Biological systems are the product of an evolutionary process of random tinkering and selection that resulted in unexpected and non-intuitive ?engineering? solutions to dynamically varying conditions. Thus, biological systems are robust, adaptive and evolvable information processing systems that operate asynchronously and in parallel on multiple scales. The examination and characterization of the design principles of biological circuits has the potential to revolutionize biology, medicine and the way computing and communication systems are built. This project is pioneering important advances at the interface between biology and computation by pursuing two complementary goals: (1) to develop a modular, parallel-ready simulator to replicate the multi-scalar architecture of complex biological systems; (2) to discover key design principles relevant to information processing systems in general by reproducing biological design in silico.
Information processing by cells encompasses multiple scales connecting molecular events to phenotypes. Current simulation techniques have limited multi-scale and modular capabilities, resulting in models that describe only a single feature of a given system and miss the relationships between architecture, function and behavior. This research effort addresses these limitations by representing biological systems as a hierarchy of functional executable modules. The design of the platform obeys four basic principles: 1) components are objects; 2) objects are governed by rules; 3) rules include some degree of stochasticity; and 4) objects and rules are organized in functional and spatial modules that compose a hierarchy. The development of the new platform is driven by the construction of simulations of key biological model systems with an unprecedented scope and precision, such as bacterial chemotaxis, epidermal growth factor receptor signaling, the acute inflammatory response, and parallel processing by bacterial colonies. The reproduction of these biological systems in silico is providing insights into their design principles, which in turn advances the future design and implementation of distributed technological systems.
