# Presentation: 525422

## In silico biology: computational path toward holistic understanding of living cells

- Andrey A. Gorin
- Computer Science and Mathematics
- Oak Ridge National Laboratory
- agor@ornl.gov

## Motivation – Predictive Biology

- Developments in Biological Sciences:
- Experimental: From Reduction to Systems Science
- Computational: From Validation to Prediction
- Development in Technologies:
- High Throughput Experimentation
- High Performance Computing
- Uniqueness of Biology:
- First Principles Approach is Impossible/Impractical
- Enormous Multitude of Scales
- Descriptive Models from Diverse Data
- Large Uncertainties in Data

**Notes:**
Biological sciences are undergoing a change in scientific approach:
Experimental biological sciences is shifting from a reductionist approach that focuses on the function of individual components to one in which the inter-relation between these components as a complex system is studied
Computational sciences and the computational resources are becoming sufficiently advanced that a shift from validation and understanding of experimental observations to the ability to predict the outcome of experiments that have not been carried out yet, and ultimately form the basis for the experimental design. 

A number of technical capabilities drive this transformation of biological sciences:
The realization that only a systems approach – one in which all components of a complex system are considered – can provide the insight that is needed to understand the function of those systems, and ultimately how they can be used or modified to address some of the most complex scientific challenges we are facing in environmental restoration, energy security and global climate change;
The emergence of high-throughput experimental technologies that provide us with vast amounts of information about individual components of these complex systems – the ability to do global proteomics and comparative genomics are examples – ;
The availability of large-scale high-performance computing capabilities that provides us the capability required to process all of this data, derive the underlying models for the function of these complex systems, and design and predict the outcome of new experiments.

While the same developments have benefited other branches of science, biological sciences are unique in the following aspects
The sheer number and complexity of constituent components of a biological system makes a first principles approach clearly impractical. 
The correlation of the multitude of scales – from nm to m, from fs to hours – presents an additional challenge
Development of descriptive models requires analysis of a very diverse set of data
This data has large uncertainties associated with the experiments – control of large number of external constraints – requiring advanced statistical methods.  

## Scientific Drivers

**Bio-energy**
- Development of  integrated experimental an computational approaches for:
-  Feedstock optimization with the goal of better cellulose deconstruction by special bacterial systems
-  Understanding of microbial communities in single batch processes (harsh environments)
-  Enzyme or regulatory circuit design to increase desirable output

**Bio-remediation**
- Environmental restoration using microbial processes requires study of:
-  Microbial attachment to mineral environments
-  Uptake of contaminant ions by microbes and metal reduction at the microbial membrane
-  Conversion of toxic chemicals by microbial systems

## Our Research Directions

**Mass-spectrometry based proteomics:** protein identification and quantification in complex biological samples
**Structural models of protein complexes: **docking from known components, fundamental principles of molecular recognition, prediction of protein complexes in novel genomes
**Network models****:** reconstruction from protein interactions and other sources, simple simulation to demonstrate predictive capabilities

**Notes:**
The key challenge in biology is to link the genetic information encoded on the genome to the function of biological systems. This process involves the translation of genetic information into the structure of proteins and protein complexes, as well as the structure of protein interaction and other networks. Once we have these structure we can construct models that can be used to simulate the kinetic or dynamic behavior of these systems that ultimately result in the function of these systems. Having the model allows descriptive, but more importantly predictive modeling and simulation to be carried out. This allows researchers the predict the behavior under different conditions, and the design of new experiments to validate these predictions. None of these research components can be addressed without the use of both data intensive and model driven computing.

## Exploring Protein Dimension of Bio Universe  

- Mass spectrometry process

**Exploring Protein Dimension of Bio Universe  **

- 1 out of 105-1011 must be selected as the correct peptide

- Entire proteome is analyzed in a few hours

## De novo Platform: Probability Profile Method  

**[510] VDDLSSLT [305]**

**Peak **
**Assignment**

**m****1****  m****2****  m****3****  m****4 ...**

*We made several advancements in the understanding of the “mass spec” mathematics. Taken together they lead to conceptually novel platform. *

**Notes:**
Slide shows main steps in database search vs de novo algorithms.  The main difference of de novo algorithms is that we first construct pretty simple objects from the spectrum (it is easy to access mathematical properties of such simpler objects and there is no dbase limitation on the initial stage of analysis), but as a drawback we maybe forced to scan database with literally 1,000,000 of such simple objects -- sequence tags - generalized version of a string.  

Two varieties of de novo are possible:  1. Pure de novo where after spectrum analysis the answer is found immediately - sequence from the target protein; 2. De novo combined with database search.   

## Results: Capabilities in Mass Spectrometry 

**Results: Capabilities in Mass Spectrometry**** **

*Advances in the mathematical understanding and dramatic acceleration of fundamental operations lead to principally new capabilities*

-  Output gains, and especially in highly confident identifications. Our method gives several times more of highly confident identifications** **
-  Capability to detect unexpected phenomena in the samples. We have found novel biological phenomena in the legacy data and uncovered mistakes in the data sets regarded as benchmarks.

- Deamidations (6 spectra)
**IHPFAQTQSLVYPFPGPIP****N   ****IHPFAQTQSLVYPFPGPIP****D**
- Incorrect peptides (7 spectra)
**VIPAADLS****Q****QISTAGTEASGTGNMK** **->**          **VIPAADLS****E****QISTAGTEASGTGNMK**
**...**
- Disulphide bond (1 spectra)
**AAANFFSAS****C****VP****C****ADQSSFPK**

*De novo methods can improve even manually verified benchmark data sets* obtained by the existing technology.

**Notes:**
Example 1: Sensitivity specificity curve goes much more closely the horizontal line - that means that as in higher ranks of the identifications we are getting very significant advantage

Example 2: Many mistakes were found and confirmed in ISB benchmark data set. A few of them shown - it is mostly mutations that we can explain as deamidations, etc as soon as we found them

## Model-dependent Science Questions

_**Structural Models Reliant:**_

**Many ****fundamental questions**** in systems biology are hampered by the lack of ****reliable ****predictive models****.**

_**Network Models Reliant:**_

- What biochemical processes in a microbe are related to its traits (hydrogen or ethanol producer, ethanol resistant)?
**How does a bacteria degrade lignin or cellulose? **
- What are the mechanisms behind the conversion of toxic waste to nontoxic substances by bacteria?
- ...

- What are biochemical or regulatory functions of the proteins that are shown to be important for hydrogen production?
- What are the hot spots of cellulases that could impair their binding?
**What are the components of hydrogen producing protein assemblies?**
- ...

## Predictive Model Building

## Protein Complexes in Genomics:GTL

- Express Bait
- Protein

- Exogenous / Endogenous
-                      Pulldown

- Mass
- Spectrometry
- Analysis

- Putative Interacting Proteins at High Throughput

- Is the interaction real or an artifact?
- What is the structure of the protein
-  complex?
- What is its function?
- What is its dynamic mechanism?
- Can we answer these questions at scale?

*GTL is focused on protein interactions that make life work*

- Xray Diffraction

**Notes:**
Useful for PROJECTIONS and for analysis of experimental data (DOE or not DOE)

For example, we have demonstrated  that often pull down find a complex even when the bate protein is not part of it   

## Modeling Protein Structures and Complexes

*Combinatorial and optimization techniques are applied for two areas: development of knowledge based potentials and analysis of ultra large structural sets.*

***Discovery of protein complexes***

***Protein folding***

***Ligand binding***

-   Graph    algorithms

- Geometry and bioinfo libraries

- Shared memory indices

- Parallel implemenations

**?**

**Notes:**
Stages of reconstruction during docking are very similar to network reconstructions

Original proposal has grown from fragment libraries ideas -- ideas of knowledge based potentials originated there 

Here we mostly present our developments in regard of docking of protein complexes 

## Computational Algorithms in Structure Modeling

*Multitude of combinatorial optimization problems with different data access patterns.*** **

_**Example**_**: *****Ab Initio***** Prediction of Protein 3-d Structure**** **

## Results: Protein Docking

-  Full implementation for Bayesian potential energy functions for protein docking
-  The size of the docking benchmarking set (~1,200) is unprecedented in the field. High quality results were obtained for over 70% of all tested complexes (native in top 5), indicating that the method is very efficient.
-  Successfully modeled protein complexes using the structures from other organisms
-  Docking calculations are scalable to 1000s processor due to surface patch approach for parallelization

**Notes:**
We have two advances here:

 We developed framework for docking -based on surface patches incorporating Bayesian energies for contacts and parallelizable to 100s and 1000s processors

2. We have excellent results on the uniquely large data set (over 1200 complexes), but we also tried smaller data sets that 
Have been done by other people with other methods (the paper to appear in PROTEINS this month)

DISTRIBUTION figure -- shows the volume of the calculation - every dot is computed conformation for subset of 225 hetero complexes (where the subunits are different)
(Blue and red curves show the distribution of natives and decoy conformations, fraction of native contacts increases with the score (black curve))

DIAGONAL figure -- our method leads not only to enormous acceleration but we also work on the mathematical questions. For example, we used Bayesian approaches to calculate probability of success for each obtained structure. The diagonal shows that our computed value is a good correspondence to the observed things on benchmark data set. (The plot shows real fraction of native contacts in the groups of decoys with similar values of Confidence Function)

## Future:_x0010_Protein Docking 

-  Develop further Protein Interface Server (PINS) database (pins.ornl.gov).
-  Develop theory and computational implementation for docking potentials taking into account orientation of the contacting residues (Bayesian). Petascale implementations for docking platforms.
-   Improve mathematical methods for prediction validation.
-  Analyze and annotate PINS interfaces related to the metabolic functions involving carbon-processing pathways in cellulose-degrading bacteria. Construct predictions for the organisms important for Bioenergy Centers in cases when full or partial genome sequence are available.

## Predictive Model Building

## LDRD D07-014 Modeling Cellular Mechanisms for Efficient Bioethanol Production through Petascale Analysis of Biological Networks

**Andrey Gorin, Nabeela Ahmad, Andrew Bordner, Robert Day,  **
**Jessie Gu, Guruprasad Kora, Chongle Pan, **
**Byung-Hoon Park, Nagiza Samatova, Edward Uberbacher, Cray. Inc**

**Oak Ridge National Laboratory, FY2007-FY2008**

**Notes:**
This long title can well serve as an Abstract for our project. 

The presentation compliments (but does NOT replace) our submitted Report here we will just highlight some of the achievement and very briefly describe things to be done during second year.  

## Future: Graph Algorithms for Networks   

**Analysis of graphs reflecting physical interactions in structures**
- Very wide set of problems, but with a uniform set of “translation” rules
- Enormously large graphs
- Directly connected to modeling petascale applications
**Developing graph representations for real cellular subsystems. **
- Nodes: genes, proteins, DNA elements, metabolites.
- Edges: translation, positive regulation, production of metabolite
**Flexible representations **
- Integration of many tools to annotate 5’ regions
- Promotor alignment
- Models to predict transcription patterns based on promotor models

**Notes:**
Continuing biology topics....

IN ADDITION TO DESIRE TO DO BIOLOGY APPLICATION - biology is providing really interesting problems 

(1) Representing molecular structures as graphs is really activity with great future: This is a very wide area of application -- where graph edge is usually interaction between two residues 

(2) We have deal with networks with only one type of edges -- many types of edges should be considered (those of course will be directed graphs) 

## Future: Mining Networks for Bioenergy 

- Switch grass paralogs/homologs has to be identified in collections of >250,000 partial gene data (EST) from rice, maize, sorghum genomes
- It is expected that 6000 Populus lines will be passed through cell wall phenotyping pipeline (expression arrays, proteomics data, etc)
- Huge data sets are already (e.g. Obauashi et al. (2007) 1,388 expression arrays)

**(From Bioenergy Center proposal)**

**Notes:**
Overall idea of Bionergy Center - and huge data sets are already available !!! 

## Future: Taking It to Petascale

*** ******Port to NLCF platforms***
- Scale Maximal Clique Enumeration from 100s to 1000s processors
- Introduce similar implementations for other codes in pGraph and BioGraphE

***Optimize ******performance on NLCF architectures***:
- Deliver efficient ***MPI-based multi-core*** implementations
***Minimize thread locking/unlocking overheads***
***Exploit data locality*** in job redistribution strategies
***Minimize I/O overheads*** via buffering, data compression, hierarchical parallel I/O.

**Notes:**
Maybe we already there but we may need to prove it 

— some algorithms are ready to be tried on thousands processors 
— other are coming SOON 

Note that some problems cannot be fit on this graph  - e.g. when we need Common subgraph between several graphs !!!

AGAIN BIOLOGY IS inspiration for computer science and mathematics !! 

## Potential Areas of Joint Work

**Potential Areas of Joint Work**

**Mathematics**
- Statistics of pattern recognition problems
- Combinatorial optimization methods beyond dynamic programming
- Bayesian system with strong feature correlations
**Computer science**
- Graph algorithms working for VERY large graphs 104-105 nodes and across many graphs (103) in parallel
- Flexible software systems for representation transcription regulation networks in the bacterial cells (4 genes)
- Petascale implementation strategies: MPI implementations, load balancing, etc
**Biological Applications:**
- Novel applications in mass-spectrometry (e.g. gene finding in new genomes, special gene expression in stem cells, mass-spec of cross-linked systems)
- Expression array analysis and metabolic pathway construction for bacteria involved in bioenergy processing