Feature context-dependency and complexity-reduction in probability landscapes for integrative genomics

Annick Lesne¹ and Arndt Benecke¹^,2

¹Institut des Hautes Études Scientifiques, Bures-sur-Yvette, France

²Institut de Recherche Interdisciplinaire – CNRS USR3078 – Université Lille I, France

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Theoretical Biology and Medical Modelling 2008, 5:21doi:10.1186/1742-4682-5-21


Published:	10 September 2008

Abstract

Background

The question of how to integrate heterogeneous sources of biological information into a coherent framework that allows the gene regulatory code in eukaryotes to be systematically investigated is one of the major challenges faced by systems biology. Probability landscapes, which include as reference set the probabilistic representation of the genomic sequence, have been proposed as a possible approach to the systematic discovery and analysis of correlations amongst initially heterogeneous and un-relatable descriptions and genome-wide measurements. Much of the available experimental sequence and genome activity information is de facto, but not necessarily obviously, context dependent. Furthermore, the context dependency of the relevant information is itself dependent on the biological question addressed. It is hence necessary to develop a systematic way of discovering the context-dependency of functional genomics information in a flexible, question-dependent manner.

Results

We demonstrate here how feature context-dependency can be systematically investigated using probability landscapes. Furthermore, we show how different feature probability profiles can be conditionally collapsed to reduce the computational and formal, mathematical complexity of probability landscapes. Interestingly, the possibility of complexity reduction can be linked directly to the analysis of context-dependency.

Conclusion

These two advances in our understanding of the properties of probability landscapes not only simplify subsequent cross-correlation analysis in hypothesis-driven model building and testing, but also provide additional insights into the biological gene regulatory problems studied. Furthermore, insights into the nature of individual features and a classification of features according to their minimal context-dependency are achieved. The formal structure proposed contributes to a concrete and tangible basis for attempting to formulate novel mathematical structures for describing gene regulation in eukaryotes on a genome-wide scale.