Information Technology Reference
In-Depth Information
As detailed molecular, as well as high throughput genomic data, accumulate, a
repertoire of gene regulatory networks (GRN) grounded on experimental data is be-
ing built [Mendoza and Alvarez-Buylla (1998); Albert and Othmer (2003); Espinosa-
Soto et al. (2004)]. Furthermore, some of the studies are suggesting that qualitative
models such as Boolean networks recover the fundamental structural characteristics
(eg., topology of GRN interconnections) and dynamical behaviors (eg., attractors
attained that correspond to multigene expression proles characteristic of certain
cell types, and robustness to transient and genetic perturbations). Thus, a con-
tinuous feedback from theoretical studies exploring the behavior of simple model
GRN, to those analyzing the behavior of experimentally grounded biological GRNs
is useful. Such interplay between theoretical and experimental GRNs is provid-
ing guidelines to understanding both generic and specic structural and dynamical
characteristics of biological GRNs, as well as more general issues concerning the
relationship between network structure and dynamics, or network behavior under
noisy environments.
Recent studies are demonstrating that the coordinated expression of multiple
molecular components is essential to sustain functionality in noisy environments
[Aldana et al. (2007)]. The global dynamics of biological GRNs must be robust in
order to guarantee stability under a broad range of external conditions. But, at the
same time, GRNs should be suciently exible to be able to recognize and integrate
external signals that specically elicit adaptive mechanisms to the environmental
challenges met by each organism.
Interestingly, the so called critical dynamical systems, that operate at the brink
of a phase transition between order and chaos, have such a compromised behavior
between robustness and adaptability. Precisely, critical dynamics is related to the
appearance of long-range time correlations observed in many natural systems, as
described at the beginning of the section. A recent study showed that biological
GRNs of a wide range of sizes (eg., large genomic as well as small modules), and
inferred with very dierent methods for contrasting organisms (eg., bacteria, yeast,
plants and animals), all operate close to a critical dynamics [Balleza et al. (2008)].
This result suggests that the capacity to compromise between being able to recover
previous stable states, as well as exploring all of them or new ones, might have
being fundamental during the evolutionary history of GRN assemblage. At the
same time, such a global trait of GRN could have been subject to natural selection
during the early evolution of life, and at the same time may provide an explanation
for the great diversity and dynamically robust forms that have evolved among living
beings.
Cells that function with a critical dynamics are able to keep previous stable
states and at the same time explore among them or even among new ones, are able
to bind past discriminations to future reliable actions. Systems that function under
a critical regime have a near parallel ow of information that optimizes the capacity
to bind past and future decisions. In contrast, ordered dynamical systems converge
