# Presentation: 222550

## Enabling Biology to Become a Predictive Science 

- William D. Beavis, Geert Wenes, John Ambrosiano
- Computational Infrastructure for GTL Workshop
- 22 January 2002

**Notes:**
Thank-you
Co Authors

In 1903 Ernst Rutherford said that Biology is akin to postage stamp collecting.  As a physicist he was alluding to the lack of models and the ability to predict biological phenomena.

## Goal: P(Phenotype|Data,Model)

**Notes:**
Pred P  Need to consider diagnoses Predict skin cancer?
              Need to consider prediction of therapeutic treatments  both efficacy and side effects (unintended consequences)

Needs to fit the DOE  mission for OBER:  
ie  energy production  Are we looking at ways to add value to farmers that are dealing with commodities that are bringing in less money today than they did in 1980?
Carbon sequestration.  Can we engineer proteins that will deposit C into lignins that will end up in 2x4s and other long lasting materials or into  humic and folic acid in the soils through the roots of plants and soil micro organisms?
Can we engineer proteins that will Sequester heavy metals and radioactive isotopes and break these down?
Cetral Dogma

## Discovery-Driven Data, Models and Computing Resources  for Predicting the Phenotype.

**Discovery-Driven Data, Models and Computing Resources  for Predicting the Phenotype.**

**Notes:**
Central Dogma provides a frame workd to develop a two dimensional landscape

## Ancestry and Environment

**Data:**
- Pedigrees, Breeding Records, Progeny Performance Trials
- Family Histories, Clinical Records, Environmental Exposure
- 105 - 107  records per trait in geographically distributed, heterogenous repositories.
**.05 - .5
**Models:**
**y = X********  **+  **Z********  **+  ****
**Compute Resources:**
- For brute-force ML, RAM needs to accommodate data from multiple sources and a few million records.

**Notes:**
Maximum Liklihood 
Lack of Fit

## Bushels

- per acre

- Year

## Predictions from DNA information

**Data:**
- Physical Maps, Linkage Maps and SNPs
- Nucleotide Sequences
- 1.6 x 1010  sequences in a few geographically distributed repositories.
- Sequencing erros,**.01; annotation errors ?
**Models:**
**y = X********  **+  **Z********  **+ **W******** **+ ****
**Blast (seq****i****  ~ seq****j**** => function) ; *****s*****ij*****=***** ln(*****q*****ij*****/P*****i***** P*****j*****) / ***********u**
**Compute Resources:**
- 200K ESTs: 2 weeks, 16-node Linux cluster with 16 Gb RAM

## Weakness of sequence annotations:

- Computerized stamp collecting

## Predictions from DNA information

**Data:**
- Physical Maps, Linkage Maps and SNPs
- Nucleotide Sequences
- 1.6 x 1010  sequences in a few geographically distributed repositories.
- Sequencing erros,**.01; annotation errors ?
**Models:**
**y = X********  **+  **Z********  **+ **W******** **+ ****
- Blast (seqi  ~ seqj => function) ; *s*ij*=* ln(*q*ij*/P*i* P*j*) / ***u
**CASP (seq => structure => function)**
**Compute Resources:**
- Moderate sized clusters of commodity-grade processors.

**Notes:**
New Algorithms are being developed as a result of the biannual competition to predict protein structure
From these predicted structures it is possible to infer function from among a broad class of similar structures, but specific functions in the context of a specific biochemical pathway or network needs additional information.  

## Slide 9

**Notes:**
Note active sites and how they change with change of one amino acid

## Understanding the Mechanisms of Protein Folding

-  Will lead to an understanding of protein function in various cellular environments
**(Misfolding is known to occur and be responsible for serious diseases)**
- Resulting in development of predictable diagnostics, novel protein-based therapeutics, novel proteins for sequestering C, heavy metals, and radioactive isotopes.

## Understanding the Mechanisms of Protein Folding

- Experimental techniques are limited for relevant time scales, thus there is a need for simulation of
**folding kinetics**
**folding pathways**
**force-field assessments**
- Allen et al. 2001.  “Blue Gene: A vision for protein science using a petaflop supercomputer”. IBM Systems Journal 40:310-327.
- Levitt et al. 2002. “Modeling Across the Scales - Atoms to Organisms”  Mathematics and Molecular Biology VII.  Program in Mathematics and Molecular Biology. Santa Fe, N. Mex.

## Computing Needs for Mechanisms of Protein Folding

**Allen et al. 2001.  “Blue Gene: A vision for protein science using a petaflop supercomputer”**
**IBM Systems Journal 40:310-327.**

**Notes:**
If we assume the use of a petaflop/sec machine, then it would take 3 years to simulate 100 micoseconds.... A typical time frame for simulating the protein  folding pathways 

## Predictions from mRNA and proteomic arrays

**Data:**
- gene-chips, micro-arrays, bead-arrays (MPSS), proteomic arrays
- very sparse data, relative to biological time scales, snap shots of average transcription from pools of similarly treated cells
- 104 - 105  genes per experiment; 104 experiments in geographically distributed, heterogenous repositories.
**.1 - .5
**Models:**
**y = X********  **+  **Z********  **+ **W******** **+ T + ****
**Support Vector Machines**
**Compute Resources:**
- RAM needs to accommodate data from multiple arrays, multiple experiments, GenBank, SwissProt, Kegg, Pfam, PathDB

**Notes:**
Poor reproducibility
Sensitivity, ie power of ~ 
specificity, ie type 1 error 
Current analysis tools are based on simple clustering techniques.that provide some intuition for the biologists
Show Cluster analysis slide.  

## Examples of data from 

- Examples of data from
- micro-arrays and 2 D gel protein arrays

## Slide 15

**Notes:**
Needs  Data Acquisition:
Note  original images are in Red Green and yellow
Image processing (>2 channel)
Integration with sequence DBs

Note Clusters

## VxInsight

- (SNL: George Davidson)

## Predictions from Expression Arrays

**Data:**
- gene-chips, micro-arrays, bead-arrays (MPSS), proteomic arrays
- very sparse data, relative to biological time scales, snap shots of average transcription from pools of similarly treated cells
- 104 - 105  genes per experiment; 104 experiments in geographically distributed, heterogenous repositories.
**.1 - .5
**Models:**
**y = X********  **+  **Z********  **+ **W******** **+ T + ****
**Support Vector Machines**
**Compute Resources:**
- RAM needs to accommodate data from multiple arrays, multiple experiments, GenBank, SwissProt, Kegg, Pfam, PathDB

**Notes:**
swissprot =========Release 40.7 of SWISS-PROT contains 103370 sequence entries, comprising38071553 amino acids abstracted from 93529 references. KEGG====146 Pathways79 organisms328344 genes46107 enzymes (here: more relevant than genes)3829 EC numbers9560 compounds2427 compounds with links to pathwaysGenbank=======14976310 sequencesPathDB======15029 Proteins26576 Steps1906 EC Numbers127 Organisms267 Pathways

## Pathways and Networks

**Data:**
- expression arrays
- cross-link arrays of protein-protein, protein-DNA and protein-RNA interactions.
- Very sparse data, i.e., snap shots at discrete time/treatment/tissue samples
- geographically distributed, hetoerogenous repositories
**Models:**
- Hypothetical networks of nodes and edges, e.g.,   Baysian Networks
**Compute Resources:**
- Similar to “radiation transport” simulations?

## Data Needed for Inferring Genetic Networks from N Genes

**D’haeseleer, 1997. Data Requirements for Gene Network Inference **
**http://www.cs.unm.edu/~patrik/networks/**

**Notes:**
If we assume the use of a petaflop/sec machine, then it would take 3 years to simulate 100 micoseconds.... A typical time frame for simulating the protein  folding pathways 

## Slide 20

## Simple network diagram including protein complexes, metabolic reactions and protein-protein interactions

## Slide 22

## Slide 23

**Notes:**
Digital Cell  Colin Hill

## A Need for Integration

- The data are located in hundreds of geographically distributed, heterogenous repositories.
- Analysis tools (implemented algorithms) are likewise widely dispersed.
- Novel algorithms will be developed redundantly by independent PI.
- High performance computing resources are limited, but geographically distributed.

**Notes:**
This is going to be one of the more difficult
Unlike high energy physics, high throughput biology is not concentrated in a few labs.  
Technologies are becoming commodity priced very fast

## Functional information highlighted on a network of yeast peroxisomal proteins

**Notes:**
Link of known functions on a  network of proteins.  The network is proposed based on evidence of protein - protein interactions.  

## Integration Taxonomy

- Software Oriented

- Data Oriented

- Data Warehouse
- Federated Databases
- Multiple Databases

- Web based (hyperlinks)
- Component based

-  proprietary
-  enterprise

## Complex database and software

**Rigidity**
**Fragility**
**Expense**

## Loose Coupling Architecture

**Tight packaging of data and application allows context sensitive display of information.**
**Plug and play architecture emphasizes communication between components via fundamental biological concepts.**
**Separate components allow parallel and independent scientific advancements.**

## Server Proxies

- Clients

- Entrez
- SP

*(Web Extension)*

*ISYS Platform*

- ATV

- Table
- Viewer

- BDGPGO
- Browser

- Web Browser
- (with ISYS wrapper)

- SimSearch
- Browser

- maxdView

- SimSearch
- Launcher

- Sequence
- Viewer

- Local CPU, Disk

- Internet Resources

- Web Proxy

- (your client)

- (your client)

- (your SP)

## Integrated biological data types with an Integration Platform.

## Slide 31

## Discovery-Driven Data, Models and Computing Resources  for Predicting the Phenotype.

**Discovery-Driven Data, Models and Computing Resources  for Predicting the Phenotype.**

- Large volume
- Highly distributed
- low error

- Huge volume
- Few repositories
- very low error

- Huge volume
- Thousands of repositories
- Multiple Platforms
- high error

- Huge volume
- Hundreds of repositories
- Multiple Platforms
- high error

- Very little experimental data

- Linear Models
- Very large LOF

- Linear Models with a large LOF
- Annotation with potentially huge
- LOF

- Linear Models with a LOF
- SVM ?

- Protein Structure Predictions
- Protein Folding Mechanisms
- via simulation modeling?

- Baysian Networks
- Radiation Transport and
- other simulation models?

- Commodity Clusters

- Commodity Clusters

- Large RAM

- Commercial  SPM
- Blue-Gene +

- Commercial  SPM
- Blue-Gene +

## NCGR
Damian Gessler
Robert Kueffner
Honghui Wan
Jeff Blanchard
Mark Waugh

- Damian Gessler
- Robert Kueffner
- Honghui Wan
- Jeff Blanchard
- Mark Waugh

- Enabling Biology to Become a Predictive Science

- Sandia National Labs
- Grant Hefflefinger
- George Davidson
- Joint Genome Institute
- Dan Rokshar
**Oak Ridge National Labs**
- Jerry Tuskan
- Frank Larimer
- Los Alamos National Labs
- Mark Wall

## Bioinformatics

**Bioinformatics**

## DISCOVERY-DRIVEN BIOLOGICAL RESEARCH

**DISCOVERY**
**IDEA**

**CONTROLLED EXPERIMENT**
**Massively Parallel**
**Data Collection**
**(LIMS, Image Processing)**

**DATA MANAGEMENT:**
**Store, Retrieve,**
**Integrate**

**IP:**
**Drug Targets,**
**Gene Targets,**
**Diagnostics,**
**Software Systems,**
**Analysis Services**

**IP: **
**Technology**
**Platforms**

**IP: **
**Software Systems**
**Information Services**

**DATA INTERPRETATION:**
**Analyses, Predictive Models, **
**Systems Biology**

**DISCOVERY-DRIVEN BIOLOGICAL RESEARCH**