| dc.description.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 the 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–dispersionreaction 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 have an effect on 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 | en_US |
