Natural Killer Cells, cell-mediated immunity, kills stressed cells, INNATE immunityД« Lymphocytes, ADAPTIVE, humoral immunity, makes antibodiesД«ound antibody catalyzes complement product formation, C3a, C5a T CYTOTOXIC lymphocytes, cell-mediated, ADAPTIVE immunity T HELPER lymphocytes, cell-mediated, ADAPTIVE immunityД¬ytotoxic T Lymphocyte Agent (CTL) Zones 2,3,1 Scavenging of dead cell debris, antigen presentation, INNATE immunity Tissue surveillance, antigen presentation, INNATE immunity Stress factors such as Heat Shock Proteins, Uric Acid, and Chemerin, chemokines such as CX3CL1, CCе…ѓ, CCL5, CCL6ДЄpoptotic bodies or dead cells associated with programmed cell deathД¬ell fragments associated with death by necrosisДendritic Cell Agent (DC1, DC2) Zones 1, 2 IMMUNE CELLS REPRESENTED AND FUNCTIONAL DESCRIPTIONД¬YTOKINES, CHEMOKINES AND MOLECULES REPRESENTED BY EACH SIGNAL We believe that cells can be treated as finite state machines that can be readily grouped into classes that would correspond to agent-classes sharing the same behavioral rules. Our rationale for this is that cells represent a well-defined biological organizational level, and that extensive information exists regarding the behaviors of cellular populations in response to extracellular stimuli. For purposes of the BIS we have chosen to focus primarily at the 'cell-as-agent' level of resolution. This abstraction is a necessary step in the translation of real-world systems to mathematical or simulation models, and is targeted at the coarsest level of granularity that can effectively reproduce the behavior of the overall system at a pre-specified level of interest. While the goal was to represent all of the basic types of cells that populate the immune system in the model, we did not attempt to replicate every known sub-type of immune cell (Table вЂ‹ (Table1). Agents and signals are used to represent the basic elements of a complex system, and the agents interact with each other in a computer-simulated environment. This technique is also known as 'individual-based modeling', 'bottom-up modeling' and 'pattern-oriented modeling'. In order to aid in the qualitative characterization and examination of this relationship, we introduce the BIS, an agent-based model (ABM) based on the cellular and molecular mechanisms of the interface between the innate and adaptive immune response.ДЄgent-based modeling has been used to study the non-linear behavior of complex systems. Both the innate and adaptive components of the host response are complex, and the interaction between the two represents another level of intricate, non-linear and potentially paradoxical behavior. Adaptive immunity is also responsible for processes such as hypersensitivity reactions, autoimmune diseases, cancer and transplant rejection. This process involves exponential proliferation of antigen-specific cells that rapidly eliminate pathogens upon a second encounter. The adaptive immune response, which follows the innate response, is responsible for fighting disease and developing into the memory response. The innate immune system is also recognized to contribute to the pathophysiology of such wide-ranging diseases as atherosclerosis, lung fibrosis, asthma and sepsis. The basic strategy of innate immunity is to kill and clear pathogens. The cells of the innate immune system recognize well conserved 'danger' signals, and innate immunity was the first part of the immune system to evolve.

The innate immune response is essential for immunity to bacterial, fungal and parasitic infections. The host response to insult is one of the most striking examples of biocomplexity. As a result, there is rapidly growing interest in the development of 'in-silico' research tools to be used as an adjunct to more traditional research endeavors. The presence and effect of biocomplexity on biomedical research is well recognized.

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