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      Scalable nonlinear programming framework for parameter estimation in dynamic biological system models

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          Abstract

          We present a nonlinear programming (NLP) framework for the scalable solution of parameter estimation problems that arise in dynamic modeling of biological systems. Such problems are computationally challenging because they often involve highly nonlinear and stiff differential equations as well as many experimental data sets and parameters. The proposed framework uses cutting-edge modeling and solution tools which are computationally efficient, robust, and easy-to-use. Specifically, our framework uses a time discretization approach that: i) avoids repetitive simulations of the dynamic model, ii) enables fully algebraic model implementations and computation of derivatives, and iii) enables the use of computationally efficient nonlinear interior point solvers that exploit sparse and structured linear algebra techniques. We demonstrate these capabilities by solving estimation problems for synthetic human gut microbiome community models. We show that an instance with 156 parameters, 144 differential equations, and 1,704 experimental data points can be solved in less than 3 minutes using our proposed framework (while an off-the-shelf simulation-based solution framework requires over 7 hours). We also create large instances to show that the proposed framework is scalable and can solve problems with up to 2,352 parameters, 2,304 differential equations, and 20,352 data points in less than 15 minutes. The proposed framework is flexible and easy-to-use, can be broadly applied to dynamic models of biological systems, and enables the implementation of sophisticated estimation techniques to quantify parameter uncertainty, to diagnose observability/uniqueness issues, to perform model selection, and to handle outliers.

          Author summary

          Constructing and validating dynamic models of biological systems spanning biomolecular networks to ecological systems is a challenging problem. Here we present a scalable computational framework to rapidly infer parameters in complex dynamic models of biological systems from large-scale experimental data. The framework was applied to infer parameters of a synthetic microbial community model from large-scale time series data. We also demonstrate that this framework can be used to analyze parameter uncertainty, to diagnose whether the experimental data are sufficient to uniquely determine the parameters, to determine the model that best describes the data, and to infer parameters in the face of data outliers.

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          Most cited references 53

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          On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming

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            • Article: not found

            Optimization of conditional value-at-risk

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              • Record: found
              • Abstract: not found
              • Article: not found

              Conditional value-at-risk for general loss distributions

                Bookmark

                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                March 2019
                25 March 2019
                : 15
                : 3
                Affiliations
                [1 ] Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
                [2 ] Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
                University of California Irvine, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Article
                PCOMPBIOL-D-18-01408
                10.1371/journal.pcbi.1006828
                6467427
                30908479
                © 2019 Shin et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 13, Tables: 2, Pages: 29
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100008959, College of Engineering, University of Wisconsin-Madison;
                Award Recipient :
                SS and VMZ acknowledge financial support from the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison. OSV acknowledges support from the Army Research Office under Young Investigator Award W911NF-17-1-0296 and the National Institutes of Health under award NIGMS 1 R35 GM124774-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Mathematics
                Differential Equations
                Physical Sciences
                Mathematics
                Algebra
                Linear Algebra
                Physical Sciences
                Physics
                Classical Mechanics
                Motion
                Inertia
                Physical Sciences
                Mathematics
                Probability Theory
                Random Variables
                Covariance
                Computer and Information Sciences
                Systems Science
                Nonlinear Dynamics
                Physical Sciences
                Mathematics
                Systems Science
                Nonlinear Dynamics
                Physical Sciences
                Mathematics
                Algebra
                Algebraic Structures
                Physical Sciences
                Mathematics
                Algebra
                Linear Algebra
                Eigenvalues
                Biology and Life Sciences
                Species Interactions
                Custom metadata
                vor-update-to-uncorrected-proof
                2019-04-16
                The full Julia script is available at https://github.com/zavalab/JuliaBox/tree/master/MicrobialPLOS.

                Quantitative & Systems biology

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