title: Feature context-dependency and complexity-reduction in probability landscapes for integrative genomics.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institut des Hautes Etudes Scientifiques, Bures-sur-Yvette, France. lesne@ihes.fr
authors.forename: Annick
authors.initials: A
authors.lastname: Lesne
authors.forename: Arndt
authors.initials: A
authors.lastname: Benecke
mesh_terms: D001483:Base Sequence; D020869:Gene Expression Profiling / Q000379:methods; D023281:Genomics / Q000379:methods* / Q000706:statistics & numerical data*; D006801:Humans; D008969:Molecular Sequence Data; D011336:Probability; D049490:Systems Biology / Q000379:methods* / Q000706:statistics & numerical data*
references.citation: HFSP J. 2008 Aug;2(4):183-8
references.pmid: 19404429
references.citation: BMC Bioinformatics. 2005 Dec 22;6:307
references.pmid: 16372901
references.citation: PLoS One. 2008 Jul 16;3(7):e2721
references.pmid: 18628956
references.citation: Theor Biol Med Model. 2008 May 20;5:9
references.pmid: 18492240
references.citation: Eur Phys J E Soft Matter. 2006 Mar;19(3):353-66
references.pmid: 16520898