Modeling and Simulation of Genetic Regulatory Systems: A Literature Review Export

@article{deJong2002mas, abstract = {In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.}, author = {de Jong, H.}, citeulike-article-id = {411464}, citeulike-linkout-0 = {http://www.liebertonline.com.ezproxy.lib.ucalgary.ca/doi/abs/10.1089/10665270252833208}, citeulike-linkout-1 = {http://dx.doi.org/10.1089/10665270252833208}, doi = {10.1089/10665270252833208}, journal = {Journal of Computational Biology}, keywords = {genetic\_regulatory\_networks, kinematics}, month = {January}, number = {1}, pages = {67--103}, posted-at = {2005-11-29 21:55:30}, priority = {2}, title = {Modeling and Simulation of Genetic Regulatory Systems: A Literature Review}, url = {http://dx.doi.org/10.1089/10665270252833208}, volume = {9}, year = {2002} }

TY - JOUR ID - deJong2002mas L3 - citeulike-article-id:411464 N2 - In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems. IS - 1 JF - Journal of Computational Biology EP - 103 TI - Modeling and Simulation of Genetic Regulatory Systems: A Literature Review VL - 9 SP - 67 KW - genetic_regulatory_networks KW - kinematics AU - de Jong, H PY - 2002/01// UR - http://dx.doi.org/10.1089/10665270252833208 ER -