This multidisciplinary proposal aims to model multicellular biological systems based on modeling of the behavior of each individual cell. This research will relate sub-cellular protein interactions to full scale cell behavior, and to the behavior of a system of many cells using computational methods. Cell division, growth, death and function will be modeled. Results from this work can have an impact both on Bioengineering and on Computer Science.
Traditional computational modeling and simulation methods are inadequate to address the biological complexity of cells. In recent years, considerable efforts have been devoted to developing multiscale models that capture biological complexity at distinct time and spatial resolutions. However, little progress has been made in developing multiscale models capable of integrating high resolution biological details with long-time cellular behavior. The primary goal of this proposal is to develop a parallel computation framework, called ParCell, for multiscale cell population modeling and simulation. ParCell will link subcellular biochemistry to long-time cell behavior determined by cell death, division, fate decision, and other cellular functions. Current multiscale population models mostly rely on serial computation-based simulation techniques. Such limitations and challenges prohibit the understanding and analysis of many important biological systems, such as tissue regeneration, clonal expansion of antigen-exposed immune cells, cell migration in wound healing, evolution of drug resistance in cells, and cellular phenotypes under disease and treatment conditions. ParCell will be the framework for modeling of heterogeneous multicellular systems that will link high resolution molecular details of signaling and gene transcription to evolutionary cell fate decisions and population dynamics. Current cell population models are mostly based on agent-based modeling (ABM) technique, where cells are represented as software objects or agents. Instead, it is proposed that cells will be represented as stand-alone parallel simulations (i.e., threads) rather than software objects. Using parallel computation, ParCell will systematically expand a single-cell biochemical network model, created using other software or languages, into a population model. Specifically, it will launch parallel simulations on a single-cell biochemical network model, and treat each simulation thread as an independent cell. It will also use a message passing interface (MPI) to link subcellular network dynamics (parallel thread corresponding to each cell) to cellular fate decisions and phenotypes based on model-specific (user-supplied) rules. Such distributed structure of the models combined with parallel computation will enable unprecedented scalability and mechanistic abstraction. Additionally, ParCell will use a novel load-balancing scheme for arbitrary model scalability in dynamic and heterogeneous cloud environments. The models can be made as mechanistic as any single-cell reaction network without adding model complexity or programming efforts. The PIs will leverage various summer camp programs organized by the Diversity, Outreach, and Women's Programs to recruit female and underrepresented minority students into the project.
