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1.
Proteins ; 60(3): 525-46, 2005 Aug 15.
Artigo em Inglês | MEDLINE | ID: mdl-15971229

RESUMO

The intrinsic dynamic response of a transcriptional regulatory network depends directly on molecular interactions in the cellular transcription, translation, and degradation machineries. These interactions can be incorporated into dynamic mathematical models of the biochemical system using the biophysical relationship with the model parameters. Modifications of such interactions bring changes to the biological behavior of the cells, and therefore, many normal and pathological cellular states depend on them. It is important for analysis, prediction, diagnosis, and treatment of cellular function to have an experimentally derived model with parameters that adequately represent the molecular interactions of interest. Finding the model and parameters of a transcriptional regulatory network is a difficult task that has been approached at different levels and with different techniques. We develop here a new analysis method (based on previous work on network inference, modeling, and parameter identification) that finds the most changed parameters from yeast oligonucleotide microarray expression patterns in cases where a phenotype difference exists between two samples. We then relate and examine the changed parameters with their associated genes, corresponding genetic functional categories, and particular subnetworks and connectivities. The biophysical bases for these changes are also identified by studying the relationship of the changed parameters with the transcription, translation, and degradation mechanisms. The method is improved to cases where there are two or more transcription factors influencing transcription, and a statistical analysis is performed to give a measurement of the uniqueness and robustness of the parameter fit.


Assuntos
Biofísica/métodos , Biologia Computacional/métodos , Proteômica/métodos , Algoritmos , Motivos de Aminoácidos , Animais , RNA Polimerases Dirigidas por DNA/química , Proteínas Fúngicas/química , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Genoma , Proteínas de Fluorescência Verde/química , Cinética , Modelos Biológicos , Modelos Genéticos , Modelos Estatísticos , Fenótipo , Ligação Proteica , Biossíntese de Proteínas , Saccharomyces cerevisiae/metabolismo , Fatores de Tempo , Fatores de Transcrição/química , Transcrição Genética
2.
Proteins ; 55(2): 339-50, 2004 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-15048826

RESUMO

Finding the causality and strength of connectivity in transcriptional regulatory networks from time-series data will provide a powerful tool for the analysis of cellular states. Presented here is the design of tools for the evaluation of the network's model structure and parameters. The most effective tools are found to be based on evolution strategies. We evaluate models of increasing complexity, from lumped, algebraic phenomenological models to Hill functions and thermodynamically derived functions. These last functions provide the free energies of binding of transcription factors to their operators, as well as cooperativity energies. Optimization results based on published experimental data from a synthetic network in Escherichia coli are presented. The free energies of binding and cooperativity found by our tools are in the same physiological ranges as those experimentally derived in the bacteriophage lambda system. We also use time-series data from high-density oligonucleotide microarrays of yeast meiotic expression patterns. The algorithm appropriately finds the parameters of pairs of regulated regulatory yeast genes, showing that for related genes an overall reasonable computation effort is sufficient to find the strength and causality of the connectivity of large numbers of them.


Assuntos
Regulação da Expressão Gênica , Modelos Genéticos , Transcrição Genética/genética , Algoritmos , Bacteriófago lambda/genética , Evolução Biológica , Simulação por Computador , Escherichia coli/genética , Escherichia coli/virologia , Perfilação da Expressão Gênica , Meiose/genética , Mutação/genética , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , Saccharomyces cerevisiae/genética , Seleção Genética , Termodinâmica , Fatores de Transcrição/metabolismo
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