Modeling, Simulation and Application of Bacterial Transduction in Genetic Algorithms

At present, all methods in Evolutionary Computation are bioinspired in the fundamental principles of neo-Darwinism as well as on a vertical gene transfer. Thus, on a mechanism in which an organism receives genetic material from its ancestor. Horizontal, lateral or cross-population gene transfer is any process in which an organism transfers a genetic segment to another one that is not its offspring. Virus transduction is one of the key mechanisms of horizontal gene propagation in microorganism (e.g. bacteria). In the present paper, we model and simulate a transduction operator, exploring a possible role and usefulness of transduction in a genetic algorithm. The genetic algorithm including transduction has been named PETRI (abbreviation of Promoting Evolution Through Reiterated Infection). The efficiency and performance of this algorithm was evaluated using a benchmark function and the 0/1 knapsack problem. The utility was illustrated designing an AM radio receiver, optimizing the main features of the electronic components of the AM radio circuit as well as those of the radio enclosure. Our results shown how PETRI approaches to higher fitness values as transduction probability comes near to 100%. The conclusion is that transduction improves the performance of a genetic algorithm, assuming a population divided among several sub-populations or ‘bacterial colonies’

Finding undetected protein associations in cell signaling by belief propagation

External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.Comment: 6 pages, 3 figures, 1 table, Supporting Informatio

Large-scale inference and graph theoretical analysis of gene-regulatory networks in B. stubtilis

We present the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B. subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. By employing a computational, linear correlative procedure to generate these networks, and by analyzing the networks from a graph theoretical perspective, we are able to verify the biological viability of our inferred networks, and we demonstrate that our networks' graph theoretical properties are remarkably similar to those of other biological systems. In addition, by comparing our inferred networks to those of a previous, noisier implementation of the linear inference process [17], we are able to identify trends in graph theoretical behavior that occur both in our networks as well as in their perturbed counterparts. These commonalities in behavior at multiple levels of complexity allow us to ascertain the level of complexity to which our process is robust to noise.Comment: 22 pages, 4 figures, accepted for publication in Physica A (2006

Distribution matching for transduction

Many transductive inference algorithms assume that distributions over training and test estimates should be related, e.g. by providing a large margin of separation on both sets. We use this idea to design a transduction algorithm which can be used without modification for classification, regression, and structured estimation. At its heart we exploit the fact that for a good learner the distributions over the outputs on training and test sets should match. This is a classical two-sample problem which can be solved efficiently in its most general form by using distance measures in Hilbert Space. It turns out that a number of existing heuristics can be viewed as special cases of our approach.