Mathematical modeling of gene expression: a guide for the perplexed biologist

Abstract

The detailed analysis of transcriptional networks holds a key for understanding central biological processes, and interest in this field has exploded due to new large-scale data acquisition techniques. Mathematical modeling can provide essential insights, but the diversity of modeling approaches can be a daunting prospect to investigators new to this area. For those interested in beginning a transcriptional mathematical modeling project, we provide here an overview of major types of models and their applications to transcriptional networks. In this discussion of recent literature on thermodynamic, Boolean, and differential equation models, we focus on considerations critical for choosing and validating a modeling approach that will be useful for quantitative understanding of biological systems.

Keywords::

• Gene regulation
• thermodynamic models
• differential equation models
• Boolean models
• model selection
• parameter estimation
• sensitivity analysis
• Drosophila melanogaster

Acknowledgement

We would like to thank Jackie Dresch, Jacob Clifford, Rupinder Sayal, Yerzhan Suleimenov, and members of the Arnosti laboratory for thoughtful discussions.

Declaration of interest

The authors declare to have no conflict of interest. The authors alone are responsible for the content and the writing of the manuscript. This project was supported by a Special Projects Grant from the MSU Foundation and NIH GM 56976 to D.N.A., and fellowships from the MSU Quantitative Biology Initiative and Gene Expression in Development and Disease focus group to A.A.

Editor: Michael M. Cox