Transcript PowerPoint 7.1MB - The Biomolecular Modeling & Computational

1. Professor Mark Ragan
(Institute for Molecular Bioscience)
2. Dr Thomas Huber
(Department of Mathematics)
Computational Biology and
Bioinformatics Environment
ComBinE
Queensland Parallel Supercomputing Foundation
National Facility Projects
The scientific problem:
Handcrafted analyses suggest that gene
transfer in nature may be not only from parents
to offspring (“vertical”), but also from one
lineage to another (“lateral” or “horizontal”)
From microbial genomics we have complete
inventories of genes & proteins in ~ 80 genomes
Comparative analysis should identify all cases
of vertical and lateral gene transfer
Queensland Parallel Supercomputing Foundation
Comparison of protein families
among completely sequenced
microbial genomes
Computational
requirement for
80 genomes:
Find all interestingly large
protein families in all microbial
genomes
1012 BLAST
comparisons
Generate structure-sensitive
multiple alignments
5000 T-Coffee
alignments
Infer phylogenetic trees with
appropriate statistics
5000 Bayesian
inference trees
Compare trees, look for
topological incongruence
107 topological
comparisons
Queensland Parallel Supercomputing Foundation
The approach
Usage of NF:
Motif-based multiple alignment
30-50 sequences = 2-5 hours per run
Will need ~5000 runs @ 4 - 60 seqs
Code not yet
parallelised
Bayesian inference
Parameterisation of (MC)3 search
NF used for trials of up to 106 Markov
chain generations (~200 hours / run)
1.5-2.0 Gb RAM per run
With each run
costing a few 10s of
hours and need for
1000s analyses, it’s
more efficient to use
many processors
simultaneously
Queensland Parallel Supercomputing Foundation
Computations on APAC
National Facility
Bayesian inference (MrBayes 2.0) applied to 34-sequence Elongation Factor 1 dataset. Eight simultaneous
Markov chains, discrete approximation of gamma distribution ( = 0.29), chain temperature 0.1000
Ln-likelihood as function of number of generations
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Ln-likelihood
Ln-likelihood
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Number of generations
Log-likelihood as a function of number of
Markov chain generations
Number of Markov chain generations
Approach to stationarity under Jones et al. (1992) and General
time-reversible models of protein sequence change
Queensland Parallel Supercomputing Foundation
Parameterisation of Metropolis-coupled
Markov chain Monte Carlo optimisation
through protein tree space
Mark Borodovsky, Georgia Tech
Robert Charlebois, NGI Inc. (Ottawa)
Tim Harlow, University of Queensland
Jeffrey Lawrence, University of Pittsburgh
Thomas Rand, St Mary’s University
Queensland Parallel Supercomputing Foundation
With thanks to collaborators
1. Professor Mark Ragan
(Institute for Molecular Bioscience)
2. Dr Thomas Huber
(Department of Mathematics)
Computational Biology and
Bioinformatics Environment
ComBinE
Queensland Parallel Supercomputing Foundation
National Facility Projects
Protein Structure Prediction
• The bioinformatics approach
– Compare sequence to other sequence
– huge datasets (0.5*106 sequences)
– Match sequence with known structure
– (Low resolution force field development)
• The biophysics approach
– Simulations that mimic natural
behaviour
Queensland Parallel Supercomputing Foundation
Two Lineages
Protein Structure Prediction
• The bioinformatics approach
Hardware
Requirements:
– Compare sequence to other sequence CPU: minutes/seq
– huge datasets (0.5*106 sequences)
Mem:  1 GB
– Match sequence with known structure CPU: hours/seq
– (Low resolution force field development)
Mem:  100s MB
• The biophysics approach
– Simulations that mimic natural
behaviour
CPU: 100s hours
Mem: 10s MB
Queensland Parallel Supercomputing Foundation
Two Lineages
Protein Structure Prediction
Parallelism:
• The bioinformatics approach
– Compare sequence to other sequence
Trivial parallel
– huge datasets (0.5*106 sequences)
– Match sequence with known structure Trivial parallel
– (Low resolution force field development)
• The biophysics approach
– Simulations that mimic natural
behaviour
Hard parallel
High bandwidth +
low latency
requirement
Queensland Parallel Supercomputing Foundation
Two Lineages
MD Simulation
Propagating Molecular Models in
Time
Start With Old
System State
New System State
Time step required: 10-15s
Time scale wanted: >10-3s
 System is split in different
domains
•
•
Add Information
On Energy And
Force
Mechanical
Description
Apply Numerical
Integrator
Newton’s Laws of
Motion
Fast varying forces (cheap
to calculate) are integrated
more frequent
Slow varying forced
(expensive to calculate) are
integrated less frequent
+ More efficient integration
+ Easy to expand to parallel
simulations
Queensland Parallel Supercomputing Foundation
Force splitting and multiple
time step integration
(Ian Lenane)
What if start and end points are given?
• proteins: unfolded  folded
• Molecular machines: 1 cycle
( x1 , y1 )
• Shortest path calculations
– Floyd, Dijkstra
• Hamilton’s principle of least action
t1

min arg{ S}   dt 0.5mv(t ) 2  U (q(t ))

t0
+ Computationally very attractive
• Extremely long time steps
• Very well suited for parallel architectures
(Floyd
algorithm parallelized, but performance problems >4PE
on -GS NUMA architecture)
( x2 , y2 )
Queensland Parallel Supercomputing Foundation
Path simulations
(Ben Gladwin)
• 2001 CPU quota: 2*5250 + 8000 service units
– Total use  12000 units (3000 units in parallel)
• 2002 CPU quota: 4 * 6000 service units
– First quarter: 2000 units
– Second quarter: 85 units
• Collaborators
• Dr A. Torda (ANU) Low resolution force fields /
protein structure prediction
• Prof. D. Hume, A/Prof. B. Kobe and Dr. J. Martin
(UQ) Structural genomics project
• Prof. K. Burrage, I. Lenane and B. Galdwin (UQ)
Numerical integration and path simulations
• Special Thanks
• Mrs J. Jenkinson and Dr D. Singleton (NF/ANUSF)
Queensland Parallel Supercomputing Foundation
National Facility
supercomputer use