Parallel Tempering with Lasso for model reduction in systems biology

Systems Biology models reveal relationships between signaling inputs and observable molecular or cellular behaviors. The complexity of these models, however, often obscures key elements that regulate emergent properties. We use a Bayesian model reduction approach that combines Parallel Tempering wit...

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Bibliographic Details
Published in:PLoS computational biology 2020-03, Vol.16 (3), p.e1007669-e1007669
Main Authors: Gupta, Sanjana, Lee, Robin E C, Faeder, James R
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Bayes Theorem
Bayesian analysis
Biology
Biology and Life Sciences
Complexity
Computational biology
Computer and Information Sciences
Feedback loops
Feedback, Physiological - physiology
Gene expression
Mathematical models
Mediating effects (Research)
Medicine and Health Sciences
Methods
Model reduction
Models, Biological
Multilevel analysis
NF-kappa B - metabolism
NF-κB protein
Ordinary differential equations
Parameter estimation
Physical Sciences
Regularization
Research and Analysis Methods
Signal Transduction
Signalling systems
Software
Systems Biology - methods
Tempering
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Summary:Systems Biology models reveal relationships between signaling inputs and observable molecular or cellular behaviors. The complexity of these models, however, often obscures key elements that regulate emergent properties. We use a Bayesian model reduction approach that combines Parallel Tempering with Lasso regularization to identify minimal subsets of reactions in a signaling network that are sufficient to reproduce experimentally observed data. The Bayesian approach finds distinct reduced models that fit data equivalently. A variant of this approach that uses Lasso to perform selection at the level of reaction modules is applied to the NF-κB signaling network to test the necessity of feedback loops for responses to pulsatile and continuous pathway stimulation. Taken together, our results demonstrate that Bayesian parameter estimation combined with regularization can isolate and reveal core motifs sufficient to explain data from complex signaling systems.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1007669