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Sequential construction of a model for modular gene expression control, applied to spatial patterning of the Drosophila gene hunchback

Computer Science and CEWIT, SUNY Stony Brook, 1500 Stony Brook Road, Stony Brook, NY 11794, USA

Lab Modeling of Evolution, I. M. Sechenov Institute of Evolutionary Physiology and Biochemistry, Russian Academy of Sciences, pr. Torez 44, St. Petersburg 194223, Russia

E-mail Address: [email protected]; [email protected]

Corresponding author.

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Ekaterina M. Myasnikova

Center for Advanced Studies, Peter the Great St. Petersburg Polytechnical University, 29 Polytechnicheskaya St. Petersburg 195251, Russia

Department of Bioinformatics, Moscow Institute of Physics and Technology, 9 Institutskiy per., Dolgoprudny, Moscow 141700, Russia

E-mail Address: [email protected]

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, and

David M. Holloway

Mathematics Department, British Columbia Institute of Technology, 3700 Willingdon Avenue, Burnaby, BC, Canada V5G 3H2, Canada

Department of Biology, University of Victoria, Victoria, BC, Canada V8W 2Y2, Canada

E-mail Address: [email protected]

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https://doi.org/10.1142/S0219720016410055Cited by:7 (Source: Crossref)

Abstract

Gene network simulations are increasingly used to quantify mutual gene regulation in biological tissues. These are generally based on linear interactions between single-entity regulatory and target genes. Biological genes, by contrast, commonly have multiple, partially independent, cis-regulatory modules (CRMs) for regulator binding, and can produce variant transcription and translation products. We present a modeling framework to address some of the gene regulatory dynamics implied by this biological complexity. Spatial patterning of the hunchback (hb) gene in Drosophila development involves control by three CRMs producing two distinct mRNA transcripts. We use this example to develop a differential equations model for transcription which takes into account the cis-regulatory architecture of the gene. Potential regulatory interactions are screened by a genetic algorithms (GAs) approach and compared to biological expression data.

Keywords:

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