Of MODS and Models: Predicting and Validating Phenotypes from - - PowerPoint PPT Presentation

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Of MODS and Models: Predicting and Validating Phenotypes from - - PowerPoint PPT Presentation

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Of MODS and Models: Predicting and Validating Phenotypes from Pathway Tools Metabolic Models Peter D. Karp Bioinformatics Research Group SRI International pkarp@ai.sri.com SRI International Bioinformatics 1 Overview l Pathway

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SRI International Bioinformatics

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  • Of MODS and Models: Predicting

and Validating Phenotypes from Pathway Tools Metabolic Models

Peter D. Karp Bioinformatics Research Group SRI International pkarp@ai.sri.com

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SRI International Bioinformatics

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  • Overview

l Pathway Tools approach to metabolic modeling l What’s coming up for Pathway Tools

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SRI International Bioinformatics

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  • Literate Modeling
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  • I believe that the time is ripe for significantly better

documentation of programs, and that we can best achieve this by considering programs to be works of

  • literature. Hence, my title: “Literate Programming.”

Let us change our traditional attitude to the construction

  • f programs: Instead of imagining that our main task is

to instruct a computer what to do, let us concentrate rather on explaining to humans what we want the computer to do.

Donald E. Knuth, 1984

  • Literate Programming
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  • Literate Programming

l Combined a programming language with a

document preparation language

l The resulting hyper-document integrated a

program with well-styled documentation

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  • Literate Modeling

l Collaboration around models will be impossible if

models are as inscrutable as most software

l => Models and model results must be

 Readily understandable  Web browsable  Connected to the genome  Connected to pathways  Connected to the regulatory network  Connected to multiple online databases  Queryable  Accessible through graphical visualizations  Beautiful

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  • “The Database is the Model”

l Marriage of models and databases l Generate steady state metabolic flux models

directly from Pathway/Genome Databases such as EcoCyc

 To update the model, update the database  To browse the model, browse the database  To view model results, use database-generated viewers

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  • From MODS to Models

l The evolution of Model Organism Databases l SGD, MGI, FlyBase, WormBase, etc. l EcoCyc as MMOD

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  • Perspective 1:

EcoCyc as Textual Review Article

l All gene products for which experimental literature

exists are curated with a minireview summary

 3,730 gene products contain summaries  Summaries cover function, interactions, mutant phenotypes,

crystal structures, regulation, and more

l Additional summaries found in pages for operons,

pathways

l EcoCyc data derived from 24,000 publications

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  • Perspective 2: EcoCyc as

Computational Symbolic Theory

l Highly structured, high-fidelity knowledge

representation provides computable information

l Each molecular species defined as a DB object

 Genes, proteins, small molecules

l Each molecular interaction defined as a DB object

 Metabolic and transport reactions  Regulation of enzyme activity, gene expression

l 220 database fields capture object properties and

relationships

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  • Perspective 3:

EcoCyc as Predictive Metabolic Model

l A steady-state quantitative model of E. coli

metabolism can be generated from EcoCyc

l Predicts phenotypes of E. coli knock-outs, and

growth/no-growth of E. coli on different nutrients

l Model is updated on each EcoCyc release l Serves as a quality check on the EcoCyc data

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  • Two Paradigms of Flux-Balance

Modeling

l FBA models as spreadsheets / SBML l FBA models derived from MODs

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  • Approach: FBA Model as a Database

l Store and update metabolic model within Pathway Tools

PGDB

l Export to constraint solver for model execution l Close coupling to genome and regulatory information l Extensive PTools schema

 Associate a wealth of information with each model  Unique identifiers for each component of the model

l Extensive query and visualization tools

 Metabolites, reactions, pathways, growth media  Visualize reaction flux and omics data using overviews

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  • FBA Model as a Database

l Also store within a PGDB the growth observation

data needed to validate and refine a PGDB

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  • Curation is Critical to Systems Biology

l Common curation effort for MOD and systems-biology

models

l Biological models undergo long periods of updating and

refinement

 New information from literature  To improve consistency of predictions with experimental data

l Methodologies from MODs can benefit systems-biology

models

 Evidence codes  Mini-review summaries  Literature citations

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  • Pathway Tools Approach to

Metabolic Modeling

l Power tools to accelerate modeling l Debug/validate model using Pathway Tools

 Multiple gap filling  Dead-end metabolite analysis  Reaction balance checking

l Modeling support – ptools-support@ai.sri.com

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  • Pathway Tools Software

Pathway/Genome Editors Pathway/Genome Database PathoLogic Annotated Genome Pathway/Genome Navigator Briefings in Bioinformatics 11:40-79 2010

+

MetaFlux

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  • SRI Modeling Projects

l EcoCyc model for E. coli l HumanCyc model for H. sapiens l YeastCyc model for S. cerevisiae

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  • Recent Pathway Tools Enhancements

l Version 16.5 l Save display state

 File -> Save Display State to File

l Atom mappings l Chemical radicals l EC number changes l Web Groups enhancements

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  • Coming Soon

l Version 17.0 in late March l Pathway prediction

 Pathway abundance score for metagenomic pathway

prediction

 Improvements to enzyme name matcher

l Pathway search tool for metabolic engineering l Web omics pop-ups l Groups improvements

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  • Coming Soon

l Version 17.0 l Internals

 New faster Web image generation  Web image persistence for better caching  New installer  Relational DBMS performance improvements

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  • Coming

l Better handling of compartments and cell types l Modeling improvements

 Automatically run model across many growth conditions and

knock-outs

 Hypothesize model changes to rectify prediction errors  Expanded gap filling  FBA for microbial communities  FBA for eukaryotes

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  • Coming

l Prediction of alternative growth media for an

  • rganism from its PGDB

 Method predicts 787 alternative anaerobic media for E. coli

 72.5% accuracy for 91 media

 Automatically partitions nutrients into equivalence classes  Algorithm starts with all transportable compounds and

exhaustively considers all combinations of nutrients

 Can take months to run for E. coli

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  • Coming

l Redesign / modernize Navigator interface l Add sequence operations l Performance / scalability improvements

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  • Minimal Information about a PM Expt

l markus.goeker@dsmz.de