Systematic Bayesian posterior analysis guided by Kullback-Leibler divergence facilitates hypothesis formation.

Bayesian inference produces a posterior distribution for the parameters of a mathematical model that can be used to guide the formation of hypotheses; specifically, the posterior may be searched for evidence of alternative model hypotheses, which serves as a starting point for hypothesis formation and model refinement. Previous approaches to search for this evidence are largely qualitative and unsystematic; 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, biologically significant insights. Our approach uses KL divergence to rank parameters by how much information they gain from experimental data. Subsequently, rather than searching all model parameters at random, we use this ranking to prioritize examining the posteriors of the parameters that gained the most information from the data for evidence of alternative model hypotheses. We test our approach with two examples, which showcase the ability of our approach to systematically uncover different types of alternative hypothesis evidence. First, we test our KL divergence ranking on an established example of Bayesian hypothesis formation. Our top-ranked parameter matches the one previously identified to produce alternative hypotheses. In the second example, we apply our ranking in a novel study of a computational model of prolactin-induced JAK2-STAT5 signaling, a pathway that mediates beta cell proliferation. Within the top 3 ranked parameters (out of 33), we find a bimodal posterior revealing two possible ranges for the prolactin receptor degradation rate. We go on to refine the model, incorporating new data and determining which degradation rate is most plausible. Overall, while the effectiveness of our approach depends on having a properly formulated prior and on the form of the posterior distribution, we demonstrate that our approach offers a novel and generalizable quantitative framework for Bayesian hypothesis formation and use it to produce a novel, biologically-significant insight into beta cell signaling.

Phosphatases are predicted to govern prolactin-mediated JAK-STAT signaling in pancreatic beta cells.

Patients with diabetes are unable to produce a sufficient amount of insulin to properly regulate their blood glucose levels. One potential method of treating diabetes is to increase the number of insulin-secreting beta cells in the pancreas to enhance insulin secretion. It is known that during pregnancy, pancreatic beta cells proliferate in response to the pregnancy hormone, prolactin (PRL). Leveraging this proliferative response to PRL may be a strategy to restore endogenous insulin production for patients with diabetes. To investigate this potential treatment, we previously developed a computational model to represent the PRL-mediated JAK-STAT signaling pathway in pancreatic beta cells. Here, we applied the model to identify the importance of particular signaling proteins in shaping the response of a population of beta cells. We simulated a population of 10 000 heterogeneous cells with varying initial protein concentrations responding to PRL stimulation. We used partial least squares regression to analyze the significance and role of each of the varied protein concentrations in producing the response of the cell. Our regression models predict that the concentrations of the cytosolic and nuclear phosphatases strongly influence the response of the cell. The model also predicts that increasing PRL receptor strengthens negative feedback mediated by the inhibitor suppressor of cytokine signaling. These findings reveal biological targets that can potentially be used to modulate the proliferation of pancreatic beta cells to enhance insulin secretion and beta cell regeneration in the context of diabetes.

Prolonged Culture of Aligned Skeletal Myotubes on Micromolded Gelatin Hydrogels.

In vitro models of skeletal muscle are critically needed to elucidate disease mechanisms, identify therapeutic targets, and test drugs pre-clinically. However, culturing skeletal muscle has been challenging due to myotube delamination from synthetic culture substrates approximately one week after initiating differentiation from myoblasts. In this study, we successfully maintained aligned skeletal myotubes differentiated from C2C12 mouse skeletal myoblasts for three weeks by utilizing micromolded (µmolded) gelatin hydrogels as culture substrates, which we thoroughly characterized using atomic force microscopy (AFM). Compared to polydimethylsiloxane (PDMS) microcontact printed (µprinted) with fibronectin (FN), cell adhesion on gelatin hydrogel constructs was significantly higher one week and three weeks after initiating differentiation. Delamination from FN-µprinted PDMS precluded robust detection of myotubes. Compared to a softer blend of PDMS µprinted with FN, myogenic index, myotube width, and myotube length on µmolded gelatin hydrogels was similar one week after initiating differentiation. However, three weeks after initiating differentiation, these parameters were significantly higher on µmolded gelatin hydrogels compared to FN-µprinted soft PDMS constructs. Similar results were observed on isotropic versions of each substrate, suggesting that these findings are independent of substrate patterning. Our platform enables novel studies into skeletal muscle development and disease and chronic drug testing in vitro.