Systematic Bayesian posterior analysis facilitates hypothesis formation and guides investigation of pancreatic beta cell signaling

• Published in: bioRxiv
• Main Authors: Huber, Holly A, Georgia, Senta K, Finley, Stacey D
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 15.07.2022
• Subjects: Bayesian analysis; Beta cells; Cell growth; Cell proliferation; Cell signaling; Computer applications; Diabetes mellitus; Divergence; Feedback; Hypotheses; Hypothesis testing; Investigations; Janus kinase 2; Mathematical models; Prolactin; Signal transduction; Stat5 protein
• DOI: 10.1101/2022.07.14.500054

Bayesian inference produces a posterior distribution for the parameters and predictions from a mathematical model that can be used to guide the formation of hypotheses, thereby increasing our understanding of a biological process. Previous approaches to such Bayesian hypothesis formation are largely qualitative and time consuming; further, demonstrations of these approaches typically stop at hypothesis formation, leaving the questions they raise unanswered. Here, we introduce a Kullback-Leibler (KL) divergence-based ranking to expedite Bayesian hypothesis formation and investigate the hypotheses it generates, ultimately generating novel, therapeutically-relevant biological insights. We test our approach on a computational model of prolactin-induced JAK2-STAT5 signaling, a pathway that mediates beta cell proliferation. Because diabetes is characterized by a decrease in functional beta cell mass, replenishing this mass by harnessing beta cell proliferation signaling pathways like this one is a viable potential therapeutic strategy. Our KL divergence-based approach selects only a subset of data-informed parameters to search for qualitative evidence of alternative hypotheses, thereby expediting hypothesis formation. Within this subset, we find two plausible ranges for the receptor degradation rate, each characterized by different dynamics of the SOCS protein, which mediates a negative feedback loop in the JAK2-STAT5 pathway. To investigate these dynamics, we refine the posterior of an existing model with additional data. This refined model out-performs the original model when predicting a test data set consisting of protein dynamics that are consistent across ligand and tissue types. Further, it predicts transient SOCS activation with high certainty. We demonstrate that our approach offers a novel quantitative framework for Bayesian hypothesis testing. Further, we establish an improved model of beta cell signaling and determine that the negative feedback controlling the prolactin-induced proliferative response in beta cells is likely transient. This understanding of SOCS dynamics is particularly relevant for harnessing the proliferation signaling response of beta cells. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/FinleyLabUSC/KL-Divergence-Bayesian-Hypothesis-Formation