Theoretical Article In silico simulations suggest that Th-cell development is regulated by both selective and instructive mechanisms ANDREAS JANSSON, 1,2 MAGNUS FAGERLIND, 1,2 DIANA KARLSSON, 2,3 PATRIC NILSSON 2 and MARGARET COOLEY 1 1 School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, New South Wales, Australia; 2 Systems Biology, School of Life Sciences, University of Sko ¨ vde, Sko ¨ vde and 3 Swedish Institute for Infectious Disease Control, Microbiology and Tumor Biology Center, Karolinska Institutet, Stockholm, Sweden Summary Th-cell differentiation is highly influenced by the local cytokine environment. Although cytokines such as IL-12 and IL-4 are known to polarize the Th-cell response towards Th1 or Th2, respectively, it is not known whether these cytokines instruct the developmental fate of uncommitted Th cells or select cells that have already been committed through a stochastic process. We present an individual based model that accommodates both stochastic and deterministic processes to simulate the dynamic behaviour of selective versus instructive Th-cell development. The predictions made by each model show distinct behaviours, which are compared with experimental observations. The simulations show that the instructive model generates an exclusive Th1 or Th2 response in the absence of an external cytokine source, whereas the selective model favours coexistence of the phenotypes. A hybrid model, including both instructive and selective development, shows behaviour similar to either the selective or the instructive model depend- ent on the strength of activation. The hybrid model shows the closest qualitative agreement with a number of well- established experimental observations. The predictions by each model suggest that neither pure selective nor instructive Th development is likely to be functional as exclusive mechanisms in Th1/Th2 development. Key words: cellular automata (CA), individual based modelling (IBM), Th1 cell, Th2 cell. Introduction The use of mathematical modelling has been increasingly applied to help understand the complex dynamics of T-cell acti- vation and development. Several models of T-cell recognition and activation have been established with great success. 1,2 Individual based modelling, which has become feasible with modern computer power, has made significant advances in simulating the immune system. For example, Segovia-Juarez et al. 3 simulated granuloma formation by treating macro- phages and T cells as discrete agents, whereas Chao et al. 4 used a stage-structure approach to model CTL response to antigens. We have previously established a theoretical frame- work for the molecular basis of costimulation based on a sys- tem of ordinary differential equations. 5 The model was based on rigorous biophysical and experimental data, allowing for quantitative analysis of the molecular interactions. However, modelling Th1/Th2 differentiation has been diffi- cult for two reasons. First, the basic biology is not completely understood, and the numerous components that are involved in the process make it difficult to propose a detailed schematic model. Second, the lack of well-defined kinetic data, such as the influence of different cytokines on Th-cell differentiation, makes it difficult to predict the response even if an appropriate model were established. 6 The theoretical field of Th1/Th2 dif- ferentiation is still struggling to model the basic dynamics to help understand the role of the key players involved in the differentiation process. The majority of the previous models assume a well-mixed population of cells, using a mean-field approach with a system of deterministic ordinary differential equations. 7–12 For example, Fishman and Perelson 8 studied the role of cross-regulatory cytokines on Th-cell differentiation, whereas Yates et al. 9 investigated the effect of Fas-mediated activation-induced cell death on this process. The consistent finding of these studies is that the relative strength of Th- cell activation and the nature of the cytokine environment are critical determinants of Th-cell polarization. By using cellular automata, which are discrete in both time and space, Brass et al. 13 and Tome and Drugowich de Felicio 14 could study behaviour at a single cell level and showed that the local dens- ities of Th1/Th2 cells profoundly affect Th-cell development. The development of Th1 and Th2 effector cells starts with the activation of uncommitted naive Th cells. After the activated Th cell enters the cell cycle, it may differentiate into either a Th1 or a Th2 cell, 15 dependent on a number of influences in its local environment. One crucial influencing factor is the cytokine environment in which the Th cells differentiate. IL-12 and IFN-g promote Th1 development, whereas IL-4 promotes Th2 development. 16 Because Th1 cells produce IFN-g, which pro- motes the production of IL-12 by macrophages, and Th2 cells produce IL-4, these cytokines act in positive feedback loops by enhancing their characteristic responses. These cytokines have Correspondence: Andreas Jansson, Systems Biology, School of Life Sciences, University of Sko ¨vde, Box 408, 54128 Sko ¨vde, Sweden. Email: andreas.jansson@his.se Received 4 October 2005; accepted 30 November 2005. Immunology and Cell Biology (2006) 84, 218–226 doi:10.1111/j.1440-1711.2006.01425.x Ó 2006 The Authors Journal compilation Ó 2006 Australasian Society for Immunology Inc.