ABSTRACT
The gram-negative myxobacterium Myxococcus xanthus is equipped with an interesting motility system that allows it to reverse direction on average every 8 minutes by switching the construction of two motility engines at the ends of this rod-shaped bacterium. While the mechanisms responsible for timing and engine construction/deconstruction are relatively well understood, there are several competing hypotheses as to how they are coupled together. In this paper we examine the evidence for protein interactions underlying these possible couplings using a novel framework consisting of a probabilistic model describing protein and domain interactions and a belief propagation inference algorithm. When provided with large amount of indirect pieces of information, such as high-throughput experiment results, and protein structures, we can reliably determine the relative likelihoods of these hypotheses, even though each individual piece of evidence by itself has very limited reliability. The same framework can be used to map large protein and domain interaction networks in myxobacteria and other organisms.
Subject(s)
Bacterial Proteins/chemistry , Bacterial Proteins/physiology , Myxococcus xanthus/physiology , Algorithms , Computational Biology , Databases, Protein , Membrane Proteins/chemistry , Membrane Proteins/physiology , Models, Biological , Movement/physiology , Protein Interaction Domains and MotifsABSTRACT
Proteins and other macromolecules have coupled dynamics over multiple time scales (from femtosecond to millisecond and beyond) that make resolving molecular dynamics challenging. We present an approach based on periodically decomposing the dynamics of a macromolecule into slow and fast modes based on a scalable coarse-grained normal mode analysis. A Langevin equation is used to propagate the slowest degrees of freedom while minimizing the nearly instantaneous degrees of freedom. We present numerical results showing that time steps of up to 1000 fs can be used, with real speedups of up to 200 times over plain molecular dynamics. We present results of successfully folding the Fip35 mutant of WW domain.
Subject(s)
Molecular Dynamics Simulation/statistics & numerical data , Multiprotein Complexes/chemistry , Biophysical Phenomena , Computational Biology , Models, Molecular , Protein ConformationABSTRACT
In this paper we present the foundation of a unified, object-oriented, three-dimensional biomodelling environment, which allows us to integrate multiple submodels at scales from subcellular to those of tissues and organs. Our current implementation combines a modified discrete model from statistical mechanics, the Cellular Potts Model, with a continuum reaction-diffusion model and a state automaton with well-defined conditions for cell differentiation transitions to model genetic regulation. This environment allows us to rapidly and compactly create computational models of a class of complex-developmental phenomena. To illustrate model development, we simulate a simplified version of the formation of the skeletal pattern in a growing embryonic vertebrate limb.
Subject(s)
Models, Biological , Morphogenesis/physiology , Animals , Cattle , Cell Death , Cell Division , Cell Physiological Phenomena , Physiology/methodsABSTRACT
MOTIVATION: CompuCell is a multi-model software framework for simulation of the development of multicellular organisms known as morphogenesis. It models the interaction of the gene regulatory network with generic cellular mechanisms, such as cell adhesion, division, haptotaxis and chemotaxis. A combination of a state automaton with stochastic local rules and a set of differential equations, including subcellular ordinary differential equations and extracellular reaction-diffusion partial differential equations, model gene regulation. This automaton in turn controls the differentiation of the cells, and cell-cell and cell-extracellular matrix interactions that give rise to cell rearrangements and pattern formation, e.g. mesenchymal condensation. The cellular Potts model, a stochastic model that accurately reproduces cell movement and rearrangement, models cell dynamics. All these models couple in a controllable way, resulting in a powerful and flexible computational environment for morphogenesis, which allows for simultaneous incorporation of growth and spatial patterning. RESULTS: We use CompuCell to simulate the formation of the skeletal architecture in the avian limb bud. AVAILABILITY: Binaries and source code for Microsoft Windows, Linux and Solaris are available for download from http://sourceforge.net/projects/compucell/
