COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 157
If the correspondence between attractor and cell is assumed, malignancy can be viewed as an
attractor similar in most ways to that associated with a normal cell,^79 and the transition from normal to
malignant is represented by a “phase transition” from one attractor to another. Such a transition might
be induced by an external event (radiation, chemical exposure, lack of nutrients, and so on).
As one illustration, Szallasi and Liang argue that changes in large-scale gene expression patterns
associated with conversion to malignancy depend on the nature of attractor transition in the underlying
genetic network in three ways:
- A specific oncogene can induce changes in the state of downstream genes (i.e., genes for which
the oncogene is part of their regulatory network) and transition rules for those genes without driving
the system from one attractor to another one. If this is true, inhibition of the oncogene will result a
reversion of those downstream changes and a consequent normal phenotype. In some cases, just such
phenomenology has been suggested,^80 although whether or not this mechanism is the basis of some
forms of human cancer is unknown as yet. - A specific oncogene could force the system to leave one attractor and flow into another one. The
new attractor might have a much shorter cycle time (implying rapid cell division and reproduction)
and/or be more resistant to outside perturbations (implying difficulty in killing those cells). In this case,
inhibition of the oncogene would not result in reversion to a normal cellular state. - A set of “partial” oncogenes may force the system into a new attractor. In this case, no individual
partial oncogene would induce a phenotypical change by itself—however, the phenomenology associ-
ated with a new attractor would be similar.
These different scenarios have implications for both research and therapy. From a research perspec-
tive, the operation of the second and third mechanisms implies that the network’s trajectory through
state space is entirely different, a fact that would impede the effectiveness of traditional methodologies
that focus on one or a few regulatory pathways or oncogenes. From a therapeutic standpoint, the
operation of the latter two mechanisms implies that a focus on “knocking out the causal oncogene” is
not likely to be very effective.
5.4.3.3 Genetic Regulation as Circuits,
Genetic networks can also be modeled as electrical circuits.^81 In some ways, the electrical circuit
analogy is almost irresistible, as can be seen from a glance at any of the known regulatory pathways: the
tangle of links and nodes could easily pass for a circuit diagram of Intel’s latest Pentium chip. For
example, McAdams and Shapiro described the regulatory network that governs the course of a λ-phage
infection in E. coli as a circuit, and included factors such as time delays, which are critical in biological
networks (gene transcription and translation are not instantaneous, for example) and indeed, in electri-
cal networks, as well.
More generally, nature’s designs for the cellular circuitry seems to draw on any number of tech-
niques that are very familiar from engineering: “The biochemical logic in genetic regulatory circuits
provides real-time regulatory control [via positive and negative feedback loops], implements a branch-
(^79) S.A. Kauffman, “Differentiation of Malignant to Benign Cells,” Journal of Theoretical Biology 31:429, 1971. (Cited in Szallasi and
Liang, 1998.)
(^80) S. Baasner, H. von Melchner, T. Klenner, P. Hilgard, and T. Beckers, “Reversible Tumorigenesis in Mice by Conditional
Expression of the HER2/c-erbB2 Receptor Tyrosine Kinase,” Oncogene 13(5):901, 1996. (Cited in Szallasi and Liang, 1998.)
(^81) H.H. McAdams and L. Shapiro, “Circuit Simulation of Genetic Networks,” Science 269(5224):650-656, 1995.