ArticleFrontiers in genetics2020
A Probabilistic Approach to Explore Signal Execution Mechanisms With Limited Experimental Data.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Unified tumor growth mechanisms from multimodel inference and dataset integration.PLoS computational biology · 2023Article
- A biochemical necroptosis model explains cell-type-specific responses to cell death cues.Biophysical journal · 2023Article
- Programmatic modeling for biological systems.Current opinion in systems biology · 2021Article
- Inferring phenomenological models of first passage processes.PLoS computational biology · 2021Article
- A Probabilistic Approach to Explore Signal Execution Mechanisms With Limited Experimental Data.Frontiers in genetics · 2020Article
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2 authors.
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Abstract
Mathematical models of biochemical reaction networks are central to the study of dynamic cellular processes and hypothesis generation that informs experimentation and validation. Unfortunately, model parameters are often not available and sparse experimental data leads to challenges in model calibration and parameter estimation. This can in turn lead to unreliable mechanistic interpretations of experimental data and the generation of poorly conceived hypotheses for experimental validation. To address this challenge, we evaluate whether a Bayesian-inspired probability-based approach, that relies on expected values for quantities of interest calculated from available information regarding the reaction network topology and parameters can be used to qualitatively explore hypothetical biochemical network execution mechanisms in the context of limited available data. We test our approach on a model of extrinsic apoptosis execution to identify preferred signal execution modes across varying conditions. Apoptosis signal processing can take place either through a mitochondria independent (Type I) mode or a mitochondria dependent (Type II) mode. We first show that
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