Abstract

Trillions of chemical reactions occur in the human body every second, where the generated products are not only consumed locally but also transported to various locations in a systematic manner to sustain homeostasis. Current solutions to model these biological phenomena are restricted in computability and scalability due to the use of continuum approaches in which it is practically impossible to encapsulate the complexity of the physiological processes occurring at diverse scales. Here, we present a discrete modeling framework defined on an interacting graph that offers the flexibility to model multiscale systems by translating the physical space into a metamodel. We discretize the graph-based metamodel into functional units composed of well-mixed volumes with vascular and cellular subdomains; the operators defined over these volumes define the transport dynamics. We predict glucose drift governed by advective-dispersive transport in the vascular subdomains of an islet vasculature and cross-validate the flow and concentration fields with finite-element-based COMSOL simulations. Vascular and cellular subdomains are coupled to model the nutrient exchange occurring in response to the gradient arising out of reaction and perfusion dynamics. The application of our framework for modeling biologically relevant test systems shows how our approach can assimilate both multi-omics data from in vitro-in vivo studies and vascular topology from imaging studies for examining the structure-function relationship of complex vasculatures. The framework can advance simulation of whole-body networks at user-defined levels and is expected to find major use in personalized medicine and drug discovery.

Highlights

  • The physiological system is a complex network in which each organ forms a subsystem and different subsystems interact to maintain overall homeostasis of the body

  • Differential equations defining the interactions over nodal volumes embedded in the graph are solved by translating the physical domain into a metamodel in which the biophysical attributes are subsumed. This framework is suitable for the following key applications: 1) to reduce the computational cost involved in the spatial discretization of large tissue volumes (Section 3.1.2); our discrete approach is geared toward obtaining fast solution by reducing the system dimension, and the metamodel is scalable into any domain; 2) to probe the effect of flow topology on scalar transport and the sensitivity of concentration dynamics to network parameters and variations in physiological set points. (Section 3.2, 3.4); and 3) to assimilate multi-omics data from in vitro and in vivo studies and vascular topology from imaging studies (Section 3.3) for examining the influence of structural changes on the functional response of a tissue

  • After validating the results of nodal pressures and edge velocities with the results from COMSOL, we proceed with the simulations of advection–dispersion dynamics of glucose species in the blood vessel

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Summary

A Graph-Based Framework for Multiscale Modeling of Physiological Transport

Pal D (2022) A Graph-Based Framework for Multiscale Modeling of Physiological Transport. We present a discrete modeling framework defined on an interacting graph that offers the flexibility to model multiscale systems by translating the physical space into a metamodel. We discretize the graphbased metamodel into functional units composed of well-mixed volumes with vascular and cellular subdomains; the operators defined over these volumes define the transport dynamics. Vascular and cellular subdomains are coupled to model the nutrient exchange occurring in response to the gradient arising out of reaction and perfusion dynamics. The application of our framework for modeling biologically relevant test systems shows how our approach can assimilate both multiomics data from in vitro–in vivo studies and vascular topology from imaging studies for examining the structure–function relationship of complex vasculatures.

INTRODUCTION
METHODOLOGY
Construction of Capillary Networks
Preliminary Assumptions
Mathematical Formulation of Flow Distribution in the Network
Advection–Dispersion of Chemical Species in the Blood Vessel
Advection–Dispersion-Reaction
Equations
RESULTS AND DISCUSSION
Comparison of Flow and Concentration Fields
Influence of Flow Topology on Scalar Transport
Functional Coupling of Blood Vessel–Cell Exchange
Sensitivity of Concentration Dynamics to ΔP and Glucose Dose
DATA AVAILABILITY STATEMENT
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