Optimization Algorithms for Computational Systems Biology

Reali, Federico; Priami, Corrado; Marchetti, Luca

doi:10.3389/fams.2017.00006

Cited by 42 publications

(25 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RCGAs provided in libRCGA can also handle constrained optimization problems. Constrained optimization formulations are suited to incorporate prior knowledge in the parameter estimation problems [2], [14], [16], [18]. It has been demonstrated that the stochastic ranking is very efficient in constrained optimization problems [22], [23].…”

Section: Discussionmentioning

confidence: 99%

“…Genetic algorithms (GAs) are metaheuristic techniques developed with inspiration from the evolution of living organisms [13], [14]. The basic procedure is shown below.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…To evaluate fitting between model prediction and training experimental data, the ODE system, which is often stiff, needs to be numerically integrated, leading to a long computational time [12], [13]. Moreover, parameter estimation problems are often difficult optimization problems with multiple local 1 optima [3], [14]. For such problems, gradient-based deterministic algorithms become easily trapped in local optima.…”

Section: Introductionmentioning

confidence: 99%

“…However, constrained optimization is more useful to construct a realistic kinetic model that satisfies several biological constraints [17]. Prior knowledge or an educated guess about a model (e.g., that a certain rate constant is larger than another, or that a metabolite concentration must be less than 10 mM) can naturally be incorporated in parameter estimation problems as constraints [2], [14], [16], [18].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models

Maeda

Boogerd

Kurata

2018

IPSJ Transactions on Bioinformatics

View full text Add to dashboard Cite

Kinetic modeling is a powerful tool to understand how a biochemical system behaves as a whole. To develop a realistic and predictive model, kinetic parameters need to be estimated so that a model fits experimental data. However, parameter estimation remains a major bottleneck in kinetic modeling. To accelerate parameter estimation, we developed a C library for real-coded genetic algorithms (libRCGA). In libRCGA, two real-coded genetic algorithms (RCGAs), viz. the Unimodal Normal Distribution Crossover with Minimal Generation Gap (UNDX/MGG) and the Real-coded Ensemble Crossover star with Just Generation Gap (REX star /JGG), are implemented in C language and paralleled by Message Passing Interface (MPI). We designed libRCGA to take advantage of high-performance computing environments and thus to significantly accelerate parameter estimation. Constrained optimization formulation is useful to construct a realistic kinetic model that satisfies several biological constraints. libRCGA employs stochastic ranking to efficiently solve constrained optimization problems. In the present paper, we demonstrate the performance of libRCGA through benchmark problems and in realistic parameter estimation problems. libRCGA is freely available for academic usage at http://kurata21.bio.kyutech.ac.jp/maeda/index.html.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Genetic algorithms (GAs) are metaheuristic techniques developed with inspiration from the evolution of living organisms [13], [14]. The basic procedure is shown below.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models

Maeda

Boogerd

Kurata

2018

IPSJ Transactions on Bioinformatics

View full text Add to dashboard Cite

show abstract

“…This is a consequence of the presence of nonlinear dynamics and path constraints. As a result, local optimization methods will usually converge to bad local solutions [45,145]. Often, researchers resort to the use of a multi-start strategy, i.e.…”

Section: Challenges and Pitfalls In Numerical Optimal Controlmentioning

confidence: 99%

Using optimal control to understand complex metabolic pathways

Tsiantis

Banga

2020

Preprint

View full text Add to dashboard Cite

Background: We revisit the idea of explaining and predicting dynamics in biochemical pathways from first-principles. A promising approach is to exploit optimality principles that can be justified from an evolutionary perspective. In the context of the cell, several previous studies have explained the dynamics of simple metabolic pathways exploiting optimality principles in combination with dynamic models, i.e. using an optimal control framework. For example, dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point.Results: Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework based on multicriteria optimal control which has been designed with scalability and efficiency in mind, extending several recent methods. This framework includes mechanisms to avoid common pitfalls, such as local optima, unstable solutions or excessive computation time. We illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments. Conclusions:We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we show how the multicriteria approach allows us to consider general cost/benefit trade-offs that have been likely favored by evolution. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction networks.

show abstract

Stochastic simulation algorithms for computational systems biology: Exact, approximate, and hybrid methods

Simoni

Reali

Priami

et al. 2019

WIREs Mechanisms of Disease

Self Cite

View full text Add to dashboard Cite

Nowadays, mathematical modeling is playing a key role in many different research fields. In the context of system biology, mathematical models and their associated computer simulations constitute essential tools of investigation. Among the others, they provide a way to systematically analyze systems perturbations, develop hypotheses to guide the design of new experimental tests, and ultimately assess the suitability of specific molecules as novel therapeutic targets. To these purposes, stochastic simulation algorithms (SSAs) have been introduced for numerically simulating the time evolution of a well‐stirred chemically reacting system by taking proper account of the randomness inherent in such a system. In this work, we review the main SSAs that have been introduced in the context of exact, approximate, and hybrid stochastic simulation. Specifically, we will introduce the direct method (DM), the first reaction method (FRM), the next reaction method (NRM) and the rejection‐based SSA (RSSA) in the area of exact stochastic simulation. We will then present the τ‐leaping method and the chemical Langevin method in the area of approximate stochastic simulation and an implementation of the hybrid RSSA (HRSSA) in the context of hybrid stochastic‐deterministic simulation. Finally, we will consider the model of the sphingolipid metabolism to provide an example of application of SSA to computational system biology by exemplifying how different simulation strategies may unveil different insights into the investigated biological phenomenon. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods

show abstract

Optimization Algorithms for Computational Systems Biology

Cited by 42 publications

References 83 publications

libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models

libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models

Using optimal control to understand complex metabolic pathways

Stochastic simulation algorithms for computational systems biology: Exact, approximate, and hybrid methods

Contact Info

Product

Resources

About