PROBABILISTIC MODELING OF BIOLOGICAL INFORMATION SYSTEMS

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Work has been carried forward in three different areas. A model of cochlear processing of auditory stimuli has been developed. Hair cell transduction is the only nonlinear component in the model and this suffices to explain well known nonlinear auditory characteristics. The second area involves a study of cellular automata. Cellular automata provide a unique approach to complex biological problems such as growth and differnetiation of organisms and neural structure and function. Much current interest focuses on general properties or approaches to cellualr automata. We have developed an analytical procedure for predicting the statistical distribution of local patterns in a class of simple automatons. The method gives promise of generalization to more complex systems. The third area of study is the statistical properties of biological macromolecules. The PAM matrix model of protein evolution has been analyzed and an error discovered in the construction of the matrix and a need to modify the model demonstrated. A study is currently in progress to determine the effect of mutations on the overall fitness of a population. Preliminary results suggest a large effect for small populations.