FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2890747153> ?p ?o ?g. }

• W2890747153 abstract "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 stif 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. Competing methods reported in the computational biology literature cannot address problems of this level of complexity. The proposed framework is flexible, 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." @default.
• W2890747153 created "2018-09-27" @default.
• W2890747153 creator A5005596786 @default.
• W2890747153 creator A5012618236 @default.
• W2890747153 creator A5083590669 @default.
• W2890747153 date "2018-09-07" @default.
• W2890747153 modified "2023-10-05" @default.
• W2890747153 title "Scalable Nonlinear Programming Framework for Parameter Estimation in Dynamic Biological System Models" @default.
• W2890747153 cites W127155249 @default.
• W2890747153 cites W1501760102 @default.
• W2890747153 cites W1509329028 @default.
• W2890747153 cites W1647779468 @default.
• W2890747153 cites W180478659 @default.
• W2890747153 cites W1961184135 @default.
• W2890747153 cites W1965420754 @default.
• W2890747153 cites W1982258483 @default.
• W2890747153 cites W1995447295 @default.
• W2890747153 cites W2000150815 @default.
• W2890747153 cites W2007930753 @default.
• W2890747153 cites W2010167768 @default.
• W2890747153 cites W2011262079 @default.
• W2890747153 cites W2011315597 @default.
• W2890747153 cites W2012994611 @default.
• W2890747153 cites W2016875121 @default.
• W2890747153 cites W2022451981 @default.
• W2890747153 cites W2024060531 @default.
• W2890747153 cites W2037135781 @default.
• W2890747153 cites W2039813252 @default.
• W2890747153 cites W2044278781 @default.
• W2890747153 cites W2050841740 @default.
• W2890747153 cites W2051482558 @default.
• W2890747153 cites W2053061982 @default.
• W2890747153 cites W2054922103 @default.
• W2890747153 cites W2056486651 @default.
• W2890747153 cites W2058532528 @default.
• W2890747153 cites W2061011338 @default.
• W2890747153 cites W2066333398 @default.
• W2890747153 cites W2081781433 @default.
• W2890747153 cites W2101647774 @default.
• W2890747153 cites W2102434872 @default.
• W2890747153 cites W2103573680 @default.
• W2890747153 cites W2106320154 @default.
• W2890747153 cites W2111912124 @default.
• W2890747153 cites W2122184339 @default.
• W2890747153 cites W2123871098 @default.
• W2890747153 cites W2127419446 @default.
• W2890747153 cites W2128769815 @default.
• W2890747153 cites W2135046866 @default.
• W2890747153 cites W2141239733 @default.
• W2890747153 cites W2147333650 @default.
• W2890747153 cites W2152246075 @default.
• W2890747153 cites W2165603655 @default.
• W2890747153 cites W2168175751 @default.
• W2890747153 cites W2179681403 @default.
• W2890747153 cites W2268335277 @default.
• W2890747153 cites W2292207802 @default.
• W2890747153 cites W2295190630 @default.
• W2890747153 cites W2564588533 @default.
• W2890747153 cites W2597588799 @default.
• W2890747153 cites W2606802599 @default.
• W2890747153 cites W2607596470 @default.
• W2890747153 cites W2773368689 @default.
• W2890747153 cites W2949499968 @default.
• W2890747153 cites W3105558219 @default.
• W2890747153 cites W3124407081 @default.
• W2890747153 cites W414546996 @default.
• W2890747153 cites W4235623249 @default.
• W2890747153 cites W4250589301 @default.
• W2890747153 cites W4255975151 @default.
• W2890747153 cites W4256038730 @default.
• W2890747153 cites W4293177929 @default.
• W2890747153 cites W4312258136 @default.
• W2890747153 doi "https://doi.org/10.1101/410688" @default.
• W2890747153 hasPublicationYear "2018" @default.
• W2890747153 type Work @default.
• W2890747153 sameAs 2890747153 @default.
• W2890747153 citedByCount "1" @default.
• W2890747153 countsByYear W28907471532020 @default.
• W2890747153 crossrefType "posted-content" @default.
• W2890747153 hasAuthorship W2890747153A5005596786 @default.
• W2890747153 hasAuthorship W2890747153A5012618236 @default.
• W2890747153 hasAuthorship W2890747153A5083590669 @default.
• W2890747153 hasBestOaLocation W28907471531 @default.
• W2890747153 hasConcept C11413529 @default.
• W2890747153 hasConcept C121332964 @default.
• W2890747153 hasConcept C126255220 @default.
• W2890747153 hasConcept C134306372 @default.
• W2890747153 hasConcept C158622935 @default.
• W2890747153 hasConcept C167928553 @default.
• W2890747153 hasConcept C28826006 @default.
• W2890747153 hasConcept C33923547 @default.
• W2890747153 hasConcept C36299963 @default.
• W2890747153 hasConcept C41008148 @default.
• W2890747153 hasConcept C48044578 @default.
• W2890747153 hasConcept C62520636 @default.
• W2890747153 hasConcept C73000952 @default.
• W2890747153 hasConcept C77088390 @default.
• W2890747153 hasConceptScore W2890747153C11413529 @default.
• W2890747153 hasConceptScore W2890747153C121332964 @default.
• W2890747153 hasConceptScore W2890747153C126255220 @default.

• next