| Signal processing, as it is performed by molecular systems in biological cells, has traditionally been studied in a qualitative framework that fits the limited quantitative information available from biological instrumentation. This dissertation anticipates the increasing ability to control and observe biological cells at a fine scale, and it delivers a quantitative framework in which to study signaling behavior.; The framework expands the traditional scope of signal processing theory to cover the non-traditional nature of molecular signals, and demonstrates new insights that result from analyzing molecular detection in the proposed context. Molecular signals are shown to play a role like targets that require illumination, either passively from thermal noise or actively from mechanical sources, and receivers require energy to estimate parameters of the target.; To address the computationally demanding simulations of molecular systems as stochastic discrete event processes, a new technique is demonstrated using an alternative algorithm with fine-grained reconfigurable hardware. An initial implementation of this compiled accelerator delivered a 20x performance gain. By reducing simulation durations for a single processor, for example, from days to minutes, researchers can interactively study a more meaningful class of cell models. |
