# Presentation: 609450

## BioPilot: Data-Intensive Computingfor Complex Biological Systems

- Nagiza Samatova
**Computer Science Research Group**
**Computer Science and Mathematics Division**
**Sponsored by**the Department of Energy’s Office of ScienceAdvanced Scientific Computing Research
- Scientific Discovery through Advanced Computing

## Biological research is becoming a high-throughput, data-intensive science, requiring development and evaluation of new methods and algorithms for large-scale computational analysis, modeling, and simulation

**Biological research is becoming a high-throughput, data-intensive science, requiring development and evaluation of new methods and algorithms for large-scale computational analysis, modeling, and simulation**

- Computational algorithms for proteomics
**Improve the efficiency and reliability of protein identification and quantification**
**Peptide identification using MS/MS using pre-computed spectral databases**
**Combinatorial peptide search with mutations, post- translational modifications and cross-linked constructs**

- Inference, modeling, and simulation of biological networks
**Reconstruction of cellular network topologies using high-throughput ****biological data**
**Stochastic and differential equation-based simulations of the dynamics of cellular networks**

- Integration of bioinformatics and biomolecular modeling and simulation** **
**Structure, function, and dynamics of complex biomolecular system in appropriate environments**
**Comparative analysis of large-scale molecular simulation trajectories**
**Event recognition in biomolecular simulations**
**Multi-level modeling of protein models and their conformational space**

- Goals for enabling data-intensivecomputing in biology

## Comparative molecular trajectoryanalysis with BioSimGrid

- Comparative molecular trajectoryanalysis with BioSimGrid

**Routine protein simulations generate TB trajectories.**
**Routine analysis tools (ED) require multiple passes.**
**Correlation analyses have random access patterns.**
**Comparative analyses require multiple/distributed trajectory access.**

**The analysis of molecular dynamics****trajectories presents a large,****data-intensive analysis problem:**

- The scientific challenge:

**Integrated molecular simulation****and comparative molecular****trajectory analysis:**

**Enzymatic reaction mechanisms**
**Molecular machines**
**Molecular basis of signal and material transport**

- T. P. Straatsma, Computational Biology and Bioinformatics

## Building accurate protein structures

- Building accurate protein structures

**Homology models can be built from related protein structures, but even with proper alignment, usually have about 4A RMS error–too large to permit meaningful computational simulation/molecular dynamics.****Goal is to reduce the errors in initial models.**** **

**We multi-align the group of neighbors using sequence and structure information to find the most stable parts of the domain.  Extracted stable core structure is 20********30% closer in terms of RMSD to the target than to any of the original templates.**
**More flexible parts of target are modeled locally by choosing most sequentially similar loops from the library of local segments found in all the homologues. **
**A genetic algorithm conformation search strategy using Cray XT4 uses backbone and sidechain angles as parameters. ****Each GA step is evaluated by minimization****.**

**A computed template compared with the target, improved from 3.4A initially to 2.3A**

- E. Uberbacher, Biological and Environmental Sciences Division

## 
Ultra-scale docking. The Bayesian potentials allow exploration of 100s and 1000s of protein complexes instead of one or two. 

Docking from independently crystallized subunits. We demonstrated excellent results on complexes reconstructed from ~500 independently crystallized subunits.

Whole genome predictions. Developed technology has been used to predict 1000s of protein complexes on the genome-wide level in several organisms.

**Ultra-scale docking.**** The Bayesian potentials allow exploration of 100s and 1000s of protein complexes instead of one or two. **
**Docking from independently crystallized subunits.**** We demonstrated excellent results on complexes reconstructed from ~500 independently crystallized subunits.**
**Whole genome predictions.**** Developed technology has been used to predict 1000s of protein complexes on the genome-wide level in several organisms.**

**Best structure****. ****The set with the largest common kernel always includes nearly the best native structure present in the ensemble of 10,000 folded structures.**
**Quality estimator.**** ****The size of the largest common kernel (obtained from the connectivity graph) provides an excellent estimate of how close the selected structure is to the native ones.**

- A. Gorin, Computer Science and Mathematics Division

- Analysis of ultra-large structural ensembles

## Predictive proteome analysis using advanced computation and physical models

- Predictive proteome analysis using advanced computation and physical models

- Scientific challenge:

**Data sizes: 30,000 to 200,000 spectra from a single experiment to be****compared with as many as millions of theoretical spectra**

**Computational tools identify only 10********20%****of spectra from proteomics analysis.**
**Fragmentation patterns are highly sequence-specific.**
**Statistical characterization of noise essential.**
**Identification rate increased by 23–40%.**

- NH3-HC-CO- NH-HC-CO- NH-HC-CO- NH-HC-COOH

- 20%@5% False Pos. Rate

- Increase in identified peptides

- 40%@1% False Pos. Rate

- W. R. Cannon, Computational Biology and Bioinformatics

## In-depth analysis of MS proteomicsdata flows

- In-depth analysis of MS proteomicsdata flows

**Mass-spectrometry-based proteomics is one of the richest sources of the biological information, but the data flows are enormous (100,000s of samples each consisting of 100,000s of spectra). **

**Computationally, both advanced graph algorithms and memory-based indexes (> 1 TB are required for****in-depth analysis of the spectra).**

**Two principally new capabilities are demonstrated:**** • Quantity of identified peptides is 50% increased.**** • Among highly reliable identifications, increase is many-fold.  **

- A. Gorin, Computer Science and Mathematics Division

## ScalaBLAST 

- ScalaBLAST

**Standalone BLAST application would take >1 year for IMG (>1.6 million proteins) vs IMG and >3 years for IMG vs nr.**
**“****PERL script” approach breaks even modest clusters because****of poor memory management.**

- Scientific challenge:

**Good scaling on both shared and distributed memory architectures**

- C. S. Oehmen, Computational Biology and Bioinformatics

**120**

**0**

**150**

**Scaling factor**

**Number of processors**

**100**

**50**

**0**

**40**

**80**

**MPP2**
**ALTIX**
**HTCBLAST**

**0**

**1000**

**Queries per (proc*minute)**

**Number of processors**

**100**

**50**

**0**

**3**

**4**

**6**

**10000**

**5**

**2**

**1**

**IMG 1.6**

**IMG 2.2**

**Run-time (hours)**

**40**

**35**

**30**

**25**

**20**

**15**

**10**

**5**

**0**

**0**

**1**

**2**

**3**

**4**

**5**

**6**

**7**

**8**

**Trillions of pairwise comparisons**

## pGraph: Parallel Graph Library for analysis of biological networks

- Major features:
**Memory reduction by 1000 times**
**Scalability to 100s of processors**
**FPT-based search space reduction theory**

- Nagiza Samatova, Computer Science and Mathematics Division

## Discovering cell networks with structure and genome context

**The conserved ASD domain links ~30% of ECF sigma and anti-sigma pairs in the signal transduction cascade, e.g., response to iron and ****1****O****2**** with FecIR, RpoE-ChrR proteins.**
**Many combinations of different sigma (********) and sensor (S) domains exist.**** Two levels of**** ****positional clustering****,**** ****(1) domain and (2) gene neighbor, generate vast permutations between protein pairs.**

**The structure of the ChrR anti-sigma factor from *****Rhodobacter sphaeroides***** reveals domains which help explain the ****combinatoric complexity**** of gene regulation in response to environmental conditions. An ****advanced toolkit**** ****for graph, genome context, and visual analytics was used to study ECF sigma factor regulation.**

**FecI****	****FecR**
**RpoE****	****ChrR**

*3D structure of ChrR anti-sigma regulator*

- Sofia Heidi, Computational Biology and Bioinformatics

## Stochastic simulations of biological networks

**To model microbial communities, the smallest realistic model would include >109 cells x 100 reactions ~1011 reactions.**
**Currently can handle ~104–106 reactions on a single-processor machine.**

- Scientific challenge:

***J. Phys. Chem. B***** 105, 11026, 2001**
**Speedup over exact Gillespie SSA ~25**
**Multinomial Tau-leaping method:**
**Speedups ~40–200**

**Probability-weighted dynamic Monte Carlo method...**

- H. Resat, Computational Biology and Bioinformatics

**1**

**4**

**Number of phosphorylation binding sites**

**3**

**2**

**5**

**8**

**7**

**6**

**9**

**12**

**11**

**10**

**0**

**250**

**500**

**750**

**1000**

**1250**

**1500**

**Average running time (seconds)**

## Contacts

- Tjerk Straatsma
- Principal Investigator
- Pacific Northwest National Laboratorytps@pnl.gov
- Nagiza Samatova
- Principle Investigator
- Oak Ridge National Laboratorysamatovan@ornl.gov
- Christine Chalk
- Program Manager
- U.S. Department of Energy
- Office of Science
- Advanced Scientific Computing Research, SciDACchristine.chalk@science.doe.gov

-    Samatova_BioPilot_SC07