The homologous response == Repeating infections with the same trojan creates a homologous response, which is certainly expected to utilize the feature storage at its highest level, i.e., the feature ability from the Immune System to utilize information acquired through the first infections to be able to enhance the quality as well as the performance from the response to the next. cell-rich but inefficient clones, as storage, an asset usually, becomes a responsibility. Keywords:Viral Cross-reactivity, Cellular/Humoral Response, Agent-based Pc Model == 1. Launch == The prosperity of data, which range from the VAV1 molecular towards the organism level collected by the successful reductionist YYA-021 strategy of contemporary immunologists has led to accrued understanding of all the different parts of the disease fighting capability (Is certainly), whose complexity in any way levels concomitantly seems to have expanded. Thus, to convert the improvement into knowledge of the working principles as well as the logic from the YYA-021 connections of parts, a multidisciplinary work may be useful. Systems biologists and immunologists recognize in integrating advanced technology (such as for example imaging) and computational modelling with experimental data, and their initiatives on defined sections of molecular activation and cellular interactions have yielded understandable images and interpretations [1]. Many models of the immune system have been proposed in the last fifteen years, each understandably focused on sections of the system and most falling into one of the two categories:continuousordiscrete. Continuous models usually represent the dynamic of the several entities involved in the immune response in the form of differential equations: their solution yields an average or typical response, and its faithfulness to the real biologic reality depends on the chosen parameter values, and the simplifications and assumptions introduced to make the simulated phenomenon mathematically solvable. On the other hand, discrete models seldom reach general quantitative conclusions; however, being intrinsically apt to handle local dynamics, they fill an important complementary area of modelling. They are very dependent on computing power, but what appeared to be a severe limitation has been overcome by the rapidly increasing availability of computing capacity at low cost, which favours the applications of these kinds of models in any scientific field. Agent-based discrete models are inspired by Von Neumann cellular automata [2] which in turn may be considered equivalent to mathematical calculations. They consist of discrete dimensional entities (two/three dimensional space and time in discrete steps), where the agents are the relevant cells (or molecules) equipped with virtual receptors and capabilities, which reflect the experimental observations. Both continuous and discrete models can be useful in unravelling complexity by representing and simulating hypotheses; their virtual results may stimulate new theories that will have to be tested via new experimentsin vivo. Both kinds face the danger of becoming unreal and self-referential. One way to dodge this risk is to systematically confront the evolving biological consensus and – avoiding YYA-021 falling in love with ones creature – be willing to modify the equation or the code to accommodate any new data or emerging hypothesis. IMMSIM [3] has been conceived to allow the dynamic representation of hypotheses and their preliminary testing. This in turn may elicit new ideas and hypotheses to be eventually testedin vivo. In several applications over recent years, the model has generated emergent and sometimes surprising data, which allowed shedding light on the mechanisms and interactions of the model itself and on their counterparts in the biological immune system. For example, during the simulation of the affinity maturation of the humoral response, the varying density of cells and availability of antigen were shown to configure the shift from the severest bottleneck of the primary response, obtaining the T help, to the secondary bottleneck, winning the competition for antigen [4]. The model also offers the possibility to manipulate the elements of virtual runs like experimental biologists do, by using the computational equivalent of knock out mice or gene transfer. Stratagems of this kind were applied in parallel experiments comparing the response of the humoral branchonly, the cellular branchonly, andbothbranches, in order to relate the efficiency of responses to different viral features [5]. In a study about cross-reactive memory, the silencing of one or the other of two suspected kinds of attrition (passive vs active) was used recently, and revealed interesting cooperative effects of the combined mechanisms [6]. In the present study, selective freezing of humoral YYA-021 cross-reactive responses is obtained by increasing the bit distance in epitopes but not in peptides, while to reveal antibody mediated competition against cellular responses, the antibody lifetime was artificially shortened or extended over a 50-fold range. The aim of this article is to present a systematic study of homologous and heterologous responses against viruses, representing the variable molecular complementarity by using the mathematical concept of binary string space, and tackling the recently found asymmetry of the humoral vs cellular cross-reactivity. The obvious disclaimer is that any emergent findings and novel relations must await biological validation before being accepted and applied. A more general aim is to confirm that.