A bacterial algorithm for surface mapping using a Markov modulated Markov chain model of bacterial chemotaxis

Abstract

Bacterial chemotaxis is the locomotory response of bacteria to chemical stimuli. E. coli movement can be described as a biased random walk, and it is known that the general biological or evolutionary function is to increase exposure to some substances and reduce exposure to others. In this thesis we introduce an algorithm for surface mapping, which tracks the motion of a bacteria-like software agent (based on a low-level model of the biochemical network responsible for chemotaxis) on a surface or objective function. Towards that end, a discrete Markov modulated Markov chains model of the chemotaxis pathway is described and used. Results from simulations using one- and two-dimensional test surfaces show that the software agents, referred to as bacterial agents, and the surface mapping algorithm can produce an estimate which shares some broad characteristics with the surface and uncovers some features of it. We also demonstrate that the bacterial agent, when given the ability to reduce the value of the surface at locations it visits (analogous to consuming a substance on a concentration surface), is more effective in reducing the surface integral within a certain period of time when compared to a bacterial agent lacking the ability to sense surface information or respond to it.