department to improve course and curricular effectiveness to maximize education-linked rewards.  Given that laboratory courses, the courses most often taught by science graduate students, are multi-hour schedule disrupting events, of limited demonstrable educational effectiveness, that complicate student course scheduling, removing requirements for lab courses deemed unnecessary (or generating more effective versions), would be actively rewarded (of course, sanctions for continuing to offer ineffective courses would also be useful, but politically more problematic.)

A similar situation applies when a biology department requires its majors to take 5 credit hour physics or chemistry courses.  Currently it is “easy” for a department to require its students to take such courses without critically evaluating whether they are “worth it”, educationally.  Imagine how a department’s choices of required courses would change if the impact of high failure rates (which I would argue is a proxy for poorly designed  and delivered courses) directly impacted the rewards reaped by a department. There would be an incentive to look critically at such courses, to determine whether they are necessary and if so, well designed and delivered. Departments would serve their own interests if they invested in the development of courses  that better served their disciplinary goals, courses likely to engage their students’ interests.

So how do we measure a department’s educational efficacy?

There are three obvious metrics: i) retention of students as majors (or in the case of “service courses” for non-majors, whether students master what it is the course claims to teach); ii) time to degree (and by that I mean the percentage of students who graduate in 4 years, rather than the 6 year time point reported in response to federal regulations (six year graduation rate | background on graduation rates); and iii) objective measures of student learning outcomes attained and skills achieved. The first two are easy, Universities already know these numbers.  Moreover they are directly influenced by degree requirements – requiring students to take boring and/or apparently irrelevant courses serves to drive a subset of students out of a major.  By making courses relevant and engaging, more students can be retained in a degree program. At the same time, thoughtful course design can help students  pass through even the most rigorous (difficult) of such courses. The third, learning outcomes, is significantly more challenging to measure, since universal metrics are (largely) missing or superficial.  A few disciplines, such as chemistry, support standardized assessments, although one could argue with what such assessments measure.  Nevertheless, meaningful outcomes measures are necessary, in much the same way that Law and Medical boards and the Fundamentals of Engineering exam serve to help insure (although they do not guarantee) the competence of practitioners. One could imagine using parts of standardized exams, such as discipline specific GRE exams, to generate outcomes metrics, although more informative assessment instruments would clearly be preferable. The initiative in this area could be taken by professional societies, college consortia (such as the AAU), and research foundations, as a critical driver for education reform, increased effectiveness, and improved cost-benefit outcomes, something that could help address the growing income inequality in our country and make success in higher education an important factor contributing to an institution’s reputation.

 

A footnote or two…
 
1. My comments are primarily focused on research universities, since that is where my experience lies; these are, of course, the majority of the largest universities (in a student population sense).
 
2. Although my experience is limited, having spent my professorial career at a single institution, conversations with others leads me to conclude that it is not unique.
 
3. The one obvious exception would be the hiring of  coaches of sports teams, since their success in teaching (coaching) is more directly discernible and impactful on institutional finances and reputation).
 
minor edits – 16 March 2020