# Modeling Multiscale Control of Liver Regeneration > **NIH NIH U01** · THOMAS JEFFERSON UNIVERSITY · 2020 · $626,946 ## Abstract PROJECT SUMMARY The aim of this collaborative U01 project is to develop a novel multiscale modeling framework that takes advantage of the in depth information on cellular functional states provided by single cell data sets. Emerging technologies and analysis methods aimed at high-throughput molecular assays of hundreds to thousands of single cells have enabled an unprecedented view of the heterogeneity, hierarchy and complexity of cellular functional states. There is an unmet need for systematic methods to utilize such information-rich data sets in a multiscale modeling framework. The central innovative idea of our project with broad impact is to realize the full potential of these novel single cell data sets by developing models of cellular functional states and state transitions to bridge the molecular and tissue scales with physiological scale functions. We will focus on the following Cutting Edge Challenge: Novel computational modeling approaches for big data that account for simultaneous sources of data on multiple scales. We will develop the proposed multiscale modeling framework in the context of understanding the control principles governing the coordinated tissue response to injury. Our approach involves explicit accounting of cellular functional states of immune, stromal, endothelial and epithelial cells, and putative molecular processes driving the state transitions, with broad applicability to multiple tissue repair scenarios. The complexity of the tissue repair process makes it difficult, in a purely qualitative analysis, to identify how, to what extent, and at what time the multiscale molecular, cellular and physiological factors contribute to the coordinated control of the entire process. We will focus on the process of liver regeneration as an enabling testbed in order to fully develop, fine tune and illustrate our multiscale modeling approach for broader application and utility. We have recruited a collaborative team of investigators with expertise in computational modeling, high-throughput single cell scale molecular assays, in vivo manipulation, intravital imaging, and non-invasive methods for physiological scale analysis. Our cross-disciplinary project efforts are organized along three Aims: Aim 1 Develop a mathematical framework to model molecular networks and cellular functional states for predicting the cellular scale impact of molecular mechanisms identified by single cell data sets. Aim 2 Integrate the molecular and cellular network model with a model of spatial tissue microarchitecture and metabolic capacity to predict physiological consequences of response to liver injury. Aim 3 Evaluate and experimentally test the multiscale model for key mechanisms and dynamic shifts in network balances that provide insights into the control principles of the regenerative response to injury in the liver. Successful completion of the aims will yield an optimized approach for utilizing single cell data sets in multiscale modeling. We wi... ## Key facts - **NIH application ID:** 10000095 - **Project number:** 5U01EB023224-04 - **Recipient organization:** THOMAS JEFFERSON UNIVERSITY - **Principal Investigator:** Joannes B Hoek - **Activity code:** U01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $626,946 - **Award type:** 5 - **Project period:** 2017-09-30 → 2022-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10000095 ## Citation > US National Institutes of Health, RePORTER application 10000095, Modeling Multiscale Control of Liver Regeneration (5U01EB023224-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10000095. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*