title: Agent-based simulation of reactions in the crowded and structured intracellular environment: Influence of mobility and location of the reactants.
abstract: BACKGROUND
  In this paper we apply a novel agent-based simulation method in order to model intracellular reactions in detail. The simulations are performed within a virtual cytoskeleton enriched with further crowding elements, which allows the analysis of molecular crowding effects on intracellular diffusion and reaction rates. The cytoskeleton network leads to a reduction in the mobility of molecules. Molecules can also unspecifically bind to membranes or the cytoskeleton affecting (i) the fraction of unbound molecules in the cytosol and (ii) furthermore reducing the mobility. Binding of molecules to intracellular structures or scaffolds can in turn lead to a microcompartmentalization of the cell. Especially the formation of enzyme complexes promoting metabolic channeling, e.g. in glycolysis, depends on the co-localization of the proteins.
  
  
  RESULTS
  While the co-localization of enzymes leads to faster reaction rates, the reduced mobility decreases the collision rate of reactants, hence reducing the reaction rate, as expected. This effect is most prominent in diffusion limited reactions. Furthermore, anomalous diffusion can occur due to molecular crowding in the cell. In the context of diffusion controlled reactions, anomalous diffusion leads to fractal reaction kinetics. The simulation framework is used to quantify and separate the effects originating from molecular crowding or the reduced mobility of the reactants. We were able to define three factors which describe the effective reaction rate, namely f diff for the diffusion effect, f volume for the crowding, and f access for the reduced accessibility of the molecules.
  
  
  CONCLUSIONS
  Molecule distributions, reaction rate constants and structural parameters can be adjusted separately in the simulation allowing a comprehensive study of individual effects in the context of a realistic cell environment. As such, the present simulation can help to bridge the gap between in vivo and in vitro kinetics.
authors.affiliation: Automatic Control Laboratory, ETH Zurich, Physikstrasse 3 8092 Zurich, Switzerland. mklann@ee.ethz.ch
authors.forename: Michael T
authors.initials: MT
authors.lastname: Klann
authors.forename: Alexei
authors.initials: A
authors.lastname: Lapin
authors.forename: Matthias
authors.initials: M
authors.lastname: Reuss
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003593:Cytoplasm / Q000378:metabolism; D003599:Cytoskeleton / Q000378:metabolism; D004058:Diffusion; D004777:Environment; D007700:Kinetics; D008954:Models, Biological; D009068:Movement; D015398:Signal Transduction; D012984:Software; D013269:Stochastic Processes; D049490:Systems Biology / Q000379:methods*