Evolutionary Approaches for Strain Optimization Using Dynamic Models under a Metabolic Engineering Perspective

Evangelista, Pedro; Rocha, Isabel; Ferreira, Eugénio C.; Rocha, Miguel

doi:10.1007/978-3-642-01184-9_13

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…On the other hand, stochastic global optimization can be used to locate solutions near to the global optimum, including EAs which have shown acceptable performance for applications in biological systems (Banga, 2008 ; Rocha et al, 2008 ). Examples of stochastic optimization include: applications of robust methods for parameter estimation in nonlinear dynamic systems, that outperform significantly methods previously used for three specific benchmark problems (Rodriguez-Fernandez et al, 2006 ); EAs for predicting optimal reaction knockouts and enzyme modulation strategies for the maximization of serine production by E. coli (Evangelista et al, 2013 ); and, the exploration of a computational environment where dynamical models are used to support simulation and optimization tasks, by using metaheuristics to identify modifications of parameters so that the production of dihydroxyacetone phosphate is maximized in E. coli (Evangelista et al, 2009 ). Usual alternatives to characterize targets include making a local parameter sensitivity analysis, or simulating more significant changes in enzyme levels or other elements.…”

Section: Computational Strain Optimization (Cso)mentioning

confidence: 99%

A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering

2018

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Mathematical modeling is a key process to describe the behavior of biological networks. One of the most difficult challenges is to build models that allow quantitative predictions of the cells' states along time. Recently, this issue started to be tackled through novel in silico approaches, such as the reconstruction of dynamic models, the use of phenotype prediction methods, and pathway design via efficient strain optimization algorithms. The use of dynamic models, which include detailed kinetic information of the biological systems, potentially increases the scope of the applications and the accuracy of the phenotype predictions. New efforts in metabolic engineering aim at bridging the gap between this approach and other different paradigms of mathematical modeling, as constraint-based approaches. These strategies take advantage of the best features of each method, and deal with the most remarkable limitation—the lack of available experimental information—which affects the accuracy and feasibility of solutions. Parameter estimation helps to solve this problem, but adding more computational cost to the overall process. Moreover, the existing approaches include limitations such as their scalability, flexibility, convergence time of the simulations, among others. The aim is to establish a trade-off between the size of the model and the level of accuracy of the solutions. In this work, we review the state of the art of dynamic modeling and related methods used for metabolic engineering applications, including approaches based on hybrid modeling. We describe approaches developed to undertake issues regarding the mathematical formulation and the underlying optimization algorithms, and that address the phenotype prediction by including available kinetic rate laws of metabolic processes. Then, we discuss how these have been used and combined as the basis to build computational strain optimization methods for metabolic engineering purposes, how they lead to bi-level schemes that can be used in the industry, including a consideration of their limitations.

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Section: Computational Strain Optimization (Cso)mentioning

confidence: 99%

A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering

2018

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“…Recently, the development of methods for multiobjective optimization [10] and the simulation of mutants using dynamic models based on ordinary differential equations (ODEs) have also been approached [3].…”

Section: Projects Using Jecolimentioning

confidence: 99%

Implementing Metaheuristic Optimization Algorithms with JECoLi

Evangelista

Maia

Rocha

2009

2009 Ninth International Conference on Intelligent Systems Design and Applications

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This work proposes JECoLi -a novel Java-based library for the implementation of metaheuristic optimization algorithms with a focus on Genetic and Evolutionary Computation based methods. The library was developed based on the principles of flexibility, usability, adaptability, modularity, extensibility, transparency, scalability, robustness and computational efficiency. The project is opensource, so JECoLi is made available under the GPL license, together with extensive documentation and examples, all included in a community Wiki-based web site (http://darwin.di.uminho.pt/jecoli). JECoLi has been/is being used in several research projects that helped to shape its evolution, ranging application fields from Bioinformatics, to Data Mining and Computer Network optimization.

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“…In previous work [6], the authors have used some of these tools to identify optimal or near-optimal sets of genetic changes in E. coli to achieve a given metabolic engineering aim using the central carbon metabolism ODE model [4]. This case study is used here to show the main capabilities of the platform.…”

Section: Introductionmentioning

confidence: 99%

A Software Tool for the Simulation and Optimization of Dynamic Metabolic Models

Evangelista

Rocha

Ferreira

et al. 2009

Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living

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In Systems Biology, there is a growing need for simulation and optimization tools for the prediction of the phenotypical behavior of microorganisms. In this paper, an open-source software platform is proposed to provide support for research in Metabolic Engineering, by implementing tools that enable the simulation and optimization of dynamic metabolic models using ordinary differential equations. Its main functionalities are related with (i) phenotype simulation of both wild type and mutant strains under given environmental conditions and (ii) strain optimization tackling tasks such as gene knockout selection or the definition of the optimal level of enzyme expression, given appropriate objective functions. The central carbon metabolism of E. coli was used as a case study, to illustrate the main features of the software.

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Evolutionary Approaches for Strain Optimization Using Dynamic Models under a Metabolic Engineering Perspective

Cited by 3 publications

References 19 publications

A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering

A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering

Implementing Metaheuristic Optimization Algorithms with JECoLi

A Software Tool for the Simulation and Optimization of Dynamic Metabolic Models

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