Sherekar2023 - Modeling heterogeneity in TNFR1 signaling model

ABSTRACT: Tumor microenvironment contains abundant quantities of Tumor necrosis factor alpha (TNFα) secreted by a battery of immune cells. Signal flow through TNFα stimulated TNFR1 signaling, which is supposed to maintain a fine balance between survival and cell-death phenotypes, is often sacrificed in a diseased tissue, such as that of a cancer. Strategies to tilt this balance towards cell-death in a tumor that can help improve the therapeutic efficiency are often remain ineffective due to the cell-to-cell variability in expressing different phenotypes. This variability during TNFR1 signaling stems from the heterogeneity in signal flow through intracellular signaling entities that regulate pro-survival and apoptosis responses. This stochastic Boolean dynamic model of TNFR1 signaling focuses on understanding the dynamic cross-talk regulation of apoptosis at single-cell level. We demonstrate with this model that the signal flow path variability can be modulated to enable cells favour apoptosis. We use BM-ProSPR algorithm (GitHub - ganeshIITB/BMProSPR: Codes pertaining to BM-ProSPR) to compute reliable partial state transition graph (pSTG) for the systematic analysis of the dynamical properties of the network at single-cell level (pSTGs of two different conditions are provided as .mat files). Using the pSTG, we derive a comprehensive way to construct ensemble-level dynamics of intracellular signaling entities in order to understand the cross-talk between the pathways (Methods section in manuscript- Time varying conditional probability of finding a node being active). Model analysis juxtaposed with the experimental observations revealed that NFκB and PI3K transient responses guide XIAP to coordinate the crucial dynamic cross-talk between the pro-survival and apoptotic arms at the single-cell level. Model predicted ~31% increase in apoptosis can be achieved by arresting Comp1-IKK* activity. In summary, the analysis of TNFR1 signaling network model provides insights into 1. What are the central regulators for regulating phenotypic response at single-cell level? 2. How do these regulators coordinate the cross-talk between the pathways of different phenotypes? 3. How do the ensemble-level signal flow paths shift towards a phenotype by arresting an entity that regulates the central regulators’ levels?

SUBMITTER: Shubhank Sherekar  

PROVIDER: MODEL2504180001 | BioModels | 2025-04-22

REPOSITORIES: BioModels