Abstract
A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models.
Highlights
Large molecular complexes, like the kinetochore [1] or the spindle pole body (SPB, yeast centrosome) [2], consist of many different proteins and other components
In order to cope with this problem, we have presented a novel spatial and rule-based simulation (SRSim, Gruenert et al [13]), which extends a rule-based framework by adding information about the molecules’ geometry and which allows spatial stochastic simulations, based on the molecular dynamics software, LAMMPS [14]
We develop a 3D model of the human mitotic kinetochore in addition to the spindle assembly checkpoint pathway
Summary
Like the kinetochore [1] or the spindle pole body (SPB, yeast centrosome) [2], consist of many different proteins and other components. Time and space add further dimensions to the combinatorial complexity [4] Conventional modeling approaches, such as differential equations, Boolean networks and Bayesian networks, are insufficient to cope with combinatorial explosion, due to their use of a restricted state space with fixed dimensionality [5]. To overcome this problem of combinatorial explosion, several rule-based modeling approaches have been developed [6,7,8,9,10,11], which allow one to define a reaction network implicitly [12].
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