Biology and related fields are currently witnessing a revolution. Quantitative approaches and methods are now becoming increasingly common, largely due to remarkable achievements in sequencing technologies and high-throughput genomic screening methods. One of the major challenges is to use such approaches for understanding the underlying mechanisms and functions of genes. The approach that will be taken in this project is to model the genetic regulatory system and infer the model structure and parameters from gene expression data. The goal of the project is to further develop and refine the mathematical and computational methods and models and to apply them to a concrete biological model system. This will not only serve as a validation of the modeling approach, but will also lead to an improved understanding of the genetic regulatory mechanisms underlying cellular function and dysfunction. In particular, the specific aims are 1) to compare different network inference algorithms and validate them with experimental methods; 2) to generate and evaluate sub-networks using time-course microarray data; 3) to predict and validate the effects of gene perturbations on steady-state expressions of genes in the sub-networks. The research program will closely integrate both computational and experimental approaches to achieve our goals. ? ? ?
| Heinäniemi, Merja; Nykter, Matti; Kramer, Roger et al. (2013) Gene-pair expression signatures reveal lineage control. Nat Methods 10:577-83 |
| Knijnenburg, Theo A; Roda, Oriol; Wan, Yakun et al. (2011) A regression model approach to enable cell morphology correction in high-throughput flow cytometry. Mol Syst Biol 7:531 |
| Ratushny, Alexander V; Shmulevich, Ilya; Aitchison, John D (2011) Trade-off between responsiveness and noise suppression in biomolecular system responses to environmental cues. PLoS Comput Biol 7:e1002091 |
| Knijnenburg, Theo A; Lin, Jake; Rovira, Hector et al. (2011) EPEPT: a web service for enhanced P-value estimation in permutation tests. BMC Bioinformatics 12:411 |
| Ramsey, Stephen A; Knijnenburg, Theo A; Kennedy, Kathleen A et al. (2010) Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites. Bioinformatics 26:2071-5 |
| Galas, David J; Nykter, Matti; Carter, Gregory W et al. (2010) Biological Information as Set-Based Complexity. IEEE Trans Inf Theory 56:667-677 |
| Huang, Albert C; Hu, Limei; Kauffman, Stuart A et al. (2009) Using cell fate attractors to uncover transcriptional regulation of HL60 neutrophil differentiation. BMC Syst Biol 3:20 |
| Nykter, Matti; Lähdesmäki, Harri; Rust, Alistair et al. (2009) A data integration framework for prediction of transcription factor targets. Ann N Y Acad Sci 1158:205-14 |
| Knijnenburg, Theo A; Wessels, Lodewyk F A; Reinders, Marcel J T et al. (2009) Fewer permutations, more accurate P-values. Bioinformatics 25:i161-8 |
| Lahdesmaki, Harri; Rust, Alistair G; Shmulevich, Ilya (2008) Probabilistic inference of transcription factor binding from multiple data sources. PLoS One 3:e1820 |
Showing the most recent 10 out of 20 publications
