1 / 21

Modeling and Simulating the Biological Pathway - case study -

Systems Biology Presentation. Modeling and Simulating the Biological Pathway - case study -. 第六組. Outline. Information gathering KEGG web service Ontology-based knowledge extraction Modeling environment Stoichiometric matrix Simulating environment Kinetics model Results and discussion.

val
Télécharger la présentation

Modeling and Simulating the Biological Pathway - case study -

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Systems Biology Presentation Modeling and Simulating the Biological Pathway- case study - 第六組

  2. Outline • Information gathering • KEGG web service • Ontology-based knowledge extraction • Modeling environment • Stoichiometric matrix • Simulating environment • Kinetics model • Results and discussion

  3. Pathway modeling agent Quantitative Simulation agent Bio-ontology & thesauri Model the pathway according to the promoter and molecular interactions Extract the molecular interactions and Chemical coefficients According to the Quality of Service and use’s goal to make the Biological plan Measure the chemical valuesby calculating the coefficients and pathway structure Literature extraction agent Workflow Planning agent Information wrapper Agent Information Gathering Web service Matchmaker (Broker Agent) Connect the service Database (KEGG, NCBI, Micro-array)、Bioinformatics Toolkit Global agent Architecture

  4. Architecture Kinetics database Get the kinetics coefficient from the experiments or literature Dynamic model Biological database Get the gene name, chemical compound and its physical information chemical database Get the chemical reaction Pathway database Get the biological pathway Stoichiometric model

  5. Benefit • Relational database system for managing kinetic data, chemical structure, pathway, chemical reaction • Provide stoichiometric information and parameters for kinetics equations to the model

  6. Web service-KEGG • KEGG API provides valuable means for accessing the KEGG system, such as for searching and computing biochemical pathways in cellular processes or analyzing the universe of genes in the completely sequenced genomes. • get_genes_by_pathway, • get_enzymes_by_pathway, • get_compounds_by_pathway, • get_reactions_by_pathway • ….etc • The users can access the KEGG API server by the SOAP technology over the HTTP protocol. The SOAP server also comes with the WSDL, which makes it easy to build a client library for a specific computer language.

  7. Ontology-based knowledge extraction • Concentration (mM), Volume (m),Flux (mM/s),PH,…etc • C-mol/min*L-cytosol • where C-mol is a mol of carbon and L-cytosol is a litre of cytosolic water • Sentence: • The pyruvate concentration that is required to accommodate a flux of 0.48 C-mol/min*L-cytosol, is 8 mM.

  8. Glycolysis

  9. Enzyme Kinetics • One substrate, one product reversible Michaelis-Menten kinetics was used to describe the enzymes PGI, PGM and ENO: where a and p represent the concentrations of the corresponding substrate and product, respectively. G is the mass-action ratio, p/a, Keq is the equilibrium constant, peq/aeq. Ka and Kp are the Michaelis-Menten constants for a and p. • Reversible Michaelis-Menten kinetics for two noncompeting substrate-product couples was used for HK, GraPDH, PGK and PYK: where a and b represent the concentrations of the substrates and p and q the concentrations of the products.

  10. Example DEMO

  11. Results

  12. Results (II)

  13. Future work • The combination of flux based static modeling with dynamic modeling based on kinetic equations • The model can be initiated as a stoichiometric model that is gradually converted into a dynamic model by adding dynamic equations. • Flux distribution analysis as a method for calculating each flux in stoichiometric models. • Substances at the boundary between dynamic models and stoichiometric model are influenced by both flux.

  14. Systems Biology Presentation Biosynthesis of Ethanolby E.coli

  15. Glycolysis

  16. Problem

  17. Pyruvate Decarboxylase • Reference • Saccharomyces cerevisiaepyruvate decarboxylasePDC1 has been isolated and fused to the indicator gene Escherichia coli lacZ. • T7 RNA polymerase promoter phi 10, that a cloned Saccharomyces cerevisiaepyruvate decarboxylase gene ( pdc1) can be expressed in Escherichia coli.

  18. Alcohol Dehydrogenase • Only strain K-12 definitely have alcohol dehydrogenase (adhP) • alcohol dehydrogenase (EC 1.1.1.1)

  19. Escherichia coli strain KO11 • E. coli KO11 and three ethanol-resistant mutants of this strain (LY01-LY03). • Strain KO11 is an ethanol-producing recombinant in which the • Z. mobilis genes for ethanol production (pdc, adhB) • and the cat gene (acetyltransferase) have been integrated into the E. coli B chromosom.

  20. Two strain used for this! • Strain K-12 • definitely have alcohol dehydrogenase (adhP) • Saccharomyces cerevisiaepyruvate decarboxylase (pdc1)recombinant • Escherichia coli strain KO11 • Z. mobilis genes for ethanol production (pdc, adhB) recombinant

  21. Reference • Karp, P.D.; Riley, M.; Saier, M.; Paulsen, I.T.; Collado-Vides, J.; Paley, S.; Pellegrini-Toole, A.; Bonavides, C.; Gama-Castro, S. The Ecocyc database. Nucleic Acids Res. 2002, 30,56-58 • Yomano, L.P.; York, S.W.; Ingram, L.O. Journal of Industrial Microbiology & Biotechnology. Isolation and characterization of ethanol-tolerant mutants of Escherichia coli KO11 for fuel ethanol production. 1998, 20, 132-138

More Related