From topology to dynamics in biochemical networks
Abstract
Abstract formulations of the regulation of gene expression as random Boolean switching networks have been studied extensively over the past three decades. These models have been developed to make statistical predictions of the types of dynamics observed in biological networks based on network topology and interaction bias, p. For values of mean connectivity chosen to correspond to real biological networks, these models predict disordered dynamics. However, chaotic dynamics seems to be absent from the functioning of a normal cell. While these models use a fixed number of inputs for each element in the network, recent experimental evidence suggests that several biological networks have distributions in connectivity. We therefore study randomly constructed Boolean networks with distributions in the number of inputs, K, to each element. We study three distributions: delta function, Poisson, and power law (scale free). We analytically show that the critical value of the interaction bias parameter, p, above which steady state behavior is observed, is independent of the distribution in the limit of the number of elements N→∞. We also study these networks numerically. Using three different measures (types of attractors, fraction of elements that are active, and length of period), we show that finite, scalefree networks are more ordered than either the Poisson or delta function networks below the critical point. Thus the topology of scalefree biochemical networks, characterized by a wide distribution in the number of inputs per element, may provide a source of order in living cells.
 Publication:

Chaos
 Pub Date:
 December 2001
 DOI:
 10.1063/1.1414882
 Bibcode:
 2001Chaos..11..809F
 Keywords:

 87.15.He;
 05.45.Gg;
 87.16.Yc;
 87.15.Rn;
 05.40.a;
 Dynamics and conformational changes;
 Control of chaos applications of chaos;
 Regulatory chemical networks;
 Reactions and kinetics;
 polymerization;
 Fluctuation phenomena random processes noise and Brownian motion