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Life’s Attractors

Understanding Developmental Systems Through Reverse Engineering and In Silico Evolution
  • Johannes JaegerEmail author
  • Anton Crombach
Chapter
Part of the Advances in Experimental Medicine and Biology book series (volume 751)

Abstract

We propose an approach to evolutionary systems biology which is based on reverse engineering of gene regulatory networks and in silico evolutionary simulations. We infer regulatory parameters for gene networks by fitting computational models to quantitative expression data. This allows us to characterize the regulatory structure and dynamical repertoire of evolving gene regulatory networks with a reasonable amount of experimental and computational effort. We use the resulting network models to identify those regulatory interactions that are conserved, and those that have diverged between different species. Moreover, we use the models obtained by data fitting as starting points for simulations of evolutionary transitions between species. These simulations enable us to investigate whether such transitions are random, or whether they show stereotypical series of regulatory changes which depend on the structure and dynamical repertoire of an evolving network. Finally, we present a case study—the gap gene network in dipterans (flies, midges, and mosquitoes)—to illustrate the practical application of the proposed methodology, and to highlight the kind of biological insights that can be gained by this approach.

Keywords

Gene Regulatory Network Developmental System Reverse Engineering Regulatory Structure Phenotypic Transition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We would like to acknowledge Karl Wotton and Mónica García-Solache for creating the quantitative data sets mentioned in Sect. 4. We thank Damjan Cicin-Sain for designing computational tools and databases that were essential for data processing and quantification. Analyses and models based on those data will be published elsewhere. We are grateful to David Irons and Nick Monk for letting us use their phase portraits of the toggle switch model, shown in Figs. 5.2 and 5.3. Finally, we thank all members of the Jaeger Lab at the CRG for discussions, constructive criticism, and useful feedback on the manuscript.

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Authors and Affiliations

  1. 1.EMBL/CRG Research Unit in Systems BiologyCentre de Regulació Genòmica (CRG), and Universitat Pompeu Fabra (UPF)BarcelonaSpain

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