Computational protocol: A Kinetic Platform to Determine the Fate of Hydrogen Peroxide in Escherichia coli

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Protocol publication

[…] The identification of universal “sloppiness” in computational biological models [], meaning many parameters are poorly constrained after fitting on experimental data, led to the development of a number of methods designed to identify ensembles of parameter sets that could comparably describe the data and be used to assess the robustness of forward predictions [, ]. Methods such as “brute force” uniform sampling or Gaussian sampling become impossible with increasingly complex models, so computational biologists have turned to the use of Monte Carlo techniques to explore the viable parameter space efficiently (e.g., HYPERSPACE [] and SloppyCell []). Here, we used a previously developed MCMC method [] to explore the parameter space, initiating a random walk away from each of the 40 acceptable parameter sets and keeping 100 viable sets with an ER≤10 for each point. This resulted in an ensemble of 4,000 parameter sets that could all capture our experimental observations, and allowed us to assess robustness of our predictions to parametric uncertainty. In addition, before proceeding, we ensured that all models in the ensemble satisfied our design criteria (). […]

Pipeline specifications

Software tools HYPERSPACE, SloppyCell
Application Mathematical modeling
Organisms Escherichia coli, Bacteria
Chemicals Carbon, Hydrogen Peroxide, NAD