Opportunities of Systems EngineeringOperations Research in Bioinformatics

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Opportunities of Systems Engineering/Operations
Research in Bioinformatics

• Hyoungtae Kim
• (Joint work with Wiljeana Jackson, S.C. LIN and
  Dr. JC LU)

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Outline

• Introduction on Bioinformatics
• Paradigm Shift in Biology
• Systems Engineering/Operations Research
  for Bioinformatics
• About Funding Opportunities
• Conclusions

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What is Bioinformatics/Computational Molecular
Biology?

• An application of mathematical, statistical, and
  computational tools in the analysis of the huge
  size biological data
• Most of the cases, it involves analyzing
  information stored in large databases
• Multi-disciplinary
• -Biology -Mathematics -Statistics
• -Physics -Chemistry -Computer Science
• -Engineering

It has not yet found its own natural home
department
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Why Bioinformatics?

• Current data analysis tools are far from being
  efficient for analyzing vast amount of biological
  data
• The pace of biological understanding is much
  slower than the pace of the technology advance
  that have powered experimental discovery and data
  collection
• Benefits
• Advances in detection and treatment of disease
  and the production of genetically engineered
  foods
• ?Profound impact on health and medicine

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Three Elements of Bioinformatics Research

• Significant Biological problems
• Gene, motif, signal recognition
• Protein structure prediction
• Metabolic pathway deduction
• Etc.

Bioinformatics

• Data
• Microarrays
• Mass Spectroscopy
• Etc.

• Theory Methods
• Algorithms
• Statistical Methods
• Ontologies
• Etc.

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Prerequisites of Bioinformatics
Scientific Mind

• Basic knowledge in Molecular Biology
• Prokaryotic and Eukaryotic cells
• Genes, Codons, DNA, RNA, Central dogma of biology
• Etc.
• Computing Skills
• Program Languages Python, Perl, Java, etc.
• Knowledge in Relational Databases, etc.
• Other Skills
• General Statistical Knowledge
• Optimization Tools Math Programming, Network
  Optimization, etc.

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Various Problems in Bioinformatics
Standard Problems

• DNA and Protein Sequence Analysis
• Gene Finding and Prediction
• Etc.
• Microarray Experiment and Data Analysis
• Protein Structure Prediction
• Deduction of Metabolic Pathways
• And more

Emerging Problems
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Outline

• Introduction to Bioinformatics
• Paradigm Shift in Biology
• Systems Engineering/Operations Research
  for Bioinformatics
• About Funding Opportunities
• Concluding Remarks

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Paradigm Shift in Biology

• The Human Genome Project (HGP)
• Working Draft of the human genome (2001)

• Goal of the HGP sequencing of the human genome
• Hypothesis driven reductionism ?discovery science
  approach
• Drive-forced the development of high throughput
  technologies and computer applications to
  transmit, analyze, and model very large size data
  sets

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Paradigm Shift in Biology

• High-throughput Technologies

• Microarrays allow the expression of thousands
  of genes to be surveyed at one time
• Protein Arrays can examine all proteins in a
  cell and check if they are interacting under
  designed conditions
• Mass Spectrometry The basic modality is protein
  mass fingerprinting

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Paradigm Shift in Biology

• Microarray Chip Technology

• Allows data collection in high-throughput manner
• Can put all genes in a microbe on a chip

• Interpretation of the data is very challenging

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253x15154 Microarray Gene Expression Data 162
cancer vs 91 normal patients
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Paradigm Shift in Biology
Genes and proteins
Protein-protein interaction data
Gene activity data
Black box
Protein structure data
Proteomic data
Regulatory elements
Metabolite data
Gigantic amount of biological information is
hidden in these data and their inter-data
relationship!
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Paradigm Shift in Biology

• Concept of Systems Biology

• The Reductionist paradigm has been phenomenally
  successful in biology since 1950s
• Genomics era ? exhaustive lists of biological
  parts (i.e. genes and proteins) together with
  their functional characteristics
• A System-level perspective is required to make
  sense of how all of these individual parts emerge
  and act collectively to perform a biological
  function

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Outline

• Introduction to Bioinformatics
• Paradigm Shift in Biology
• Systems Engineering/Operations Research tools
  for Bioinformatics
• About Funding Opportunities
• Concluding Remarks

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Systems Engineering/Operations Research tools

• Three Categories

• Network Optimization
• Combinatorial
• Integer Programming
• Dynamic Programming
• Network Optimization
• Minimum Spanning Tree
• Etc.

• Stochastics
• Hidden Markov Models
• MCMC
• Simulation Models
• Etc.

• Statistics
• MLE
• Regression
• Sampling
• Linear Model
• Cross Validation
• Statistical Estimation and Test
• Multivariate Analysis (or ANOVA)
• Wavelet Transformation
• Bayesian Networks, Etc.

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Systems Engineering tools for Bioinformatics

• Some Examples

• Hidden Markov Model for Gene Finding
• Dynamic Programming for Sequence Alignment
• Integer Programming for Protein Folding
• Minimum Spanning Tree approach to Clustering for
  Motif Identification (Xu et al. (2001)
• And many more

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A Significant Biological Problem

• Identification of Transcription Factor Binding
  Sites(Motifs)

• A genes transcriptional level is regulated by
  proteins (transcription factors), which bind to
  specific sites in the genes promoter region,
  called binding sites
• The binding-site identification problem is to
  find short conserved fragments, from a set of
  genomic sequences
• ? Features of transcription factor binding site
• These short DNA fragments in the upstream regions
  of genes are generally very similar to each other
• Relatively high frequencies compared to other
  sequence fragments

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Data Collection

• Data Set (D) Set of All Short DNA fragments in
  the upstream regions of genes

• Microarray gene expression technologies allow
  simultaneous view of the transcription levels of
  many thousands of genes under various cellular
  conditions

Upstream regions of genes
GATCACCTGACATCAGGAGTTCAAGACCAGCCTGCCAACG CCATCTCTA
CTAAAAATAGGAAATTCACCTGGTGGCAGGT CCAGCTACTCGGGAGGCT
GAGGCAGAAGAATCGCTTGAAT GAGATTGCACTGAGCTGAGATCACGCC
ACTGCGCTCCAGC GAGCAAGACTCCATAAAAAAAAAAATTATAACCTAA
TGAT AGGGAAGAGCTTACCACAATTGCTGGCCCATGGCCAATGC ACAG
CTACTGCAAACAACCATGATGATGATACATCTCTTG GGTTGTTTGAGAC
ACATTCTATGCTCCTTGATTTGATTGG GGTTCCTTGGGGACTTGGAGGT
GACGAAAGCCTCCCTGGG ACCTTCACTTCTCTAATATCAAGCTTCAGCA
ACCTGCTCC CAGGGTTGGACAGGCCCAACAACAGAGGAAATCCACAAAG
CACATACATCCACGGGGTCTAACGAGGTGAGGCCAATGAC CACCCCAG
CCAGACTCTGACTTCACTCCCGGCAGGTTTCA CAGCAGTTGGAGCGAGC
TGGCTTCTTGCGGTAGGCAGCCA GCTCCCAATAGTCCTCGTTTCCTGGT
AATCTCATGCTTGG
Experiment
Find group of genes having correlated expression
profiles
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• Some testing data sets are available on the
  internet or in the literature

• For example ?
• CRP binding sites 18 sequences with 105 BPs
• Yeast binding sites 8 sequences with 1000 BPs
• Human binding sites 113 sequences with 30 BPs

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CRP binding sites 18 sequences with 105 BPs
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Theory Methods

• Traditional approaches

• Various sampling techniques including Gibbs
  sampling
• EM Algorithm
• Greedy Algorithm
• Multi-Order Markov Chain Algorithm
• All these are heuristic algorithms so this
  problem remains as a challenging and unsolved
  problem

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Brief Review Minimum Spanning Tree

• Input A graph, G (V,E), with weighted edges
• Output the cheapest subset of edges that keeps
  the graph in one connected component

• Two Popular Algorithms

• Kruskals Algorithm

• Prims Algorithm

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Theory Methods

• Minimum Spanning Tree approach

• Step1 Define a distance measure (?) on the data
  set (D), and compute distances b/w each pair of
  data points (i.e., ?(A,B) for all A, B in D)
• Higher the sequence similarity b/w two fragments,
  smaller the distance is b/w their mapped
  positions

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Theory Methods

• Minimum Spanning Tree approach

• Step2 Find the MST ,T, representing D with its
  edge weight defined by ? and treat it as a data
  clustering problem

c1
c4
T
e1
e2
c2
e3
c3
Remove three edges e1,e2,e3
4 Clusters, c1c4, are identified
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Evaluation of the MST Method

• Comparison with Other Methods

• MST is based on a combinatorial approach
• ? can identify all clusters of possible binding
  sites
• While existing heuristic methods are likely to
  miss some clusters
• Implemented result is at least as good as results
  by other methods
• While Simple structure of a tree facilitates
  efficient implementations of rigorous algorithm

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Outline

• Introduction to Bioinformatics
• Paradigm Shift in Biology
• Systems Engineering/Operations Research tools
  for Bioinformatics
• About Funding Opportunities
• Concluding Remarks

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Funding Overviews by Funding Institutions(Top)/Fie
ld of Research(Bot)
Total of 54.1 billion in FY2004
Environmental science
Physical science
Life science
Engineering
9.1 billion
29.3 billion
Percentage of Total Federal Funding Preliminary
2004 Statistics Source National Science
Foundation/Division of Science Resources
Statistics, Survey of Federal Funds for Research
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How to Search for Funding Opportunities?

• NIH Computer Retrieval of Information on
  Scientific Projects (CRISP)
• http//crisp.cit.nih.gov
• NIH Office of Extramural Research (OER)
• http//grants1.nih.gov
• Other Websites
• http//www.grants.gov
• http//fedgrants.gov
• http//www.nsf.gov/pubsys/ods/index.html

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Growing Opportunities in Bioinformatics
From CRISP Search Data
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NIH Funded Projects in 2004
From CRISP Search Data

• Searched all Related Institutes, Centers, and
  States for the 2004 Fiscal Year

NIH Grants in Bioinformatics, 826
Microarray, 214 grants
Systems Biology, 80 grants
Cancer,63 grants
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NIH Funding Opportunities for 2004
From http//grants1.nih.gov

• 2004 Program Announcement (PA)
• Total 171 PAs
• Larger variety of topics
• Cancer most prevalent topic
• Many wish to have multidisciplinary outlook on
  topics
• 2005 Requests For Application (RFA)
• Total 68 RFAs
• Although listed for 2005, some application
  deadlines have passed
• 2 directly related to bioinformatics
• Cancer still most prevalent topic

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Outline

• Introduction to Bioinformatics
• Paradigm Shift in Biology
• Systems Engineering/Operations Research
  for Bioinformatics
• About Funding Opportunities
• Conclusions

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Developing Potential Research Plans

• Two Takeaways

• Systems Engineers/Operations Research Society
  already have tools to solve various
  bioinformatics problems
• Moneys are there to support your research

Then, what do we need to start?
Biological Problems to solve
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Concluding Remarks!!

• The main driving force of bioinformatics/computati
  onal biology is the high-throughput data
  production
• I.E. tools together with computing power can play
  an important role in this process
• Funding opportunities in this area are very rich

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Thank you!
Any Questions?
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Level of Organization and Related Field of Study
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Central Dogma of Biology
Transcription
Translation
example
Transcription
Translation
TTG CTG CGG
UUG CUG CGG
Leu Leu Arg
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Transcription and Translation
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Gene

• A gene is a region of DNA that controls a
  hereditary characteristic, usually corresponding
  to a single mRNA carrying the information for
  constructing a protein.
• The human genome contains about 30,000 genes.
  (February 2001)

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Introns and Exons
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Pair-wise Sequence Alignment
VLSPADKTNVKAAWAKVGAHAAGHG
VLSEAEWQLVLHVWAKVEADVAGHG
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Sequence Alignment

• Purposes
• Learn about evolutionary relationships
• Finding genes, domains, signals
• Classify protein families (function, structure).
• Identify common domains (function, structure).

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Multiple Sequence Alignment
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Scoring Systems for Alignment
Simple case
Sequence 1 Sequence 2
A G C T A 1 0 0 0 G 0 1 0 0 C 0 0 1 0 T 0 0 0 1
Scoring matrix
Match 1 Mismatch 0 Score 5
DNA
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Scoring Systems for Alignment
Complex case
Sequence 1 Sequence 2
PTHPLASKTQILPEDLASEDLTI
PTHPLAGERAIGLARLAEEDFGM
C S T P A G N D . . C 9 S -1 4 T -1 1
5 P -3 -1 -1 7 A 0 1 0 -1 4 G -3 0 -2 -2
0 6 N -3 1 0 -2 -2 0 5 D -3 0 -1 -1 -2 -1
1 6 . .
Scoring matrix
TG -2 TT 5 Score 48
Protein
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Protein Structure
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Public Databases

• Big 3 Centers

National Center for Biotechnology Information
EBI
DNA Database Bank of Japan
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The Human Genome

• 23 pairs of chromosomes comprise the human
  genome.
• The human genome contains 3,164.7 million (or 3
  Billion) nucleotide base.
• The average gene consists of 3,000 bases, but
  sizes vary greatly, with the largest known human
  gene being dystrophin at 2.4 million bases.
• The total number of genes is estimated at 30,000
  to 40,000
• The total number of protein variant is
  estimated as 1 Million.

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Various Fields in Biology
Genomics
DNA
Transcriptomics
RNA
Proteomics
Proteins
Metabolomics
Metabolites
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Trends in Molecular Biology
Reverse Genetics
Functional Genomics
Gene
Function (Mutation)
Function
Gene
Genome Project High Throughput Tech
Genome
Genomics
Structural Genomics
Functional Genomics
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DNA Bases
A (Adenine), G (Guanine), C (Cytosine),
T(Thymine)