Biology education in the light of single cell/molecule studies

By Mike Klymkowsky

from Stochastic Gene Expression in a Single Cell by Michael B. Elowitz, Arnold J. Levine, Eric D. Siggia & Peter S. Swain

Stochastic processes are often presented in terms of random, that is unpredictable, events.  This framing obscures the reality that stochastic processes, while more or less unpredictable at the level of individual events, are well behaved at the population level.  It also obscures the role of stochastic processes in a wide range of predictable phenomena; in atomic systems, for example, unknown factors determine the timing of the radioactive decay of a particular unstable atom, at the same time the rate of radioactive decay is highly predictable in a large enough population. Similarly, in the classical double-slit experiment the passage of a single photon, electron, or C60 molecule is unpredictable while the behavior of a larger population is perfectly predictable. The macroscopic predictability of the Brownian motion (a stochastic process) enabled Einstein to argue for the reality of atoms.  Similarly, the dissociation of a molecular complex or the occurrence of a chemical reaction, driven as they are by thermal collisions, are stochastic processes, whereas dissociation constants and reaction rates are predictable. In fact this type of unpredictability at the individual level and predictability at the population level is the hallmark of stochastic, as compared to truly random, that is, unpredictable behaviors.

stochastic wordle

Single cell and single molecule studies increasingly provide mechanistic insights into a range of biological processes, from evolutionary to cognitive and pathogenic mechanisms. The effects of stochastic events are complicated by the developing and adaptive

FIG.1  A schematic of the mutational origin of antibiotic resistance over time (and space) from McNally & Brown, 2016).

nature of biological systems and appears to be influenced by the genetic background. In some cases, homeostatic (feedback) mechanisms return the system to its original state.  In others, the stochastic expression (or mutation) of a particular gene (or set of genes) leads to a cascade of downstream effects that change the system, such that subsequent events become more or less probable, a process nicely illustrated in recent real time studies of the evolution of antibiotic resistance in bacteria (←FIG & a seriously cool video). The stochastic (molecular clock) nature of an organism’s intrinsic mutation rate has recently been used with the EXAC system to visualize the impact of selective and non-adaptive effects on human genes.

Pedagogical studies:  The “Framework for K12 science education”  ignores stochastic processes altogether, while the Vision and Change in Undergraduate Biology Education” document contains a single point that calls for “incorporating stochasticity into biological models” (p. 17), but omits details of what this means in practice. People (even scholars) often have a difficult time developing an accurate understanding of stochastic processes (see “Understanding Randomness and its Impact on Student Learning” and “Fooled by Randomness“.  The failure to appreciate the ubiquity of stochastic processes in biological system has been an obstacle to the acceptance of Darwinian evolution. In this light, it seems well past time to rethink the foundational roles of stochastic processes in biological (as well as chemical and physical) systems and how best to introduce such processes to students through coherent course narratives and supporting materials.

A number of studies indicate that students call upon deterministic models to explain a range of stochastic processes. The fact that all too often students are introduced to the behaviors of cellular and molecular level biological systems through depictions that are overtly deterministic does not help the situation. In the majority of instructional videos, for example, molecules appear to know were they are heading and move there with a purpose. Similarly the folding of polypeptides is often depicted as a deterministic process[1] although the proliferation of model-based simulations offers a more realistic depiction (see below).  That said the widespread involvement of chaperones is rarely acknowledged. Macromolecules are commonly depicted as rigid rather than as dynamic. The thermally driven opening and closing of the DNA double-helix (a consequence of the weakness of intermolecular interactions) is rarely illustrated.  Molecules recognize one another and (apparently) stay locked in their mutual embrace forever; the role of thermal collisions in driving molecular dissociation (and binding specificity) is rarely considered in most textbooks, and presumably, in the classes that use these books. Moreover, the factors involved in inter-molecular interactions are often poorly understood, even after the completion of conventional university level chemistry courses. The energetic factors that determine enzyme specificity and reaction rates and the binding of transcription factors to their target DNA sequences, as well as the effects of mutations on these and other processes, often go uncommented on. It is not at all clear whether students appreciate that thermal collisions are responsible for the reversal of molecular interactions or that they supply a reaction’s activation energy. Cells with the same genotype are implicitly expected to behave in identical ways (display the same phenotypes), a situation at odds with direct observation (see “Stochastic Gene Expression in a Single Cell” and “What’s Luck Got to Do with It: Single Cells, Multiple Fates, and Biological Non-determinism” and the general processes involved in cellular differentiation and social behaviors. Phenotypic penetrance and expressivity also involve stochastic behaviors, together with genetic background effects. It certainly does not help when instructors introduce a stochastic process, such as genetic drift, in the context of the Hardy-Weinberg model, a situation in which genetic drift does not occur. Such presentations are likely to increase student confusion.

FIG. 2 A comparison between the expression of lacZ as measured in a bulk population and in individual cells (adapted from Vilar et al., 2003).

It is our impression that the typical instructional approach is to present molecular level processes in terms of large populations of molecules that behave in a deterministic manner. Consider the bacteria Escherichia coli’s lac operon, a group of genes that has been a workhorse in modern molecular biology and a common context through which to present the regulation of gene expression. Expression of the lac operon results in the synthesis of two proteins (lactose permease and β-galactosidase) that enable lactose to enter the cell and convert lactose into the monosaccharides glucose and galactose (which can be metabolized futher) and allolactone, which binds to, and inhibits the binding of the lac repressor protein to DNA, allowing the expression of the lac operon. When the bulk behavior of a bacterial culture is analyzed, the expression of the lac operon increases as a smooth function over time (in the absence of other energy sources)(FIG. ↑). The result is that the expression of the proteins required for lactose metabolism is restricted to situations in which lactose is present.

The mechanistic quandary, rarely if ever considered explicitly as far as we can tell, is how the lac operon can “turn on” when the entry of lactose into the cell and the inactivation of the lac repressor both depend upon the operon’s expression?  The situation becomes clear only when we consider the behavior of individual cells; LacZ expression goes from off to fully on in a stochastic manner (FIG. 2↑).[2]  Given that there are ~5 to 10 lac repressor molecules and one to two copies of the lac operon per cell, the lac operon can be expressed when the operon is free of bound repressor.  If such a “noisy” event happens to occur when lactose is present in the media, expression of the lac operon allows lactose to enter the cell, the conversion of lactose into allolactone, the inactivation of the lac repressor, and stable expression of the lac operon. The stochastic behavior of the system enables individual cells to sample their environment and respond when useful metabolites are present while minimizing unnecessary metabolic expense (the synthesis of irrelevant polypeptides) when they are not. A similar logic is involved in the quorum sensing, the emission of light (via the luciferase system), the regulation of the DNA uptake system, the generation of persister phenotypes, and programmed cell death (to benefit genetically related neighbors).


What is a biology educator to do? The question that faces the reflective educational designer and enlightened instructor is how should their course address the multiple roles of stochastic processes within biological systems?  I have a short set of recommendations that I think both designers and instructors might want to consider; many have been incorporated into ongoing efforts at course design, which I have only recently (2019) begun to think of as educational engineering. First, it should be explicitly recognized, and conveyed to students, that stochastic processes are difficult to understand, as witness the common belief in the Gambler’s fallacy and the “hot hand”. Students need to be given adequate time to work with, and appropriate feedback on the behavior of stochastic systems. Secondly, and rather obviously, instructors should illustrate and articulate the role of stochastic processes in range of biological systems, from phenotypic variation and evolutionary events, including the effects of mutations and various non-adaptive processes (such as genetic drift) to de novo gene formation, gene expression, drug-target interactions, and reaction kinetics. Finally, the stochastic behaviors of molecular (and cellular) level processes should be accurately and explicitly illustrated .[3] Among currently available examples there are those that illustrate the movement of a water molecule through a membrane either through an aquaporin molecule or on its own,[4] as well as a PhET applet that illustrates the Elowitz et al study on stochastic GFP-expression in E. coli (and allows for student manipulation of key regulatory parameters).[5]  A simulation of the nature of intermolecular interactions and the role of molecular collisions in their formation has been developed for use with the CLUE Chemistry curriculum.[6]

FIG.3 Students are asked to predict the trajectory of a cannon ball (A) or a molecule (B); few students recognize the transition from projectile to Brownian motion (adapted from Klymkowsky et al., 2016).

Students’ understanding of stochastic processes can be revealed to instructors (and their students) through the use of various targeted assessment tools (such as the Biology Concepts Instrument or BCI, the Genetic Drift Instrument, and diagnostic assessments of student thinking about stochastic processes). For example, students can be asked to draw a graph that reflects the movement of a macroscopic projectile FIG. 3A→) versus a molecular (microscopic) object (FIG.3B→); such a task can reveal whether students can make the transition from the well behaved (deterministic) to the stochastic.  Drawing (and explanation) has been used extensively in the analysis of student understanding with the context of the CLUE project (“lost in lewis structures“, “noncovalent interactions“, and “relationships between molecular structure and properties“).  In a similar vein, network dynamics, including the cascade effects driving cell level divergence and the feedback and regulatory interactions involved in limiting the effects of noise and generating various outcomes (cellular differentiation) can be presented to students (see “Network motifs: simple building blocks of complex networks“, “Using graph-based assessments within socratic tutorials” and “Noise facilitates transcriptional control under dynamic inputs“).  One can consider the role of stochastic events within social systems, including responses to various aberrant behaviors (social cheating, cancer) and in terms of social feedback mechanisms (apoptosis, positive and negative feedback, lateral inhibition of cell differentiation) and in the context of the decisions involved in stem cells division,  differentiation, and cancer formation.  By introducing students to the roles and implications of stochastic processes in biological systems, we can help them develop a coherent understanding of the predictable, but not completely deterministic, nature of such systems.

Some minor edits: 5 May 2019 and 2 October 2020 – figures re-inserted 20 June 2020

[1] Scientific animation: protein production and folding

[2] Lac repressor numbers

[3] See this recording of stem cells

[4] Aquaporin and a lipid membrane

[5] PhET gene expression basics  – see panel 3

[6] http://virtuallaboratory.colorado.edu/LDF+binding-interactions/1.2-interactions-0.html

Recognizing scientific illiteracy

This is the first of the blog posts that I prepared for the PLOS science-education blog, a blog that later moved to bioliteracy.  I am a PLOS ONE author and Academic Editor. For more about my work click @ ORCID or my lab website.

Scientific literacy – what is it, how to recognize it, and how to help people achieve it through educational efforts, remains a difficult topic.  The latest attempt to inform the conversation is a recent National Academy report “Science Literacy: concepts, contexts, and consequences”.  While there is lots of substance to take away from the report, three quotes seem particularly telling to me. The first is from Roberts [1] that points out that scientific literacy has “become an umbrella concept with a sufficiently broad, composite meaning that it meant both everything, and nothing specific, about science education and the competency it sought to describe.”   The second quote, from the report’s authors, is that “In the field of  education, at least, the lack of consensus surrounding science literacy has not stopped it from occupying a prominent place in policy discourse” (p. 2.6).  And finally, “the data suggested almost no relationship between general science knowledge and attitudes about genetically modified food, a potentially negative relationship between biology-specific knowledge and attitudes about genetically modified food, and a small, but negative relationship between that same general science knowledge measure and attitudes toward environmental science” (p. 5.4).

cosmology

Recognizing the scientifically illiterate

Perhaps it would be useful to consider the question of scientific literacy from a different perspective, namely, how can we recognize a scientifically illiterate person based on what they write or say? What clues imply illiteracy?[1]   To start, let us consider the somewhat simpler situation of standard literacy.  Constructing a literate answer implies two distinct abilities: the respondent needs to be able to  accurately interpret what the question asks and they need to recognize what an adequate answer contains. These are not innate skills; students need feedback and practice in both, particularly when the question is a scientific one. In my own experience with teaching, as well as data collected in the context of an introductory course [2], all to often a student’s answers consist of a single technical term, spoken (or written) as if a word = an argument or explanation. We need a more detailed response in order to accurately judge whether an answer addresses what the question asks (whether it is relevant) and whether it has a logical coherence and empirical foundations, information that is traditionally obtained through a Socratic interrogation.[2]  At the same time, an answer’s relevance and coherence serve as a proxy for whether the respondent understood (accurately interpreted) what was being asked of them.

So what is added when we move to scientific literacy, what is missing from the illiterate response.  At the simplest level we are looking for mistakes, irrelevancies, failures in logic, or in recognizing contradictions within the answer, explanation or critique. The presence of unnecessary language suggests, at the very least, a confused understanding of the situation.[3]  A second feature of a scientifically illiterate response is a failure to recognize the limits of scientific knowledge; this includes an explicit recognition of the tentative nature of science, combined with the fact that some things are, theoretically, unknowable scientifically.  For example, is “dark matter” real or might an alternative model of gravity remove its raison d’être?[4]  (see “Dark Matter: The Situation has Changed“

When people speculate about what existed before the “big bang” or what is happening in various unobservable parts of the multiverse, they have left science for fantasy.  Similarly, speculation on the steps to the origin of life on Earth (including what types of organisms, or perhaps better put living or pre-living systems, existed before the “last universal common ancestor”), the presence of “consciousness” outside of organisms, or the probability of life elsewhere in the universe can be seen as transcending either what is knowable or likely to be knowable without new empirical observations.  While this can make scientific pronouncements somewhat less dramatic or engaging, respecting the limits of scientific discourse avoids doing violence to the foundations upon which the scientific enterprise is built.  It is worth being explicit, universal truth is beyond the scope of the scientific enterprise.

The limitations of scientific explanations

Acknowledging the limits of scientific explanations is a marker of understanding how science actually works.  As an example, while a drug may be designed to treat a particular disease, a scientifically literate person would reject the premise that any such drug could, given the nature of interactions with other molecular targets and physiological systems, be without side effects and that these side effects will vary depending upon the features (genetic, environmental, historic, physiological) of the individual taking the drug.  While science knowledge reflects a social consensus, it is constrained by rules of evidence and logic (although this might appear to be anachronistic in the current post- and more recently alternative-fact age).

Even though certain ideas are well established (Laws of Conservation and Thermodynamics, and a range of evolutionary mechanisms), it is possible to imagine exceptions (and revisions).  Moreover, since scientific inquiry is (outside of some physics departments) about a single common Universe, conclusions from different disciplines cannot contradict one another – such contradictions must inevitably be resolved through modification of one or the other discipline.  A classic example is Lord Kelvin’s estimate of the age of the Earth (~20 to 50 million years) and estimates of the time required for geological and evolutionary processes to produce the observed structure of the Earth and the diversity of life (hundreds of millions to billions of years), a contradiction resolved in favor of an ancient Earth by the discovery of radioactivity.

Scientific illiteracy in the scientific community

There are also suggestions of scientific illiteracy (or perhaps better put, sloppy and/or self-serving thinking) in much of the current “click-bait” approach to the public dissemination of scientific ideas and observations.  All too often, scientific practitioners, who we might expect to be as scientifically literate as possible, abandon the discipline of science to make claims that are over-arching and often self-serving (this is, after all, why peer-review is necessary).

A common example [of scientific illiteracy practiced by scientists and science communicators] is provided by studies of human disease in “model” organisms, ranging from yeasts to non-human primates. While there is no doubt that such studies have been, and continue to be critical to understanding how organisms work (and certainly deserving of public and private support) – their limitations need to be made explicit. While a mouse that displays behavioral defects (for a mouse) might well provide useful insights into the mechanisms involved in human autism, an autistic mouse may well be a scientific oxymoron.

Discouraging scientific illiteracy within the scientific community is challenging, particularly in the highly competitive, litigious,[5] and high stakes environment we find ourselves in.[6]  How to best help our students, both within and without scientific disciplines, avoid scientific illiteracy remains unclear, but is likely to involve establishing a culture of Socratic discourse (as opposed to posturing).  Understanding what a person is saying, what empirical data and assumptions it is based on, and what it implies and or predicts are necessary features of literate discourse.

Minor edits 23 October 2020; added SH Dark Matter video 15 June 2021. Twitter @mikeklymkowsky

Literature cited:

  1. Roberts, D.A., Scientific literacy/science literacy. I SK Abell & NG Lederman (Eds.). Handbook of research on science education (pp. 729-780). 2007, Mahwah, NJ: Lawrence Erlbaum.
  2. 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, in press., 2016. in press.
  3. Lee, H.-S., O.L. Liu, and M.C. Linn, Validating measurement of knowledge integration in science using multiple-choice and explanation items. Applied Measurement in Education, 2011. 24(2): p. 115-136.
  4. Henson, K., M.M. Cooper, and M.W. Klymkowsky, Turning randomness into meaning at the molecular level using Muller’s morphs. Biol Open, 2012. 1: p. 405-10.

[1] Assuming, of course, that what a person’s says reflects what they actually think, something that is not always the case.

[2] This is one reason why multiple-choice concept tests consistently over-estimate students’ understanding ( 3. Lee, H.-S., O.L. Liu, and M.C. Linn, Validating measurement of knowledge integration in science using multiple-choice and explanation items. Applied Measurement in Education, 2011. 24(2): p. 115-136.)

[3] We have used this kind of analysis to consider the effect of various learning activities

[4] http://curious.astro.cornell.edu/physics/108-the-universe/cosmology-and-the-big-bang/dark-matter/659-could-a-different-theory-of-gravity-explain-the-dark-matter-mystery-intermediate

[5] http://science.sciencemag.org/content/353/6303/977 and http://www.cjr.org/the_observatory/local_science_fraud_misses_nat.php

[6] See as an example: http://www.nature.com/news/bitter-fight-over-crispr-patent-heats-up-1.17961 &  http://www.sciencemag.org/news/2016/03/accusations-errors-and-deception-fly-crispr-patent-fight