# Multiscale Computational Modeling to Design Patterned Tissue Assembloids for Biomanufacturing > **NIH NIH R21** · UNIVERSITY OF VIRGINIA · 2024 · $169,702 ## Abstract PROJECT SUMMARY The mass production of tissues and organs is the ultimate goal of biomanufacturing, but this goal will not be achieved without the assistance of engineering design tools that predict how three-dimensional, multicellular structures, comprised of cells that dynamically respond to each other and their environment, self-organize into spatially patterned tissues. To date, no such design tools exist, and our high-risk, high-reward proposal seeks to develop and validate the first multiscale computational model to inform the design, fabrication, and self- assembly of tissues comprised of heterogeneous cell types engineered with synthetic gene circuits regulating cell adhesion. We recently published a relatively simple agent-based computational model, that when coupled with machine learning algorithms, identifies design parameters that generate multicell spheroids, or simple tissues, comprised of heterogeneous subpopulations of cells that self-organize into specific patterns (e.g., striped, soccer ball, core/shell, and core/pole). The proposed work will greatly elaborate this simple model to a multiscale computational model to predict how collections of bioprinted spheroids form into spatially patterned “assembloids.” We will utilize mixed populations of two cell types genetically engineered with highly modular synNotch synthetic gene circuits, which propagate intracellular signals to regulate cell-cell adhesion strength based on cell-cell interactions. We will also leverage state-of-the-art 3D printing spheroid positioning technology developed at our institution to precisely place three-dimensional spheroids adjacent to one another within a synthetic biomaterial that facilitates the formation of engineered tissue constructs. In Aim 1, we will develop a novel multiscale agent-based computational model that predicts how synNotch-mediated intercellular signaling and intracellular signaling in individual cells gives rise to self-sorting of heterogeneous cell populations, leading to the emergent patterning of three-dimensional tissues. We will run tens of thousands of simulations and apply clustering algorithms to extract design parameters that favor certain three-dimensional tissue patterns over others. In Aim 2, we will experimentally validate that the computational model can be reliably used to design three-dimensional multicellular tissue constructs by challenging it to identify the design parameters (e.g., initial number of cells, ratio of cell subpopulations, heterotypic and homotypic cell-cell adhesion strengths) that will generate three-dimensional assembloids with specific multicellular patterns. We will culture synNotch cells into spheroids and spatially position them into tissue assembloids according to the design parameters predicted by the computer model, and then we will assess if the experimental tissues, imaged using confocal microscopy, exhibit the spatial patterns predicted by the computational model. We expect that our proje... ## Key facts - **NIH application ID:** 10999951 - **Project number:** 1R21EB035402-01A1 - **Recipient organization:** UNIVERSITY OF VIRGINIA - **Principal Investigator:** Matthew J Lazzara - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $169,702 - **Award type:** 1 - **Project period:** 2024-08-01 → 2026-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10999951 ## Citation > US National Institutes of Health, RePORTER application 10999951, Multiscale Computational Modeling to Design Patterned Tissue Assembloids for Biomanufacturing (1R21EB035402-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10999951. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*