124 CATALYZING INQUIRY
5.2.10 Models Can Link What Is Known to What Is Yet Unknown,
In the words of Pollard, “Any cellular process involving more than a few types of molecules is too
complicated to understand without a mathematical model to expose assumptions and to frame the
reactions in a rigorous setting.”^10 Reviewing the state of the field in cell motility and the cytoskeleton,
he observes that even with many details of the mechanism as yet controversial or unknown, modeling
plays an important role. Referring to a system (of actin and its interacting proteins) modeled by Mogilner
and Edelstein-Keshet,^11 he points to advantages gained by the mathematical framework: “A math-
ematical model incorporating molecular reactions and physical forces correctly predicts the steady-state
rate of cellular locomotion.” The model, he notes, correctly identifies what limits the motion of the cell,
predicts what manipulations would change the rate of motion, and thus suggests experiments to per-
form. While details of some steps are still emerging, the model also distinguishes quantitatively be-
tween distinct hypotheses for how actin filaments are broken down for purposes of recycling their
components.
5.2.11 Models Can Be Used to Generate Accurate Quantitative Predictions,
Where detailed quantitative information exists about components of a system, about underlying
rules or interactions, and about how these components are assembled into the system as a whole,
modeling may be valuable as an accurate and rigorous tool for generating quantitative predictions.
Weather prediction is one example of a complex model used on a daily basis to predict the future. On
the other hand, the notorious difficulties of making accurate weather predictions point to the need for
caution in adopting the conclusions even of classical models, especially for more than short-term pre-
dictions, as one might expect from mathematically chaotic systems.
5.2.12 Models Expand the Range of Questions That Can Meaningfully Be Asked,
For much of life science research, questions of purpose arise about biological phenomena. For
instance, the question, Why does the eye have a lens? most often calls for the purpose of the lens—to
focus light rays—and only rarely for a description of the biological mechanism that creates the lens.
That such an answer is meaningful is the result of evolutionary processes that shape biological entities
by enhancing their ability to carry out fitness-enhancing functions. (Put differently, biological entities
are the result of nature’s engineering of devices to perform the function of survival; this perspective is
explored further in Chapter 6.)
Lander points out that molecular biologists traditionally have shied away from teleological matters,
and that geneticists generally define function not in terms of the useful things a gene does, but by what
happens when the gene is altered. However, as the complexity of biological mechanism is increasingly
revealed, the identification of a purpose or a function of that mechanism has enormous explanatory
power. That is, what purpose does all this complexity serve?
As the examples in Section 5.4 illustrate, computational modeling is an approach to exploring the
implications of the complex interactions that are known from empirical and experimental work. Lander
notes that one general approach to modeling is to create models in which networks are specified in
terms of elements and interactions (the network “topology”), but the numerical values that quantify
those interactions (the parameters) are deliberately varied over wide ranges to explore the functionality
of the network—whether it acts as a “switch,” “filter,” “oscillator,” “dynamic range adjuster,” “pro-
ducer of stripes,” and so on.
(^10) T.D. Pollard, “The Cytoskeleton, Cellular Motility and the Reductionist Agenda,” Nature 422(6933):741-745, 2003.
(^11) A. Mogilner and L. Edelstein-Keshet, “Regulation of Actin Dynamics in Rapidly Moving Cells: A Quantitative Analysis,”
Biophysical Journal 83(3):1237-1258, 2002.
(^12) Section 5.2.12 is based largely on A.D. Lander, “A Calculus of Purpose,” PLoS Biology 2(6):e164, 2004.