Biological networks 101: computational modeling for molecular biologists

Abstract

Computational modeling of biological networks permits the comprehensive analysis of cells and tissues to define molecular phenotypes and novel hypotheses. Although a large number of software tools have been developed, the versatility of these tools is limited by mathematical complexities that prevent their broad adoption and effective use by molecular biologists. This study clarifies the basic aspects of molecular modeling, how to convert data into useful input, as well as the number of time points and molecular parameters that should be considered for molecular regulatory models with both explanatory and predictive potential. We illustrate the necessary experimental preconditions for converting data into a computational model of network dynamics. This model requires neither a thorough background in mathematics nor precise data on intracellular concentrations, binding affinities or reaction kinetics. Finally, we show how an interactive model of crosstalk between signal transduction pathways in primary human articular chondrocytes allows insight into processes that regulate gene expression.

Keywords: ANIMO; AXIN2; Analysis of Networks with Interactive MOdeling; Axin 2 gene product; B2M; Biologist; Chondrocyte; Computational modeling; ERK; Extracellular Signal-Related Kinase 1, MAPK3; FZD; Frizzled; GINsim; Gene Interaction Network simulation; Gene expression; IL-1B; IL-1BR; IL-1b; JNK; KEGG; Kyoto Encyclopedia of Genes and Genomes; MAPK; MMP; ODE; Ordinary Differential Equation; PP96; Proteome profiler 96; Signal transduction; Wnt; beta-2-microglobulin mRNA; c-Jun N-terminal kinase protein; hours; hrs; interleukin 1, beta mRNA; interleukin 1, beta protein; interleukin 1, beta receptor; matrix metalloprotease; mitogen-activated protein kinase; wingless-type MMTV integration site family.