Recent Papers

"Estimating hypermutation rates from clonal tree data". Steven H. Kleinstein, Yoram Louzoun and Mark J. Shlomchik. The Journal of Immunology (2003) Vol. 171 No. 9, 4639-4649.

"Why are there so few key mutant clones? The influence of stochastic selection and blocking on affinity maturation in the germinal center". Steven H. Kleinstein and Jaswinder Pal Singh. International Immunology (2003) Vol. 15 No. 7, 871-884.

"Toward quantitative simulation of germinal center dynamics: Biological and modeling insights from experimental validation". Steven H. Kleinstein and Jaswinder Pal Singh.The Journal of Theoretical Biology (2001) Vol. 211 No. 3, 253-275.

"A systematic approach to vaccine complexity using an automaton model of the cellular and humoral immune system", B. Kohler, R. Puzone, P. E. Seiden and F. Celada, Vaccine 19, 862-876 (2000).

"Simulating the Immune System", Steven H. Kleinstein and Philip E. Seiden, Computing in Science and Engineering, pp69-77. July/August 2000.

"The Transition between Immune and Disease States in a Cellular Automaton Model of Clonal Immune Response", M. Bezzi, F. Celada, S. Ruffo and P. E. Seiden, Physica A 245, 145-163 (1997).

A Simulation of the Immune System, Experiments "in machina" P. E. Seiden and F. Celada, in Some New Directions in Science on Computers, Eds., G. Bhanot, S. Y. Chen and P. E. Seiden, (World Scientific Press, Singapore, 1997), pp.1-17.

"A Solution to the Rheumatoid Factor Paradox", J. J. Stewart, H. Agosto, S. Litwin, J. D. Welsh, M. Shlomchik, M. Weigert and P. E. Seiden, J. Immunology 159, 1728-1738 (1997).

"Insights into Rheumatoid Factor Production using a Cellular Automaton Model of the Immune System", O. Lefèvre, P. E. Seiden and F. Celada, Advances in Mathematical Modeling of Biological Processes, Ed. D. Kirschner, Int. J. Appl. Sci. Computation 3, 32-47 (1996).

"Affinity Maturation and Hypermutation in a Simulation of the Humoral Response", F. Celada, and P. E. Seiden, European J. Immunology 26, 1350-1358 (1996).

"Modelling Thymic Function in a Cellular Automaton", D. Morpurgo, R. Serentha, P. E. Seiden and F. Celada, Int. Immunology 7, 505-516 (1995).

"A Computer Model of Cellular Interactions in the Immune System", F. Celada and P. E. Seiden, Immunol. Today 13, 56 (1992).

"A Model for Simulating Cognate Recognition and Response in the Immune System ", P. E. Seiden and F. Celada, J. Theor. Biology. 158, 329 (1992).

"A systematic approach to vaccine complexity using an automaton model of the cellular and humoral immune system", B. Kohler, R. Puzone, P. E. Seiden and F. Celada, Vaccine 19, 862-876 (2000).

(click here for model parameters)

A modern approach to vaccination faces the compound complexity of microorganism behavior and immune response triggering and regulation. Since computational modeling can yield useful guidelines for biological experimentation we have used IMMSIM3, a cellular automaton model for simulating humoral- and cell- mediated responses, to explore a wide range of virus-host relations. Sixty-four virtual viruses were generated by an assortment of speed of growth, infectivity level and lethal load. The outcome of the infections, as influenced by the immune response and the bolstering of cures, obtained by vaccine presensitization are illustrated in this first article. The results allow us to relate the success rate of responses to certain combinations of viral parameters and to show that some viruses are more susceptible to humoral, and others to cellular responses, allowing prediction of which infection may be susceptible to polarized (Th1>Th2 and Th1<Th2) responses. They reveal certain qualities emerging from the cooperation of the two responses, and a significantly higher improvement of the cell-mediated response following vaccination.

"Insights into Rheumatoid Factor Production using a Cellular Automaton Model of the Immune System", O. Lefevre, P. E. Seiden and F. Celada, Advances in Mathematical Modeling of Biological Processes, Ed. D. Kirschner, Int. J. Appl. Sci. Computation, 3, 32-47 (1996). In rheumatoid arthritis, one of the best studied autoimmune diseases, the immune response is directed against the patient's own immunoglobulins, via antibodies (called rheumatoid factors) which are both a cause and a marker of the disease. In this paper we discuss how the immune system can be triggered to produce these autoantibodies. Taking advantage of a flexible computer model of the immune system, we are experimenting on two possible ways by which self tolerance may be broken: idiopeptides and intermolecular help. Our experiments have outlined the requirements of both pathways and determined the relative importance of a number of factors such as the lifetime of immune complexes, the relative affinity of the various cell types for the antigen and the incidence of anergy.

Rheumatoid factors (RF) associated with arthritic joint erosion are only seen transiently, if at all, in nondiseased individuals. Therefore, a tolerance mechanism must exist that prevents pathologic RF B cells from expressing Abs. Surprisingly, it has been shown that pathologic RF B cells are not tolerized by any previously established tolerance mechanism such as deletion, receptor editing, anergy, or prevention of memory establishment. Hoe are pathologic RF cells tolerized? By simulating the RF response with a cellular automaton model immune system, we demonstrate that pathologic RFs can be tolerized by the novel mechanism of "competitive tolerance" with natural, nonpathologic RFs. We then demonstrate that competitive tolerance can be broken when a sequestered pool of expanding B cells are inappropriately subjected to chronic stimulation (as appears to occur in MRL/lpr mice and in patients with rheumatoid arthritis).

In this paper we extend the Celada-Seiden model of the humoral immune response to include infectious virus and killer T cells (cellular response). The model represents molecules and cells with bitstrings. The response of the system to virus involves a competition between the ability of the virus to kill the host cells and the host's ability to eliminate the virus. We find two basins of attraction in the dynamics of this system, one is identified with disease and the other with the immune state. There is also an oscillating state that exists on the border of these two stable states. Fluctuations in the population of virus or antibody can end the oscillation and drive the system into one of the stable states. The introduction of mechanisms of cross-regulation between the two responses can bias the system towards one of them. We also study a mean field model, based on coupled maps, to investigate virus-like infections. This simple model reproduces the attractors for average populations observed in the cellular automaton. All the dynamical behavior connected to spatial extension is lost, as is the oscillating feature. Thus the mean field approximation introduced with coupled maps destroys oscillations.

Computer modelling can be a valuable adjunct in immunological research. It can be used both as a theoretical tool to explore and understand the behavior of immunological processes as well as in a mode to provide an additional experimental tool, "experiments in machina", experiments in the computer. Cellular automata are particularly useful in this regard since they can easily handle the complexities and special cases inherent in the immune system. We describe a generalized cellular automaton that has been designed to model the humoral component of the immune system.

The function of the immune system is not only to produce a specific response to any of a zillion possibly dangerous foreign molecules, but also to improve on it by continuously changing the quality of the antibody or effector cell, an important evolutionary measure to stay abreast of the mutability and infectivity of the foreign invaders. In this study, based on experiments conducted in a cellular automaton model of the immune system, we have reproduced affinity maturation of the antibodies and determined the relative roles of affinity selection and hypermutation of antibody genes in bringing about the final result, i.e., optimization of the effector function. We have found that hypermutation is not necessary for affinity maturation if the repertoire of B-cell specificities is sufficiently complete, while in the realistic case of very incomplete repertoires, only mutation can fill in the holes before affinity selection can take place. We find that for mutation confined to the CDR regions of the antibody, the most efficient affinity maturation occurs for mutation rates of 0.2 per paratope per cell division. When mutations also occur in the framework region, the most efficient CDR mutation rate moves to lower values. A most sensitive parameter is the speed of maturation, which reflects the rate of expansion of mutated clones. Comparing it with biological observation can help discriminate between alternative hypotheses on the phenomenon of hypermutation.

Interest in the theoretical modeling of the immune system grew rapidly following Jerne's suggestion of the idiotypic network in 1974. Most of the calculations carried out, both for the network and other models, relied on the solution of differential equations. This often meant that the connection between the theory and real biology was abstract and comparisons with experiment difficult. More recently the cellular automaton approach has been applied to immunological problems. This approach offers a closer connection between theory and experiment and allows the problem to be set up in more biological language complete with some of the messy complications intrinsic to biology. In particular the present authors emphasize a modification they call a generalized cellular automaton that they suggest is rich enough to allow computer experiments to be used as practical adjuncts to the usual biological experiments at a great saving of cost and time.