Parameter optimization and sensitivity analysis for large kinetic models using a real-coded genetic algorithm

Tohsato, Yukako; Ikuta, Kunihiko; Shionoya, Akitaka; Mazaki, Yusaku; Ito, Masahiro

doi:10.1016/j.gene.2012.11.080

Cited by 25 publications

(25 citation statements)

References 18 publications

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“…Previous reports [93] on the sensitivity analysis of the E. coli kinetic model by Chassagnole et al [76] showed that simulation results are only sensitive to the values of nine (out of 25) enzyme activities in the model. In light of this analysis, we perturbed the enzyme activities of two sensitive ones ( and ) and two rather insensitive ( and ) by +/−20% from their reference levels and repeated the k-OptForce calculations.…”

Section: Resultsmentioning

confidence: 99%

k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design

2014

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Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.

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Section: Resultsmentioning

confidence: 99%

k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design

2014

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“…Most glycolysis models are based on ODE [12,26,27]. Chassagnole et al (2002) constructed an integrated ODE model of glycolysis and PP pathways [12,28], which is often used as a base model in many studies [26,27,29,30]. By assembling TCA cycle with the model of [12], Kadir et al (2010) set up an ODE model together with six pieces of logical controlling rules [27], and Usuda et al (2010) included gene regulation in [26].…”

Section: Resultsmentioning

confidence: 99%

“…By assembling TCA cycle with the model of [12], Kadir et al (2010) set up an ODE model together with six pieces of logical controlling rules [27], and Usuda et al (2010) included gene regulation in [26]. Kinetic parameters of these ODE model were taken from the databases, such as BRENDA [31], SABIO-RK [32], and BioModels [33], and some works focused on parameter optimization [30]. PTS are usually represented by one or a few equations in these ODE models.…”

Section: Resultsmentioning

confidence: 99%

Identification of key regulators in glycogen utilization in E. coli based on the simulations from a hybrid functional Petri net model

et al. 2013

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BackgroundGlycogen and glucose are two sugar sources available during the lag phase of E. coli, but the mechanism that regulates their utilization is still unclear.MethodsAttempting to unveil the relationship between glucose and glycogen, we propose an integrated hybrid functional Petri net (HFPN) model including glycolysis, PTS, glycogen metabolic pathway, and their internal regulatory systems.Results and conclusionsBy comparing known biological results to this model, basic necessary regulatory mechanism for utilizing glucose and glycogen were identified as a feedback circuit in which HPr and EIIAGlc play key roles. Based on this regulatory HFPN model, we discuss the process of glycogen utilization in E. coli in the context of a systematic understanding of carbohydrate metabolism.

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“…In chemistry, there is also a long history of applying SENSA in chemical kinetics. The SENSA of rate constants on the reaction kinetics is an important way to understand a kinetic scheme 43,44 . It also has profound implications on parameter optimization 44 .…”

Section: Introductionmentioning

confidence: 99%

Breathing dynamics based parameter sensitivity analysis of hetero-polymeric DNA

Talukder

Sen

Chakraborti

et al. 2014

The Journal of Chemical Physics

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We study the parameter sensitivity of hetero-polymeric DNA within the purview of DNA breathing dynamics. The degree of correlation between the mean bubble size and the model parameters are estimated for this purpose for three different DNA sequences. The analysis leads us to a better understanding of the sequence dependent nature of the breathing dynamics of hetero-polymeric DNA. Out of the fourteen model parameters for DNA stability in the statistical Poland-Scheraga approach, the hydrogen bond interaction ǫ hb (AT) for an AT base pair and the ring factor ξ turn out to be the most sensitive parameters. In addition, the stacking interaction ǫ st (TA − TA) for an TA − TA nearest neighbor pair of base-pairs is found to be the most sensitive one among all stacking interactions. Moreover, we also establish that the nature of stacking interaction has a deciding effect on the DNA breathing dynamics, not the number of times a particular stacking interaction appears in a sequence. We show that the sensitivity analysis can be used as an effective measure to guide a stochastic optimization technique to find the kinetic rate constants related to the dynamics as opposed to the case where the rate constants are measured using the conventional unbiased way of optimization.

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Parameter optimization and sensitivity analysis for large kinetic models using a real-coded genetic algorithm

Cited by 25 publications

References 18 publications

k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design

k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design

Identification of key regulators in glycogen utilization in E. coli based on the simulations from a hybrid functional Petri net model

Breathing dynamics based parameter sensitivity analysis of hetero-polymeric DNA

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