ArticlePLoS computational biology2016
Non-monotonic Response to Monotonic Stimulus: Regulation of Glyoxylate Shunt Gene-Expression Dynamics in Mycobacterium tuberculosis.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Cumulative dose responses for adapting biological systems.Journal of the Royal Society, Interface · 2025Article
- A computational framework for a Lyapunov-enabled analysis of biochemical reaction networks.PLoS computational biology · 2020Article
- NF-κB signaling dynamics is controlled by a dose-sensing autoregulatory loop.Science signaling · 2019Article
- Hierarchical and Dynamic Regulation of Defense-Responsive Specialized Metabolism by WRKY and MYB Transcription Factors.Frontiers in plant science · 2019Article
- Subharmonics and Chaos in Simple Periodically Forced Biomolecular Models.Biophysical journal · 2018Article
- Oscillatory stimuli differentiate adapting circuit topologies.Nature methods · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Understanding how dynamical responses of biological networks are constrained by underlying network topology is one of the fundamental goals of systems biology. Here we employ monotone systems theory to formulate a theorem stating necessary conditions for non-monotonic time-response of a biochemical network to a monotonic stimulus. We apply this theorem to analyze the non-monotonic dynamics of the σB-regulated glyoxylate shunt gene expression in Mycobacterium tuberculosis cells exposed to hypoxia. We first demonstrate that the known network structure is inconsistent with observed dynamics. To resolve this inconsistency we employ the formulated theorem, modeling simulations and optimization along with follow-up dynamic experimental measurements. We show a requirement for post-translational modulation of σB activity in order to reconcile the network dynamics with its topology. The results of this analysis make testable experimental predictions and demonstrate wider applicability of the developed methodology to a wide class of biological systems.
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Registered trials
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