Cell-by-cell and bidomain models for cardiac derived stem cell tissue: innovative numerical and deep learning tools for regenerative medicine

The project has the two-fold ambition of developing a novel and accurate mathematical and numerical framework for the simulation of the phenotype-specific human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) and of developing the computationally and energy efficient simulation of engineered hiPSC-CMs tissue. Tools developed will be able to interact with those already present in the vast field of computational medicine for the investigation and treatment of genetic cardiac pathologies, thus providing support for real-time medical decisions.

hiPSC-CMs have enormously advanced the field of Regenerative Medicine since their discovery in 2006 by Yamanaka et al. , which then led to the Nobel Prize in Medicine in 2012. In recent years, mathematical models of the ionic current through the hiPSC-CM’s membrane have focused on the immature ventricular-like phenotype, developing a system of stiff ordinary differential equations (ODEs) .Nowadays, the Dynamic Clamp technique (DC) allows for the growth of hiPSC-CMs towards more adult cardiac phenotype . Thus, the employment of electronic maturation highlights the chamber-specific AP phenotype of the cells, supporting the first aim of the project. In scientific literature, in-vitro data about mature cells is still missing. This work seeks to develop a new phenotype-specific in-silico model of the hiPSC-CM ionic current.

Resulting ionic models are the starting point for the cardiac in-silico model, based on the biophysically accurate representation of the heart tissue which is constituted by multiscale models of different physical processes and described by systems of partial differential equations (PDEs). In this project, we are going to focus on the Bidomain electrophysiological model, where the space-time evolution of electric potentials (a parabolic system of nonlinear PDEs) is coupled with an hiPSC-CM specific ionic model.

Research and innovation objectives in this area can follow two parallel computational challenges.

Existing models provide approximations of the tissue using a spatial scale which is several times larger than the actual cell size. Thus, a formidable challenge is to provide a cell-by-cell mathematical formulation of the problem in order to simulate the interaction of many different proteins embedded in the cell membrane. This model needs computationally efficient numerical methods to improve the economic and environmental sustainability of scientific computing, by enabling energy savings in the numerical simulation.

An emerging alternative approach to solving PDEs and to improving the efficiency of standard methods in scientific computing is scientific machine learning. Physics-Informed Neural Networks (PINNs) and Deep Operator Networks (DeepONets) are starting to be used in the context of cardiac modeling . This project aims to build PINNs for coupled PDEs, specifically for integrated hiPSC-CMs models of engineered tissue.

Better: The project will lead to new collaborations and will hopefully establish a research network with international leaders in this field

The last ambitious aim deals with genetic disease modeling. Atrial fibrillation (AF), for instance, is a common disease affecting atrial cells The best option currently used for treating the disease is interventional therapy, namely ablation.

The clinical aim of this project is to use the computational model created as a virtual representation of the patient’s tissue and to virtually evaluate drugs through a patient-specific model, by paving the way for improving therapeutic targets or ablation techniques .

Several ion channel mutations, along with a range of other genetic variants and broader risk factors, are known to increase the likelihood of developing AF . Therefore, hiPSC-CMs technology perfectly fits this challenge, since these cells have the same genetic heritage as the donor.

The development of a patient-specific simulator reflects the need of personalized medicine, by reducing the expenses related to the testing of specific drugs and in-vitro cell maturation experiments.

The project is challenging, ambitious, and original. It is designed to provide innovative contributions in mathematics, with significant implications in regenerative and cardiac computational medicine. The project is fully feasible as the researcher and supervisors possess proven experience in mathematical and numerical methods for PDEs, scientific computing, High-Performance Computing (HPC), and cardiac modeling. Fundamental research in mathematical and numerical modeling will be driven by clinical applications, leading to an innovative tool for both the scientific numerical community and clinicians operating at the forefront of medicine.

The high impact of this project and the creation of synergy between different fields will lead to other applications as well as being the first step towards a higher integration of regenerative medicine in computational cardiology models. This project gives perspective to other knowledge and fields. Thus, the resulting forge of ideas will be the starting point of this researcher’s academic career, as she aspires to a future position in academia, dealing with international groups.

The international perspective of this challenging project is proved also by the international host. The researcher will reside in Switzerland for two years, conducting her research in Lugano under the supervision of Prof. Rolf Krause, full Professor at Università della Svizzera Italiana (USI). Nevertheless, international groups are interested in this research area so and two visiting experiences are planned during the two-year project for a total duration of eight months: the first one to Zuse Institute Berlin (Germany), under the supervision of Dr. Martin Weiser and the second one to Università degli Studi di Pavia (Italy), under the supervision of Prof. Luca F. Pavarino.

This project goes beyond the state of the art because it offers the first cell-by-cell model for hiPSC-CMs coupled with the mathematical model for atrial-like hiPSC-CMs, not yet present in scientific literature.