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Inferring reaction systems from ordinary differential equations

François Fages, Steven Gay, Sylvain Soliman
2015 Theoretical Computer Science  
We derive from this study a heuristic algorithm which, given a system of ODEs as input, computes a system of reactions with the same ODE semantics, by inferring well-formed reactions whenever possible.  ...  In Mathematical Biology, many dynamical models of biochemical reaction systems are presented with Ordinary Differential Equations (ODE).  ...  reaction systems automatically inferred from their ODE semantics.  ... 
doi:10.1016/j.tcs.2014.07.032 fatcat:qkkt6mta75hhvi2n2jdakszxku

Inference for reaction networks using the linear noise approximation

Paul Fearnhead, Vasilieos Giagos, Chris Sherlock
2014 Biometrics  
We consider inference for the reaction rates in discretely observed networks such as those found in models for systems biology, population ecology and epidemics.  ...  The former ignores the stochasticity in the true model, and can lead to inaccurate inferences.  ...  Epidemic Data Analysis We now consider analysing real-data under the SIR model (Example 2).  ... 
doi:10.1111/biom.12152 pmid:24467590 fatcat:b674mxg2ezgixpppzbhpcl2d2m

MOCCASIN: converting MATLAB ODE models to SBML

Harold F. Gómez, Michael Hucka, Sarah M. Keating, German Nudelman, Dagmar Iber, Stuart C. Sealfon
2016 Bioinformatics  
We developed MOCCASIN (Model ODE Converter for Creating Automated SBML INteroperability) to help. MOCCASIN can convert ODEbased MATLAB models of biochemical reaction networks into the SBML format.  ...  MATLAB is popular in biological research for creating and simulating models that use ordinary differential equations (ODEs).  ...  Funding Modeling Immunity for Biodefense contracts HHSN266200500021C (M.H., G.N., S.C.S.), U19AI117873 (G.N., S.C.S.) and HHSN272201000053C (H.F.G., Thomas B.  ... 
doi:10.1093/bioinformatics/btw056 pmid:26861819 pmcid:PMC4908318 fatcat:fmafuqfxczbhvmmtkpkuejelnm

StochDecomp - Matlab package for noise decomposition in stochastic biochemical systems [article]

Tomasz Jetka, Agata Charzynska, Anna Gambin, Michael P.H. Stumpf, Michal Komorowski
2013 arXiv   pre-print
We also demonstrate and exemplify using the JAK-STAT signalling pathway that it is possible to infer noise contributions resulting from individual reactions directly from experimental data.  ...  This is the first computational tool that allows to decompose noise into contributions resulting from individual reactions.  ...  Stored ODEs are then used to numerically analyse the model, particularly decompose the variability into contributions resulting from each of the model reactions either in steady state or out-of-steady-state  ... 
arXiv:1308.3103v1 fatcat:nszo36c4ubf23chzq5wz2wz2ji

Topological augmentation to infer hidden processes in biological systems

Mikael Sunnåker, Elias Zamora-Sillero, Adrián López García de Lomana, Florian Rudroff, Uwe Sauer, Joerg Stelling, Andreas Wagner
2013 Computer applications in the biosciences : CABIOS  
Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data.  ...  We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure.  ...  In step 7, we make an informed decision about reaction terms to be added to the ODE model based on the information from step 5 (reactions to which these terms should be added) and step 6 (form of the reaction  ... 
doi:10.1093/bioinformatics/btt638 pmid:24297519 pmcid:PMC3892687 fatcat:35vxgatwyffq5mmidi4vlerdbi

StochDecomp—Matlab package for noise decomposition in stochastic biochemical systems

Tomasz Jetka, Agata Charzyńska, Anna Gambin, Michael P.H. Stumpf, Michał Komorowski
2013 Computer applications in the biosciences : CABIOS  
We also demonstrate and exemplify using the JAK-STAT signalling pathway that the noise contributions resulting from individual reactions can be inferred from data experimental data along with Bayesian  ...  parameter inference.  ...  Stored ODEs are then used to numerically analyze the stochastic model, particularly decompose the variability into contributions resulting from each of the model reactions either in steady-state or out-of-steadystate  ... 
doi:10.1093/bioinformatics/btt631 pmid:24191070 fatcat:jxmhbvwpyzfzzp3tozserrwt5m

Causal network inference using biochemical kinetics [article]

C. J. Oates, F. Dondelinger, N. Bayani, J. Korola, J. W. Gray, S. Mukherjee
2014 arXiv   pre-print
Results, based on (i) data simulated from a mechanistic model of mitogen-activated protein kinase signaling and (ii) phosphoproteomic data from cancer cell lines, demonstrate that nonlinear formulations  ...  Inference regarding both parameters and the reaction graph itself is carried out within a fully Bayesian framework.  ...  In Silico MAPK Pathway Data were generated from a mechanistic model of the MAPK signaling pathway due to Xu et al. (2010) , specified by a system of 25 ODEs of Michaelis-Menten type whose reaction graph  ... 
arXiv:1406.0063v1 fatcat:j5o23k65gbcy5dqyfvoybrs64e

Causal network inference using biochemical kinetics

Chris J. Oates, Frank Dondelinger, Nora Bayani, James Korkola, Joe W. Gray, Sach Mukherjee
2014 Computer applications in the biosciences : CABIOS  
Results, based on (i) data simulated from a mechanistic model of mitogen-activated protein kinase signaling and (ii) phosphoproteomic data from cancer cell lines, demonstrate that non-linear formulations  ...  can yield gains in causal network inference and permit dynamical prediction and uncertainty quantification in the challenging setting where the reaction graph is unknown.  ...  In silico MAPK pathway Data were generated from a mechanistic model of the MAPK signaling pathway described by Xu et al. (2010) , specified by a system of 25 ODEs of Michaelis-Menten type whose reaction  ... 
doi:10.1093/bioinformatics/btu452 pmid:25161235 pmcid:PMC4147905 fatcat:u4gjrr4daja4zb6h6rfi2525aa

A Unique Transformation from Ordinary Differential Equations to Reaction Networks

Sylvain Soliman, Monika Heiner, Jean Peccoud
2010 PLoS ONE  
We describe a method to extract a structured reaction network model, represented as a bipartite multigraph, for example, a continuous Petri net (CPN), from a system of Ordinary Differential Equations (  ...  Our method is implemented and available; we illustrate it on some signal transduction models from the BioModels database.  ...  On the contrary, in our article we discuss conditions for unique structure inference directly from a given system of ODEs.  ... 
doi:10.1371/journal.pone.0014284 pmid:21203560 pmcid:PMC3008708 fatcat:mdhnouec4rdjzejpn7jm4frnay

ODEion — A SOFTWARE MODULE FOR STRUCTURAL IDENTIFICATION OF ORDINARY DIFFERENTIAL EQUATIONS

PETER GENNEMARK, DAG WEDELIN
2014 Journal of Bioinformatics and Computational Biology  
There is therefore a lack of methods and software that can handle more general models and realistic data. We present ODEion, a software module for structural identification of ODEs.  ...  In the systems biology field, algorithms for structural identification of ordinary differential equations (ODEs) have mainly focused on fixed model spaces like S-systems and/or on methods that require  ...  The initial model corresponds to prior knowledge of the structure of the system and is included as reactions (terms) from the model space on the right hand side of the ODEs.  ... 
doi:10.1142/s0219720013500157 pmid:24467754 fatcat:2rs5nxwpwnb55km2rrahihwtre

Exploiting network topology for large-scale inference of nonlinear reaction models

Nikhil Galagali, Youssef M. Marzouk
2019 Journal of the Royal Society Interface  
The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion.  ...  Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models  ...  ODE-based species interaction models have recently been incorporated into model inference frameworks [5, 6] .  ... 
doi:10.1098/rsif.2018.0766 pmid:30862281 pmcid:PMC6451393 fatcat:qkshtabz2jda5g3yuckla52pum

MEANS: python package for Moment Expansion Approximation, iNference and Simulation

Sisi Fan, Quentin Geissmann, Eszter Lakatos, Saulius Lukauskas, Angelique Ale, Ann C. Babtie, Paul D. W. Kirk, Michael P. H. Stumpf
2016 Bioinformatics  
There is also a parser enabling model definition from SBML-files. Furthermore, four predefined models are available from the examples package.  ...  inference of ODE systems generated by the approximation or defined by the user; and customizable visualization for the output.  ... 
doi:10.1093/bioinformatics/btw229 pmid:27153663 pmcid:PMC5018365 fatcat:wxx7fq7rt5csjikcsw6kbuf37y

Approximate Latent Force Model Inference [article]

Jacob D. Moss, Felix L. Opolka, Bianca Dumitrascu, Pietro Lió
2022 arXiv   pre-print
Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems.  ...  However, the existing inference techniques associated with these models rely on the exact computation of posterior kernel terms which are seldom available in analytical form.  ...  Approximate Latent Force Models In this section we propose Alfi, an approximate latent force inference framework which significantly expands the class of models that can benefit from latent force interpretability  ... 
arXiv:2109.11851v3 fatcat:fx6tcxwgcbaxznudxshy4ziiba

Inference of differential equation models by genetic programming

H IBA
2008 Information Sciences  
This paper describes an evolutionary method for identifying a causal model from the observed time series data. We use a system of ordinary differential equations (ODEs) as the causal model.  ...  ., bioinformatics, chemical reaction models, controlling theory etc.  ...  For this purpose, we are working on the development of the interactive inference system, in which users will be able to pick up the correct equations or discard the meaningless equations from the suggested  ... 
doi:10.1016/j.ins.2008.07.029 fatcat:6wsm7vx7pzhftcbnovrhh2lxai

Neural Differential Equations for Inverse Modeling in Model Combustors [article]

Xingyu Su, Weiqi Ji, Long Zhang, Wantong Wu, Zhuyin Ren, Sili Deng
2021 arXiv   pre-print
In addition, we augment physical models for combustion with neural differential equations to enable learning from sparse measurements.  ...  This work proposes an approach based on neural differential equations to approximate the unknown quantities from available sparse measurements.  ...  Similarly, solving ordinary differential equations (ODEs) of reaction network models is equivalent to solving infinite-depth deep residual networks [6] , which further rationalizes exploiting SGD in training  ... 
arXiv:2107.11510v1 fatcat:sq7jdimsmnbw3in4qpu676srpy
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