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Comparative Generalized Logic Modeling Reveals Differential Gene Interactions during Cell Cycle Exit in Drosophila Wing Development. 比较广义逻辑模型揭示了果蝇翅膀发育过程中细胞周期退出过程中的差异基因相互作用。

GI-Edition. Proceedings Pub Date : 2009-01-01

Mingzhou Joe Song, Chung-Chien Hong, Yang Zhang, Laura Buttitta, Bruce A Edgar

{"title":"Comparative Generalized Logic Modeling Reveals Differential Gene Interactions during Cell Cycle Exit in Drosophila Wing Development.","authors":"Mingzhou Joe Song, Chung-Chien Hong, Yang Zhang, Laura Buttitta, Bruce A Edgar","doi":"","DOIUrl":"","url":null,"abstract":"A comparative interaction detection paradigm is proposed to study the complex gene regulatory networks that control cell proliferation during development. Instead of attempting to reconstruct the entire cell cycle regulatory network from temporal transcript data, differential interactions - represented by generalized logic - are detected directly from time course transcript data under two distinct conditions. This comparative approach is scale- and shift-invariant and is capable of detecting nonlinear differential interactions. Simulation studies on E. coli circuits demonstrated that the proposed comparative method has substantially increased statistical power over the intuitive reconstruct-then-compare approach. This method was therefore applied to a microarray experiment, profiling gene expression in the fruit fly wing as cells exit the cell cycle, and under a condition which delays this exit, over-expression of the cell cycle regulator E2F. One statistically significant differential interaction was identified between two gene clusters that is strongly influenced by E2F activity, and suggests the involvement of the Hippo signaling pathway in response to E2F, a finding that may provide additional insights on cell cycle control mechanisms. Furthermore, the comparative modeling can be applied to both static and dynamic gene expression data, and is extendible to deal with more than two conditions, useful in many biological studies.","PeriodicalId":90508,"journal":{"name":"GI-Edition. Proceedings","volume":"157 ","pages":"143-152"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4181381/pdf/nihms-158079.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32721653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Identification of Differential Interactions from Trajectories of Dynamic Biological Networks. 动态生物网络轨迹中微分相互作用的比较识别。

GI-Edition. Proceedings Pub Date : 2009-01-01

Zhengyu Ouyang, Mingzhou Joe Song

{"title":"Comparative Identification of Differential Interactions from Trajectories of Dynamic Biological Networks.","authors":"Zhengyu Ouyang, Mingzhou Joe Song","doi":"","DOIUrl":"","url":null,"abstract":"It is often challenging to reconstruct accurately a complete dynamic biological network due to the scarcity of data collected in cost-effective experiments. This paper addresses the possibility of comparatively identifying qualitative interaction shifts between two dynamical networks from comparative time course data. An innovative approach is developed to achieve differential interaction detection by statistically comparing the trajectories, instead of numerically comparing the reconstructed interactions. The core of this approach is a statistical heterogeneity test that compares two multiple linear regression equations for the derivatives in nonlinear ordinary differential equations, statistically instead of numerically. In detecting any shift of an interaction, the uncertainty in estimated regression coefficients is taken into account by this test, while it is ignored by the reconstruction-based numerical comparison. The heterogeneity test is accomplished by assessing the gain in goodness-of-fit from using a single common interaction to using a pair of differential interactions. Compared with previous numerical comparison methods, the proposed statistical comparison always achieves higher statistical power. As sample size decreases or noise increases in a certain range, the improvement becomes substantial. The advantage is illustrated by a simulation study on the statistical power as functions of the noise level, the sample size, and the interaction complexity. This method is also capable of detecting interaction shifts in the oscillated and excitable domains of a dynamical system model describing cdc2-cyclin interactions during cell division cycle. Generally, the described approach is applicable to comparing dynamical systems of additive nonlinear ordinary differential equations.","PeriodicalId":90508,"journal":{"name":"GI-Edition. Proceedings","volume":"157 ","pages":"163-172"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4181597/pdf/nihms-158078.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32721654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0