Journal
BULLETIN OF MATHEMATICAL BIOLOGY
Volume 62, Issue 2, Pages 247-292Publisher
SPRINGER
DOI: 10.1006/bulm.1999.0155
Keywords
-
Categories
Funding
- NATIONAL CENTER FOR RESEARCH RESOURCES [R01RR011626] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [T32NS007373, P01NS038310] Funding Source: NIH RePORTER
- NCRR NIH HHS [R01 RR11626] Funding Source: Medline
- NINDS NIH HHS [T32 NS07373, P01 NS38310] Funding Source: Medline
Ask authors/readers for more resources
Mathematical models are useful for providing a framework for integrating data and graining 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. (C) 2000 Society for Mathematical Biology.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available