; and under the stimulus of noise are capable of differentiating directly from any mode of behavior to at most a few other modes of behavior. Cellular differentation is modeled as a Markov chain among the modes of behavior of a genetic net. The possibility of a general theory of metabolic behavior

/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able

Summary: We designed a Java applet which enables the visualization of genetic networks and can be used as a Web publishing tool by molecular biologists studying the mechanisms of gene interactions.

learning to compute the velocity. A new fitness function is introduced that forces the neural net...

formalism; in the second stage a near optimal schedule is generated by a Genetic Algorithm (GA) which uses the Petri Net model for its chromosomal representation. This GA is also used as a reactive scheduler in the third stage where the system is simulated. In particular we focus on the impact

and enables users to maintain data integrity throughout the process. An ever-growing list of more than 50 different statistical analyses for genetic markers has been implemented in PowerMarker. Availability: www.powermarker.net

information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species

We consider neural nets whose connections are defined by growth rules taking the form of recursion relations. These are called genetic neural nets. Learning in these nets is achieved by simulated annealing optimization of the net over the space of recursion relation parameters. The method is tested

A genetic algorithm (GA) is used to prune the connections of backpropagation trained neural nets, used for identification of faces and of sonar signals. A fully-connected net is first trained on the target problem. The GA then prunes connections prior to retraining of the remainder. This process

this paper is concerned, have been derived in several ways. Levitt (1976) generated potentials of mean force by averaging energies over all relative orientations of pairs of side-chains. More recently these kinds of energy functions have been derived as potentials of mean force from the ever-growing database of known protein structures (see the references in Sippl, 1995). Huang et al. (1995) have devised a potential which does not explicitly use the database of known structures; they use only a simple classification of different residues as hydrophobic or hydrophilic, reminiscent of the theoretical energy models of Dill et al. (reviewed by Dill et al., 1995; Yue & Dill, 1995). Maiorov & Crippen (1992) generated a potential function by an optimization procedure which sought to maximize the difference in energy between correct and incorrect protein conformations.