Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch

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dc.contributor.authorXu, Peng
dc.date.accessioned2020-05-15T14:03:44Z
dc.date.available2020-05-15T14:03:44Z
dc.date.issued2020-04-24
dc.description.abstractLiving organism is an intelligent system coded by hierarchically-organized information to perform precisely-controlled biological functions. Biophysical models are important tools to uncover the design rules underlying complex genetic-metabolic circuit interactions. Based on a previously engineered synthetic malonyl-CoA switch (Xu et al., PNAS, 2014), we have formulated nine differential equations to unravel the design principles underlying an ideal metabolic switch to improve fatty acids production in E. coli. By interrogating the physiologically accessible parameter space, we have determined the optimal controller architecture to configure both the metabolic source pathway and metabolic sink pathway. We determined that low protein degradation rate, medium strength of metabolic inhibitory constant, high metabolic source pathway induction rate, strong binding affinity of the transcriptional activator toward the metabolic source pathway, weak binding affinity of the transcriptional repressor toward the metabolic sink pathway, and a strong cooperative interaction of transcriptional repressor toward metabolic sink pathway benefit the accumulation of the target molecule (fatty acids). The target molecule (fatty acid) production is increased from 50% to 10-folds upon application of the autonomous metabolic switch. With strong metabolic inhibitory constant, the system displays multiple steady states. Stable oscillation of metabolic intermediate is the driving force to allow the system deviate from its equilibrium state and permits bidirectional ON-OFF gene expression control, which autonomously compensates enzyme level for both the metabolic source and metabolic sink pathways. The computational framework may facilitate us to design and engineer predictable genetic-metabolic switches, quest for the optimal controller architecture of the metabolic source/sink pathways, as well as leverage autonomous oscillation as a powerful tool to engineer cell function.en_US
dc.description.sponsorshipDr. Xu would like to acknowledge the Cellular & Biochem Engineering Program of the National Science Foundation under grant no.1805139 for funding support. Dr. Xu would also like to acknowledge the discussion of this project with the ENCH482/682 and ENCH640 students at the University of Maryland Baltimore County in the Fall 2018 and Spring 2019.en_US
dc.description.urihttps://www.sciencedirect.com/science/article/pii/S2214030119300495en_US
dc.format.extent36 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2blgy-elzp
dc.identifier.citationPeng Xu,Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch, Metabolic Engineering Communications (2020), doi: https://doi.org/10.1016/j.mec.2020.e00127en_US
dc.identifier.urihttps://doi.org/10.1016/j.mec.2020.e00127
dc.identifier.urihttp://hdl.handle.net/11603/18638
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Chemical, Biochemical & Environmental Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.titleBranch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switchen_US
dc.typeTexten_US

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