A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
Filters
Inferring reaction systems from ordinary differential equations
2015
Theoretical Computer Science
Preserved Fulltext
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
2014
Biometrics
Preserved Fulltext
The Internet Archive has a preservation copy of this work in our general collections.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2012 pre-print arXiv:1205.6920v1 fatcat:4ljxrzyeqzgzzcspdvydkwhroaOther Versions
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
2016
Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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]
2013
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2013
Computer applications in the biosciences : CABIOS
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
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
2013
Computer applications in the biosciences : CABIOS
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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]
2014
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2014
Computer applications in the biosciences : CABIOS
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2010
PLoS ONE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2014
Journal of Bioinformatics and Computational Biology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2019
Journal of the Royal Society Interface
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2016
Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2008
Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 41,011 results