Dataset Information

Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies.

ABSTRACT: BACKGROUND: To understand the dynamic behavior of cellular systems, mathematical modeling is often necessary and comprises three steps: (1) experimental measurement of participating molecules, (2) assignment of rate laws to each reaction, and (3) parameter calibration with respect to the measurements. In each of these steps the modeler is confronted with a plethora of alternative approaches, e. g., the selection of approximative rate laws in step two as specific equations are often unknown, or the choice of an estimation procedure with its specific settings in step three. This overall process with its numerous choices and the mutual influence between them makes it hard to single out the best modeling approach for a given problem. RESULTS: We investigate the modeling process using multiple kinetic equations together with various parameter optimization methods for a well-characterized example network, the biosynthesis of valine and leucine in C. glutamicum. For this purpose, we derive seven dynamic models based on generalized mass action, Michaelis-Menten and convenience kinetics as well as the stochastic Langevin equation. In addition, we introduce two modeling approaches for feedback inhibition to the mass action kinetics. The parameters of each model are estimated using eight optimization strategies. To determine the most promising modeling approaches together with the best optimization algorithms, we carry out a two-step benchmark: (1) coarse-grained comparison of the algorithms on all models and (2) fine-grained tuning of the best optimization algorithms and models. To analyze the space of the best parameters found for each model, we apply clustering, variance, and correlation analysis. CONCLUSION: A mixed model based on the convenience rate law and the Michaelis-Menten equation, in which all reactions are assumed to be reversible, is the most suitable deterministic modeling approach followed by a reversible generalized mass action kinetics model. A Langevin model is advisable to take stochastic effects into account. To estimate the model parameters, three algorithms are particularly useful: For first attempts the settings-free Tribes algorithm yields valuable results. Particle swarm optimization and differential evolution provide significantly better results with appropriate settings.

SUBMITTER: Drager A

PROVIDER: S-EPMC2661887 | biostudies-other | 2009

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies.

Dräger Andreas A Kronfeld Marcel M Ziller Michael J MJ Supper Jochen J Planatscher Hannes H Magnus Jørgen B JB Oldiges Marco M Kohlbacher Oliver O Zell Andreas A

BMC systems biology 20090114

<h4>Background</h4>To understand the dynamic behavior of cellular systems, mathematical modeling is often necessary and comprises three steps: (1) experimental measurement of participating molecules, (2) assignment of rate laws to each reaction, and (3) parameter calibration with respect to the measurements. In each of these steps the modeler is confronted with a plethora of alternative approaches, e. g., the selection of approximative rate laws in step two as specific equations are often unknow ...[more]

PMID: 19144170

Similar Datasets

Project description:BackgroundWe address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods.ResultsWe apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input.ConclusionsOverall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology.

Project description:To compare the Helicobacter pylori (H. pylori) eradication rate of clarithromycin-based triple therapy, metronidazole-based triple therapy, sequential therapy and concomitant therapy.A total of 680 patients infected with H. pylori were divided into 4 groups and each group was treated with a different eradication therapy. Clarithromycin-based triple therapy was applied to the first group [rabeprazole, amoxicillin and clarithromycin (PAC) group: proton pump inhibitor (PPI), amoxicillin, clarithromycin], whereas the second group was treated with metronidazole-based triple therapy [rabeprazole, amoxicillin and metronidazole (PAM) group: PPI, amoxicillin, metronidazole]. The third group was treated with rabeprazole and amoxicillin, followed by rabeprazole, clarithromycin and metronidazole (sequential group). The final group was simultaneously treated with rabeprazole, amoxicillin clarithromycin and metronidazole (concomitant therapy group). In the case of a failure to eradicate H. pylori, second-line quadruple and third-line eradication therapies were administered.The per protocol (PP) analysis was performed on 143, 139, 141 and 143 patients in the PAC, PAM, sequential and concomitant groups, respectively. We excluded patients who did not receive a C(13)-urea breath test (22, 20, 23 and 22 patients, respectively) and patients with less than an 80% compliance level (5, 11, 6 and 5 patients, respectively). The eradication rates were 76.2% (109/143) in the PAC group, 84.2% (117/139) in the PAM group, 84.4% (119/141) in the sequential group and 94.4% (135/143) in the concomitant group (P = 0.0002). All 14 patients who failed second-line therapy were treated with third-line eradication therapy. Among these 14 patients, 6 infections were successfully eradicated with the third-line therapy. Both PP and intention-to-treat analysis showed an eradication rate of 42.9% (6/14). In the PAC group, 3 of 4 patients were successfully cured (3/4, 75%); 2 of 2 patients in the PAM group (2/2, 100%) and 1 of 5 patients in the sequential group (1/5, 20%) were also cured. In the concomitant group, all 3 patients failed (0/3, 0%).The eradication rate for the concomitant therapy was much higher than those of the standard triple therapy or sequential therapy (ClinicalTrials.gov number NCT01922765).

Dataset Information

Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies.

Publications

Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets