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BIOLOGICAL DISCOVERY 5 COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR
Enablers for Biological Discovery
While the previous chapter deals with the ways in which computers and algorithms could support
existing practices of biological research, this chapter introduces a different type of opportunity. The
quantities and scopes of data being collected are now far beyond the capability of any human, or team
of humans, to analyze. And as the sizes of the datasets continue to increase exponentially, even existing
techniques such as statistical analysis begin to suffer. In this data-rich environment, the discovery of
large-scale patterns and correlations is potentially of enormous significance. Indeed, such discoveries
can be regarded as hypotheses asserting that the pattern or correlation may be important—a mode of
“discovery science” that complements the traditional mode of science in which a hypothesis is gener-
ated by human beings and then tested empirically.
For exploring this data-rich environment, simulations and computer-driven models of biological
systems are proving to be essential.
5.1 ON MODELS IN BIOLOGY
In all sciences, models are used to represent, usually in an abbreviated form, a more complex and
detailed reality. Models are used because in some way, they are more accessible, convenient, or familiar
to practitioners than the subject of study. Models can serve as explanatory or pedagogical tools, repre-
sent more explicitly the state of knowledge, predict results, or act as the objects of further experiments.
Most importantly, a model is a representation of some reality that embodies some essential and interest-
ing aspects of that reality, but not all of it.
Because all models are by definition incomplete, the central intellectual issue is whether the essen-
tial aspects of the system or phenomenon are well represented (the term “essential” has multiple
meanings depending on what aspects of the phenomenon are of interest). In biological phenomena,
what is interesting and significant is usually a set of relationships—from the interaction of two mol-
ecules to the behavior of a population in its environment. Human comprehension of biological systems
is limited, among other things, by that very complexity and by the problems that arise when attempting
to dissect a given system into simpler, more easily understood components. This challenge is com-
pounded by our current inability to understand relationships between the components as they occur in
reality, that is, in the presence of multiple, competing influences and in the broader context of time and
space.