Stochastic simulation of biological cellular processes using VHDL-AMS

Accurate, predictive models of biological cellular processes are a key component of the quest to transform the engineering of genetic controls within organisms. Biological systems include a dizzying variety of interacting biochemical pathways, each consisting of numerous reactions and chemical species. Moreover, such classical assumptions of differential equations models as equilibrium (including well-stirred species) and a large number of reactants do not hold for mesoscalar, intra-cellular modeling. In fact, these systems require the seamless, accurate modeling of interacting discrete and continuous behaviors. Previous research has demonstrated the potential of using portions of biochemical pathways as switches to control behavior, with emergent behaviors such as logic gates created. Accurate, efficient models require the ability to mix discrete and continuous descriptions while providing for the high-fidelity interaction between these domains. Although a number of previous researchers have discussed the use of analog and mixed signal hardware description languages (AMS HDLs) such as VHDL-AMS and Verilog-AMS for representing the behavior of MEMS, microfluidic, optical, and thermal systems, similar benefits can result from the use of AMS HDLs for biological systems modeling. We present preliminary research into the VHDL-AMS representation of biological systems at different levels of abstraction and the capability to support multi-resolution modeling. We discuss the role of this research within the context of the DARPA BioSPICE research program, our modeling and simulation approach, and future plans.