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Biomedical subjects

Patrik D'haeseleer

Publications and source records attributed to Patrik D'haeseleer.

8 recordsLinked to original sources
PubMed2006

Deciphering principles of transcription regulation in eukaryotic genomes.

Transcription regulation has been responsible for organismal complexity and diversity in the course of biological evolution and adaptation, and it is determined largely by the context-dependent behavior of cis-regulatory elements (CREs). Therefore, understanding principles underlying CRE behavior in regulating transcription constitutes a fundamental objective of quantitative biology, yet these remain poorly understood. Here we present a deterministic mathematical strategy, the motif expression decomposition (MED) method, for deriving principles of transcription regulation at the single-gene resolution level. MED operates on all genes in a genome without requiring any a priori knowledge of gene cluster membership, or manual tuning of parameters. Applying MED to Saccharomyces cerevisiae transcriptional networks, we identified four functions describing four different ways that CREs can quantitatively affect gene expression levels. These functions, three of which have extrema in different positions in the gene promoter (short-, mid-, and long-range) whereas the other depends on the motif orientation, are validated by expression data. We illustrate how nature could use these principles as an additional dimension to amplify the combinatorial power of a small set of CREs in regulating transcription.

Computational Biology↗
PubMed2006

What are DNA sequence motifs?

Sequence motifs are becoming increasingly important in the analysis of gene regulation. How do we define sequence motifs, and why should we use sequence logos instead of consensus sequences to represent them? Do they have any relation with binding affinity? How do we search for new instances of a motif in this sea of DNA?

Base Sequence↗
PubMed2005

How does gene expression clustering work?

Clustering is often one of the first steps in gene expression analysis. How do clustering algorithms work, which ones should we use and what can we expect from them?

Algorithms↗
PubMed2004

Estimating and improving protein interaction error rates.

High throughput protein interaction data sets have proven to be notoriously noisy. Although it is possible to focus on interactions with higher reliability by using only those that are backed up by two or more lines of evidence, this approach invariably throws out the majority of available data. A more optimal use could be achieved by incorporating the probabilities associated with all available interactions into the analysis. We present a novel method for estimating error rates associated with specific protein interaction data sets, as well as with individual interactions given the data sets in which they appear. As a bonus, we also get an estimate for the total number of protein interactions in yeast. Certain types of false positive results can be identified and removed, resulting in a significant improvement in quality of the data set. For co-purification data sets, we show how we can reach a tradeoff between the "spoke" and "matrix" representation of interactions within co-purified groups of proteins to achieve an optimal false positive error rate.

Algorithms↗
PubMed2003

From annotated genomes to metabolic flux models and kinetic parameter fitting.

Significant advances in system-level modeling of cellular behavior can be achieved based on constraints derived from genomic information and on optimality hypotheses. For steady-state models of metabolic networks, mass conservation and reaction stoichiometry impose linear constraints on metabolic fluxes. Different objectives, such as maximization of growth rate or minimization of flux distance from a reference state, can be tested in different organisms and conditions. In particular, we have suggested that the metabolic properties of mutant bacterial strains are best described by an algorithm that performs a minimization of metabolic adjustment (MOMA) upon gene deletion. The increasing availability of many annotated genomes paves the way for a systematic application of these flux balance methods to a large variety of organisms. However, such a high throughput goal crucially depends on our capacity to build metabolic flux models in a fully automated fashion. Here we describe a pipeline for generating models from annotated genomes and discuss the current obstacles to full automation. In addition, we propose a framework for the integration of flux modeling results and high throughput proteomic data, which can potentially help in the inference of whole-cell kinetic parameters.

Algorithms↗
PubMed2002

Automated modelling of signal transduction networks.

BACKGROUND: Intracellular signal transduction is achieved by networks of proteins and small molecules that transmit information from the cell surface to the nucleus, where they ultimately effect transcriptional changes. Understanding the mechanisms cells use to accomplish this important process requires a detailed molecular description of the networks involved. RESULTS: We have developed a computational approach for generating static models of signal transduction networks which utilizes protein-interaction maps generated from large-scale two-hybrid screens and expression profiles from DNA microarrays. Networks are determined entirely by integrating protein-protein interaction data with microarray expression data, without prior knowledge of any pathway intermediates. In effect, this is equivalent to extracting subnetworks of the protein interaction dataset whose members have the most correlated expression profiles. CONCLUSION: We show that our technique accurately reconstructs MAP Kinase signaling networks in Saccharomyces cerevisiae. This approach should enhance our ability to model signaling networks and to discover new components of known networks. More generally, it provides a method for synthesizing molecular data, either individual transcript abundance measurements or pairwise protein interactions, into higher level structures, such as pathways and networks.

Computational Biology↗