Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models.

Stefano Casarin,Jose Felix Rodriguez Matas,Monika Colombo,Anna Corti,Francesco Migliavacca,Claudio Chiastra

doi:10.3389/fbioe.2021.744560

Abstract

The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.

Highlights

In the past two decades the widespread incidence of cardiovascular diseases and associated mortality and morbidity (Virani et al, 2021), together with the increase in computer resources, promoted an extensive research in the field of vascular pathophysiology computational modeling
Since the shifting from reductionist to “systems biology” approach, a biological system is seen as a complex network involving environmental conditions, feedback mechanisms and mutual interactions across different scales, rather than as the mere sum of its components (Kohl et al, 2010; Mazzocchi, 2012; Kesić, 2016)
The aim of the present review is to point out works on multiscale modeling of vascular remodeling, with special emphasis on those frameworks integrating continuum models and agent-based approaches in a systems biology perspective

Summary

Introduction

In the past two decades the widespread incidence of cardiovascular diseases and associated mortality and morbidity (Virani et al, 2021), together with the increase in computer resources, promoted an extensive research in the field of vascular pathophysiology computational modeling. A systems biology approach allows tracking the propagation of a physical quantity across the multiscale network and quantifying its effect at tissue/organ level This is fundamental to elucidate intracellular patterns, feedback mechanisms and cause-effect relations that are difficult to discern from in-vitro or in-vivo experiments, as well as from single-scale in-silico models (Qu et al, 2011). Such a level of detail offers a powerful instrument in the optic of personalized medicine, which is thought to revolutionize the therapeutic/ diagnostic approach (Vogenberg et al, 2010). This is supported by the progresses in biomedical technologies (e.g., imaging, high-throughput genomic sequencing) and the availability of high-performance computational resources, which allow elaborating huge quantity of data and integrating them in well-established computational infrastructures (Schadt et al, 2010; Hoekstra et al, 2019)

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