title: Conserved and differential gene interactions in dynamical biological systems.
abstract: BACKGROUND
  While biological systems operated from a common genome can be conserved in various ways, they can also manifest highly diverse dynamics and functions. This is because the same set of genes can interact differentially across specific molecular contexts. For example, differential gene interactions give rise to various stages of morphogenesis during cerebellar development. However, after over a decade of efforts toward reverse engineering biological networks from high-throughput omic data, gene networks of most organisms remain sketchy. This hindrance has motivated us to develop comparative modeling to highlight conserved and differential gene interactions across experimental conditions, without reconstructing complete gene networks first.
  
  
  RESULTS
  We established a comparative dynamical system modeling (CDSM) approach to identify conserved and differential interactions across molecular contexts. In CDSM, interactions are represented by ordinary differential equations and compared across conditions through statistical heterogeneity and homogeneity tests. CDSM demonstrated a consistent superiority over differential correlation and reconstruct-then-compare in simulation studies. We exploited CDSM to elucidate gene interactions important for cellular processes poorly understood during mouse cerebellar development. We generated hypotheses on 66 differential genetic interactions involved in expansion of the external granule layer. These interactions are implicated in cell cycle, differentiation, apoptosis and morphogenesis. Additional 1639 differential interactions among gene clusters were also identified when we compared gene interactions during the presence of Rhombic lip versus the presence of distinct internal granule layer. Moreover, compared with differential correlation and reconstruct-then-compare, CDSM makes fewer assumptions on data and thus is applicable to a wider range of biological assays.
  
  
  BACKGROUND
  Source code in C++ and R is available for non-commercial organizations upon request from the corresponding author. The cerebellum gene expression dataset used in this article is available upon request from the Goldowitz lab (dang@cmmt.ubc.ca, http://grits.dglab.org/).
  
  
  BACKGROUND
  joemsong@cs.nmsu.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA.
authors.forename: Zhengyu
authors.initials: Z
authors.lastname: Ouyang
authors.forename: Mingzhou
authors.initials: M
authors.lastname: Song
authors.forename: Robert
authors.initials: R
authors.lastname: Güth
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Ha
authors.forename: Matt
authors.initials: M
authors.lastname: Larouche
authors.forename: Dan
authors.initials: D
authors.lastname: Goldowitz
mesh_terms: D000818:Animals; D002531:Cerebellum / Q000196:embryology / Q000254:growth & development / Q000378:metabolism; D015870:Gene Expression; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D051379:Mice; D008954:Models, Biological