ArticlePLoS computational biology2020
Parallel Tempering with Lasso for model reduction in systems biology.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- A novel inflammatory response-related signature predicts the prognosis of cutaneous melanoma and the effect of antitumor drugs.World journal of surgical oncology · 2022Article
- Identification of a pyroptosis-related lncRNA risk model for predicting prognosis and immune response in colon adenocarcinoma.World journal of surgical oncology · 2022Article
- Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods.PLoS computational biology · 2022Article
- Automated model calibration with parallel MCMC: Applications for a cardiovascular system model.Frontiers in physiology · 2022Article
- Characterization and validation of a ferroptosis-related LncRNA signature as a novel prognostic model for lung adenocarcinoma in tumor microenvironment.Frontiers in immunology · 2022Article
- Multimodal parameter spaces of a complex multi-channel neuron model.Frontiers in systems neuroscience · 2022Article
- Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage.PLoS computational biology · 2021Article
- Identification of a Ferroptosis-Related LncRNA Signature as a Novel Prognosis Model for Lung Adenocarcinoma.Frontiers in oncology · 2021Article
- Four Immune-Related Long Non-coding RNAs for Prognosis Prediction in Patients With Hepatocellular Carcinoma.Frontiers in molecular biosciences · 2020Article
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3 authors.
Funding
Abstract
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.
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