A Logical Model Provides Insights into T Cell Receptor Signaling

Abstract

Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our large-scale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications.

Logic Modeling in Quantitative Systems Pharmacology

“…The examples where logic modeling has been applied to understand biological mechanisms are numerous and go from the study of T‐cell receptor signaling34, 35 to cell‐fate decision,36 mammalian cell cycle,37 or host immune response 38. In particular, cancer research has motivated many of the applications of logic modeling and fostered several methodological developments, because it requires studying large signaling and regulatory networks 39.…”

Boolean Biology: Introducing Boolean Networks and Finite Dynamical Systems Models to Biology and Mathematics Courses

“…For larger systems, however, this is as impractical as an attempt to compute the state space transition diagram. As an example, the Boolean model of T-cell receptor signaling [16] contains 94 nodes and, thus, 2 94 different states. Clearly, as the number of states increases exponentially with the number of variables, a more computationally efficient approach than simple enumeration is needed.…”

“…They have proven useful in cases where network dynamics are determined by the logic of interactions rather than by finely tuned kinetics, the details of which often are not known. Today, many FDS models appear in the literature, including a model of the metabolic network in E. coli [17], the abscisic acid signaling pathway [22], and T-cell receptor signaling [16]. However, in contrast with the abundance of undergraduate textbooks and educational modules focusing on DE models, very few curricular materials focusing on Boolean models and FDS models have been created, regardless of their high educational potential [13].…”

Bioinformatics analyses for signal transduction networks

“…It is difficult to estimate a large number of unknown parameters, thus quantitative modeling and simulation methods have a number of drawbacks. As the middle steps and simplified models of quantitative models, semi-quantitative and qualitative models contribute to the understanding of signaling pathways, including Boolean network [24] , logic model [25] , the probability model [26] and Petri network [27,28] . The quantitative models of dynamic networks include ordinary, partial and stochastic differential equations [29] .…”

“…Based on well-established qualitative knowledge and gene knockout phenotypes, Julio et al [24] constructed a largescale Boolean model to analyze the dynamic process in the signaling networks of T cells. This model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated.…”