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Mesoscopic Stochastic Spatial Simulations of Biochemical Networks

Explore simulation goals, suitable problems, current approaches, sources of error, measurements of error, and current activities for spatial stochastic simulations of biochemical networks. Discuss error sources, accuracy, pathways, and improving processes.

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Mesoscopic Stochastic Spatial Simulations of Biochemical Networks

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  1. Mesoscopic Stochastic Spatial Simulationsof Biochemical Networks CellMath presentation by: Jordi Vidal Rodriguez

  2. Goals • Simulations of biochemical networks… • Space: for concentration inhomogeneities • Stochastic: to account for molecular fluctuations • Mesoscopic: to cope with enormous number of molecules CellMath presentation

  3. Suitable Problems to Solve • Few particle systems • But how much is few? • Spatial inhomogeneity • Membranes are sources of concentration gradients • Prokaryotic cells • Simple cytosol that allows mesoscopic simulations • Pathways: • Oscillators, bifurcations, signalling,…? CellMath presentation

  4. Current Approach • Local Reaction with Gillespie method • Multiparticle, multispecies Diffusion • Membrane diffusion • Membrane is 1 site thick • Molecular fluctuations captured by both method. But how accurate are in this configuration? CellMath presentation

  5. Sources of Error • Membrane surface • Current model doesn’t simulate a surface • The site is still homogeneous affecting both • Reaction: homogeneous sub-volume • Diffusion: center of mass of particles in the center of site’s sub-volume • 2D-3D geometries CellMath presentation

  6. Measurements of Error Membrane site time evolution (Reaction) Profile evolution in time (L=20) CellMath presentation

  7. PTS pathway Simple case 2D circular domain CellMath presentation

  8. Current Activities • Reproduce PTS results • 3D geometries • Diffusion in 3D + Membrane diffusion • Arbitrary geometries (sphere, rods,…) • Lattice size effects • On molecular fluctuations (are thermodynamically correct?) • Membrane RD • Ways to improve membrane processes’ accuracy without compromising the mesoscopic model CellMath presentation

  9. Other Simulators of Interest • Smoldyn • StochSim, with membrane reactions! • VirtualCell • Going to include stochastic algorithm • E-Cell • Multi-algorithm, multi-scale • GENESIS • Neuron simulators • COPASI • Gillespie, inspired in Gepasi (Shall we expect stochastic control analysis?) CellMath presentation

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