Using an agent-based model to analyze the dynamic communication network of the immune response

<p>Abstract</p> <p>Background</p> <p>The immune system behaves like a complex, dynamic network with interacting elements including leukocytes, cytokines, and chemokines. While the immune system is broadly distributed, leukocytes must communicate effectively to respond to a pathological challenge. The Basic Immune Simulator 2010 contains agents representing leukocytes and tissue cells, signals representing cytokines, chemokines, and pathogens, and virtual spaces representing organ tissue, lymphoid tissue, and blood. Agents interact dynamically in the compartments in response to infection of the virtual tissue. Agent behavior is imposed by logical rules derived from the scientific literature. The model captured the agent-to-agent contact history, and from this the network topology and the interactions resulting in successful versus failed viral clearance were identified. This model served to integrate existing knowledge and allowed us to examine the immune response from a novel perspective directed at exploiting complex dynamics, ultimately for the design of therapeutic interventions.</p> <p>Results</p> <p>Analyzing the evolution of agent-agent interactions at incremental time points from identical initial conditions revealed novel features of immune communication associated with successful and failed outcomes. There were fewer contacts between agents for simulations ending in viral elimination (<it>win</it>) versus persistent infection (<it>loss</it>), due to the removal of infected agents. However, early cellular interactions preceded successful clearance of infection. Specifically, more Dendritic Agent interactions with TCell and BCell Agents, and more BCell Agent interactions with TCell Agents early in the simulation were associated with the immune <it>win </it>outcome. The Dendritic Agents greatly influenced the outcome, confirming them as hub agents of the immune network. In addition, unexpectedly high frequencies of Dendritic Agent-self interactions occurred in the lymphoid compartment late in the <it>loss </it>outcomes.</p> <p>Conclusions</p> <p>An agent-based model capturing several key aspects of complex system dynamics was used to study the emergent properties of the immune response to viral infection. Specific patterns of interactions between leukocyte agents occurring early in the response significantly improved outcome. More interactions at later stages correlated with persistent inflammation and infection. These simulation experiments highlight the importance of commonly overlooked aspects of the immune response and provide insight into these processes at a resolution level exceeding the capabilities of current laboratory technologies.</p

Idiopathic pulmonary fibrosis is strongly associated with productive infection by herpesvirus saimiri

Idiopathic pulmonary fibrosis is a fatal disease without effective therapy or diagnostic test. To investigate a potential role for c�herpesviruses in this disease, 21 paraffin-embedded lung biopsies from patients diagnosed with idiopathic pulmonary fibrosis and 21 lung biopsies from age-matched controls with pulmonary fibrosis of known etiology were examined for a series of c�herpesviruses’ DNA/RNA and related proteins using in situ hybridization and reverse transcriptase-polymerase chain reaction (RT-PCR)-based methods. We detected four proteins known to be in the genome of several c�herpesviruses (cyclin D, thymidylate synthase, dihydrofolate reductase, and interleukin-17) that were strongly co-expressed in the regenerating epithelial cells of each of the 21 idiopathic pulmonary fibrosis cases and not in the benign epithelia of the controls. Among the c� herpesviruses, only herpesvirus saimiri expresses all four of these ‘pirated’ mammalian proteins. We found herpesvirus saimiri DNA in the regenerating epithelial cells of 21/21 idiopathic pulmonary fibrosis cases using four separate probe sets but not in the 21 controls. RT-PCR showed that the source of the cyclin D RNA in active idiopathic pulmonary fibrosis was herpesvirus saimiri and not human. We cloned and sequenced part of genome corresponding to the DNA polymerase herpesvirus saimiri gene from an idiopathic pulmonary fibrosis sample and it matched 100% with the published viral sequence. These data are consistent with idiopathic pulmonary fibrosis representing herpesvirus saimiri-induced pulmonary fibrosis. Thus, treatment directed against viral proliferation and/or viral-associated proteins may halt disease progression. Further, demonstration of the viral nucleic acids or proteins may help diagnose the disease

Theoretical Biology and Medical

Software The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunit

The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunity-1

<p><b>Copyright information:</b></p><p>Taken from "The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunity"</p><p>http://www.tbiomed.com/content/4/1/39</p><p>Theoretical Biology & Medical Modelling 2007;4():39-39.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC2186321.</p><p></p>ndicated agent type was missing, in combination with initial conditions of 20, 50 or 80 DCs. The control has all cell types present. The number of simulation runs for each data bar is as follows: No Bs with 20 DCs, n = 82; with 50 DCs, n = 73; with 80 DCs, n = 92; no CTLs with 20 DCs, n = 64; with 50 DCs, n = 61; with 80 DCs, n = 106; no DCs, n = 100; no MΦs with 20 DCs, n = 50; with 50 DCs, n = 55; with 80 DCs, n = 76; no NKs with 20 DCs, n = 53; with 50 DCs, n = 71; with 80 DCs, n = 66; no Ts with 20 DCs, n = 75; with 50 DCs, n = 50; with 80 DCs, n = 50; no Granulocyte agents with 20 DCs, n = 93; with 50 DCs, n = 54; with 80 DCs, n = 54. The asterisks indicate significant differences from the control conditions using the Chi-squared test. The p-value for the bars marked **** is

The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunity-3

<p><b>Copyright information:</b></p><p>Taken from "The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunity"</p><p>http://www.tbiomed.com/content/4/1/39</p><p>Theoretical Biology & Medical Modelling 2007;4():39-39.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC2186321.</p><p></p>ons) for selected agent types are shown. The data are grouped by the outcome of each simulation run. Blue diamonds represent the mean of the immune wins (n = 58), pink squares represent the mean of the immune losses (n = 48) and green triangles represent the mean of the immune hyper-response data (n = 9), for every tick of the simulation runs (see inset in Figure 5h). The fine lines of matching color represent the standard deviation for each outcome at every tick. The inset plots contain the same data means (as the plots that contain them) for the initial ticks of the simulation, on a scale to show greater detail. Except for part h which shows data from infected Parenchymal agent counts in Zone 1, all of the other agent counts were recorded from Zone 2. Note that the scales for the numbers of agents differ for each plot

The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunity-2

<p><b>Copyright information:</b></p><p>Taken from "The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunity"</p><p>http://www.tbiomed.com/content/4/1/39</p><p>Theoretical Biology & Medical Modelling 2007;4():39-39.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC2186321.</p><p></p>of the indicated agent type was recruited, in combination with initial conditions of 20, 50 or 80 DCs. The control in each case is the same as shown in Figures 2 and 3. More DCs added with 20 DCs, n = 49; with 50 DCs, n = 52; with 80 DCs, n = 83; more CTLs added with 20 DCs, n = 61; with 50 DCs, n = 107; with 80 DCs, n = 50; more MΦs added with 20 DCs, n = 62; with 50 DCs, n = 80; with 80 DCs, n = 72; more NKs added with 20 DCs, n = 108; with 50 DCs, n = 80; with 80 DCs, n = 96; more Gran added with 20 DCs, n = 89; with 50 DCs, n = 50; with 80 DCs, n = 50. The asterisks indicate significant differences from the control conditions using the Chi-squared test. The p-values are as follows: ****

The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunity-4

<p><b>Copyright information:</b></p><p>Taken from "The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunity"</p><p>http://www.tbiomed.com/content/4/1/39</p><p>Theoretical Biology & Medical Modelling 2007;4():39-39.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC2186321.</p><p></p> and the data for selected agent types are shown. Only activated agents are included. The data are grouped by outcome and color coded as in Figure 5

Expression of specific chemokines and chemokine receptors in the central nervous system of multiple sclerosis patients

Chemokines direct tissue invasion by specific leukocyte populations. Thus, chemokines may play a role in multiple sclerosis (MS), an idiopathic disorder in which the central nervous system (CNS) inflammatory reaction is largely restricted to mononuclear phagocytes and T cells. We asked whether specific chemokines were expressed in the CNS during acute demyelinating events by analyzing cerebrospinal fluid (CSF), whose composition reflects the CNS extracellular space. During MS attacks, we found elevated CSF levels of three chemokines that act toward T cells and mononuclear phagocytes: interferon-γ–inducible protein of 10 kDa (IP-10); monokine induced by interferon-γ (Mig); and regulated on activation, normal T-cell expressed and secreted (RANTES). We then investigated whether specific chemokine receptors were expressed by infiltrating cells in demyelinating MS brain lesions and in CSF. CXCR3, an IP-10/Mig receptor, was expressed on lymphocytic cells in virtually every perivascular inflammatory infiltrate in active MS lesions. CCR5, a RANTES receptor, was detected on lymphocytic cells, macrophages, and microglia in actively demyelinating MS brain lesions. Compared with circulating T cells, CSF T cells were significantly enriched for cells expressing CXCR3 or CCR5. Our results imply pathogenic roles for specific chemokine–chemokine receptor interactions in MS and suggest new molecular targets for therapeutic intervention

Gulf War Illness: Is there Lasting Damage to the Endocrine-Immune Circuitry?

We reported previously that the persistence of complex immune, endocrine and neurological symptoms that afflict up to one third of veterans from the 1990-91 Gulf War might be supported by a misdirected regulatory drive. Here we use a detailed model of immune signaling in concert with an overarching circuit model of known sex and stress hormone co-regulation to explore how the failure of regulatory elements may further establish a self-perpetuating imbalance that closely resembles Gulf War Illness (GWI). Defects to the model were imparted iteratively and the stable regulatory modes supported by these altered immune-endocrine circuits were identified using repeated simulation experiments. In each case the predicted homeostatic regimes were compared to experimental data collected in male GWI (n=20 ) and matched healthy veterans (n=22 ). We found that alignment of GWI with a new homeostatic regime improved significantly when cortisol’s normal anti-inflammatory activity was interrupted. Alignment improved further when this cortisol insensitivity was compounded by the loss of the normal antagonistic effects of Th1 cytokines on Th2 lymphocyte activation. Together these simulation results suggest altered glucocorticoid gene regulation compounded by possible changes in IGF-1 regulation of Th1:Th2 immune balance may be key underlying features of GWI