Faculty Bibliography

The study of spontaneous mutation rates in mammalian cells has been hampered by the lack of an alternative to the cumbersome Luria and Delbruck fluctuation test. A brief review of mathematical treatments of spontaneous mutagenesis, along with some of the limitations of the fluctuation test, is presented. A new experimental method and a simple mathematical model for deriving the spontaneous mutation rate are described. Data from the transgenic Chinese hamster G12 cell line growing at two different rates is analyzed according to this model. The results support the concept that, at least for growing cells, the spontaneous mutation rate is independent of the growth rate, and the mutant fraction increases in a linear fashion with the number of generations

We classify points in R(d) (feature vector space) by functions related to feedforward artificial neural networks. These functions, dubbed "stochastic neural nets", arise in a natural way from probabilistic as well as from statistical considerations. The probabilistic idea is to define a classifying bit locally by using the sign of a hidden state-dependent noisy linear function of the feature vector as a new (d+1)th coordinate of the vector. This (d+1)-dimensional distribution is approximated by a mixture distribution. The statistical idea is that the approximating mixtures, and hence the a posteriori class probability functions (stochastic neural nets) defined by them, can be conveniently trained either by maximum likelihood or by a Bayes criterion through the use of an appropriate expectation-maximization algorithm.