We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the immune response, C-ImmSim, such that it represents pathogens, as well as lymphocytes receptors, by means of their amino acid sequences and makes use of bioinformatics methods for T and B cell epitope prediction. This is a key step for the simulation of the immune response, because it determines immunogenicity. The binding of the epitope, which is the immunogenic part of an invading pathogen, together with activation and cooperation from T helper cells, is required to trigger an immune response in the affected host. To determine a pathogen's epitopes, we use existing prediction methods. In addition, we propose a novel method, which uses Miyazawa and Jernigan protein–protein potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC) haplotype heterozygosity and homozygosity with respect to the influenza virus and show that there is an advantage to heterozygosity. Finally, we investigate the emergence of one or more dominating clones of lymphocytes in the situation of chronic exposure to the same immunogenic molecule and show that high affinity clones proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives for a better understanding of the immune system.
A multicomponent lattice Boltzmann model recently introduced [R. Benzi et al., Phys. Rev. Lett. 102, 026002 (2009)] to describe some dynamical behaviors of soft-flowing materials is theoretically analyzed. Equilibrium and transport properties are derived within the framework of a continuum free-energy formulation and checked against numerical simulations. Due to the competition between short-range interspecies repulsion and midrange intraspecies attraction, the model is shown to give rise to a very rich configurational dynamics of the density field, exhibiting numerous features of soft-flowing materials such as long-time relaxation due to caging effects, enhanced viscosity and structural arrest, aging under moderate shear, and shear-thinning flow above a critical shear threshold. (C) 2009 American Institute of Physics. [doi: 10.1063/1.3216105
We present a parallel version of MUPHY, a multi-physics/scale code based upon the combination of microscopic Molecular Dynamics (MD) with a hydro-kinetic Lattice Boltzmann (LB) method. The features of the parallel version of MUPHY are hereby demonstrated for the case of translocation of biopolymers through nanometer-sized, multi-pore configurations, taking into explicit account the hydrodynamic interactions of the translocating molecules with the surrounding fluid. The parallel implementation exhibits excellent scalability on the IBM BlueGene platform and includes techniques which may improve the flexibility and efficiency of other complex multi-physics parallel applications, such as hemodynamics, targeted-drug delivery and others.
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