An Introduction to Rule-based Modeling of Immune Receptor Signaling

Cells process external and internal signals through chemical interactions. Cells that constitute the immune system (e.g., antigen presenting cell, T-cell, B-cell, mast cell) can have different functions (e.g., adaptive memory, inflammatory response) depending on the type and number of receptor molecules on the cell surface and the specific intracellular signaling pathways activated by those receptors. Explicitly modeling and simulating kinetic interactions between molecules allows us to pose questions about the dynamics of a signaling network under various conditions. However, the application of chemical kinetics to biochemical signaling systems has been limited by the complexity of the systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune, PySB) is an approach to address this complexity. In this chapter, by application to the Fc$\varepsilon$RI receptor system, we will explore the origins of complexity in macromolecular interactions, show how rule-based modeling can be used to address complexity, and demonstrate how to build a model in the BioNetGen framework. Open source BioNetGen software and documentation are available at http://bionetgen.org.Comment: 5 figure

Memory of Germinant Stimuli in Bacterial Spores

Bacterial spores, despite being metabolically dormant, possess the remarkable capacity to detect nutrients and other molecules in their environment through a biochemical sensory apparatus that can trigger spore germination, allowing the return to vegetative growth within minutes of exposure of germinants. We demonstrate here that bacterial spores of multiple species retain memory of transient exposures to germinant stimuli that can result in altered responses to subsequent exposure. The magnitude and decay of these memory effects depend on the pulse duration as well as on the separation time, incubation temperature, and pH values between the pulses. Spores of Bacillus species germinate in response to nutrients that interact with germinant receptors (GRs) in the spore’s inner membrane, with different nutrient types acting on different receptors. In our experiments, B. subtilis spores display memory when the first and second germinant pulses target different receptors, suggesting that some components of spore memory are downstream of GRs. Furthermore, nonnutrient germinants, which do not require GRs, exhibit memory either alone or in combination with nutrient germinants, and memory of nonnutrient stimulation is found to be more persistent than that induced by GR-dependent stimuli. Spores of B. cereus and Clostridium difficile also exhibit germination memory, suggesting that memory may be a general property of bacterial spores. These observations along with experiments involving strains with mutations in various germination proteins suggest a model in which memory is stored primarily in the metastable states of SpoVA proteins, which comprise a channel for release of dipicolinic acid, a major early event in spore germination

Domain-Oriented Reduction of Rule-Based Network Models

The coupling of membrane-bound receptors to transcriptional regulators and other effector functions is mediated by multi-domain proteins that form complex assemblies. The modularity of protein interactions lends itself to a rule-based description, in which species and reactions are generated by rules that encode the necessary context for an interaction to occur, but also can produce a combinatorial explosion in the number of chemical species that make up the signaling network. We have shown previously that exact network reduction can be achieved using hierarchical control relationships between sites/domains on proteins to dissect multi-domain proteins into sets of non-interacting sites, allowing the replacement of each “full” (progenitor) protein with a set of derived auxiliary (offspring) proteins. The description of a network in terms of auxiliary proteins that have fewer sites than progenitor proteins often greatly reduces network size. We describe here a method for automating domain-oriented model reduction and its implementation as a module in the BioNetGen modeling package. It takes as input a standard BioNetGen model and automatically performs the following steps: 1) detecting the hierarchical control relationships between sites; 2) building up the auxiliary proteins; 3) generating a raw reduced model; and 4) cleaning up the raw model to provide the correct mass balance for each chemical species in the reduced network. We tested the performance of this module on models representing portions of growth factor receptor and immunoreceptor-mediated signaling networks, and confirmed its ability to reduce the model size and simulation cost by at least one or two orders of magnitude. Limitations of the current algorithm include the inability to reduce models based on implicit site dependencies or heterodimerization, and loss of accuracy when dynamics are computed stochastically