High‐throughput rational design of artificial tissue moulds using a biophysical in‐silico model and machine learning

Hague, James; Andrews, Allison and Dickinson, Hugh (2023). High‐throughput rational design of artificial tissue moulds using a biophysical in‐silico model and machine learning. In: TERMIS 2023 – European Chapter Manchester Central Conference Centre Manchester, UK March 28–31, 2023.Tissue Engineering Part A.

DOI: https://doi.org/10.1089/ten.tea.2023.29043.abstracts

Abstract

INTRODUCTION: We use a microscopic biophysical model for self‐organization and reshaping of lab grown polarised tissues to identify candidate moulds for growth of artificial tissue from hydrogels. Designing moulds and scaffolds that lead to specific patterns of self‐organisation in artificial tissues is a slow and painstaking effort. Computational models could help with this process. We use a biophysical model of self‐organisation in polarised tissues that includes both extracellular matrix (ECM) and cells. The model uses a mix of contractile networks and dipole orientations (CONDOR) to simulate the feedback between microscopic active forces mediated between cells and ECM, and macroscopic forces that develop on tissue length‐scales. When large numbers of cells act together these forces drive, and are affected by, macroscopic‐scale self‐organization and reshaping of tissues in a feedback loop.

METHODS: We use the CONDOR model [1], which consists of a contractile network representing the ECM, that interacts with large numbers of cells via dipole forces. By simulating the interactions between large numbers of cells and the ECM in this way, we track both individual cells and the ECM, like the situation within tissues. This allows us to describe the macroscopic self‐organization and reshaping of tissue. We solve the model using simulated annealing, Monte Carlo and machine learning techniques. This allows high‐throughput simulation of large numbers of tissue moulds.

RESULTS: Close agreement has previously been shown with experiments on artificial neural tissue [1]. We used the CONDOR model to make high‐throughput calculations for a wide range of computer‐generated moulds. We identified a fitness function so that we can identify (computationally) the relative merit of moulds for growing artificial neural tissue. Using this process, we have identified candidate mould designs for growth of artificial neural tissue with favourable properties.

DISCUSSION & CONCLUSIONS: Feedback between cells and ECM is a key factor in driving macroscopic self‐organization and reshaping of tissue. The CONDOR model has previously been shown to match well with experimental data. We demonstrated proof‐of‐concept for using the model for high‐throughput rational design of artificial tissues. Model extensions will lead to further tools for rational design of moulds, scaffolds, and other approaches for growing artificial tissue.

ACKNOWLEDGEMENTS: We acknowledge support from the STFC Impact Acceleration Account

REFERENCES: [1] Microscopic biophysical model of self‐organization in tissue due to feedback between cell‐ and macroscopic‐scale forces. Physical Review Research 2, 043217 (2020). J. P. Hague, P. W. Mieczkowski, C. O'Rourke, A. J. Loughlin, J. B. Phillips

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