128 CATALYZING INQUIRY
relates to more realistic situations. In this case, simulation takes over where analysis ends.^26 Some systems
are simply too large or elaborate to be understood using analytical techniques. In this case, simulation is a
primary tool. Forecasts requiring heavy “number-crunching” (e.g., weather prediction, prediction of cli-
mate change), as well as those involving huge systems of diverse interacting components (e.g., cellular
networks of signal transduction cascades), are only amenable to exploration using simulation methods.
More detailed models require a detailed consideration of chemical or physical mechanisms involved
(i.e., these models are mechanistic^27 ). Such models require extensive details of known biology and have
the largest data requirements. They are, in principle, the most predictive. In the extreme, one can imagine
a simulation of a complete cell—an “in silico” cell or cybercell—that provides an experimental framework
in which to investigate many possible interventions. Getting the right format, and ensuring that the in
silico cell is a reasonable representation of reality, has been and continues to be an enormous challenge.
No reasonable model is based entirely on a bottom-up analysis. Consider, for example, that solving
Schrödinger’s equation for the millions of atoms in a complex molecule in solution would be a futile
exercise, even if future supercomputers could handle this task. The question to ask is how and why such
work would be contemplated: finding the correct level of representation is one of the key steps to good
scientific work. Thus, some level of abstraction is necessary to render any model both interesting
scientifically and feasible computationally. Done properly, abstractions can clarify the sources of con-
trol in a network and indicate where more data are necessary. At the same time, it may be necessary to
construct models at higher degrees of biophysical realism and detail in any event, either because
abstracted models often do not capture the essential behavior of interest or to show that indeed the
addition of detail does not affect the conclusions drawn from the abstracted model.^28
It is also helpful to note the difference between a computational artifact that reproduces some
biological behavior (a task) and a simulation. In the former case, the relevant question is: “How well
does the artifact accomplish the task?” In the latter case, the relevant question is: “How closely does the
simulation match the essential features of the system in question?”
Most computer scientists would tend to assign higher priority to performance than to simulation.
The computer scientist would be most interested in a biologically inspired approach to a computer
science problem when some biological behavior is useful in a computational or computer systems
context and when the biologically inspired artifact can demonstrate better performance than is possible
through some other way of developing or inspiring the artifact. A model of a biological system then
becomes useful to the computer scientist only to the extent that high-fidelity mimicking of how nature
accomplishes a task will result in better performance of that task.
By contrast, biologists would put greater emphasis on simulation. Empirically tested and validated
simulations with predictive capabilities would increase their confidence that they understood in some
fundamental sense the biological phenomenon in question. However, it is important to note that be-
cause a simulation is judged on the basis of how closely it represents the essential features of a biological
system, the question “What counts as essential?” is central (Box 5.3). More generally, one fundamental
focus of biological research is a determination of what the “essential” features of a biological system are,
(^26) At times, it is also desirable to employ a mix of analysis and simulation. Analysis would be used to generate the basic
equations underlying a complex phenomenon. Solutions to these equations would then be explored and with luck, considerably
simplified. The simplified models can then be simulated. See, for example, E.A. Ezrachi, R. Levi, J.M. Camhi, and H. Parnas,
“Right-Left Discrimination in a Biologically Oriented Model of the Cockroach Escape System,” Biological Cybernetics 81(2):89-99,
1999.
(^27) Note that mechanistic models can be stochastic—the term “mechanistic” should not be taken to mean deterministic.
(^28) Tensions between these perspectives were apparent even in reviews of the draft of this report. In commenting on neuro-
science topics in this report, advocates of the first point of view argued that ultrarealistic simulations accomplish little to further
our understanding about how neurons work. Advocates of the second point of view argued that simple neural models could not
capture the implications of the complex dynamics of each neuron and its synapses and that these models would have to be
supplemented by more physiological ideas. From the committee’s perspective, both points of view have merit, and the scientific
challenge is to find an appropriate simplification or abstraction that does capture the interesting behavior at reasonable fidelity.