Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic 'two component system' protein network.
Adaptive behavior in unicellular organisms (i.e., bacteria) depends on highly organized networks of proteins governing purposefully the myriad of molecular processes occurring within the cellular system. For instance, bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a sophisticated biochemical navigation system that samples the environmental conditions surrounding the cell, searching for nutrients or moving away from toxic substances or dangerous physical conditions. In this paper we discuss how proteins of the intervening signal transduction network could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor. This simulation of bacterial taxis has been carried out on a hardware realization of a McCulloch-Pitts artificial neuron using an operational amplifier. Based on the behavior of the operational amplifier we produce a model of the interaction between CheY and FliM, elements of the prokaryotic two component system controlling chemotaxis, as well as a simulation of learning and evolution processes in bacterial taxis. On the one side, our simulation results indicate that, computationally, these protein 'switches' are similar to McCulloch-Pitts artificial neurons, suggesting a bridge between evolution and learning in dynamical systems at cellular and molecular levels and the evolutive hardware approach. On the other side, important protein 'tactilizing' properties are not tapped by the model, and this suggests further complexity steps to explore in the approach to biological molecular computing.
Di Paola
Vieri
V
Department of Applied Mathematics, Faculty of Biological Sciences, Complutense University of Madrid, Madrid 28040, Spain.
Marijuán
Pedro C
PC
Lahoz-Beltra
Rafael
R
D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D000669:Amplifiers, Electronic; D001185:Artificial Intelligence; D018407:Bacterial Physiological Phenomena; D001426:Bacterial Proteins / Q000378:metabolism*; D005075:Biological Evolution; D003198:Computer Simulation; D004867:Equipment Design; D019544:Equipment Failure Analysis; D007858:Learning / Q000502:physiology; D008954:Models, Biological; D020409:Molecular Motor Proteins / Q000502:physiology; D009038:Motion; D016571:Neural Networks, Computer; D015398:Signal Transduction / Q000502:physiology*
D001426:Bacterial Proteins; D020409:Molecular Motor Proteins