RESUMEN
Biological systems by default involve complex components with complex relationships. To decipher how biological systems work, we assume that one needs to integrate information over multiple levels of complexity. The songbird vocal communication system is ideal for such integration due to many years of ethological investigation and a discreet dedicated brain network. Here we announce the beginnings of a songbird brain integrative project that involves high-throughput, molecular, anatomical, electrophysiological and behavioral levels of analysis. We first formed a rationale for inclusion of specific biological levels of analysis, then developed high-throughput molecular technologies on songbird brains, developed technologies for combined analysis of electrophysiological activity and gene regulation in awake behaving animals, and developed bioinformatic tools that predict causal interactions within and between biological levels of organization. This integrative brain project is fitting for the interdisciplinary approaches taken in the current songbird issue of the Journal of Comparative Physiology A and is expected to be conducive to deciphering how brains generate and perceive complex behaviors.
Asunto(s)
Encéfalo/fisiología , Pájaros Cantores/fisiología , Animales , Vías Auditivas , Teorema de Bayes , Encéfalo/anatomía & histología , Mapeo Encefálico , Biología Computacional , Simulación por Computador , Proteínas de Unión al ADN/metabolismo , Electrofisiología , Perfilación de la Expresión Génica , Regulación del Desarrollo de la Expresión Génica , Biblioteca de Genes , Aprendizaje , Modelos Neurológicos , Actividad Motora/fisiología , Red Nerviosa , Redes Neurales de la Computación , Factores de Transcripción/metabolismo , Vocalización Animal/fisiologíaRESUMEN
We propose a model-driven approach for analyzing genomic expression data that permits genetic regulatory networks to be represented in a biologically interpretable computational form. Our models permit latent variables capturing unobserved factors, describe arbitrarily complex (more than pair-wise) relationships at varying levels of refinement, and can be scored rigorously against observational data. The models that we use are based on Bayesian networks and their extensions. As a demonstration of this approach, we utilize 52 genomes worth of Affymetrix GeneChip expression data to correctly differentiate between alternative hypotheses of the galactose regulatory network in S. cerevisiae. When we extend the graph semantics to permit annotated edges, we are able to score models describing relationships at a finer degree of specification.
Asunto(s)
Perfilación de la Expresión Génica/estadística & datos numéricos , Modelos Genéticos , Teorema de Bayes , Galactosa/metabolismo , Regulación Fúngica de la Expresión Génica , Genoma Fúngico , Modelos Estadísticos , Análisis de Secuencia por Matrices de Oligonucleótidos/estadística & datos numéricos , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismoRESUMEN
We present techniques for automating the design of computational systems built using DNA, given a set of high-level constraints on the desired behavior and performance of the system. We have developed a program called SCAN that exploits a previously implemented computational melting temperature primitive to search a 'nucleotide space' for sequences satisfying a pre-specified set of constraints, including hybridization discrimination, primer 5' end and 3' end stability, secondary structure reduction, and prevention of oligonucleotide dimer formation. The first version of SCAN utilized 24 h of computer time to search a space of over 7.5 billion unary counter designs and found only nine designs satisfying all of the pre-specified constraints. One of SCAN's designs has been implemented in the laboratory and has shown a marked improvement in performance over the products of previous attempts at manual design. We conclude with some novel ideas for improving the overall speed of the program that offer the promise of an efficient method for selecting optimal nucleotide sequences in an automated fashion.