The continuing development of high throughput experiments (HTEs) in catalysis has dramatically increased the amount of data that can be collected in relatively short periods of time. Even when HTEs can afford "Edisonian" discovery, how can the increasing amounts of data be converted to knowledge to guide the next search in the vast design space of catalytic materials? To address this question, we recently proposed a catalyst design architecture that uses detailed kinetic models. In this paper, we describe Reaction Modeling Suite-a rational, automated, and intelligent environment, based on systems, artificial intelligence, and optimization techniques that aid the development of kinetic models. We demonstrate its utility in developing a kinetic model for propane aromatization on zeolite. We also show the proof-of-concept of how a genetic algorithm-based search strategy can be used to search for kinetic parameters that correspond to an improved catalyst.