Aug 20, 2015

A statistical approach reveals designs for the most robust stochastic gene oscillators

BioRxiv : the Preprint Server for Biology
Mae WoodsChris P Barnes


The engineering of transcriptional networks presents many challenges due to the inherent uncertainty in the system structure, changing cellular context and stochasticity in the governing dynamics. One approach to address these problems is to design and build systems that can function across a range of conditions; that is they are robust to uncertainty in their constituent components. Here we examine the robustness landscape of transcriptional oscillators, which underlie many important processes such as circadian rhythms and the cell cycle, plus also serve as a model for the engineering of complex and emergent phenomena. The central questions that we address are: Can we build genetic oscillators that are more robust than those already constructed? Can we make genetic oscillators arbitrarily robust? These questions are technically challenging due to the large model and parameter spaces that must be efficiently explored. Here we use a measure of robustness that coincides with the Bayesian model evidence combined with an efficient Monte Carlo method to traverse model space and concentrate on regions of high robustness, which enables the accurate evaluation of the relative structural robustness of gene network models governed by sto...Continue Reading

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Mentioned in this Paper

Complex (molecular entity)
Transcription, Genetic
Anatomical Space Structure
Cell Cycle
Circadian Rhythm Pathway 2
Gene Regulatory Networks
RNA, Messenger

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