Category: Evaluating understanding

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.

 

Sounds like science, but it ain’t …

we are increasingly assailed with science-related “news” – stories that too often involve hype and attempts to garner attention (and no, half-baked ideas are not theories, they are often non-scientific speculation or unconstrained fantasies).

The other day, as is my addiction, I turned to the “Real Clear Science” website to look for novel science-based stories (distractions from the more horrifying news of the day). I discovered two links that seduced me into clicking: “Atheism is not as rare or as rational as you think” by Will Gervais and Peter Sjöstedt-H’s “Consciousness and higher spatial dimensions“.  A few days later I encountered “Consciousness Is the Collapse of the Wave Function” by Stuart Hameroff. On reading them (more below), I faced the realization that science itself, and its distorted popularization by both institutional PR departments and increasingly by scientists and science writers, may be partially responsible for the absurdification of public discourse on scientific topics [1].  In part the problem arises from the assumption that science is capable of “explaining” much more than is actually the case. This insight is neither new nor novel. Timothy Caulfield’s essay Pseudoscience and COVID-19 — we’ve had enough already focuses on the fact that various, presumably objective data-based, medical institutions have encouraged the public’s thirst for easy cures for serious, and often incurable diseases.  As an example, “If a respected institution, such as the Cleveland Clinic in Ohio, offers reiki — a science-free practice that involves using your hands, without even touching the patient, to balance the “vital life force energy that flows through all living things” — is it any surprise that some people will think that the technique could boost their immune systems and make them less susceptible to the virus?” That public figures and trusted institutions provide platforms for such silliness [see Did Columbia University cut ties with Dr. Oz?] means that there is little to distinguish data-based treatments from faith- and magical-thinking based placebos. The ideal of disinterested science, while tempered by common human frailties, is further eroded by the lure of profit and/or hope of enhanced public / professional status and notoriety.  As noted by Pennock‘ “Science never guarantees absolute truth, but it aims to seek better ways to assess empirical claims and to attain higher degrees of certainty and trust in scientific conclusions“. Most importantly, “Science is a set of rules that keep the scientists from lying to each other. [2]

It should surprise no one that the failure to explicitly recognize the limits, and evolving nature of scientific knowledge, opens the door to self-interested hucksterism at both individual and institutional levels. Just consider the number of complementary/alternative non-scientific “medical” programs run by prestigious institutions. The proliferation of pundits, speaking outside of their areas of established expertise, and often beyond what is scientifically knowable (e.g. historical events such as the origin of life or the challenges of living in the multiverse which are, by their very nature, unobservable) speaks to the increasingly unconstrained growth of pathological, bogus, and corrupted science  which, while certainly not new [3], has been facilitated by the proliferation of public, no-barrier, no-critical feedback platforms [1,4].  Ignoring the real limits of scientific knowledge and rejecting, or ignoring, the expertise of established authorities, rejects the ideals that have led to science that “works”.  

Of course, we cannot blame the distortion of science for every wacky idea; crazy, conspiratorial and magical thinking may well be linked to the cognitive “features” (or are they bugs) of the human brain. Norman Cohn describes the depressing, and repeated pattern behind the construction of dehumanizing libels used to justify murderous behaviors towards certain groups [5].  Recent studies indicate that brains, whether complex or simple neural networks, appear to construct emergent models of the world, models they use to coordinate internal perceptions with external realities [6].  My own (out of my area of expertise) guess is that the complexity of the human brain is associated with, and leads to the emergence of internal “working models” that attempt to make sense of what is happening to us, in part to answer questions such as why the good die young and the wicked go unpunished. It seems likely that our social nature (and our increasing social isolation) influences these models, models that are “checked” or “validated” against our experiences. 

It was in this context that Gervais’s essay on atheism caught my attention. He approaches two questions: “how Homo sapiens — and Homo sapiens alone — came to be a religious species” and “how disbelief in gods can exist within an otherwise religious species?”  But is Homo sapiens really a religious species and what exactly is a religion? Is it a tool that binds social groups of organisms together, a way of coping with, and giving meaning to, the (apparent) capriciousness of existence and experience, both, or something else again?  And how are we to know what is going on inside other brains, including the brains of chimps, whales, or cephalopods? In this light I was struck by an essay by Sofia Deleniv “The ‘me’ illusion: How your brain conjures up your sense of self” that considers the number of species that appear to be able to recognize themselves in a mirror. Turns out, this is not nearly as short a list as was previously thought, and it seems likely that self-consciousness, the ability to recognize yourself as you, may be a feature of many such systems.  Do other organisms possess emergent “belief systems” that help process incoming and internal signals, including their own neural noise? When the author says, “We then subtly gauge participants’ intuitions” by using “a clever experiment to see how people mentally represent atheists” one is left to wonder whether there are direct and objective measures of “intuitions” or “mental representations”?   Then the shocker, after publishing a paper claiming that “Analytic Thinking Promotes Religious Disbelief“, the authors state that “the experiments in our initial Science paper were fatally flawed, the results no more than false positives.’ One is left to wonder did the questions asked make sense in the first place. While it initially seemed scientific (after all it was accepted and published in a premiere scientific journal), was it ever really science? 

Both “Consciousness and Higher Spatial Dimensions” and “Consciousness Is the Collapse of the Wave Function”, sound very scientific. Some physicists (the most sciencey of scientists, right?) have been speculating via “string theory” and “multiverses”, a series of unverified (and likely unverifiable) speculations, that they universe we inhabit has many many more than the three spatial dimensions we experience.  But how consciousness, an emergent property of biological (cellular) networks, is related to speculative physics is not clear, no matter what Nobel laureates in physics may say.  Should we, the people, take these remarks seriously?  After all these are the same folks who question the reality of time (for no good reason, as far as I can tell, as I watch my new grandchild and myself grow older rather than younger). 

Part of the issue involves what has been called “the hard problem of consciousness”, but as far as I can tell, consciousness is not a hard problem, but a process that emerges from systems of neural cells, interacting with one another and their environment in complex ways, not unlike the underlying processes of embryonic development, in which a new macroscopic organism composed of thousands to billions of cells emerges from a single cell.  And if the brain and body are generating signals (thoughts) then in makes sense these in turn feed back into the system, and as consciousness becomes increasingly complex, these thoughts need to be “understood” by the system that produced them.  The system may be forced to make sense of itself (perhaps that is how religions and other explanatory beliefs come into being, settling the brain so that it can cope with the material world, whether a nematode worm, an internet pundit, a QAnon wack-o, a religious fanatic, or a simple citizen, trying to make sense of things.

Thanks to Melanie Cooper for editorial advice and Steve Pollock for checking my understanding of physics; all remaining errors are mine alone!

  1. Scheufele, D. A. and Krause, N. M. (2019). Science audiences, misinformation, and fake news. Proceedings of the National Academy of Sciences 116, 7662-7669
  2. Kenneth S. Norris, cited in False Prophet by Alexander Kohn (and cited by John Grant in Corrupted Science. 
  3.  See Langmuir, I. (1953, recovered and published in 1989). “Pathological science.” Research-Technology Management 32: 11-17; “Corrupted Science: Fraud, Ideology, and Politics in Science” and “Bogus Science: or, Some people really believe these things” by John Grant (2007 and 2009)
  4.  And while I personally think Sabine Hossenfelder makes great explanatory videos, even she is occasionally tempted to go beyond the scientifically demonstrable: e.g. You don’t have free will, but don’t worry and An update on the status of superdeterminism with some personal notes  
  5.  Norman Cohn’s (1975) “Europe’s Inner Demons” will reveal.
  6. Kaplan, H. S. and Zimmer, M. (2020). Brain-wide representations of ongoing behavior: a universal principle? Current opinion in neurobiology 64, 60-69.

Anti-Scientific & anti-vax propaganda (1926 and today)

“Montaigne concludes, like Socrates, that ignorance aware of itself is the only true knowledge” – from “Forbidden Knowledge” by Roger Shattuck

A useful review of the history of the anti-vaccination movement: Poland & Jacobson 2011. The Age-Old Struggle against the Antivaccinationists NEJM

Science educators and those who aim to explain the implications of scientific or clinical observations to the public have their work cut out for them. In large part, this is because helping others, including the diverse population of health care providers and their clients, depends upon more than just critical thinking skills. Equally important is what might be termed “disciplinary literacy,” the ability to evaluate whether the methods applied are adequate and appropriate and so whether a particular observation is relevant to or able to resolve a specific question. To illustrate this point, I consider an essay from 1926 by Peter Frandsen and a 2021 paper by Ou et al. (2021) on the mechanism of hydroxychloroquine inhibition of SARS-CoV-2 replication in tissue culture cells.                

In Frandsen’s essay, well before the proliferation of unfettered web-based social pontification and ideologically-motivated distortions, he notes that “pseudo and unscientific cults are springing up and finding it easy to get a hold on the popular mind,” and “are making some headway in establishing themselves on an equally recognized basis with scientific medicine,” in part due to their ability to lobby politicians to exclude them from any semblance of “truth in advertising.”  Of particular resonance were the efforts in Minnesota, California, and Montana to oppose mandatory vaccination for smallpox. Given these successful anti-vax efforts, Frandsen asks, “is it any wonder that smallpox is one thousand times more prevalent in Montana than in Massachusetts in proportion to population?”  One cannot help but analogize to today’s COVID-19 statistics on the dramatically higher rate of hospitalization for the unvaccinated (e.g. Scobie et al., 2021). The comparison is all the more impactful (and disheartening) given the severity of smallpox as a disease, its elimination, in 1977, together with the near elimination of other dangerous viral human diseases (poliomyelitis and measles) primarily via vaccination efforts (Hopkins, 2013), and the discouraging number of high profile celebrities, some of whom I for one previously considered admirable figures (various forms of influencers in modern parlance) who actively promulgate positions that directly contradict objective and reproducible observation and embrace blatantly scientifically untenable beliefs (the vaccine-autism link serves as a prime example).                 

While much is made of the idea that education-based improvements in critical thinking ability can render its practitioners less susceptible to unwarranted conspiracy theories and beliefs (Lantian et al., 2021), the situation becomes more complex when we consider how it is that presumably highly educated practitioners, e.g. medical doctors, can become conspiracists (ignoring for the moment the more banal, and likely universal, reasons associated with greed and the need to draw attention to themselves).  As noted, many is the conspiracist who considers themselves to be a “critical freethinker” (see Lantian et al). The fact that they fail to recognize the flaws in their own thinking leads us to ask, what are they missing?            

A point rarely considered is what we might term “disciplinary literacy.” That is, do the members of an audience have the background information necessary to question foundational presumptions associated with an observation? Here I draw on personal experience. I have (an increasingly historical) interest in the interactions between intermediate filaments and viral infection (Doedens et al., 1994; Murti et al., 1988). In 2020, I found myself involved quite superficially with studies by colleagues here at the University of Colorado Boulder; they reproduced the ability of hydroxychloroquine to inhibit coronavirus replication in cultured cells.  Nevertheless, and in the face of various distortions, it quickly became apparent that hydroxychloroquine was ineffective for treating SARS-CoV-2 infection in humans. So, what disciplinary facts did one need to understand this apparent contradiction (which appears to have fueled unreasonable advocacy of hydroxychloroquine treatment for COVID)? The paper by Ou et al. (2021) provides a plausible mechanistic explanation. The process of in vitro infection of various cells appears to involve endocytosis followed by proteolytic events leading to the subsequent movement of viral nucleic acid into the cytoplasm, a prerequisite for viral replication. Hydroxychloroquine treatment acts by blocking the acidification of the endosome, which inhibits the capsid cleavage reaction and the subsequent cytoplasmic transport of the virus’s nucleic acid genome (see figure 1, Ou et al. 2021).  In contrast, in vivo infection involves a surface protease, rather than endocytosis, and is therefore independent of endosomal acidification.  Without a (disciplinary) understanding of the various mechanisms involve in viral entry, and their relevance in various experimental contexts, it remains a mystery for why hydroxychloroquine treatment blocks viral replication in one system (in vitro cultured cells) and not another (in vivo).             

 In the context of science education and how it can be made more effective, it appears that helping students understand underlying cellular processes, experimental details, and their often substantial impact on observed outcomes is central. This is in contrast to the common focus (in many courses) on the memorization of largely irrelevant details. Understanding how one can be led astray by the differences between experimental systems (and inadequate sample sizes) is essential. One cannot help but think of how mouse studies on diseases such as sepsis (Kolata, 2013) and Alzheimer’s (Reardon, 2018) have been haunted by the assumption that systems that differ in physiologically significant details are good models for human disease and the development of effective treatments. Helping students understand how we come to evaluate observations and the molecular and physiological mechanisms involved should be the primary focus of a modern education in the biological sciences, since it helps build up the disciplinary literacy needed to distinguish reasoned argument from anti-scientific propaganda. 

Acknowledgement: Thanks to Qing Yang for bringing the Ou et al paper to my attention.  

Literature cited:
Shattuck, R. (1996). Forbidden knowledge: from Prometheus to pornography. New York: St. Martin’s Press.

Doedens, J., Maynell, L. A., Klymkowsky, M. W. and Kirkegaard, K. (1994). Secretory pathway function, but not cytoskeletal integrity, is required in poliovirus infection. Arch Virol. suppl. 9, 159-172.

Hopkins, D. R. (2013). Disease eradication. New England Journal of Medicine 368, 54-63.

Kolata, G. (2013). Mice fall short as test subjects for some of humans’ deadly ills. New York Times 11, 467-477.

Lantian, A., Bagneux, V., Delouvée, S. and Gauvrit, N. (2021). Maybe a free thinker but not a critical one: High conspiracy belief is associated with low critical thinking ability. Applied Cognitive Psychology 35, 674-684.

Murti, K. G., Goorha, R. and Klymkowsky, M. W. (1988). A functional role for intermediate filaments in the formation of frog virus 3 assembly sites. Virology 162, 264-269.
 
Ou, T., Mou, H., Zhang, L., Ojha, A., Choe, H. and Farzan, M. (2021). Hydroxychloroquine-mediated inhibition of SARS-CoV-2 entry is attenuated by TMPRSS2. PLoS pathogens 17, e1009212.

Reardon, S. (2018). Frustrated Alzheimer’s researchers seek better lab mice. Nature 563, 611-613.

Scobie, H. M., Johnson, A. G., Suthar, A. B., Severson, R., Alden, N. B., Balter, S., Bertolino, D., Blythe, D., Brady, S. and Cadwell, B. (2021). Monitoring incidence of covid-19 cases, hospitalizations, and deaths, by vaccination status—13 US jurisdictions, April 4–July 17, 2021. Morbidity and Mortality Weekly Report 70, 1284.

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.”).

Thinking about biological thinking: Steady state, half-life & response dynamics

Insights into student thinking & course design, part of the biofundamentals project. 

Something that often eludes both instructors and instructional researchers is a clear appreciation of what it is that students do and do not know, what ideas they can and cannot call upon to solve problems and generate clear, coherent, and plausible explanations. What information – thought to have been presented effectively through past instruction, appears to be unavailable to students. As an example, few instructors would believe that students completing college level chemistry could possibly be confused about the differences between covalent and non-covalent molecular interactions, yet there is good evidence that they are (Williams et al., 2015). Unless these ideas, together with their  conceptual bases and practical applications, are explicitly called out in the design and implementation of instructional materials, they often fail to become a working (relevant) part of the students’ conceptual tool-kit.   

To identify ideas involved in understanding biological systems, we are using an upper division undergraduate course in developmental biology (blog link) to provide context; this is a final “capstone” junior/senior level course that comes after students have completed multiple required courses in chemistry and biology.  Embryonic development integrates a range of molecular level processes, including the control of gene expression, cellular morphology and dynamics, through intrinsic and extrinsic signaling systems.   

A key aspect of the course’s design is the use of formative assessment activities delivered through the beSocratic system. These activities generally include parts in which students are asked to draw a graph or diagram. Students are required to complete tasks before the start of each class meeting; their responses are used to inform in-class discussions, a situation akin to reviewing game film and coaching in sports. Analysis of student drawings and comments, carried out in collaboration with Melanie Cooper and her group at Michigan State University, can reveal unexpected aspects of students’ thinking (e.g. Williams et al., 2015). What emerges from this Socratic give and take is an improved appreciation of the qualities of the tasks that engage students (as well as those that do not), and insights into how students analyze specific tasks, what sets of ideas they see as necessary and which necessary ideas they ignore when generating explanatory and predictive models. Most importantly, they can reveal flaws in how necessary ideas are developed. While at an admittedly early stage in the project, here I sketch out some preliminary findings: the first of these deal with steady state concentration and response dynamics.

The ideas of steady state concentration and pathway dynamics were identified by Loertscher et al (2014)as two of five “threshold concepts” in  biochemistry and presumably molecular biology as well. Given the non-equilibrium nature of biological systems, we consider the concentration of a particular molecule in a cell in dynamic terms, a function of its rate of synthesis (or importation from the environment) together with its rate of breakdown.  On top of this dynamic, the activity of existing molecules can be regulated through various post-translational mechanisms.  All of the populations of molecules within a cell or organism have a characteristic steady state concentration with the exception of genomic DNA, which while synthesized is not, in living organisms, degraded, although it is repaired.

In biological systems, molecules are often characterized by their “half life” but this can be confusing, since it is quite different from the way the term is used in physics, where students are likely to first be introduced to it.[1]  Echos from physics can imply that a molecule’s half-life is an intrinsic feature of the molecule, rather than of the system in which the molecule finds itself.  The equivalent of half-life would be doubling time, but these terms make sense only under specific conditions.  In a system in which synthesis has stopped (synthesis rate = 0) the half life is the time it takes for the number of molecules in the system to decrease by 50%, while in the absence of degradation (degradation rate = 0), the doubling time is the time it takes to double the number of molecules in the system.  Both degradation and synthesis rates are regulateable and can vary, often dramatically, in response to various stimuli.

In the case of RNA and polypeptide levels, the synthesis rate is determined by many distinct processes, including effective transcription factor concentrations, the signals that activate transcription factors, rates of binding of transcription factors to transcription factor binding sites (which can involve both DNA sequences and other proteins), as well as relevant binding affinities, and the rates associated with the recruitment and activation of DNA-dependent, RNA polymerase. Once activated, the rate of gene specific RNA synthesis will be influenced  by the rate of RNA polymerization (nucleotide bases added per second) and the length of the RNA molecules synthesized.  In eukaryotes, the newly formed RNA will generally need to have introns removed through interactions with splicing machinery, as well as other  post-transcriptional reactions, after which the processed RNA will be transported from the nucleus to the cytoplasm through the nuclear pore complex. In the cytoplasm there are rates associated with the productive interaction of RNAs with the translational machinery (ribosomes and associated factors), and the rate at which polypeptide synthesis occurs (amino acids added per second) together with the length of the polypeptide synthesized (given that things are complicated enough, I will ignore processes such as those associated with the targeting of membrane proteins and codon usage, although these will be included in a new chapter in biofundamentals reasonably soon, I hope). On the degradative side, there are rates associated with interactions with nucleases (that breakdown RNAs) and proteinases (that breakdown polypeptides).  These processes are energy requiring; generally driven by reactions coupled to the hydrolysis of adenosine triphosphate (ATP). 

That these processes matter is illustrated nicely in work from Harima and colleagues (2014).   The system, involved in the segmentation of the anterior region of the presomitic mesoderm, responds to signaling by activating the Hes7 gene, while the Hes7 gene product act to inhibit Hes7 gene expression. The result is an oscillatory response that is “tuned” by the length of the transcribed region (RNA length). This can be demonstrated experimentally by generating mice in which two of the genes three introns (Hes7-3) or all three introns (intron-less) are removed. Removing introns changes the oscillatory behavior of the system (Hes7 mRNA -blue and Hes7 protein – green)(Harima et al., 2013).

In the context of developmental biology, we use beSocratic activities to ask students to consider a molecule’s steady state concentration as a function of its synthesis and degradation rates, and to predict how the system would change when one or the other is altered. These ideas were presented in the context of observations by Schwanhausser et al (2011) that large discrepancies between steady state RNA and polypeptide concentrations are common and that there is an absence of a correlation between RNA and polypeptide half-lives (we also use these activities to introduce the general idea of correlation). In their responses, it was common to see students’ linking high steady state concentrations exclusively to long half-lives. Ask to consider the implications in terms of system responsiveness (in the specific context of a positively-acting transcription factor and target gene expression), students often presumed that a longer half-life would lead to higher steady state concentration which in turn would lead to increased target gene expression, primarily because collisions between the transcription factor and its DNA-binding sites would increase, leading to higher levels of target gene expression. This is an example of a p-prim (Hammer, 1996) – the heuristic that “more is more”, a presumption that is applicable to many systems. 

In biological systems, however, this is generally not the case – responses “saturate”, that is  increasing transcription factor concentration (or activity) above a certain level generally does not lead to a proportionate, or any increase in target gene expression. We would not call this a misconception, because this is an example of an idea that is useful in many situations, but generally isn’t in biological systems – where responses are generally inherently limited. The ubiquity and underlying mechanisms of response saturation need to be presented explicitly, and its impact on various processes reinforced repeatedly, preferably by having students use them to solve problems or construct plausible explanations. A related phenomenon that students seemed not to recognize involves the non-linearity of the initial response to a stimulus, in this case, the concentration of transcription factor below which target gene expression is not observed (or it may occur, but only transiently or within a few cells in the population, so as to be undetectable by the techniques used).

So what ideas do students need to call upon when they consider steady state concentration, how it changes, and the impact of such changes on system behavior?  It seems we need to go beyond synthesis and degradation rates and include the molecular processes associated with setting the system’s response onset and saturation concentrations.  First we need to help students appreciate why such behaviors (onset and saturation) occur – why doesn’t target gene expression begin as soon as a transcription factor appears in a cell?  Why does gene expression level off when transcription factor concentrations rise above a certain level?  The same questions apply to the types of threshold behaviors often associated with signaling systems.  For example, in quorum sensing among unicellular organisms, the response of cells to the signal occurs over a limited concentration range, from off to full on.  A related issue is associated with morphogen gradients (concentration gradients over space rather than time), in which there are multiple distinct types of “threshold” responses. One approach might be to develop a model in which we set the onset concentration close to the saturation concentration. The difficulty (or rather instructional challenge) here is that these are often complex processes involving cooperative as well as feedback interactions.

Our initial approach to steady state and thresholds has been to build activities based on the analysis of a regulatory network presented by Saka and Smith (2007), an analysis based on studies of early embryonic development in the frog Xenopus laevis. We chose the system because of its simplicity, involving only four components (although there are many other proteins associated with the actual system).  Saka and Smith modeled the regulatory network controlling the expression of the transcription factor proteins Goosecoid (Gsc) and Brachyury (Xbra) in response to the secreted signaling protein activin (↓), a member of

the TGFβ superfamily of secreted signaling proteins (see Li and Elowitz, 2019).   The network involves the positive action of Xbra on the gene encoding the transcription factor protein Xom.  The system’s behavior depends on the values of various parameters, parameters that include response to activator (Activin), rates of synthesis and the half-lives of Gsc, Xbra, and Xom, and the degrees of regulatory cooperativity and responsiveness.

Depending upon these parameters, the system can produce a range of complex responses.  In different regimes (→),  increasing concentrations of activin (M) can lead, initially, to increasing, but mutually exclusive, expression of either Xba (B) or Gsc (A) as well as sharp transitions in which expression flips from one to the other, as Activin concentration increases, after which the response saturates. There are also conditions at very low Activin concentration (marked by ↑) in which both Xbra and Gsc are expressed at low levels, a situation that students are asked to explain.

Lessons learned: Based on their responses, captured through beSocratic and revealed during in class discussions, it appears that there is a need to be more explicit (early in the course, and perhaps the curriculum as well) when considering the mechanisms associated with response onset and saturation, in the context of how changes in the concentrations of regulatory factors (through changes in synthesis, turn-over, and activity) impact system responses. This may require a more quantitative approach to molecular dynamics and system behaviors. Here we may run into a problem, the often phobic responses of biology majors (and many faculty) to mathematical analyses.  Even the simplest of models, such as that of Saka and Smith, require a consideration of factors generally unfamiliar to students, concepts and skills that may well not be emphasized or mastered in prerequisite courses. The trick is to define realistic, attainable, and non-trivial goals – we are certainly not going to succeed in getting late stage molecular biology students with rudimentary math skills to solve systems of differential equations in a developmental biology course.  But perhaps we can build up the instincts needed to appreciate the molecular processes involved in the behavior of systems whose behavior evolves overtime in response to various external signals (which is, of course, pretty much every biological system).

Footnotes: 

[1] A similar situation exists in the context of the term “spontaneous” in chemistry and biology.  In chemistry spontaneous means thermodynamically favorable, while in standard usage (and generally in biology) spontaneous implies that a reaction is proceeding at a measurable, functionally significant rate.  Yet another insight that emerged through discussions with Melanie Cooper. 

Mike Klymkowsky

Literature cited

Hammer, D. (1996). Misconceptions or p-prims. How might alternative perspectives of cognitive structure influence instructional perceptions and intentions. Journal of the Learning Sciences 5, 97-127.

Harima, Y., Imayoshi, I., Shimojo, H., Kobayashi, T. and Kageyama, R. (2014). The roles and mechanism of ultradian oscillatory expression of the mouse Hes genes. In Seminars in cell & developmental biology, pp. 85-90: Elsevier.

Harima, Y., Takashima, Y., Ueda, Y., Ohtsuka, T. and Kageyama, R. (2013). Accelerating the tempo of the segmentation clock by reducing the number of introns in the Hes7 gene. Cell Reports 3, 1-7.

Li, P. and Elowitz, M. B. (2019). Communication codes in developmental signaling pathways. Development 146, dev170977.

Loertscher, J., Green, D., Lewis, J. E., Lin, S. and Minderhout, V. (2014). Identification of threshold concepts for biochemistry. CBE—Life Sciences Education 13, 516-528.

Saka, Y. and Smith, J. C. (2007). A mechanism for the sharp transition of morphogen gradient interpretation in Xenopus. BMC Dev Biol 7, 47.

Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W. and Selbach, M. (2011). Global quantification of mammalian gene expression control. Nature 473, 337.

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.

Latest & past (PLoS Sci-Ed)

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Please note, given the move from PLoS some of the links in the posts may be broken; some minor editing in process.  All by Mike Klymkowsky unless otherwise noted

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.