Microarray technology enables investigators to simultaneously measure the expression of thousands of genes and holds the promise to cast new light onto the regulatory mechanisms of the genome. A main avenue of experimental investigation, leveraging on this technology, is based on the temporal dissection of cellular mechanisms. Temporal experiments offer the possibility of observing these mechanisms in action and to break down the genome into sets of genes involved in the same processes. The overall goal of this project is to develop an unsupervised approach and an integrated software environment to automatically discover regulatory mechanisms from temporal microarray experiments. The hypothesis underpinning our approach is that complex interaction patterns can be identified through analysis of conditional rather than marginal gene expression profiles. This novel approach also provides principled guidance to experimental design and sampling strategies, and it naturally extends to a large class of statistical models, able to capture a wider range of dynamic behaviors and experimental designs. We plan to develop a comprehensive framework to design and analyze microarray data collected through temporal experiments. This framework will be used to specify and answer the critical design questions of sample size and sampling frequency determination. Using this framework, we will develop a new model-based approach and an iterative search algorithm, called Conditional Clustering, to identify different patterns of behavior determined by a set of genes through the analysis of the behavior of a gene given a set of other genes, rather than the behavior of each gene in isolation. We will implement this design and analysis framework in a computer program that will be distributed over the Internet.This project brings together researchers in artificial intelligence, theoretical statistics and experimental design with a long track record of methodological contributions to bioinformatics to develop a novel methodological approach to a critical question at the forefront of genomic research.
| Ferrazzi, Fulvia; Engel, Felix B; Wu, Erxi et al. (2011) Inferring cell cycle feedback regulation from gene expression data. J Biomed Inform 44:565-75 |
| Chang, Hsun-Hsien; Dreyfuss, Jonathan M; Ramoni, Marco F (2011) A transcriptional network signature characterizes lung cancer subtypes. Cancer 117:353-60 |
| Alterovitz, Gil; Xiang, Michael; Hill, David P et al. (2010) Ontology engineering. Nat Biotechnol 28:128-30 |
| Kanjilal, Sanjat; Citorik, Robert; LaRocque, Regina C et al. (2010) A systems biology approach to modeling vibrio cholerae gene expression under virulence-inducing conditions. J Bacteriol 192:4300-10 |
| Chang, Hsun-Hsien; McGeachie, Michael; Alterovitz, Gil et al. (2010) Mapping transcription mechanisms from multimodal genomic data. BMC Bioinformatics 11 Suppl 9:S2 |
| Alterovitz, Gil; Muso, Taro; Ramoni, Marco F (2010) The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms. Brief Bioinform 11:80-95 |
| Ramoni, Rachel Badovinac; Himes, Blanca E; Sale, Michele M et al. (2009) Predictive genomics of cardioembolic stroke. Stroke 40:S67-70 |
| Chang, Hsun-Hsien; Ramoni, Marco F (2009) Transcriptional network classifiers. BMC Bioinformatics 10 Suppl 9:S1 |
| Gerhardinger, Chiara; Dagher, Zeina; Sebastiani, Paola et al. (2009) The transforming growth factor-beta pathway is a common target of drugs that prevent experimental diabetic retinopathy. Diabetes 58:1659-67 |
| Abad-Grau, Maria M; Ierache, Jorge; Cervino, Claudio et al. (2008) Evolution and challenges in the design of computational systems for triage assistance. J Biomed Inform 41:432-41 |
Showing the most recent 10 out of 17 publications
