PROJECT SUMMARY/ABSTRACT Rule-based modeling approaches, which are based on the principles of chemical kinetics and diffusion and enabled by an expanding armamentarium of sophisticated software tools (e.g., BioNetGen/NFsim), offer spe- cial advantages for studying the dynamics of interactions among multisite signaling proteins. Rule-based mod- els can capture the effects of polymerization-like reactions and multisite post-translational modifications over time scales of seconds to hours while incorporating constraints imposed by molecular structures. Furthermore, with a rule-based approach to model formulation, it is possible to construct and analyze larger, more compre- hensive models for cellular regulatory systems than with traditional modeling approaches because of the op- portunity to represent systems concisely and at a high level of abstraction using formal rules for biomolecular interactions. Rules can often be processed to automatically derive traditional model forms, such as a coupled system of ordinary differential equations (ODEs). However, when the system state space implied by rules is exceedingly large, the use of simulation engines based on network-free algorithms becomes necessary and model analysis is limited by the high computational cost of the stochastic simulations. In addition, in these cir- cumstances and others, parameter identification and uncertainty quantification (UQ) are extremely challenging. We will address these problems by improving the efficiency of simulation, fitting, and UQ tools and by leverag- ing distributed computing resources. Recently, we developed novel algorithms for accelerating stochastic simu- lations, a toolbox of parallelized metaheuristic optimization methods for fitting, and implementations of Markov chain Monte Carlo (MCMC) methods for Bayesian UQ. This toolbox, called PyBioNetFit (PyBNF), leverages standardized formats for defining and sharing models (e.g., core SBML and BNGL) and is compatible with var- ious simulators. Here, we propose to develop general-purpose software implementations for accelerated net- work-free (stochastic) simulation and for restructuring rule-based models (i.e., optimizing rules so as to mini- mize the number of rule-implied equations). We will also provide a new interface to CVODE and CVODES for numerical integration of ODEs, forward sensitivity analysis, and adjoint sensitivity analysis. Furthermore, we will extend the biological property specification language (BPSL) of PyBNF to make this means for formalizing qualitative data more expressive. In addition, we will add gradient-based optimization and MCMC methods to PyBNF and built-in support for Smoldyn, a simulator for (rule-based) spatial stochastic models. These im- 𝜀𝜀 provements will facilitate grounding of models in data. We will test and validate new tools by building models 𝜀𝜀 for IgE receptor (Fc RI) signaling in collaboration with quantitative experimentalists. We will focus on models 𝜀𝜀 for Fc RI-Lyn ...