DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES
Ph.D. Thesis Defense
Speaker : Ms. Deepa Maheshvare M
S.R. Number : 06-18-01-10-12-16-1-14025
Title : “Modeling physiological transport at scales: connecting cells to organs”
Research Supervisor : Prof. Debnath Pal
Date & Time : June 21, 2024 (Friday), 11:00 AM
Venue : The Thesis Defense will be held on MICROSOFT TEAMS
Please click on the following link to join the Thesis Defense:
MS Teams link
ABSTRACT
The physiological system is a complex network in which each organ forms a subsystem, and the functional networks in different subsystems communicate to maintain body’s overall homeostasis. The ability to simultaneously capture local and global dynamics by hierarchically bridging communication networks at different scales is a key challenge in holistic physiology modeling.
We present a scalable hierarchical framework that allows us to bridge diverse scales to model biochemicals’ production, consumption, and distribution in tissue microenvironments. We developed a discrete modeling framework to simulate the gradient-driven advection–dispersion-reaction physics of multispecies transport in multiscale systems. The physical space is translated into a metamodel, and we define graph operators on the finite connected network representation of the discrete functional units embedded in the metamodel. The governing differential equations capture the inter-compartment dynamics of the well-mixed nodal volumes by formulating the transport dynamics in the vascular domain, transcapillary exchange, and metabolism in the tissue domain as a ‘tank-in-series’ model. This allows our framework to scale to large networks and provides the flexibility to fuse multiscale models by encoding imaging data of vascular topology and omics data to enhance systems-level understanding. Our framework is suitable for reducing the computational cost of spatially discretizing large tissue volumes and for probing the effect of flow topology on biochemical transport to study structure-function relationships in tissues.
Next, we developed a comprehensive and standardized data-driven modeling workflow to address the challenges faced in developing kinetic models of metabolism in single cells. We have created open, free, and FAIR (findable, accessible, interoperable, and reusable) assets to study pancreatic physiology and glucose-stimulated insulin secretion (GSIS). The data curation, integration, normalization and data fitting workflow, and a large database of metabolic data from 39 studies spanning 50 years of pancreatic, islet, and β-cell research in humans, rats, mice, and cell lines were used to construct a novel data-driven kinetic SBML (Systems Biology Markup Language) model. The model consists of detailed glycolysis and phenomenological equations for biphasic insulin secretion coupled to ATP dynamics, and (ATP/ADP ratio). The predictions of glycolytic intermediates and biphasic insulin secretion are in good agreement with experimental data, and our model predicts the factors affecting ATP consumption, ATP formation, hexokinase, phosphofructokinase, and ATP/ADP-dependent insulin secretion influence GSIS.
Finally, we present KiPhyNet, an online network simulation tool connecting cellular kinetics and physiological transport. It allows users to simulate and interactively visualize pressure, velocity, and concentration fields for applications such as flow distribution, glucose transport, and glucose-lactate exchange in microvascular networks. When extended for translational purposes in clinical settings, the framework and pipeline developed in this work can advance the simulation of whole-body models and are expected to have major applications in personalized medicine.
ALL ARE WELCOME
