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Catalyzing Inquiry at the Interface of Computing and Biology
Different fields of science have traditionally used models for different purposes; thus, the nature of the models, the criteria for selecting good or appropriate models, and the nature of the abbreviation or simplification have varied dramatically. For example, biologists are quite familiar with the notion of model organisms.1 A model organism is a species selected for genetic experimental analysis on the basis of experimental convenience, homology to other species (especially to humans), relative simplicity, or other attractive attributes. The fruit fly Drosophila melanogaster is a model organism attractive at least in part because of its short generational time span, allowing many generations in the course of an experiment.
At the most basic level, any abstraction of some biological phenomenon counts as a model. Indeed, the cartoons and block diagrams used by most biologists to represent metabolic, signaling, or regulatory pathways are models—qualitative models that lay out the connectivity of elements important to the phenomenon. Such models throw away details (e.g., about kinetics) implicitly asserting that omission of such details does not render the model irrelevant.
A second example of implicit modeling is the use of statistical tests by many biologists. All statistical tests are based on a null hypothesis, and all null hypotheses are based on some kind of underlying model from which the probability distribution of the null hypothesis is derived. Even those biologists who have never thought of themselves as modelers are using models whenever they use statistical tests.
Mathematical modeling has been an important component of several biological disciplines for many decades. One of the earliest quantitative biological models involved ecology: the Lotka-Volterra model of species competition and predator-prey relationships described in Section 5.2.4. In the context of cell biology, models and simulations are used to examine the structure and dynamics of a cell or organism’s function, rather than the characteristics of isolated parts of a cell or organism.2 Such models must consider stochastic and deterministic processes, complex pleiotropy, robustness through redundancy, modular design, alternative pathways, and emergent behavior in biological hierarchy.
In a cellular context, one goal of biology is to gain insight into the interactions, molecular or otherwise, that are responsible for the behavior of the cell. To do so, a quantitative model of the cell must be developed to integrate global organism-wide measurements taken at many different levels of detail.
The development of such a model is iterative. It begins with a rough model of the cell, based on some knowledge of the components of the cell and possible interactions among them, as well as prior biochemical and genetic knowledge. Although the assumptions underlying the model are insufficient and may even be inappropriate for the system being investigated, this rough model then provides a zeroth-order hypothesis about the structure of the interactions that govern the cell’s behavior.
Implicit in the model are predictions about the cell’s response under different kinds of perturbation. Perturbations may be genetic (e.g., gene deletions, gene overexpressions, undirected mutations) or environmental (e.g., changes in temperature, stimulation by hormones or drugs). Perturbations are introduced into the cell, and the cell’s response is measured with tools that capture changes at the relevant levels of biological information (e.g., mRNA expression, protein expression, protein activation state, overall pathway function). Box 5.1 provides some additional detail on cellular perturbations.
The next step is comparison of the model’s predictions to the measurements taken. This comparison indicates where and how the model must be refined in order to match the measurements more closely. If the initial model is highly incomplete, measurements can be used to suggest the particular components required for cellular function and those that are most likely to interact. If the initial model is relatively well defined, its predictions may already be in good qualitative agreement with measurement, differing only in minor quantitative ways. When model and measurement disagree, it is often
Section 5.1 draws heavily on excerpts from T. Ideker, T. Galitski, and L. Hood, “A New Approach to Decoding Life: Systems Biology,” Annual Review of Genomics and Human Genetics 2:343-372, 2001; and H. Kitano, “Systems Biology: A Brief Overview,” Science 295(5560):1662-1664, 2002.