# Presentation: 005191

## Systems Biology at Argonne

- Natalia Maltsev, Gong Xin Yu, Elizabeth Marland, Alex Rodrigez, Dinnanth Sulakhe, Ramya Krisnamurthi, Pavan Anumula, Tanuja Bompada, Praveen Chandramohan,  Luke Ulrich, Richard Lusk...

## What are we going to talk about?

- Systems Biology – hmm... what is it?
- Systems biology at Argonne
- Bioinformatics at Argonne: Puma’s, WIT’s and Gwiz’es
- Some questions and problems

## All this data... more the better

- 114 completely sequenced genomes
- 735 sequencing projects on various stages of completeness

**High-throughput experimental technologies**
**High-throughput computational resources**

## Approaches in Biology

- Analysis of components (dissection)
- Bioinformatics – insights from large datasets (pattern recognition, comparative analysis)
- Systems biology – studies of emergent behavior (integration, modeling, perturbation)

## Biology as Systems Science:Integrated Whole vs Parts

**Cartesian view: any complex system can be analyzed in terms of properties of it’s parts**
**Systems approach: Living systems are integrated wholes whose properties cannot be reduced to those of the parts**
**Systemic properties of the whole arise from the “organizing relations” of the parts – that is from a configuration of ordered relationships that is characteristic of that of particular class of organisms or systems. Systemic properties are destroyed when a system is dissected into isolated elements**

## Enzymes and Metabolic Pathways Databasehttp://emp.mcs.anl.gov

**1. Access to EMP - a database of Enzymes and Metabolic Pathways**** **
- 	**EMP contains ****31,100 records****, which encode full factual content of 19,500 publications describing biochemistry of more than 8,000 organisms. EMP is publicly available on the Argonne Web server at http://wit.mcs.anl.gov/EMP. **
**This year we have reached an agreement and started extensive collaboration with EMPproject company on**
**establishing of public EMPproject server at Argonne**
**joint development of tools for analysis of metabolic data**
**2. Access to MCS Computational Resources**
- 	(scalable computational backend, integrated resources, platform-independent access)
**3.** **Access to Experimental data** via collaboration with Argonne Proteomics group (Dr. C. Giometti), and Pacific Northwest National Laboratory (Dr. R. Smith) et al.

## PUMA (1995) -- R. Overbeek, E.Selkov, N. Maltsev, T. Gaasterland
WIT     (1996) -- R. Overbeek, E.Selkov, N. Maltsev, N. Larsen          
WIT2  (1998->) --  R. Overbeek, E.Selkov, N. Maltsev, E.Selkov Jr., M. D'Souza, G. Pusch (http://wit.mcs.anl.gov/WIT2) 
WIT3 prototype (2001-2002) G. X. Yu, N. Maltsev, E. Marland et al.  
      http://www-wit.mcs.anl.gov/wit3
Gwiz (2002->)   -- under development

**WIT     (1996)** -- R. Overbeek, E.Selkov, N. Maltsev, N. Larsen
**WIT2  (1998**->**)** --  R. Overbeek, E.Selkov, N. Maltsev, E.Selkov Jr., M. D'Souza, G. Pusch **(http://wit.mcs.anl.gov/WIT2)**
**WIT3 prototype ****(2001-2002)** G. X. Yu, N. Maltsev, E. Marland et al.
-       **http://www-wit.mcs.anl.gov/wit3**
**Gwiz (2002->)**   -- under development

**WIT/PUMA family of Integrated Systems for Genetic sequence Analysis and Metabolic Reconstructions**

**Step 1.**** ****Identification of components of the system**
**Identify potential ORFs and assignf functions to the genes**

**Sequenced Genome**

**Step 2. The development of a  static model**
**.**** Reconstruct **
** ****Metabolic and Functional **
**networks using sequence data, **
**biochemical, expression and **
**other experimental data**

**Step 3.The Development of **
**Dynamic Models**
**Verification and refinement **
**of models using **
**Experimental data**

**Characterization of Biological Systems**

## Systems Biology Project at MCS

**Major goal:**
-       the development of integrated scalable automated system for high-throughput analysis of the sequenced genomes and reconstruction of genetic networks from the sequence data in order to:
- support the core metabolic modeling program at MCS
- advance comprehensive understanding of microbial metabolism and physiology through comparative and evolutionary analysis of genes and gene networks

**Bioinformatics**

## So far so good... We have Data  we have tools, and we know what to do...  So what’s the Problem...!?! Integrated environment for high-throughput analysis of the genomes and reconstruction of genetic networks from the sequence data

*Integrated computational environment *includes:
***The supporting database*** containing: data obtained from various electronic data sources; computational models, results of computational and experimental (in the future) analyses of the genomes and gene networks
***High-throughput genetic sequence analysis module ***consisting of:
- a computational infrastructure, tools and algorithms for high-throughput assignments of function to the genes in sequenced genomes (SVM- and HMM-based, voting algorithms, etc)
- The results of automated and interactive genetic sequence analysis of ~80 prokaryotic genomes
**M*****etabolic and regulatory networks reconstruction module ***contains:
- Tools and algorithms for reconstruction, representation, navigation and analysis of metabolic and regulatory networks
- Reconstructions of metabolic and regulatory networks for at least 50 organisms
***A library of hypotheses for experimental validation*** concerning functions of hypothetical proteins and architecture of metabolic and regulatory networks

## WIT3http://www-wit.mcs.anl.gov/wit3

**We have developed WIT3 – a working prototype of a new generation of integrated systems for high-throughput genetic sequence analysis and metabolic reconstructions (G.X. Yu et al.)**
**WIT3 now contains:**
**Automated analysis of 53 completely sequenced genomes**
**Metabolic reconstructions for over 40 prokaryotic genomes**
**Unique Knowledge Base for analysis of evolution of Protein families**
**Integrated Oracle-based database containing sequence and metabolic information**
**New algorithms for High-throughput genetic sequence analysis and metabolic reconstructions (e.g. voting algorithm, SVM and HMM-based algorithms)**
**Flexible user interface**

- Data Analysis Module

- Data Storage Module

- Data Acquisition Module

- GADU System – demonization and gridification of WIT

## Genetic Sequence analysis: Bottlenecks

- 	1. ***Incompleteness of the domains and motives libraries*****. **
- 	Currently available domains and motifs libraries (e.g. InterPro, Blocks) while containing a wealth of information for characterization and identification of proteins are still incomplete and do not yet contain information for a large number of enzymatic functions.
- 	2**. *****Low resolution of some sequence profiles***
- 	Some of the sequence profiles from the domain libraries can identify large protein families (e.g. aminotransferases), but unable to discern specific enzymatic functions.
-      3***. Blast doesn’t help much either...***

## Evolution of Enzymes

- A number of evolutionary mechanisms participate in establishing enzymatic functions. These include:
-  divergent evolution or enzyme recruitment,
-  convergent evolution or non-homologous replacement,
-  horizontal transfer of genes from one organism to another
-  and inheritance of a biological pathway from an ancestor.
- Uncovering and understanding of evolutionary history of metabolic pathways and enzymes could provide information about past and present metabolic and evolutionary potential of the species. It can also help to guide engineering and discovery of new metabolic activities.

##  Sources of Protein Families

**Base(WIT3)**

- Subunits
- DB (ANL)

- COGs

- BLAST

- Hobacgen

**HMM-Based**
**PhyloBLOCKS**

** ****Sources of Protein Families**

**SVM -Based**
**Classification**
**Characterization**
**of the Protein Families**

**Motivation:** The current
- resolution of tools such
- as BLOCKs and Pfam are
- unable discriminate closely
- related  homologous sequences

**Motivation: **
- To develop a library of Blocks
- HMM profiles corresponding
- to particular enzymatic
- functions or evolutionary
- versions of enzymes

**Output: **
- 1. A library of SVM Models for
- identification of certain
- enzymatic functions
- andd computational tools for
- predictions of protein
- functions based on these
- models

**Applications:**
- 1.Identification of conserved
- amino acid residues
- responsible for the
- functionality of a protein
- sequence.
- 2. Automated class-
- ification and predicting
- functions of proteins

**Applications:**
- 1. Identification,
- Classification and
- characterization of
- Proteins utilizing refined
- BLOCKs.
- 2. Phylogenetic
- analysis using BLOCKs
- distribution to identify
- cases of
- convergent/divergent
- Evolution of enzymes

**Output:**
- Refined BLOCKs
- specific for particular
- Enzymatic functions

**Tools: Algorithms for Automated Assignment of functions to the Genes **
**and Evolutionary Analysis of Protein Families**

- GongXin Yu, Praveen Chandromohan,
-        Luke Ulrich, Liz Marland,
-          Natalia Maltsev – ANL
-      Nagiza Samatova - ORNL

## Example of the need for further refined BLOCKs

**Porphyrin Biosynthesis - HEM**
- 5-aminolevulinate synthase EC 2.3.1.37 (gi|7433712)

**ALA domain        Aminotransferase 1,2 domain**

**ALA domain        Aminotransferase 1,2 domain**

**ALA domain        Aminotransferase 1,2 domain**

**Threonine biosynthesis - Kbl**
- 2-amino-3-ketobutyrate coenzyme A  ligase EC 2.1.3.29 (gi|6685543)

**Biotin Biosynthesis - BioF**
- 8 AMINO-7-OXONENANOATE SYNTHASE EC 2.3.1.47 (gi|14521874)

**Example of the need for further refined BLOCKs**
**Case: Related aminotransferases**

**Divergent Evolution (Enzyme Recruitment)**
**ALA synthase (EC 2.3.1.37) ****ALA Pathway**
**Kbl (EC 2.3.1.29)**** ****Threonine Biosynthesis**
**BioF (EC 2.3.1.47) ****Biotin Biosynthesis**

- No resolution from BLAST –
- however **phylogenetic trees** reveal groupings and BLOCKs can be made to differentiate from

## Evolution of Metabolic Pathways by Enzyme Recruitment (divergent Evolution)

- recruitment of promiscuous enzymes to metabolize novel substrates

-    E1

-     S

**S1**

**E2**

**S2**

**S1**

- E1

**S1**

**S2**

**E1**

- Promiscuous
- Enzyme

**KBL  EC 2.3.1.29**
**Glycine C-acetyltransferase**
**Acetyl-CoA + Glycine = CoA + 2-Amino-3-oxobutanoate**

- Acetyl-CoA

- Succinyl-CoA

- 6-Carboxyhexanoyl-CoA

**ALA EC 2.3.1.37**
**5-Aminolevulinate synthase**
**Succinyl-CoA + Glycine = 5-Aminolevulinate + CoA + CO2**

**BioF EC 2.3.1.47**
**8-Amino-7-oxononanoate synthase**
**6-Carboxyhexanoyl-CoA + L-Alanine =  8-Amino-7-oxononanoate + CoA + CO2**

## Results: 

**PhyloBLOCK**

## PhyloBLOCKS

- Base

- BLAST

- Habacgen
- Families

- COGS

- Selected Set of
- Sequences
- for Particular
- Protein Families

- CLUSTALW :
-  MSA and
- create tree files

- View tree files and user selection
- of functional groups

- Schema
- for creating BLOCKS from selected functional groups.

- CLUSTALW – MSA
- designated groups and then create BLOCKS using BLOCKMaker

- Refers to work in progress

- 1. Run LAMA to verify uniqueness
- of BLOCKS between the groups
- BLAST using consensus
- for additional verification.
- 2. Add BLOCKS to existing ANL DB

**Protein Families**

## SVM – based Classification and Characterization Schema

- Selected
- Sequences
- For Each
- Functional
- Group

- MSA

- Training

- Create profile
- Build position specific
- feature vectors based on
- profile
- 3.	Build models for
- each functional group

- Testing / Classification

- Align unknown or
- random sequence to
- MSA for homologous
- protein family

- 1. Build position specific
- feature vectors (based on
- profile made from training
- set) for unknown or
- test sequence.
- 2. Feed feature vectors
- Into different models and
- Predict if it belongs to the
- Functional group it was
- trained for.

**Predictions**

## PhyloBlocks, SVMmer: Some applications

**PhyloBlocks**
- Identification of different evolutionary forms of the enzymes
- Identification of novel functional domains
- Identification of versions of enzymes related to a certain phenotypes (e.g. thermophilic, halophilic)
- Identification of cases of horizontal transfer (?)
- PCR primenrs
**SVMmer**
- Identification of functionally important residues
- Identification of highly diverged versions of enzymes

## Some other algorithms: Conserved Chromosomal Gene Clusters – Clusters of functionally related genes in Prokaryotes.

**Some other algorithms: Conserved Chromosomal Gene Clusters – Clusters of functionally related genes in Prokaryotes.**

## Networks Analysis

- SBML, CellML, BSML, BML, GO....
- Solution?
- Identification of the Pathway Modules
- Graphs, fluxes, phenotypes, evolution of the networks
- Hierarchy of metabolic Networks

## Bioinformatics: future plans

**Systems Architecture development**
**Incorporate of the grid technologies for the needs of computational biology**
**Further develop GADU – the automated pipeline for data and process management to support high-throughput genetic sequence analysis in Gwiz**
**Incorporate new classes of data in Gwiz integrated database (e.g. structural data, regulatory networks data) **** **
**High-throughput sequence Analysis of Genomes**
**Incorporate in Gwiz at least 30 additional genomes (to reach the total number of more than 100 genomes in Gwiz)**
**Perform automated predictions of functions to the genes in at least 80 genomes**
**Establish partnerships with the experts on analysis of genomes of particular interest to DOE (e.g. for the needs of bioremediation, carbon sequestration, biomedical interest)**
**Gene networks analysis**
**Develop computational environment, for representation, comparative analysis and visualization of gene networks**
**Develop metabolic reconstructions for at least 20 additional organisms in Gwiz**
**Develop CyanoDB – a database of metabolic reconstructions for Cyanobacteria**
**The development of tools and algorithms**
**For genetic sequence analysis:**** continue to develop SVM-based tools, PhyloBlock clustering algorithms for analysis of protein families; enhance algorithms for analysis of conserved chromosomal gene clusters and chromosomal rearrangements; tools for whole genome comparisons**
**For analysis of gene networks****: develop tools for visualization and comparative analysis of networks; tools for representation fo gene networks in a form of stoichiometric matricies**
**User interface and visualization tools for public GWIZ server**

## Collaborations

**At ANL:**
**NIH Midwest structural Biology Center (Dr. A. Joachimiak)** – **on selection of priority protein targets for 3D structure determination. During the last year 2682 potential protein targets were selected and thoroughly characterized in TarGet database. **
**Globus group (Dr. Ian Foster, et al) – on adopting Grid technologies for the needs of bioinformatics**
**Dr. Jorge More et al. – on the development of clustering algorithms for the needs of sequence analysis**
**Other Collaborations:**
**Dr. Wen-Hsiung Li (University of Chicago) – on analysis of evolution of metabolic systems**
**Dr. Olaf Schreewind (University of Chicago) – on analysis of secretory proteins in pathogenic Staphylococcus **
**Dr. Nagiza Samatova et al. (ORNL) – on the development of the algorithms for high-throughput genetic sequence analysis**
**Dr. I. Zhulin – (Georgia Tech) – on analysis of prokaryotic signal transduction networks  **
**Dr. Bruce Roe – (University of Oklahoma) on analysis of Bacillus stearothermophilus genome**
**Drs. Michael Hucka and A. Finney (CalTech, Erato-Kitano project) – on development of systems biology markup language**
**Alex Selkov (EMPproject Inc, Russia) –  the development of tools for representation of metabolic pathways**