Since time-course microarrav data are short but contain a large number of genes, most of statistical models should be extended so that they can handle such statistically irregular situations. We introduce biological s...Since time-course microarrav data are short but contain a large number of genes, most of statistical models should be extended so that they can handle such statistically irregular situations. We introduce biological state space models that are established as suitable computational models for constructing gene networks from microarray gene expression data. This chapter elucidates theory and methodology of our biological state space models together with some representative analyses including discovery of drug mode of action. Through the applications we show the whole strategy of biological state space model analysis involving experimental design of time-course data, model building and analysis of the estimated networks.展开更多
Genes encoding Wnt ligands, which have important roles in cell communication and organ development, are restricted to multicellular animals. We systematically studied W nt genes from eumetazoan genomes, with emphasis ...Genes encoding Wnt ligands, which have important roles in cell communication and organ development, are restricted to multicellular animals. We systematically studied W nt genes from eumetazoan genomes, with emphasis on the poorly studied superphylum Lophotrochozoa(four annelids, seven mollusks, eight platyhelminths, one bdelloid rotifer, and one brachiopod species). Between 3 and 39 W nt loci were identified in each genome, and the protostome-specific loss of Wnt3 genes was confirmed. We identified gastropod-specific loss of Wnt8, refining the previously proposed mollusk-specific loss. Some duplicated Wnt genes belonging to a same subfamily or closely related subfamilies showed tandem distribution in the lophotrochozoan genomes, indicating tandem duplication events during Wnt family evolution. Members of the conserved Wnt10-Wnt6-Wnt1-Wnt9 cluster showed highly correlated expression patterns over time in two assayed lophotrochozoans, the oyster C rassostrea gigas and the brachiopod L ingula anatina, reflecting the possible similar function of the clustered W nt genes.展开更多
Modeling inter-relationships of genes over a specific genetic network is one of the most challenging studies in systems biology. Among the families of models proposed one commonly used is the discrete stochastic, base...Modeling inter-relationships of genes over a specific genetic network is one of the most challenging studies in systems biology. Among the families of models proposed one commonly used is the discrete stochastic, based on conditionally independent Markov chains. In practice, this model is estimated from time sequential sampling, usually obtained by microarray experiments. In order to improve the accuracy of the estimation method, we can use biological knowledge. In this paper, we decided to apply this idea to study the role of estrogen in breast cancer proliferation. The n-influence zone of a set S of genes in a given multi-layer genetic network is a set L of genes regulated, directly or indirectly, by genes in S, after at most n-1 layers. In this manuscript we describe a new approach for computing the n-influence zone of S through the estimation of a multi-layer genetic network from gene expression time series, measured by microarrays, and biological knowledge. Using seed genes related to cell proliferation, our method was able to add to the third layer of the network other genes related to this biological function and validated in the literature. Using a set of genes directly influenced by estrogen, we could find a new role for cell adhesion genes estrogen dependent. Our pipeline is user-friendly and does not have high system requirements. We believe this paper could contribute to improve the data mining for biologists in microarray time series.展开更多
文摘Since time-course microarrav data are short but contain a large number of genes, most of statistical models should be extended so that they can handle such statistically irregular situations. We introduce biological state space models that are established as suitable computational models for constructing gene networks from microarray gene expression data. This chapter elucidates theory and methodology of our biological state space models together with some representative analyses including discovery of drug mode of action. Through the applications we show the whole strategy of biological state space model analysis involving experimental design of time-course data, model building and analysis of the estimated networks.
基金Supported by the National Natural Science Foundation of China(Nos.31402285,31530079)the Scientific and Technological Innovation Project financially supported by the Qingdao National Laboratory for Marine Science and Technology(No.2015ASKJ02)the earmarked fund for Modern Agro-Industry Technology Research System(No.CARS-48)
文摘Genes encoding Wnt ligands, which have important roles in cell communication and organ development, are restricted to multicellular animals. We systematically studied W nt genes from eumetazoan genomes, with emphasis on the poorly studied superphylum Lophotrochozoa(four annelids, seven mollusks, eight platyhelminths, one bdelloid rotifer, and one brachiopod species). Between 3 and 39 W nt loci were identified in each genome, and the protostome-specific loss of Wnt3 genes was confirmed. We identified gastropod-specific loss of Wnt8, refining the previously proposed mollusk-specific loss. Some duplicated Wnt genes belonging to a same subfamily or closely related subfamilies showed tandem distribution in the lophotrochozoan genomes, indicating tandem duplication events during Wnt family evolution. Members of the conserved Wnt10-Wnt6-Wnt1-Wnt9 cluster showed highly correlated expression patterns over time in two assayed lophotrochozoans, the oyster C rassostrea gigas and the brachiopod L ingula anatina, reflecting the possible similar function of the clustered W nt genes.
基金FAPESP (99/12765-2, 01/094 01-0, 04/03967-0 and 05/00587-5) CNPq (300722/98-2, 468 413/00-6, 521097/01-0 474596/04-4 and 491323/ 05-0)CAPES
文摘Modeling inter-relationships of genes over a specific genetic network is one of the most challenging studies in systems biology. Among the families of models proposed one commonly used is the discrete stochastic, based on conditionally independent Markov chains. In practice, this model is estimated from time sequential sampling, usually obtained by microarray experiments. In order to improve the accuracy of the estimation method, we can use biological knowledge. In this paper, we decided to apply this idea to study the role of estrogen in breast cancer proliferation. The n-influence zone of a set S of genes in a given multi-layer genetic network is a set L of genes regulated, directly or indirectly, by genes in S, after at most n-1 layers. In this manuscript we describe a new approach for computing the n-influence zone of S through the estimation of a multi-layer genetic network from gene expression time series, measured by microarrays, and biological knowledge. Using seed genes related to cell proliferation, our method was able to add to the third layer of the network other genes related to this biological function and validated in the literature. Using a set of genes directly influenced by estrogen, we could find a new role for cell adhesion genes estrogen dependent. Our pipeline is user-friendly and does not have high system requirements. We believe this paper could contribute to improve the data mining for biologists in microarray time series.
