Think Simulation - Think Experiment: The Virtual Cell Paradigm

The Virtual Cell modeling and simulation framework is the product of interdisciplinary research in biology that applies the diverse strengths and experiences of individuals from engineering, the physical sciences, the biological sciences, and mathematics. A key feature is the separation of layers (core technologies and abstractions) representing biological models, physical mechanisms, geometry, mathematical models and numerical methods. This reduces software complexity, allowing independent development and verification, but most importantly it clarifies the impact of modeling decisions, assumptions, and approximations. The result is a physically consistent, mathematically rigorous, spatial modeling and simulation framework for cell biology. The Virtual Cell has a rich, interactive user interface which connects to remote services providing scalable access to a modeling database and a large dedicated cluster for shared computation and storage. In addition to new modeling capabilities, future developments will emphasize data and tool interoperability, extensibility, and experimentally oriented model analysis tools

[1]  L. Loew,et al.  An image-based model of calcium waves in differentiated neuroblastoma cells. , 2000, Biophysical journal.

[2]  Jason M. Haugh,et al.  Quantitative elucidation of a distinct spatial gradient-sensing mechanism in fibroblasts , 2005, The Journal of cell biology.

[3]  Leslie M. Loew,et al.  Kinetic analysis of receptor-activated phosphoinositide turnover , 2003, The Journal of cell biology.

[4]  P. Iglesias,et al.  Two complementary, local excitation, global inhibition mechanisms acting in parallel can explain the chemoattractant-induced regulation of PI(3,4,5)P3 response in dictyostelium cells. , 2004, Biophysical journal.

[5]  Karsten Weis,et al.  Analysis of a RanGTP-regulated gradient in mitotic somatic cells , 2006, Nature.

[6]  L. Loew,et al.  Systems Analysis of Ran Transport , 2002, Science.

[7]  L. Loew,et al.  Modeling and analysis of calcium signaling events leading to long-term depression in cerebellar Purkinje cells. , 2005, Biophysical journal.

[8]  L M Loew,et al.  A general computational framework for modeling cellular structure and function. , 1997, Biophysical journal.

[9]  K. Mackie,et al.  Regulation of KCNQ2/KCNQ3 Current by G Protein Cycling , 2004, The Journal of general physiology.

[10]  B. Slepchenko,et al.  Cyclin aggregation and robustness of bio-switching. , 2003, Molecular biology of the cell.

[11]  D. Gillespie Approximate accelerated stochastic simulation of chemically reacting systems , 2001 .

[12]  J H Carson,et al.  RNA trafficking in oligodendrocytes. , 2001, Results and problems in cell differentiation.

[13]  James C. Schaff,et al.  Electrodiffusion of ions inside living cells , 1999 .

[14]  L. Loew,et al.  Quantitative cell biology with the Virtual Cell. , 2003, Trends in cell biology.

[15]  Bertil Hille,et al.  Phospholipase C in Living Cells , 2005, The Journal of general physiology.

[16]  Leslie M Loew,et al.  Intracellular signaling: spatial and temporal control. , 2005, Physiology.

[17]  Ion I. Moraru,et al.  Morphological Control of Inositol-1,4,5-Trisphosphate–Dependent Signals , 1999, The Journal of cell biology.

[18]  Louis H Philipson,et al.  Modeling of Ca2+ flux in pancreatic beta-cells: role of the plasma membrane and intracellular stores. , 2003, American journal of physiology. Endocrinology and metabolism.

[19]  James C. Schaff,et al.  Analysis of nonlinear dynamics on arbitrary geometries with the Virtual Cell. , 2001, Chaos.

[20]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .