COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 195
of the system. “The challenge, then, is to develop mechanistic models that begin from what is under-
stood about the interactions of the individual units, and to use computation and analysis to explain
emergent behavior in terms of the statistical mechanics of ensembles of such units.” Such models must
extrapolate from the effects of change on individual plants and animals to changes in the distribution of
individuals over longer time scales and broader space scales and hence in community-level patterns
and the fluxes of nutrients.
5.4.8.2.1 Reconstruction of the Saccharomyces Phylogenetic TreeAlthough the basic structure and
mechanisms underlying evolution and genetics are known in principle, there are many complexities
that force researchers into computational approaches in order to gain insight. Box 5.23 addresses com-
plexities such as multiple loci, spatial factors, and the role of frequency dependence in evolution, and
discusses a computational perspective on the evolution of altruism, a behavioral characteristic that is
counterintuitive in the context of individual organisms doing all that they can to gain advantage in the
face of selection pressures.
Box 5.22
The Dynamics of EvolutionAvida is a simulation software system developed at the Digital Life Laboratory at the California Institute of
Technology.^1 In it, digital organisms have genomes comprised of a sequence of instructions that operate on a
virtual machine. These instructions include the ability to perform simple mathematical operations, copy val-
ues from memory location to memory location, provide input and output, and check conditions. Through a
sequence of instructions, these organisms can copy their genome, thereby reproducing asexually. Since the
software can simulate many hundreds of thousands of generations of evolution for thousands of organisms,
their digital evolution not only can be observed in reasonable lengths of time, but also can be precisely
inspected (since there are no inconvenient gaps in the fossil record). Moreover, alternate scenarios can be
explored by going back into evolutionary history and reversing the effects of mutations, for example. At a
minimum, this can be seen as experiment by analogy, revealing potential avenues for investigation or hypoth-
eses to test in actual biological evolution. A stronger argument holds that evolution is an abstract mathemat-
ical process and will operate under similar dynamics whether embodied in DNA in the physical world or in
digital simulations of it.Avida has been used to explore how complex features can arise through mutation, competition, and selective
pressure.^2 In a series of experiments, organisms were provided with a limited supply of energy units necessary
for the execution of their genome of instructions. However, organisms that performed any of a set of complex
logical operations were rewarded with an increased allowance and thus increased opportunities to reproduce.
More complicated logical operations provided proportionally greater rewards.The experiment was seeded with an ancestral form that could perform none of those operations, containing
only the instructions to reproduce. Mutation arose through imperfect copying of the genome during reproduc-
tion. EQU, the most complex logical operation checked for [representing the logical statement (A and B) or
(~A and ~B)], arose in 23 out of 50 populations studied where the simpler operations also provided rewards.
The sequence of instructions that evolved to perform the operation varied widely in length and implementa-
tion. However, in other simulations where only EQU was rewarded, no lineages ever evolved it. This evi-
dence agrees with the standard theory of biological evolution—stated as early as Darwin—that complex
structures arise through the combination and modification of useful intermediate forms.(^1) C. Adami, Introduction to Artificial Life, Springer-Verlag, New York, 1998.
(^2) R.E. Lenski, C. Ofria, R.T. Pennock, and C. Adami, “The Evolutionary Origin of Complex Features,” Nature 423:139-144, 2003.