title: Spatial modeling of cell signaling networks.
abstract: The shape of a cell, the sizes of subcellular compartments, and the spatial distribution of molecules within the cytoplasm can all control how molecules interact to produce a cellular behavior. This chapter describes how these spatial features can be included in mechanistic mathematical models of cell signaling. The Virtual Cell computational modeling and simulation software is used to illustrate the considerations required to build a spatial model. An explanation of how to appropriately choose between physical formulations that implicitly or explicitly account for cell geometry and between deterministic versus stochastic formulations for molecular dynamics is provided, along with a discussion of their respective strengths and weaknesses. As a first step toward constructing a spatial model, the geometry needs to be specified and associated with the molecules, reactions, and membrane flux processes of the network. Initial conditions, diffusion coefficients, velocities, and boundary conditions complete the specifications required to define the mathematics of the model. The numerical methods used to solve reaction-diffusion problems both deterministically and stochastically are then described and some guidance is provided in how to set up and run simulations. A study of cAMP signaling in neurons ends the chapter, providing an example of the insights that can be gained in interpreting experimental results through the application of spatial modeling.
authors.affiliation: R D Berlin Center for Cell Analysis and Modeling, University of Connecticut Heath Center, Farmington, CT, USA.
authors.forename: Ann E
authors.initials: AE
authors.lastname: Cowan
authors.forename: Ion I
authors.initials: II
authors.lastname: Moraru
authors.forename: James C
authors.initials: JC
authors.lastname: Schaff
authors.forename: Boris M
authors.initials: BM
authors.lastname: Slepchenko
authors.forename: Leslie M
authors.initials: LM
authors.lastname: Loew
mesh_terms: D000465:Algorithms; D000818:Animals; D048430:Cell Shape / Q000502:physiology; D003198:Computer Simulation; D000242:Cyclic AMP / Q000378:metabolism; D017868:Cyclic AMP-Dependent Protein Kinases / Q000378:metabolism; D003593:Cytoplasm / Q000378:metabolism; D007700:Kinetics; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D020928:Mitogen-Activated Protein Kinases / Q000378:metabolism; D008954:Models, Biological; D009010:Monte Carlo Method; D009474:Neurons / Q000166:cytology* / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology*; D012984:Software
chemical_list: D000242:Cyclic AMP; D017868:Cyclic AMP-Dependent Protein Kinases; D020928:Mitogen-Activated Protein Kinases