Thu, 24 Jul 2008 22:58:22 BST CiteULike: Author Tagkopoulos http://www.citeulike.org/author/Tagkopoulos CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/stringertheory/article/2773606 <i>Science (8 May 2008), 1154456.</i><br /><br />We question whether homeostasis alone adequately explains microbial responses to environmental stimuli, and explore the capacity of intra-cellular networks for predictive behavior in a fashion similar to metazoan nervous systems. We show that in silico biochemical networks, evolving randomly under precisely defined complex habitats, capture the dynamical, multi-dimensional structure of diverse environments by forming internal models that allow prediction of environmental change. We provide evidence for such anticipatory behavior by revealing striking correlations of Escherichia coli transcriptional responses to temperature and oxygen perturbations--precisely mirroring the co-variation of these parameters upon transitions between the outside world and the mammalian gastrointestinal-tract. We further show that these internal correlations reflect a true associative learning paradigm, since they show rapid de-coupling upon exposure to novel environments. 10.1126/science.1154456 Anticipatory Behavior Within Microbial Genetic Networks Ilias Tagkopoulos Yir-Chung Liu Saeed Tavazoie doi:10.1126/science.1154456 Science (8 May 2008), 1154456. 2008-05-08T21:24:02-00:00 2008 Science 1154456 no-tag http://www.citeulike.org/user/tabu/article/741324 <i>J Bioinform Comput Biol, Vol. 4, No. 2. (April 2006), pp. 275-298.</i><br /><br />Machine learning techniques offer a viable approach to cluster discovery from microarray data, which involves identifying and classifying biologically relevant groups in genes and conditions. It has been recognized that genes (whether or not they belong to the same gene group) may be co-expressed via a variety of pathways. Therefore, they can be adequately described by a diversity of coherence models. In fact, it is known that a gene may participate in multiple pathways that may or may not be co-active under all conditions. It is therefore biologically meaningful to simultaneously divide genes into functional groups and conditions into co-active categories - leading to the so-called biclustering analysis. For this, we have proposed a comprehensive set of coherence models to cope with various plausible regulation processes. Furthermore, a multivariate biclustering analysis based on fusion of different coherence models appears to be promising because the expression level of genes from the same group may follow more than one coherence models. The simulation studies further confirm that the proposed framework enjoys the advantage of high prediction performance. SYMMETRIC AND ASYMMETRIC MULTI-MODALITY BICLUSTERING ANALYSIS FOR MICROARRAY DATA MATRIX. Sun-Yuan Kung Man-Wai Mak Ilias Tagkopoulos J Bioinform Comput Biol, Vol. 4, No. 2. (April 2006), pp. 275-298. 2006-07-05T23:17:32-00:00 2006 J Bioinform Comput Biol 0219-7200 4 2 275 298 microarray