%0 Journal Article %1 Penas2017Parameter %A Penas, David R. %A González, Patricia %A Egea, Jose A. %A Doallo, Ramón %A Banga, Julio R. %D 2017 %J BMC bioinformatics %K kinetic-modelling parameter-estimation %N 1 %T Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy. %U http://view.ncbi.nlm.nih.gov/pubmed/28109249 %V 18 %X The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.

@article{Penas2017Parameter, abstract = {The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search ({saCeSS}), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. The performance and robustness of {saCeSS} is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, baker\'{e}s yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that {saCeSS} is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Penas, David R. and Gonz\'{a}lez, Patricia and Egea, Jose A. and Doallo, Ram\'{o}n and Banga, Julio R.}, biburl = {https://www.bibsonomy.org/bibtex/23559239a395d797589e55c821f1ac35e/karthikraman}, citeulike-article-id = {14260621}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/28109249}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=28109249}, day = 21, interhash = {b9baf69cb0c4a2411c4b9e3d2c9dde28}, intrahash = {3559239a395d797589e55c821f1ac35e}, issn = {1471-2105}, journal = {BMC bioinformatics}, keywords = {kinetic-modelling parameter-estimation}, month = jan, number = 1, pmid = {28109249}, posted-at = {2017-01-23 13:57:32}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/28109249}, volume = 18, year = 2017 }