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- W2965505874 abstract "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 based on Group Lasso is applied to the NF- κ B 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." @default.
- W2965505874 created "2019-08-13" @default.
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- W2965505874 date "2019-08-01" @default.
- W2965505874 modified "2023-09-26" @default.
- W2965505874 title "Parallel Tempering with Lasso for Model Reduction in Systems Biology" @default.
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- W2965505874 doi "https://doi.org/10.1101/720631" @default.
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