Can Mesoscopic Models Test Spatial Mechanisms of Cell Signaling?

3 April 2007

UPINDER BHALLA

Dr. Shillcock has asked how feasible it is to make good particle- level models of membrane-associated signaling, for example, for analyzing sensitivity to perturbation. I am optimistic about this.

While there are still only a few studies of spatio-temporal mechanisms in membrane signaling, I think that this is an area poised for rapid development (e.g., Coggan et al. 2005). Advances in microscopy and reporters have made it possible to track movements of molecules at high time and spatial resolution, sometimes down to the individual molecule level (e.g., Bats et al. 2007).

Currently, the number of molecules we can track in this way is limited. However, the data are more direct and 'in-vivo' than, say, enzyme rate constants. There are also increasingly detailed studies on cytoskeletal organization at and near the membrane that will help set up pretty good models of the signaling environment. Studies such as that of Fogacs et al. (2004) further refine the picture by enumerating interaction sets. Overall, it looks like the data side of the modeling equation is in pretty good shape for beginning detailed spatial modeling.

What simulation techniques could be used? To first order, there are already at least three kinds of simulators for 3-dimensional stochastic reaction-diffusion systems at the single-particle/mesoscopic level: The 'MesoRD' class (Hattne et al. 2005) starts with a spatial grid and follows reactions within grid volumes, and movements of molecules between grid voxels. The 'MCell' class of simulators (Coggan et al. 2005) follows individual molecules and their collisions and can handle complex geometries. The 'Smoldyn' class (Andrews and Bray 2004) uses Smoluchowski dynamics and is also a single-particle simulator. To my knowledge, no one has attempted to compare these methods for the specific problem of reactions in the context of dense cytoskeletal networks near the plasma membrane.

One could probably go a long way with these spatial simulation methods, but I feel that there are two more levels at which the simulator toolkit needs to be enhanced. One is the capability to keep track of multiple states of individual molecular complexes, which grows combinatorially with the number of binding sites. The program Stochsim has had this capability for several years (Le Novere and Shimizu 2001). A more difficult problem is to also keep track of the movement and growth of cytoskeletal elements themselves, including their mechanical properties (reviewed by Karsenti et al. 2006).

To summarize: I think it is indeed becoming feasible to make models to study spatio-temporal signaling in and under the membrane. Many suitable kinds of data are already becoming available. Simulation methods still have some way to go, and the computational load is high, but useful calculations should already be possible.

Citations

• J. S. Coggan, T. M. Bartol, E. Esquenazi, J. R. Stiles, S. Lamont, M. E. Martone, D. K. Berg, M. H. Ellisman, T. J. Sejnowski, Evidence for ectopic neurotransmission at a neuronal synapse. Science 309, 446-451 (2005). [Abstract] [Full Text]
• C. Bats, L. Groc, D. Choquet, The interaction between Stargazin and PSD-95 regulates AMPA receptor surface trafficking. Neuron 53, 719-734 (2007). [PubMed]
• G. Forgacs, S. H. Yook, P. A. Janmey, H. Jeong, C. G. Burd, Role of the cytoskeleton in signaling networks.J. Cell Sci. 117 2769-2775 (2004). [Abstract] [Full Text]
• J. Hattne, D. Fange, J. Elf, Stochastic reaction-diffusion simulation with MesoRD. Bioinformatics 21 2923-2924 (2005) [PubMed]
• S. S. Andrews, D.Bray, Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys. Biol. 1 137-151 (2004). [PubMed]
• N. Le Novere, T. S. Shimizu, STOCHSIM: modelling of stochastic biomolecular processes. Bioinformatics.17 575-576 (2001). [PubMed]
• E. Karsenti, F. Nedelec, T. Surrey, Modelling microtubule patterns. Nat. Cell Biol. 8 1204-1211 (2006). [PubMed]