Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Abstract

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body.

Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues

“…Mechanistic mathematical modeling can be applied to parametrize systemic processes under data insufficiency [ 21 ]. Physiologically based pharmacokinetic (PBPK) models aim to describe the absorption, distribution, metabolism, and elimination of a drug in a physiologically relevant compartmental structure, where each compartment represents an organ or a tissue.…”

Machine learning‐based model predictive controller design for cell culture processes

“…Two paradigms for modeling cell culture processes are first‐principle and data‐driven (Craven et al, 2013; Tulsyan et al, 2018). The first principle models are mostly nonlinear state models derived based on Monod kinetics and enzymatic schemes, which are formulated with many unknown free parameters (Craven et al, 2014; Nolan & Lee, 2011; Stalidzans et al, 2020). These models often cannot capture bioprocess stochastic uncertainty and model uncertainty, which normally arise from the process variability and cause deviations from operating conditions.…”

Model comparison via simplicial complexes and persistent homology

“…There are many approaches to modelling biological systems, including continuum models [6][7][8], rule-based models [9][10][11], network models [12][13][14] and mechanistic models [5,15], which are not necessarily mutually exclusive classifications. While a model should represent the corresponding phenomenon as faithfully as possible, it is also a requirement that the model is manageable.…”