Parallel Metaheuristics in Computational Biology: An Asynchronous Cooperative Enhanced Scatter Search Method

Penas, David R.; González, Patricia; Egea, José A.; Banga, Julio R.; Doallo, Ramón

doi:10.1016/j.procs.2015.05.331

Cited by 16 publications

(17 citation statements)

References 6 publications

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“…For most methods that employ repeated local optimization, the individual local optimization runs can trivially be run in parallel [75], which enables efficient use of high performance computing structure. Moreover, multiple global runs can be asynchronously parallelized to enhance efficiency through cooperativity [76]. Following recent studies [38,71], we deem this repeated local optimization a suitable candidate for scalable optimization and will in the following discuss the properties of respective local gradient based methods (see Figure 1B) in more detail.…”

Section: Optimization Methodsmentioning

confidence: 99%

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

Fröhlich

Loos

Hasenauer

2018

Methods in Molecular Biology

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Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small-to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of state-of-the-art methods for parameter and model inference, with an emphasis on scalability.

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Section: Optimization Methodsmentioning

confidence: 99%

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

Fröhlich

Loos

Hasenauer

2018

Methods in Molecular Biology

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“…In the last two decades, metaheuristic algorithms have gained significant attention as efficient solvers for hard global optimization problems appearing in real engineering and science modeling applications. A metaheuristic is a higher-level procedure designed to find or generate a heuristic that may provide a sufficiently good solution to an optimization problem, especially when the set of solutions is too large to be fully sampled; see, e.g., [22,27,28,36]. The central common feature of all heuristic optimization methods is that they start off with a more or less arbitrary initial guess, iteratively produce new solutions by combining randomness and a generation rule, evaluate these new solutions using a suitable merit function, and eventually report the best solution found during the search process; see, e.g., [2,31].…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

A metaheuristic penalty approach for the starting point in nonlinear programming

Penas

Raydan²

2020

RAIRO-Oper. Res.

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Solving nonlinear programming problems usually involve difficulties to obtain a starting point that produces convergence to a local feasible solution, for which the objective function value is sufficiently good. A novel approach is proposed, combining metaheuristic techniques with modern deterministic optimization schemes, with the aim to solve a sequence of penalized related problems to generate convenient starting points. The metaheuristic ideas are used to choose the penalty parameters associated with the constraints, and for each set of penalty parameters a deterministic scheme is used to evaluate a properly chosen metaheuristic merit function. Based on this starting-point approach, we describe two different strategies for solving the nonlinear programming problem. We illustrate the properties of the combined schemes on three nonlinear programming benchmark-test problems, and also on the well-known and hard-to-solve disk-packing problem, that possesses a huge amount of local-nonglobal solutions, obtaining encouraging results both in terms of optimality and feasibility.

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“…Scalable methods have been developed to address the problem of computational complexity, e.g. adjoint sensitivity analysis (Fujarewicz et al, 2005;Lu et al, 2012;Fröhlich et al, 2017b) and parallelization (Penas et al, 2015;Fröhlich et al, 2018). Complementary, large-scale transcriptomics, proteomics and pharmacological datasets have been acquired and have been made publicly available in databases such as the Cancer Cell Line Encyclopedia (CCLE) (Barretina et al, 2012), the Genomics of Drug Sensitivity in Cancer (GDSC) project (Eduati et al, 2017) and the MD Anderson Cell Lines Project (MCLP) (Li et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Efficient parameterization of large-scale dynamic models based on relative measurements

Schmiester

Schälte

Froehlich

et al. 2019

Preprint

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Motivation: Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets.However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale.Results: Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset, and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (>1000 state variables, >4000 parameters) using relative protein, phospho-protein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of largescale models based on heterogeneous relative measurements.Contact: jan.hasenauer@helmholtz-muenchen.de Supplementary information: Supplementary information are available at bioRxiv online.Supplementary code and data are available online at

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Parallel Metaheuristics in Computational Biology: An Asynchronous Cooperative Enhanced Scatter Search Method

Cited by 16 publications

References 6 publications

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

A metaheuristic penalty approach for the starting point in nonlinear programming

Efficient parameterization of large-scale dynamic models based on relative measurements

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