Date of Award:
5-1-2005
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Biology
Department name when degree awarded
Life Sciences: Biology
Committee Chair(s)
Keith Mott
Committee
Keith Mott
Committee
James Haefner
Committee
David Peak
Abstract
This research presents an exploration of the qualitative and quantitative properties of emergent, distributed computation in artificial cellular networks with the goal of better understanding the same computational mechanism as it might occur in biological systems, particularly plants. Stomata are tiny pores on the surface of a plant. As the sole pathway for the exchange of gasses between a plant and the atmosphere, stomata are responsible for both CO2 assimilation and water loss. By adjusting their pore size in response to environmental conditions, stomata appear to maximize carbon assimilation while minimizing water loss—a constrained optimization problem. Stomata interact with each other locally to solve the optimization task on a plant-wide scale. The ability of stomata to collectively solve a global computational task with only local communication is suggestive of emergent, distributed computation. The statistical properties of the problem-solving dynamics of stomatal networks and a class of artificial computational systems that perform emergent, distributed computation (density-classifying cellular automata) are indistinguishable. This quantitative comparison supports the possibility that stomatal networks process information via emergent, distributed computation. Two particular properties of emergent, distributed computation as a mechanism of biological information processing are explored: 1) the effects of spatial and temporal noise and 2) the effects of network topology and the sophistication of interactions between network components. To approach the first objective, spatial and temporal noise was mapped into several density-classifying cellular automata and the ensuing computational efficiency measured. The results indicate that noise may improve emergent, distributed computation. Stomatal networks and other locally connected computational systems may not only coexist with noise but actually use it to enhance adaptive, information processing ability. Also, although emergent, distributed computation can be accomplished with varying combinations of network topology and interaction rules, there are fundamental differences between the information processing dynamics depending on the sophistication of the network interactions and the network topology. Stomatal networks appear to be an example of a computational biological system that performs an emergent, distributed computation with sophisticated behavioral rules and simple network topology.
Recommended Citation
Messinger, Susanna M., "A Qualitative and Quantitative Characterization of Emergent, Distributed Computation as a Possible Mechanism for Biological Information Processing in Stomatal Networks" (2005). Biology. 707.
https://digitalcommons.usu.edu/etd_biology/707
Included in
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .