OTHE Subgroup Contributed Talks

Ashlee Ford Versypt

University at Buffalo, The State University of New York

"A Multi-Cellular Network Model Predicts Changes in Glomerular Endothelial Structure in Diabetic Kidney Disease"

Diabetic kidney disease (DKD) progression is often marked by early glomerular endothelial cell (GEC) dysfunction, including alterations in the fenestration size and number linked to impaired glomerular filtration. However, the cellular mechanisms regulating GEC fenestrations remain poorly understood due to limitations in existing in vitro models, challenges in imaging small fenestrations in vivo, and inconsistencies between in vitro and in vivo findings. This study used a logic-based protein-protein interaction network model with normalized Hill functions for dynamics to explore how glucose-mediated signaling dysregulation impacts fenestration dynamics in GECs. We identified key drivers of fenestration loss and size changes by incorporating signaling pathways related to actin remodeling, myosin light chain kinase, Rho-associated kinase, calcium, and VEGF and its receptor. The model predicted how hyperglycemia in diabetic mice leads to significant fenestration loss and increased size of fenestrations. We found that glycemic control in the pre-DKD stage mitigated signaling dysregulation but was less effective as DKD developed and progressed. The model suggested alternative disease intervention strategies to maintain fenestration structure integrity, such as targeting Rho-associated kinase, VEGF-A, NFκB, and actin stress fibers.

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Mojgan Ezadian

Lindi Wahl, Western University

"A Continuous-Time Stochastic Model for Mutation Effects in Microbial Population"

Mutation accumulation (MA) experiments are crucial for understanding evolution. In microbial populations, these experiments typically involve periods of population growth, where a single individual forms a visible colony, followed by severe bottlenecks. Studies on the effects of positive and negative selection in MA experiments have shown that, for example, with 20 generations of growth between bottlenecks, beneficial mutations will be substantially over-represented cite{}; this effect is known as ``selection bias''. In previous work, we developed a fully stochastic discrete-time model that includes realistic offspring distributions, accounting for genetic drift and allowing for the loss of rare lineages. We demonstrated that when drift is included, selection bias is even stronger than previously predicted cite{}. Since bacterial division is unlikely to remain synchronized over 20 or more generations, this study extends the discrete-time model to a continuous-time framework. Since lineages that start reproducing early accrue a compounded advantage, a continuous-time model offers an even more accurate correction for selection in MA experiments.

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