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Computer modeling of cellular processes

Computer modeling of cellular processes. Glycolysis: a few reactions By Raquell M. Holmes, Ph.D. Boston University. Computational Biology. Computational Biology  Bioinformatics More than sequences, database searches, and statistics. A part of Computational Science

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Computer modeling of cellular processes

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  1. Computer modeling of cellular processes Glycolysis: a few reactions By Raquell M. Holmes, Ph.D. Boston University

  2. Computational Biology • Computational Biology Bioinformatics • More than sequences, database searches, and statistics. • A part of Computational Science • Using mathematical modeling, simulation and visualization • Complementing theory and experiment

  3. Today’s journey • Bioinformatics • From protein sequence • Metabolic pathway databases • Metabolic behaviors • Computer modeling glycolysis

  4. Pathway for discussion Karp: Cell and Molecular Biology Textbook

  5. Bioinformatics Common starting point

  6. Many types of Protein Databases • Sequence (103): • publication, organism, sequence type, function • Protein properties (80): • Motifs, active sites, individual families, localization • Structure (15) • Resolution, experiment type, type of chain, space group. Nucl. Ac. Res. 32, D3 2004

  7. Generic Protein Seq. Record • Sequence: hexokinase • E.C. #: 2.7.1.1 • Publication: Stachelek et al 1986 • Organism: Yeast • Function: main glucose phosphorylating enzyme • Links: other databases or tools • Pre-determined Properties: • families, folds… • Swissprot example for enzyme.

  8. What we learn from protein sequence? • Similarity searches : • Homology with other proteins • Conserved domains • Active sites • How do we find the related pathway? • Are there metabolism databases?

  9. Databases on Molecular Networks Metabolic Pathways from NAR (5): • EcoCyc:http://ecocyc.PangeaSystems.com/ecocyc/ • Began with E. coli Genes and Metabolism • BioCyc includes additional genomes • KEGG: http://www.genome.ad.jp/kegg/ • Encyclopedia of genes and genomes • Others: WIT2, PathDB, UMBDD

  10. What is a metabolic pathway? Contains a series of reactions. Reactions: Substrate, product (metabolites), enzyme, co-factors. • Metabolism pathway databases • Search by name or sequence • Pathway, Compounds, Reactions, Genes

  11. Example search results KEGG: search by compound or enzyme by key word. Glucose: 82 hits 1.cpd:C00029 UDPglucose; UDP-D-glucose; UDP-glucose; Uridine diophosphate glucose 2. cpd:C00031 D-Glucose; Grape sugar; Dextrose 3. cpd:C00092 D-Glucose 6-phosphate; Glucose 6-phosphate; Robison ester ……. 79. cpd:C11911 dTDP-D-desosamine; dTDP-3-dimethylamino-3,4,6-trideoxy-D-glucose 80. cpd:C11915 dTDP-3-methyl-4-oxo-2,6-dideoxy-L-glucose 81. cpd:C11922 dTDP-4-oxo-2,6-dideoxy-D-glucose 82. cpd:C11925 dTDP-3-amino-3,6-dideoxy-D-glucose Grape sugar: FORMULA C6H1206 NAME D-Glucose grape sugar Dextrose List of reactions involving compound List of enzymes acting on compound

  12. Home page • Navigation bar • List of categories • Compounds • List of results • Oligosaccharides • Polysaccharides • Fructans • Glycogens • Large-branched-glucans • Long-linear-glucans • Pectin • Short-glucans • Starch • All-Carbohydrates • a sugar • a sugar phosphate • a sugar-1-phosphate • Aldonic-Acids • Carbohydrate-Derivatives • Carbohydrates • Disaccharides

  13. KEGG: E.coli EcoCyc: E.Coli

  14. Summary I • Protein ->enzyme->pathway database • Pathway Database Content: • Species specific (BioCyc), general (KEGG) • Known and proposed enzymes, co-factors, metabolites. • Searches return similar results (compounds, reactions…) with different appearance.

  15. From Data to Dynamics • Static Data • What is the behavior of the pathway? • Expression data • Dynamics

  16. Expression data • KEGG: • retrievable expression data sets • EcoCyc: • input expression data to view in relation to metabolic data. • Expression data is one way of viewing the behavior of a system.

  17. Submitted by: Hirotada MORI Organism: E.coli Raw data

  18. MicroArray Data: fold changes in expression Hypothetical data Changes in gene expression: Single time point Various conditions

  19. Dynamics:Answering different questions • If glucose concentration is 300 mM outside of the cell, • How quickly is glucose converted to glucose 6-phosphate? • How does the concentration of glucose 6 phosphate change over time?

  20. Yeast Glycolysis suspended Liquid, flasks, temp, ph Experiment: Sample media or characterize cell content over time Repeat under different conditions immobile Agar, temp, ph

  21. Results may look like

  22. Computer Modeling • Method for analyzing what we know about a biological process • Used to describe mechanisms behind changes • Determines what can be seen or predicted

  23. Walking through a Computational Model • Concept Map • Factors and relationships between factors • Describe relationships mathematically • Solve equations: using computer tools • View and interpret results

  24. Designing a dynamic experiment • What components are involved? • Glucose, glucose 6 phosphate, fructose 6 phosphate… • What chemical reactions involved? • Transport, chemical conversions…

  25. Glycolysis: Concept Map Often drawings, schematics or chemical reactions v1 v2 v4 v3 Fructose 6-phosphate Glucose 6-phosphate Glucose Internal Glucose Ethanol Cell

  26. v1 v2 Glucose 6-phosphate Fructose 6-phosphate Examples of relationships Internal Glucose [Glucose 6-phosphate] is determined by increase from Glucose conversion and decrease by conversion to Fructose 6-phosphate Amount of glucose 6 phosphate= amount produced- amount converted

  27. Designing a dynamic experiment • Describing relationship mathematically

  28. Relationship in terms of rates of change The rate of change of Glucose-6-phosphate (S2) is the rate of Glucose conversion (v1) minus the rate of conversion (v2) to Fructose-6-phosphate.

  29. Designing a dynamic experiment • Describing relationship mathematically • What rate laws are known to describe the enzymatic reaction? • Types of rate laws/kinetic models • Constant, mass action, michaelis menten…

  30. Glucose transport (v1) Facilitated diffusion Simplify

  31. Substrates Glucose: Glucose- 6-phosphate: Rate constants Enzyme1: Enzyme2: Rate Equations • Mass action kinetics are used here to describe the enzymatic reactions. • This is a simplification of the enzyme kinetics for this example.

  32. Initial conditions • Concentrations of components • External glucose (i.e. 300mM) • Enzymatic rates • Rate constant k (i.e. 50mM/min) • Michaelis-Menten constants, Hill Coefficients

  33. v1 v2 Internal Glucose Glucose 6-phosphate Fructose 6-phosphate The model Initial conditions Rate equations Ordinary differential equation S1=300mM k1=55mM/min k2=9.8mM/min

  34. Walking through a Computational Model • Concept Map • Factors and relationships between factors • Describe relationships mathematically • Solve equations: using computer tools • View and interpret results

  35. General Stella Install Mac or PC Excel Install Mac or PC Customized GEPASI Install Mac or PC Virtual Cell Browser: Java Mac or PC Concept mapping and system dynamics (changes over time). Discrete events, algebraic equations Biochemical kinetics and kinetic analyses. Icon mapping, dynamics and space Some Available Tools

  36. Stella Concept Map Rules as math Initial Conditions

  37. GEPASI Chemical Equations Math http://www.gepasi.org/gepasi.html

  38. Online Glycolysis models Concept map Math-rule Initial conditions http://jjj.biochem.sun.ac.za/database/

  39. Results Fructose 6-phosphate Glucose 6 phosphate

  40. Conclusions… • Model based discoveries in glycolysis:: • Oscillations in concentrations of some but not all metabolites. • Control of process distributed throughout pathway • Development of theoretical models • Method integrates knowledge of pathway factors to examine pathway behaviors.

  41. Examples of other models Calcium dynamics: Wave patterns in neuronal cells Virtual Cell Receptor signaling: IgE triggering mast cells Personal computer codes Cell cycle regulation: length of wee 1 mutant cell divisions Matlab, Mathematica, personal computer codes

  42. Calcium dynamics in neuroblastoma cells

  43. Modeling • Requires formalizing assumptions • Rate equations • Inclusion or exclusion from model • Worst case scenario • See what you believe • Best case scenario • See something unexplainable • Create new laboratory experiments

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