SIMMUNE, a tool for simulating and analyzing immune system behavior

We present a new approach to the simulation and analysis of immune system behavior. The simulations that can be done with our software package called SIMMUNE are based on immunological data that describe the behavior of immune system agents (cells, molecules) on a microscopial (i.e. agent-agent interaction) scale by defining cellular stimulus-response mechanisms. Since the behavior of the agents in SIMMUNE can be very flexibly configured, its application is not limited to immune system simulations. We outline the principles of SIMMUNE's multiscale analysis of emergent structure within the simulated immune system that allow the identification of immunological contexts using minimal a priori assumptions about the higher level organization of the immune system.Comment: 23 pages, 10 figure

Exact Green's Function of the reversible diffusion-influenced reaction for an isolated pair in 2D

We derive an exact Green's function of the diffusion equation for a pair of spherical interacting particles in 2D subject to a back-reaction boundary condition.Comment: 6 pages, 1 Figur

Toward a comprehensive language for biological systems

Rule-based modeling has become a powerful approach for modeling intracellular networks, which are characterized by rich molecular diversity. Truly comprehensive models of cell behavior, however, must address spatial complexity at both the intracellular level and at the level of interacting populations of cells, and will require richer modeling languages and tools. A recent paper in BMC Systems Biology represents a signifcant step toward the development of a unified modeling language and software platform for the development of multi-level, multiscale biological models

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through “wildcards” representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the “type” concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes amedium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through “wildcards” representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the “type” concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes amedium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications

High production rates sustain in vivo levels of PD-1high simian immunodeficiency virus-specific CD8 T cells in the face of rapid clearance

Programmed Death 1 (PD-1) expression by human/simian immunodeficiency virus (HIV/SIV)-specific CD8 T cells has been associated with defective cytokine production and reduced in vitro proliferation capacity. However, the cellular mechanisms that sustain PD-1high virus-specific CD8 T cell responses during chronic infection are unknown. Here, we show that the PD-1high phenotype is associated with accelerated in vivo CD8 T cell turnover in SIV-infected rhesus macaques, especially within the SIVspecific CD8 T cell pool. Mathematical modeling of 5-bromo-2= deoxyuridine (BrdU) labeling dynamics demonstrated a significantly increased generation rate of PD-1high compared to PD-1low CD8 T cells in all memory compartments. Simultaneous analysis of Ki67 and BrdU kinetics revealed a complex in vivo turnover profile whereby only a small fraction of PD-1high cells, but virtually all PD-1low cells, returned to rest after activation. Similar kinetics operated in both chronic and acute SIV infection. Our data suggest that the persistence of PD-1high SIV-specific CD8 T cells in chronic infection is maintained in vivo by a mechanism involving high production coupled with a high disappearance rate

Progressive CD4+ central–memory T cell decline results in CD4+ effector–memory insufficiency and overt disease in chronic SIV infection

Primary simian immunodeficiency virus (SIV) infections of rhesus macaques result in the dramatic depletion of CD4+ CCR5+ effector–memory T (TEM) cells from extra-lymphoid effector sites, but in most infections, an increased rate of CD4+ memory T cell proliferation appears to prevent collapse of effector site CD4+ TEM cell populations and acute-phase AIDS. Eventually, persistent SIV replication results in chronic-phase AIDS, but the responsible mechanisms remain controversial. Here, we demonstrate that in the chronic phase of progressive SIV infection, effector site CD4+ TEM cell populations manifest a slow, continuous decline, and that the degree of this depletion remains a highly significant correlate of late-onset AIDS. We further show that due to persistent immune activation, effector site CD4+ TEM cells are predominantly short-lived, and that their homeostasis is strikingly dependent on the production of new CD4+ TEM cells from central–memory T (TCM) cell precursors. The instability of effector site CD4+ TEM cell populations over time was not explained by increasing destruction of these cells, but rather was attributable to progressive reduction in their production, secondary to decreasing numbers of CCR5− CD4+ TCM cells. These data suggest that although CD4+ TEM cell depletion is a proximate mechanism of immunodeficiency, the tempo of this depletion and the timing of disease onset are largely determined by destruction, failing production, and gradual decline of CD4+ TCM cells

Development of Immune-Specific Interaction Potentials and Their Application in the Multi-Agent-System VaccImm

Peptide vaccination in cancer therapy is a promising alternative to conventional methods. However, the parameters for this personalized treatment are difficult to access experimentally. In this respect, in silico models can help to narrow down the parameter space or to explain certain phenomena at a systems level. Herein, we develop two empirical interaction potentials specific to B-cell and T-cell receptor complexes and validate their applicability in comparison to a more general potential. The interaction potentials are applied to the model VaccImm which simulates the immune response against solid tumors under peptide vaccination therapy. This multi-agent system is derived from another immune system simulator (C-ImmSim) and now includes a module that enables the amino acid sequence of immune receptors and their ligands to be taken into account. The multi-agent approach is combined with approved methods for prediction of major histocompatibility complex (MHC)-binding peptides and the newly developed interaction potentials. In the analysis, we critically assess the impact of the different modules on the simulation with VaccImm and how they influence each other. In addition, we explore the reasons for failures in inducing an immune response by examining the activation states of the immune cell populations in detail