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An Information Integration System for Automated Reconstruction and Dynamical Modeling of Gene Regulatory Networks An Information Integration System for Automated Reconstruction and Dynamical Modeling of Gene Regulatory Networks Michael Baitaluk 1, Amarnath Gupta 1, Shubhada Godbole 2, Xufei Qian 1, Vijay Chickarmane 2, and Animesh Ray 2 1 San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92039, 2 Keck Graduate Institute, Claremont, CA 91711 I. AIMS This poster describes methods for data integration, automated retrieval and dynamic simulation of gene/protein interaction networks as a tool to understand cellular regulation from a systems biology perspective. Three questions are addressed: 1. How to develop a truly integrated computational platform that allows intelligent retrieval of any genomic-scale or single-gene information. 2. How to automate the retrieval of cellular interaction networks by data integration approaches. 3. How to derive dynamic simulations from the static networks? II. RATIONALE Large Interaction datasets Internal Data Model Basics: 1. 2. 3. Curated data from literature Gene expression datasets III. SIMULATION OF EARLY MEIOSIS NETWORK Primary Nodes (States): All molecules (e. g. DNAs, RNAs, proteins), small molecules (e. g. ions, ATP, lipids), physical events (heat, radiation, stress). Connector Nodes (Transitions): All types of interactions (binding, chemical reaction, expression, etc. ). Graph Nodes (hypernodes): Complex objects (protein complexes, pathways, cell processes) that might contain graphs Vegetative Cell (2 N) Meiosis S. cerevisiae Tetrad 4 x (1 N) Cellular compartment: part of the model. Warehouse of data Network query Annotation, Ontology Static interaction network model Ime 1 p NDT 80 Ndt 80 p Transcriptional activation of IME 1 gene is the trigger of sporulation/meiosis. Ime 1 p is a transcription factor that activates Ime 2 p, a kinase, which allows autoactivation of Ime 1 p, and together activates a second transcription factor gene, NDT 80 Data Model: Path. Sys’s data model is relevant to Hybrid Functional Petri Net (HFPN) data model in the way that HFPN modeling has places (states) and transitions as nodes in the interaction graph and certain concepts such as node attributes and abstractions may be defined. One could treat our primary and connector nodes together with their attributes as a “state” and transition specifiers respectively. Path. Sys query: Retrieved a network of early meiosis genes: Dynamical model Petri Net (A state-transition model), Ordinary Differential Equations Biological. Networks architecture Developed Software: GUI Level Querying Templates Path. Sys: Path. Sys is a data integration platform that provides dynamic integration over diverse databases containing genetic, protein-protein and DNA-protein interactions, protein localization, and microarray data available through published literature. Querying Wizard DAG Querying Window Ontologies Annotation Server Gene Ontology Kegg Ontology … E External DB Sources (SOAP) X T Biological. Networks: provides access to Path. Sys through a novel query engine that stores and queries directed acyclic graphs such as ontologies and taxonomies in addition to molecular network interactions, allowing easy retrieval and visualization of complex biological networks. http: //brak. sdsc. edu/pub/Biological. Networks E KEGG Ternary Relations Schema … Generic Schema Graph Representation … Graph Editing and Selection R N A Graph Editing and Selection Layout Engine L Visual Mapper D DB Interfaces B DB Interfaces Petri Net-equivalent of Ime 1 network Petri. Net GUI Petri Net simulation results Project Interfaces S O Graph Editing and Selection U Path. Sys system architecture R Project Interfaces C Simulation Engine User Privileges E Update/Editing Graph, DB S Layout Engine DB Interfaces Visual Mapper … Graph Engine Middle Tier Data. Base Tier DAG Engine Cytoscape GRN Schema (Ternary) Gene Ontology Schema Petri. Net Engine Biological Networks GRN+GO+… Generic Schema BIRN Ontologies From static networks to dynamic simulation: the method a) GO function libraries (for biological/molecular processes) b) Parameters and initial conditions PN-software Static network Path. Sys: The system is equipped with two novel query engines with built in SQLlike querying language, allowing paths, trees, graphs operations. The server provides querying services and an information management framework over Path. Sys. The system integrates over 20 curated and publicly contributed data sources for the budding yeast (S. cerevisiae) and fly (D. melanogaster). Path. Sys is capable of generating and simulating dynamical gene regulatory models from molecular interaction graphs based on Hybrid Functional Petri Nets and XML technology, allowing the user to simulate and predict gene expression dynamics. SAN DIEGO SUPERCOMPUTER CENTER Petri Net Model Query Path. Sys Experiment In the above diagrams, we show the query for a particular set of genes, allows us to acquire a network, for which a Petri Net simulation can be done. Microarray expression data (fitted here, by polynomials) suggest that IME 1 m. RNA may indeed oscillate over time. [Data from: Primig et al. , Nat Genet. 26: 415 -23 (2000)] Summary We describe Path. Sys – database system which integrates over 20 curated and publicly contributed data surces for the budding yeast (S. cerevisiae) and fly (D. melanogaster) and Biological. Networks- a bioinformatics software platform for visualizing molecular interaction networks, integrating these interactions with other graph-structured data such as ontologies (e. g. gene ontology) and taxonomies (like the enzyme classification system and functional classification of yeast proteins), integrating interactions with gene expression data and other state data, querying all types of data to extract biologically meaningful relations, pathway modeling and simulation. Model describes automated reconstruction and dynamic simulation of gene regulatory networks. This is achieved by first querying a database to obtain a network, using a Petri Net to create a logical statetransition flow chart and then using this to construct a reaction scheme, which is described by a set of Pure Dynamical Model Petri Nets: Petri Nets nets are a promising tool for modeling systems with concurrency and resource sharing. In addition they can coupled ODE’s. easily represent hybrid systems comprising of both continuous and discrete dynamics. For data poor biological systems petri nets are a useful descriptive intermediate and as additional data becomes available they can be converted to full continuous models which are then amenable to a wide range and analytical and numerical techniques. Acknowledgements: We thank Herbert Sauro, Kiri. Lynn Svay, Aditya Bagchi. This project is supported by National Scientific Foundation grants EIA -0205061 and EIA-0130059.