title: MIRA: mutual information-based reporter algorithm for metabolic networks.
abstract: BACKGROUND
  Discovering the transcriptional regulatory architecture of the metabolism has been an important topic to understand the implications of transcriptional fluctuations on metabolism. The reporter algorithm (RA) was proposed to determine the hot spots in metabolic networks, around which transcriptional regulation is focused owing to a disease or a genetic perturbation. Using a z-score-based scoring scheme, RA calculates the average statistical change in the expression levels of genes that are neighbors to a target metabolite in the metabolic network. The RA approach has been used in numerous studies to analyze cellular responses to the downstream genetic changes. In this article, we propose a mutual information-based multivariate reporter algorithm (MIRA) with the goal of eliminating the following problems in detecting reporter metabolites: (i) conventional statistical methods suffer from small sample sizes, (ii) as z-score ranges from minus to plus infinity, calculating average scores can lead to canceling out opposite effects and (iii) analyzing genes one by one, then aggregating results can lead to information loss. MIRA is a multivariate and combinatorial algorithm that calculates the aggregate transcriptional response around a metabolite using mutual information. We show that MIRA's results are biologically sound, empirically significant and more reliable than RA.
  
  
  RESULTS
  We apply MIRA to gene expression analysis of six knockout strains of Escherichia coli and show that MIRA captures the underlying metabolic dynamics of the switch from aerobic to anaerobic respiration. We also apply MIRA to an Autism Spectrum Disorder gene expression dataset. Results indicate that MIRA reports metabolites that highly overlap with recently found metabolic biomarkers in the autism literature. Overall, MIRA is a promising algorithm for detecting metabolic drug targets and understanding the relation between gene expression and metabolic activity.
  
  
  METHODS
  The code is implemented in C# language using .NET framework. Project is available upon request.
authors.affiliation: Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213 and Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, OH, USA 44106.
authors.forename: A Ercument
authors.initials: AE
authors.lastname: Cicek
authors.affiliation: Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213 and Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, OH, USA 44106.
authors.forename: Kathryn
authors.initials: K
authors.lastname: Roeder
authors.affiliation: Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213 and Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, OH, USA 44106.
authors.forename: Gultekin
authors.initials: G
authors.lastname: Ozsoyoglu
mesh_terms: D000465:Algorithms; D002648:Child; D002659:Child Development Disorders, Pervasive / Q000235:genetics / Q000378:metabolism; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D014158:Transcription, Genetic