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Designing experiments to understand the variability in biochemical reaction networks

, , and . Journal of The Royal Society Interface, (Nov 6, 2013)
DOI: 10.1098/rsif.2013.0588

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Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks., and . ACM Trans. Model. Comput. Simul., 25 (2): 8:1-8:25 (2015)Estimating information in time-varying signals., , and . PLoS Comput. Biol., (2019)Molecular noise of innate immunity shapes bacteria-phage ecologies., , , and . PLoS Comput. Biol., (2019)Approximating the solution of the chemical master equation by combining finite state projection and stochastic simulation., , and . CDC, page 751-756. IEEE, (2013)On the Use of the Moment Equations for Parameter Inference, Control and Experimental Design in Stochastic Biochemical Reaction Networks., and . CMSB, volume 8130 of Lecture Notes in Computer Science, page 1-4. Springer, (2013)Designing experiments to understand the variability in biochemical reaction networks, , and . Journal of The Royal Society Interface, (Nov 6, 2013)Optimal control of an artificial microbial differentiation system for protein bioproduction., , , , , , and . ECC, page 2663-2668. IEEE, (2019)Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level., , , and . PLoS Comput. Biol., (2022)Beyond the chemical master equation: Stochastic chemical kinetics coupled with auxiliary processes., , , and . PLoS Comput. Biol., (2021)Can optimal experimental design serve as a tool to characterize highly non-linear synthetic circuits?, , , and . ECC, page 1176-1181. IEEE, (2019)