# Presentation: 353480

## A Database Centric Approach for Analyzing the Influence of Environmental Perturbations on Phenotypic Traits

- Jeff Blanchard
- National Center for Genome Resources
- (A private non-profit research and community resource development institute)
- Santa Fe, NM

## Outline - Two approaches to integration 

- Pathways as a nucleating area for data integration
- Need semantic integration before IT solutions
- Provide a context to explore microarray data
- Provide a context to explain phenotypes
- Provide a framework to build predictive models upon
- A loose integration framework
- No single group will be dominate in the near future
- Allow passing of objects between applications
- Simple data model
- Synchronize objects in different applications

## Goals of System Biology

- To effectively utilize genomic data from different sources to discover functions for genes and proteins.
- To uncover rules governing and constraining biological systems.
- To enable predictive models of biological systems
- Graphical models
- Probabilistic models
- Mathematical models

**Notes:**
Many toxicologists take a systems approaches

## Modeling approaches: three parts to a predictive mathematical model

- Set of components and proposed interactions
- Parameter values (concentrations and rate constants
- Numerical methods (computational algorithms)

## The role of graphical models in biology

- Capture qualitative relationships between variables
- Represent a conceptual understanding of a biological system
- Intuitive
- Parameter-free
- Represent areas of complete understanding and imperfect knowledge within the same model

## Ligand

- MEKK1-3

- MEK
- 1,2

- P

- Erk

- P

- PDK1

- P

- P

- P

- SRE

- E-Box

- (TCDD, BaP, 7,8-BPQ)

- AhR

- 90

- 90

- 90

- 90

- ARNT

- IkBa

- DREs

- P

- Caspase

**TGFa**

- release/transcriptional
- activation of NFkB

- SEK

- P

- JNK

- P

- MEK
- 3,6

- P

- p38

- P

- CRE

- AP-1

- P

- ATF-2

- P

- NFkB

- NFkB

- kBRE

- c-myc

- Elk

- Jun

- AhR

- AhR

***Nucleus***

***Cell Membrane***

***Cytoplasm***

- Courtesy of Scott Burchiel

- Akt

## Courtesy of

- Nora Perrone-Bizzozero

## Using information networks as an interpretive layer between phenotypes and the underlying genes, proteins and metabolites  

- Highly connected genes are often critical in the onset of cancer and metabolic diseases. However, drug treatment targeting less connected genes will have fewer side effects.

- Highlight and investigate expression data

- DNA chip/microarray

- Jeong et al. 2001

## Unification of Pathway/Network Data

- Cellular Complexes
- Metabolic Pathways
- Signaling Pathways
- Protein-Protein Interaction Networks
- Regulatory Networks

## Representation of Cellular Networks

- Cellular location
- Organismal location
- Developmental State
- Environmental conditions
- Genotype
- Aging
- Large gene families (apparent duplicate genes)

- Need Flexible Graphical Models

## Petri Net representation of control systems

- Petri Net graphs are based on two types of nodes:
-  1.  Places: interpreted as biochemical entities containing state attributes
- 2.  Transitions:  interpreted as rules, processes or actions.  We also use this node to represent associations.
- Petri net rules fire (occur) only if a logical precondition is satisfied.
- Can be used to represent kinetics!

## Petri Nets are a unifying abstraction for systems biology

- A

- C

- D

- B

- Biochemistry

- Control System

- Petri Net

- Computer
- Code

- +

- -

- +

- +

- -

- A

- B

- C

- p2

- p1

- isomorphism

- isomorphism

- isomorphism

- Courtesy of John Ambrosiano

## Mathematical and computation exploration of “functional modules - Petri Nets can be used to represent biological modules and computational modules.  

- Function A

- Function B

- Program A

- Program B

- Courtesy of John Ambrosiano

## PathDB - A database project for the building and analyzing cellular networks  

- Public
- Information Resouces
- (GenBank)

- Private Databases (excel files, LIMS)

- Specialized
- Research
- Databases
- (PathDB)

**Notes:**
PathDB can be more flexible and have more freedom in developing new tools because the public archives exist.  

## PathDB Data Model

-   Post-translational modifications
-   Activation and inhibition
-     information
-   Protein-molecule interactions
-      (metabolism)
-   Protein-protein interactions
-   Genetic interactions

-   Protein complexes
-   Metabolic and signaling pathways
-   Complex network models
-   Compound structures
-   Phenotype categories
-   Kinetic parameters

- PathDB 2.0 contains a curated relational database of information on interactions between cellular building blocks (i.e. genes, proteins and metabolites). We also curate data describing attributes of the objects and transitions.  Here is a subset of what we can store

## Examples of large scale data sets in PathDB 2.0

- Protein-Protein (Two-hybrid)
- Protein-Protein Complex (TAP)
- Protein-DNA (DNA microarray)
- Protein-lipid (protein microarray)
- Synthetic Lethal
- Computational predictions
- Other Databases

- (5390) Ito et al. 2001. PNAS
- (1572) Uetz et al. 2000. Nature
- (3397) Gavin et al. 2002. Nature
- (4190) Ho et al. 2002. Nature
- (1248) Simon et al. 2001. Cell
- (87) Ren et al. 2000. Science
- (433) Zhu et al. 2001. Science
- (291) Tong et al. 2001. Science
- (13166) Mellor et al. 2002. NAR
- MIPS
- KEGG

## Ontology: Bind genes to hierarchies

## PathDB data model

**Mediator**

**Substrate**

**Product**

**Biochemical**
**Entity**

**Step**

***Transition***
***of Entities***

## Building a network and expanding your neighborhood

## Starting from a GO term

## Transformation of data types

## Transform to steps to build a graph

## Graph is constructed from steps in QueryTool

## Proteins annotated as being involved in mating type synchronized with viewer

## Get Top1 interactions

## Add Top1 interactions to existing graph

## A complex model from classical pathways

## Find routes between two entities

## ISYS provides a solution to the integration problem

- ISYS

- NCGR

- Stanford

- Berkeley

- Wash. U

- Manchester

- Web

- Other third
- party software

- Your organization’s tools

- PathDB
- CMD Tool
- Table Viewer
- Sequence Viewer
- Similarity Search
- Viewer

- X-Cluster

- GO Browser

- ATV

- MaxD

- Entrez - NCBI
- BLAST - NCBI
- GeneScan - MIT
- Google
- TAIR - NCGR
- GeneX - NCGR

## Isys enables

- Passing of genes, proteins and compounds between applications
- Developer control in level of integrations and naming objects
- Synchronization of Java applications
- Automatically loading of data to and from web pages (NCBI, TAIR, etc)

## Isys client

## Use PathDB to find interactions among genes in a cluster

## Use PathDB to find interactions among genes in a cluster

## Overlay of cell cycle defect mutants on genes sent to PathDB from a cluster

## Overlay of cell cycle defect mutants on genes sent to PathDB from a cluster

## Using the Data File loader any set of related objects such as SNPs or metabolic profiling data can be used to query PathDB

## Using the Data File loader any set of related objects such as SNPs or metabolic profiling data can be used to query PathDB

## Using the Data File loader any set of related objects such as SNPs or metabolic profiling data can be used to query PathDB

## Model Scoring Concepts

-   Competing models are constructed.
-   Models are scored against the experimental data.
-   Score describes the probability of observing the experimental data in the context of the models.
-   Thus we are able to evaluate competing hypotheses.
-   Automated searches can be designed for high scoring models.
-   When many models have the same score the features the share in common can be extracted.
-   The shared features of high scoring models represent relationships that are likely to be biologically important.

## PathDB for Mammals

- Import mouse, rat and human nomenclature and ontologies.
- Import mappings of genes to human diseases and disorders
- Import KEGG and map to gene names.
- Import other electronic sources.
- Start literature-based neuroscience curation project.
- All data will be made available under an Open Source license (LGPL).
- Currently no curation possible into NCGR public database.
- However, can add data into a local instance of the database.

## Bringing biology and chemistry together

- Conceptually and technically this is not a difficult thing.
- Data is the key to analyses and information flow between domains.
- However, most chemistry-oriented databases are proprietary and cost a lot of money.
- Will biology become like chemistry?
- Toxicogenomics could help bring biology and chemistry together.

## Acknowledgements

- Robert Kuffner
- Peter Steadman
- David Bulmore
- Lonnie Montoya
- Todd Peterson
- Andrew Farmer
- Adam Siepel
- Damian Gessler

-   Michael Gonzales
-   Danuta Wlodek
-   Rachel Jankowitz
-   Jessica Boinoff
-   Army Research Office
-   NCGR internal funding

## PathDB/ISYS

- _[www.ncgr.org/pathdb](http://www.ncgr.org/pathdb)_
- Use client over the web
- Have a set of CDs available for local installations (Oracle or Sybase)
- Free for non-commercial users
- Free 60 day evaluation for commercial users
- Stand alone MySQL and Postgres available
- ISYS is being used as part of the open source Moby project
- We are interested in providing alternative open source clients to the database.