A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology

Teijeiro, Diego; Pardo, Xoán C.; Penas, David R.; González, Patricia; Banga, Julio R.; Doallo, Ramón

doi:10.1007/s10586-017-0860-1

Cited by 20 publications

(24 citation statements)

References 44 publications

(49 reference statements)

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“…Many of these methods can be parallelized to overcome computational burden (Mancini et al, 2015;Teijeiro et al, 2017). Parallel implementation of data fitting algorithms employing Spark, MapReduce and MPI messaging are considered in (Teijeiro et al, 2017). Computational complexity of global methods can be mitigated by incremental identification strategies (Michalik et al, 2009).…”

Section: Model Fitting Methodsmentioning

confidence: 99%

“…Computational complexity of global methods can be mitigated by incremental identification strategies (Michalik et al, 2009). Global methods also require proper selection of search parameters which is usually achieved by multiple initial exploratory runs (Penas et al, 2017). Another strategy for global search is to assume transformations followed by non-uniform sampling (Kleinstein et al, 2006).…”

Section: Model Fitting Methodsmentioning

confidence: 99%

“…Hussain et al (2015);Sun et al (2012);Ashyraliyev et al (2009);Dai and Lai (2010);Chou and Voit (2009);Cedersund et al (2016) Differential evolution(DE) derivative free method, linearly combining randomly selected candidate solutions to obtain iterative improvementsTeijeiro et al (2017); Liu and Wang(2009); Sun et al (2012); Chong et al (2014); Srinivas and Rangaiah (2007); Chong et al (2012) Scatter search (SS) often combined with tabu search, it is local search with temporarily accepting worse solutions and avoiding already visited regions Particle swarm optimiz. (PSO) derivative free method, moving particles (i.e., samples or candidate solutions) towards better solution Nobile et al (2016); Sun et al (2014); Besozzi et al (2009); Cazzaniga et al (2015); Abdullah et al (2013c); Tangherloni et al (2016)…”

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confidence: 99%

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Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

2019

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The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.

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Section: Model Fitting Methodsmentioning

confidence: 99%

Section: Model Fitting Methodsmentioning

confidence: 99%

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confidence: 99%

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Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

2019

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“…The island method has been applied to various diverse optimization problems such as nuclear power plant feedwater monitoring (15), vehicle routing (16), and trajectory planning for spacecraft (8,11). The island method has also been applied to large-scale kinetic systems biology models (17)(18)(19)(20). In this article, we apply the island method to a set of very large, challenging dynamical models from systems biology (21) in order to quantify the performance of different algorithms and the performance gains due to parallelization using the island model.…”

Section: R a F Tmentioning

confidence: 99%

Accelerated Biochemical Kinetic Model Fitting via the Asynchronous, Generalized Island Method

Medley

Asifullah

Hellerstein

et al. 2019

Preprint

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Mechanistic kinetic models of biological pathways are an important tool for understanding biological systems. Constructing kinetic models requires fitting the parameters to experimental data. However, parameter fitting on these models is a non-convex, non-linear optimization problem. Many algorithms have been proposed to addressing optimization for parameter fitting including globally convergent, population-based algorithms. The computational complexity of the this optimization for even modest models means that parallelization is essential. Past approaches to parameter optimization have focused on parallelizing a particular algorithm. However, this requires re-implementing the algorithm usinga distributed computing framework, which requires a significant investment of time and effort. There are two major drawbacks of this approach: First, the choice of best algorithm may depend on the model. Given the large variety of optimization algorithms available, it is difficult to re-implement every potentially useful algorithm. Second, when new advances are made in a given optimization algorithm, the parallel implementation must be updated to take advantage of these advantages. Thus, there is a continual burden placed on the parallel implementation.The drawbacks of re-implementing algorithms lead us to a different approach to parallelizing parameter optimization. Instead of parallelizing the algorithms themselves, we run many instances of the algorithm on single cores. This provides great flexibility as to the choice of algorithms by allowing us to reuse previous implementations. Also, it does not require the creation and maintenance of parallel versions of optimization algorithms. This approach is known as the island method. To our knowledge, the utility of the island method for parameter fitting in systems biology has not been previously demonstrated. For the parameter fitting problem, we allow islands to exchange information about their "best" solutions so that all islands leverage the discoveries of the few. This turns out to be avery effective in practice, leading to super-linear speedups. That is, if a single processor finds the optimal value of parameters in time t, then N processors exchanging information in this way find the optimal value much faster than t/N . We show that the island method is able to consistently provide good speedups for these problems. We also benchmark the island method against a variety of large, challenging kinetic models and show that it is able to consistently improve the quality of fit in less time than a single-threaded implementation.Our software is available at https://github.com/sys-bio/sabaody under a Apache 2.0 license. Contact: mailto:medjk@comcast.net sbml | systems biology | web Correspondence: medleyj@uw.edu Models in systems biology are based on varying degrees of granularity. Increased detail often comes at the expense of decreased scalability. For example, constraint-based models exhibit excellent scalability and are often used as a formalism for genome-scale reconstruction...

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“…The sixth paper "a cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology" is authored by Teijeiro, Pardo, Penas, González, Banga, and Doallo (see [7]). The authors propose a parallel implementation of an enhanced Differential Evolution algorithm using Spark.…”

Section: Detailed Contentmentioning

confidence: 99%

Parallelism-based approaches in computational biology: a view from diverse case studies

Vega‐Rodríguez

Santander-Jiménez

2017

Cluster Comput

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Computational biology allows and encourages the application of many different parallelism-based approaches. This special issue brings together high-quality state-ofthe-art contributions about parallelism-based approaches in computational biology, from different points of view or perspectives, that is, from diverse case studies. The special issue collects considerably extended and improved versions of the best papers, accepted and presented in PBio 2016 (4th International Workshop on Parallelism in Bioinformatics, and part of Euro-Par 2016). The domains and topics covered in these 6 papers are timely and important, and the authors have done an excellent job of presenting the material.

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A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology

Cited by 20 publications

References 44 publications

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

Accelerated Biochemical Kinetic Model Fitting via the Asynchronous, Generalized Island Method

Parallelism-based approaches in computational biology: a view from diverse case studies

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