PROBABILISTIC MODELING OF BIOLOGICAL INFORMATION SYSTEMS

在三个不同领域开展了工作。 的典范 已经开发了听觉刺激的耳蜗处理。 毛细胞 转换是模型中唯一的非线性分量，这就足够了。 来解释众所周知的非线性听觉特性。 第二区域 涉及到细胞自动机的研究。 元胞自动机提供了一个独特的 研究复杂的生物学问题，如生长和分化 生物体和神经结构和功能。 当前兴趣 着重于元胞自动机的一般性质或方法。 我们有 开发了一种分析程序，用于预测 一类简单自动机中局部模式的分布。 述的方法 使其有望推广到更复杂的系统。 的第三个领域 研究的是生物大分子的统计特性。 述PAM 分析了蛋白质进化的矩阵模型，发现了一个错误 在矩阵的建设和需要修改的模型 演示。 目前正在进行一项研究， 突变对种群整体适应性的影响 初步结果 表明对小群体有很大影响。

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.