Modeling transcriptional control in gene networks--methods, recent results, and future directions.
Mathematical models are useful for providing a framework for integrating data and gaining insights into the static and dynamic behavior of complex biological systems such as networks of interacting genes. We review the dynamic behaviors expected from model gene networks incorporating common biochemical motifs, and we compare current methods for modeling genetic networks. A common modeling technique, based on simply modeling genes as ON-OFF switches, is readily implemented and allows rapid numerical simulations. However, this method may predict dynamic solutions that do not correspond to those seen when systems are modeled with a more detailed method using ordinary differential equations. Until now, the majority of gene network modeling studies have focused on determining the types of dynamics that can be generated by common biochemical motifs such as feedback loops or protein oligomerization. For example, these elements can generate multiple stable states for gene product concentrations, state-dependent responses to stimuli, circadian rhythms and other oscillations, and optimal stimulus frequencies for maximal transcription. In the future, as new experimental techniques increase the ease of characterization of genetic networks, qualitative modeling will need to be supplanted by quantitative models for specific systems.
Smolen
P
P
Department of Neurobiology and Anatomy, W.M. Keck Center for the Neurobiology of Learning and Memory, University of Texas-Houston Medical School 77225, USA.
Baxter
D A
DA
Byrne
J H
JH
D000818:Animals; D004926:Escherichia coli / Q000235:genetics; D005246:Feedback; D005786:Gene Expression Regulation; D006801:Humans; D008568:Memory; D008957:Models, Genetic; D012617:Sea Urchins / Q000235:genetics; D015398:Signal Transduction / Q000235:genetics; D013269:Stochastic Processes; D014157:Transcription Factors / Q000235:genetics; D014158:Transcription, Genetic / Q000235:genetics*
D014157:Transcription Factors