Center for Quantiative Biological Simulation Microphysiology Gateway

Introduction to Microphysiological Simulations

Cells and tissues are very complex for two principle reasons. First, the biochemical reactions that underlie life are extraordinarily complex, and the study of biochemical networks and pathways is a major initiative of the Systems Biology community and the National Institutes of Health. Second, the spatial design of cells and tissues is extraordinarily complex, and it is in these complex spaces that the complex biochemistry takes place.

Computer modeling is critical to understanding such interwoven layers of cellular complexity. A major challenge for modeling is development of new methods and tools that enable simulation of biochemical complexity in spatially realistic cellular models. This combination of biochemical and spatial realism constitutes microphysiological modeling, and requires a software pipeline to build the models and run the simulations. In general, the pipeline must include:

1. tools to generate of cellular geometry, i.e., the topology and distribution of relevant molecules, organelles, and/or cells. Such geometry can be created either from image segmentation and meshing, or directly from CAD-type operations.
2. tools to segmentand annotate of the geometry to specify space- and time-dependent properties of molecular constituents (e.g., size, weight, charge, diffusion coefficient, binding sites), membrane properties (e.g., elasticity, resistance, capacitance), properties of intra- and extracellular solutions (e.g., viscosity, solute concentrations, ionic strength),  and a mapping between molecular identities and mechanisms of interaction (i.e., state diagrams of first and second-order transitions, including all associated rates).
3. tools to generate meshes, i.e., discretized representations of the geometry, that may be composed of 2-D or 3-D elements or both, and which include the properties defined during geometry segmentation and annotation.
4. a simulation engine, based on Monte Carlo, finite element, or hybrid algorithms, and supporting a wide variety of chemical kinetic data output (numerical time series and various statistical measures) as well as detailed, large-scale visualization output.
5. a visualization engine, preferably linked to the tool used for segmentation and annotation of geometry, so that visual pre- and post-processing of simulation data can be done in an integrated environment.

Certain aspects of these steps have yet to be implemented in any model building and simulation software, and remain a primary research focus of the Center for Quantitative Biological Simulation (CQBS).

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