Richard D. Berlin
Center for Cell Analysis & Modeling

Cellular Modeling

Analysis and Simulation

Cowan, Moraru, Schaff

Complex imaging data contains a wealth of quantitative information, but it is often a non-trivial process to extract quantitative parameters from the data. Absolute or relative concentrations of a molecule within the cell, the fraction of molecules that are freely diffusing and their diffusion coefficients, kinetic constants for binding to large complexes, and velocities of directed motion are all parameters that can be obtained from imaging experiments. In most cases, cellular geometries preclude the use of simple analytic solutions to extract the relevant information. Spatial simulations within the same cellular geometry as the experiment provide a tool to determine such quantitative parameters. CCAM is building a suite of software tools specifically geared to these applications.

Data Integration

Moraru, Schaff, Slepchenko

The development of quantitative models of biological processes often requires explicitly accounting for the experimental conditions involved in capturing experimental data. Thus experimental data is not only what is measured but what is used to take such measurements. In order to facilitate researchers' ability to rapidly create quantitative models of cellular physiology, faculty at CCAM are developing the computational technology ncessary to extract and reduce experimental data to standard formats that can be readily input into quantitative computational models. We develop software interfaces to data sources such as immunofluorescence images and Fluorscence Correlation Spectroscopy to derive spatial distributions, intracellular diffusion coefficients, concentrations and interactions. These data linkages require skills in computing and computation to accurately link experimental data directly to computational models.

Modeling Moving Boundaries

Huber, Wolgemuth

The dynamic interplay between biochemical pathways and cell shape is increasingly recognized as an important means of regulation of cell signaling and cytoskeletal organization. In order to advance the capability of computational models to elucidate the relationship between cell shape and biochemical reactions, we need to be able to account for the simultaneous deformation of the cell and its cytoskeleton. CCAM faculty research the physical, mathematical and computational methods to model how cytoskeletal deformations induce feedback mechanisms in biochemical reactions by, in effect, altering the geometry of the cellular "reaction vessel".

Modularity and Multistate Complexes

Blinov, Schaff, Moraru

Quantitative models of cell biological systems are increasingly accessible to researchers via public databases such as BioModels.net and the public models within the Virtual Cell modeling environment. A significant challenge to the broad use and enhancement of these models is the need for tools and technologies that permit definition and extraction of re-usable model components from within these quantitative cell biological models. CCAM faculty are developing informatic and computational tools that enable: i) creation of a repository of well-validated, reusable “sub-models” of cellular processes, ii) assembly of quantitative models of large, complex networks, iii) separation and enhancement of reusable experimental protocols (measurements and/or manipulations), iv) representation of multi-state, multi-molecular complexes as distinct entities.

Molecular Flux in Crowded Spaces

Novak, Slepchenko

Modeling biochemical reactions and cellular processes by the use of reaction-diffusion equations is a well established approach based on the assumption that the cellular environment is homogenous and ideal. But cellular environments which feature finite molecular size, finite numbers, and small accessible volumes (crowded spaces) limit the applicability of these equations. Mathematical models for the behavior of reactions in the crowded environment of cellular systems are still being developed. CCAM faculty are pushing the boundaries of the usefulness of well characterized physical laws and creating new ways to model movement and reactions of molecules in cells.

Stochastic Modeling and Discrete Particles

Novak, Schaff, Slepchenko

Experimental biology methods at CCAM involve single molecule imaging and FCS. When studying populations of cells one molecule at a time, we are able to ask the question what is the best way to understand and model the behavior of the system. Faculty at CCAM are developing tools for stochastic and discrete particle modeling within the Virtual Cell computational framework. The goal is to create an algorithm that is scalable with increasing concentration of molecules, and allows interactions with continuum fields and macromolecular environments, such as RNA granules, cytoskeletal macromolecules and flow.