COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 153
The computational model undoubtedly provides a compact representation of the relationships
between different inputs and different outputs.^70 Perhaps a more interesting question, however, is the
extent to which it is meaningful to ascribe a computational function to the biochemical substrate under-
lying the regulatory system. Davidson et al. argue that the DNA sequence in this case specifies “what is
essentially a hard-wired, analog computational device,” resulting in system properties that are “all
explicitly specified in the genomic DNA sequence.”^71
It is highly unlikely that the precise computational structure of endo16’s regulatory system will
generalize to the regulatory systems of other genes. From the perspective of the biologist, the reason is
clear—organisms are not designed as general-purpose devices. Indeed, the evolutionary process virtu-
ally guarantees that individualized solutions and architectures will be abundant, because specific adap-
tations are the rule of the day. Nevertheless, insight into the computational behavior of the endo16 cis-
regulatory system provides a new way of looking at biological behavior.
Can the regulatory systems of some other genes be cast in similar computational terms? If and when
future work demonstrates that such casting is possible, it will become increasingly meaningful to view
the genome as thousands of simple computational devices operating in tandem. Davidson’s work
suggests the possibility that a class of regulatory mechanisms, complex though they might be with
respect to their behavior, may be governed by what are in essence hard-wired devices whose essential
functionality can be understood in computational terms through a logic of operation that is in fact
relatively simple at its core. Prior to Davidson’s work and despite extensive research, the literature had
not revealed any apparent regularity in the organization of regulatory elements or in the ways in which
they interact to regulate gene expression.
Indeed, while many promoters appear either to have a simpler organization or to operate less
logically than that of endo16, few promoters have been examined with the many precise quantitative
assays that were carried out by Davidson et al., and nonquantitative assays would have completely
missed most of the functions that the majority of the regulatory system’s elements encode.^72 So, it is at
this point an open question whether this computational view has applicability beyond the specific case
of endo16.
5.4.3.2 Genetic Regulatory Networks as Finite-state Automata,
Trans-regulation (as contrasted to cis-regulation) is based on the notion that some genes can have
regulatory effects on others.^73 In reality, the network of connections between genes that regulate and
genes that are regulated is highly complex. In an attempt to gain insight into genetic regulatory net-
works from a gross oversimplification, Kaufmann proposed that actual genetic regulatory networks
might be modeled as randomly connected Boolean networks.^74
Kaufmann’s model made several simplifying assumptions:
(^70) E.F. Keller, Making Sense of Life: Explaining Biological Development with Models, Metaphors, and Machines, Harvard University
Press, Cambridge, MA, 2002, p. 241.
(^71) This is not to argue that DNA sequence alone is responsible for the specification of system properties. Epigenetic control
mechanisms also influence system properties as do environmental conditions and cell state that are not specified in DNA. An
analogy might be that although a memory dump of a computer specifies the state of the computer, many contingent activities
may affect the actual execution path. For example, the behavior (and timing) of specific input-output activities are likely to be
relevant.
(^72) G.A. Wray, “Promoter Logic,” Science 279(5358):1871-1872, 1998.
(^73) For purposes of the discussion in this subsection (Section 5.4.3.2), regulation refers to trans-regulation.
(^74) Much of this work is due to the pioneering work of Stuart Kauffman. See for example, S.A. Kauffman, The Origins of Order:
Self-Organization and Selection in Evolution, Oxford University Press, New York, 1993. An alternative discussion of this material
can be found at http://www.smi.stanford.edu/projects/helix/bmi214/ (May 13); lecture notes of Russell Altman.