M
Mauricio Barahona
Researcher at Imperial College London
Publications - 266
Citations - 11931
Mauricio Barahona is an academic researcher from Imperial College London. The author has contributed to research in topics: Complex network & Computer science. The author has an hindex of 44, co-authored 252 publications receiving 10076 citations. Previous affiliations of Mauricio Barahona include California Institute of Technology & Massachusetts Institute of Technology.
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Bounding the stationary distributions of the chemical master equation via mathematical programming.
TL;DR: This work introduces mathematical programming approaches that yield approximations of stationary distributions with computable error bounds which enable the verification of their accuracy and illustrates the methodology through several biochemical examples taken from the literature.
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Great cities look small
TL;DR: A mathematical model of human interactions in terms of a local strategy of maximizing the number of beneficial connections attainable under the constraint of limited individual travelling-time budgets is proposed and suggests that the scaling of different urban indicators with population size has an explicitly mechanistic origin.
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Hhex and Cer1 Mediate the Sox17 Pathway for Cardiac Mesoderm Formation in Embryonic Stem Cells
Yu Liu,Ruri Kaneda,Thomas Leja,Thomas Leja,Tatiana Subkhankulova,Tatiana Subkhankulova,Oleg Tolmachov,Oleg Tolmachov,Gabriella Minchiotti,Robert J. Schwartz,Mauricio Barahona,Michael D. Schneider +11 more
TL;DR: Hhex and Cer1 are indispensable components of the Sox17 pathway for cardiopoiesis in mESCs, acting at a stage downstream from Mesp1/2, and forced expression of Cer1 was sufficient to rescue cardiac differentiation in Hhex‐deficient cells.
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Rewiring cell signalling through chimaeric regulatory protein engineering
TL;DR: It is envisaged that engineered chimaeric regulatory proteins can play an important role to aid both forward and reverse engineering of biological systems for many desired applications.
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Stochastic models of gene transcription with upstream drives: exact solution and sample path characterization
TL;DR: In this paper, the authors present a framework to model gene transcription in populations of cells with time-varying (stochastic or deterministic) transcription and degradation rates, which can be understood as upstream cellular drives representing the effect of different aspects of the cellular environment.