ArticleBioinformatics (Oxford, England)2014
Causal network inference using biochemical kinetics.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- LazyNet: Interpretable ODE Modeling of Sparse CRISPR Single-Cell Screens Reveals New Biological Insights.Biology · 2025Article
- Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.International journal of molecular sciences · 2024Review
- Reconstructing gene regulatory networks of biological function using differential equations of multilayer perceptrons.BMC bioinformatics · 2022Article
- Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline.Journal of visualized experiments : JoVE · 2021Article
- Gene regulatory network inference from sparsely sampled noisy data.Nature communications · 2020Article
- Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters.Bioinformatics (Oxford, England) · 2020Article
- Learning stable and predictive structures in kinetic systems.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Reconstructing phosphorylation signalling networks from quantitative phosphoproteomic data.Essays in biochemistry · 2018Review
- Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data.Scientific reports · 2018Article
- How to deal with parameters for whole-cell modelling.Journal of the Royal Society, Interface · 2017Review
- Elucidation of molecular kinetic schemes from macroscopic traces using system identification.PLoS computational biology · 2017Article
- Approximate Bayesian inference in semi-mechanistic models.Statistics and computing · 2017Article
- Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series.PLoS computational biology · 2016Article
- The Local Edge Machine: inference of dynamic models of gene regulation.Genome biology · 2016Article
- Learning stochastic process-based models of dynamical systems from knowledge and data.BMC systems biology · 2016Article
- Article
- Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data.Bioinformatics (Oxford, England) · 2015Article
- Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression.PloS one · 2015Article
- Topological sensitivity analysis for systems biology.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
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6 authors.
Funding
Abstract
motivationNetworks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of biochemical systems are generally non-linear, suggesting that suitable non-linear formulations may offer gains with respect to causal network inference and aid in associated prediction problems.
resultsWe present a general framework for network inference and dynamical prediction using time course data that is rooted in non-linear biochemical kinetics. This is achieved by considering a dynamical system based on a chemical reaction graph with associated kinetic parameters. Both the graph and kinetic parameters are treated as unknown; inference is carried out within a Bayesian framework. This allows prediction of dynamical behavior even when the underlying reaction graph itself is unknown or uncertain. 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. AVAILABILITY AND IMPLEMENTATION: MATLAB R2014a software is available to download from warwick.ac.uk/chrisoates. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.