title: Sequential logic model deciphers dynamic transcriptional control of gene expressions.
abstract: BACKGROUND
  Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here.
  
  
  METHODS
  Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed.
  
  
  RESULTS
  SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin) during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage.
  
  
  CONCLUSIONS
  The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet providing rich biological insight. The demonstration of the efficacy of this approach in endo16 is a promising step for further application of the proposed method.
authors: Genome Institute of Singapore, Singapore, Singapore.
authors: Zhen Xuan
authors: ZX
authors: Yeo
authors: Sum Thai
authors: ST
authors: Wong
authors: Satya Nanda Vel
authors: SN
authors: Arjunan
authors: Vincent
authors: V
authors: Piras
authors: Masaru
authors: M
authors: Tomita
authors: Kumar
authors: K
authors: Selvarajoo
authors: Alessandro
authors: A
authors: Giuliani
authors: Masa
authors: M
authors: Tsuchiya
mesh_terms: D000818:Animals; D015815:Cell Adhesion Molecules / Q000235:genetics / Q000378:metabolism; D002454:Cell Differentiation; D004742:Enhancer Elements, Genetic; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D009024:Morphogenesis / Q000502:physiology; D016296:Mutagenesis; D012617:Sea Urchins / Q000502:physiology; D014158:Transcription, Genetic
chemical_list: D015815:Cell Adhesion Molecules; C061515:endo16 protein, sea urchin
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