1. BIOINFORMATICS.
Dr. Etienne Z. GNIMPIEBA
Etienne.gnimpieba@usd.edu
May 2012 1

2. Plan
• PART I: Fundamentals
• PART II: Career
• PART III: Applications
22

3. BIOINFORMATICS
PART I
FUNDAMENTALS.
3
3

4. Bioinformatics Fundamentals
Plan
PART I: Fundamentals • From biotechnology to
PART II: Career
PART III: Applications bioinformatics
• Bioinformatics world
• FOCUS: main areas
• 2 Key concepts between biology
and computer science
4
4

5. Bioinformatics Fundamentals
From Biotechnology to Bioinformatics 1
"Any technological application that uses biological
systems, living organisms, or derivatives thereof,
to make or modify products or processes for specific use.“1
1The United Nations Convention on Biological Diversity, 2008 5

6. Bioinformatics Fundamentals
From Biotechnology to Bioinformatics: apply area 2
Reduce dependence
on fertilizer,
pesticide,
agrochemical Good yield
Increase
nutritional quality
Novel substance
Agriculture Reduce
vulnerability
in crop plant
Pharmacogenomics Bio-process
Biochemical
Gene therapy
Biosystems
Genetic test
(DNA)
Medicine Bioinformatics
DNA Vaccines
Organism adapt.
Cloning
Environment
Clinical trials contamination
Education
Biotechnology Training Programs (BTPs)
2 Spellex BioScientific, v.2011 6 6

7. Bioinformatics Fundamentals
Bio World 3
BIO => life
• Environment
• Ecology – Contaminants
– Ecosystem – Factors
– Adaptation
• Chemistry
– Growth
– Reaction
– kinetic
– Compound
• Nutrition – Compartment
– Food
– Inhibition • Clinics (health)
– Nutrient
– Micronutrient – Activation – Pharmacy
– Macromolecule • Drug
– Vitamin
– Molecule • Material
– Proteins
• Epidemiology – Hospital
• Biology – Population – Pathology/ Organ
– Organism – Pandemic /specialist
– Epidemic • Cardio
– Organ – mortality
– Tissue • Onco (Cancer)
– Morbidity
– Cell • Neuro
– Metabolites • Pneumo
• Pharmaceutics
– Proteins (enzymes) – Molecule screening & modeling
• Dermato
– RNA (TF) –Pharmacogenomics
–Pharmacokinetics
– Gene – Pharmacodynamics
– Clinical trial (data management, e-CRF)
3 Etienne Gnimpieba, 2012
7

8. Bioinformatics Fundamentals
Challenges 4
• Accumulating mass of data
• Biological systems complexity
• Development of new research
interest on DNA
1950 1960 1970 1980 1990 2000 2010
4 Attwood T. K., 2012 8

9. Bioinformatics Fundamentals
Challenges 5
• Accumulating mass of data
• Biological systems complexity
• Development of new research
interest on DNA
9
5 MiPPI, 2007 9

10. Bioinformatics Fundamentals
Informatics world 6
• Data Manipulation / • Math
– Calculus
Management – Representation tools
– Modeling & predicting tools
–Creation (Learning, interpreting,
deducing, simulation, .. )
–Acquire / Collect
– Formalisms
– Exploration tools
– Optimization tools
• Process
– Theories –Experiment
–Organize
– Inference tools process design
–Store
– Statistics –Algorithm
–Secure – Graphics (Surfaces, Volumes) –Process
–Validate (standard, norms, safety) – Comparison and 3D Matching
(Vision, recognition)
–Workflow
–Analyze (statistics, mining)
–Visualize • Material
–Share (security, import, export, clean, …) – Server
– Archiving – Network
– Storage supports
– Processor
• Art & music
• Physics
• Software – Quantum computing
– Design (Human
machine interaction)
– Data manipulation tools – Signal treatment tools – Usefulness (beauty,
– Programming tools attractiveness)
– Biomedical material
– Artificial intelligence tools interaction (electric, optic – Philosophy
– High computing tools fiber, Wi-Fi, radio wave) – Signal
– Singling tools – Electrostatics
– Web 10
6Etienne Gnimpieba, 2012 – Robotics 10

11. Bioinformatics Fundamentals
Bioinformatics World: some topics 7
Genome Sequence Protein Sequence
• Finding Genes in Genomic DNA • Sequence Alignment
• Characterizing Repeats in Genomic DNA Dynamic Programming for Local vs Global Alignment
• Duplications in the Genome • Multiple Alignment and Consensus Patterns
• Secondary Structure “Prediction” • Scoring schemes and Matching statistics
(How to tell if a given alignment or match is statistically significant)
Genomics
• Expression Analysis Structures
• Large scale cross referencing of information
• Basic Protein Geometry and Least-Squares Fitting
• Function Classification and Orthologs
• Calculating a helix axis in 3D via fitting a line
• The Genomic vs. Single molecule Perspective
• Calculation of Volume and Surface
• Genome Comparisons
• Structural Alignment
• Structural Genomics
• Genome Trees
Databases
• Relational Database Concepts
• Natural Join as "where“ selection on cross product
Modeling & Simulation • Array Referencing (perl/dbm)
• Protein Units?
• Molecular Simulation • sequence, structure
• How to measure the change in a vector • motifs, modules, domains
(gradient) • Clustering and Trees
• UPGMA
• Parameter Sets
• single-linkage
• Number Density • multiple linkage
• Poisson-Boltzman Equation • Parsimony, Maximum likelihood
• Lattice Models and Simplification • The Bias Problem
7 Etienne Gnimpieba, 2012 11

12. Bioinformatics Fundamentals
Bioinformatics World: some topics 8
Experiment Compulation
Information Technology
Hardware & Instrumentation Mathematical & Physical Models
Methodology & Expertise
DNA Sequence
Genome sequencing Geomonic data Statistical
Gene & Genome
Organization analysis genetics
Sequence  Physiology (and beyond)
Molecular
Evolution
Proteomics Protein structure prediction,
Protein Structure,
Folding, Function,
protein dynamics, protein folding
& Interaction and design
Metabolic
Pathways
Functional
Regulation genomics Data standards,
Signaling (microarrays, data representations, Dynamical
Networks
2D-PAGE, etc.) and analytical tools for systems modeling
Physiology & Cell complex biological data
Biology
Interspecies
Interaction High-tech
Ecology & field ecology
Environment
Computational ecology
8 SABU M. THAMPI, Dept. of CSE, LBS College of Engineering, Kasaragod, Kerala-671542, 2011 12

13. Bioinformatics Fundamentals
Key concept: central dogma of Molecular Biology 9,10
DNA DNA
E Transcription
Degradation
Gene mRNA
Repression
Translation
Degradation
E
Catalyse
S P
13
9 Barbeillini, 2003 10 Etienne Gnimpieba, 2012 13

14. Bioinformatics Fundamentals
Key concept: Lactose Operon (Lac) 11
Genes and its binding
sites
In the "induced" state, the lac repressor In the "repressed" state, the repressor IS
is NOT bound to the operator site bound to the operator.
11 blc.arizona.edu
14

15. Bioinformatics Fundamentals
Summary Part I
15

16. *
PART II
Career.
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16

17. Bioinformatics Career
PART I: Fundamentals WHAT?
PART II: Career
Doing by
PART III: Applications
WHO?
17

18. Bioinformatics Career
Where can you be a bioinformatician? 12
• Public institution
– University( research project, training)
– Research center (research project)
– State & Federal agency (FDA, )
• Companies
– Pharmaceuticals
– Biotech
– Agricultural & food
– Health
– Information systems
Fundamental research Development research (product) Used, commercialization, market
Apply research
• Owner (your own boss)
– Contractor (entrepreneur)
– Consultant
• International institutions
– WHO
– UN
12 Etienne Gnimpieba, 2012 18

19. Bioinformatics Career
What do you do in Bioinformatics?
As informaticians, you have a lot of tasks • DNA computing
• Algorithms • Neural computing
• Databases and information systems • Evolutionary computing
• Web technologies • Immuno-computing
• Artificial intelligence and soft • Swarm-computing
computing • Cellular-computing
• Information and computation theory • Visualization
• Software engineering • Decisions making
• Data mining • Sequence Assembly
• Image processing • Genomic Sequence Analysis
• Modeling and simulation • Functional genomics
• Signal processing • Genotyping
• Discrete mathematics • Proteomics
• Control and system theory • Pharmacogenomics
• Statistics
• Integrative computing
• Database Administration
19

20. Bioinformatics Career
How to become a bioinformatician?
Skills Needed
• Database administration and programming skills
• (SQL Server, Oracle, Sybase, MySQL, CORBA, PERL, Java, C, C++,
web scripting).
• Genomic sequence analysis ,
• Molecular modeling programs,
• Biologist and computers scientists,
• Skills for data analysis, storage and retrieval.
• Skills filter information and from possible relationships between
datasets.
Training Eligibility biopharmaceutical :
• Bachelor • Life Sciences Graduates
• Master • Computer Sciences Graduates
• Databases Specialists
• MD
• Engineering Graduates
• PhD
• Marketing and Management Graduates
• High school diploma • MD-s, RN-s and Medical Professionals
20

21. Bioinformatics Career
Who does bioinformatics?
More than 100 profile denominations according to: country, company, domain, experience,
education profile, competence
From BIO based profile to Informatics based profile
• Bioinformatician • Biostatistician
– Cheminformatician • Scientist
– Computational Biologist • Biomedical Chemist
– Gene Analyst • Clinical Data Manager
– Genomic Scientist • Molecular Microbiologist
– Molecular Modeler • Software/Database
– Phylogenitist Programmer
– Protein Analyst • Medical Writer/Technical
– Scientific Curator Writer
– Structural Analyst • Research Associates and
• Biomedical Computer Scientist Research Scientists
• Geneticist • Data analyst
• Computational Biologist • Data designer
21

22. Bioinformatics Career
Career profile: an example
An example of a
Bioinformatician
work profile
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22

23. Bioinformatics Career
Summary Part II 13
Data manipulation
• Cloud
• Databank
• Database
• Data designer
• Information manipulation
Informatics
• Create/collect information
Bio/life
• Statistic analysis
• Date inference, learning
• Model from data
• Model from SB
• Large scale model
Modeling & learning SB
13 Etienne Gnimpieba, 2012 23

24. PART III
Applications.

25. Bioinformatics Applications
Overview 14
Pharma-
Biology Ad Hoc Interface
cology
PART I: Fundamentals
PART II: Career
PART III: Applications
Tools Tools
Ad Hoc Interface
Ad Hoc Interface
Tools
Tools
Ecology Medicine
CORE
Tools
Tools
Tools Tools
Computer Molecular
Science Ad Hoc Interface Nutrition
14 COSBI Report, 2010 25

26. Bioinformatics Applications
Small synopsis view of bioinformatics 15
15 Korean Bioinformation Center, 2010 26

27. Bioinformatics Applications
Informatician’s view of bioinformatics
• Data manipulation
– Data analysis
– Designing database and databank
– Management (collect, store, explore, secure)
– Inference/ mining
– Statistics
• Model design
– From biological process to mathematical formalism
– Model checking and validation
• Program building
– Data analyzing tools (implement algorithm)
– Integration tools (data, program, model)
– Modeling & Simulink tools
– Data protection tools
– …
27

28. Bioinformatics Applications
Exeample 1
Data Manipulation
Molecular online tools and Bioextract Server.
28

29. Lab #1 Molecular online tools and server 16
Context Biological Hypothesis
Statement of problem / Case study:
The FXN gene provides instructions for making a protein called frataxin. This protein is found in cells throughout the body, with the highest levels in the heart, spinal cord, liver, pancreas, and muscles. The Reduced expression of frataxin is the
protein is used for voluntary movement (skeletal muscles). Within cells, frataxin is found in energy-producing structures called mitochondria. Although its function is not fully understood, frataxin appears to help assemble cause of Friedrich's ataxia (FRDA), a
clusters of iron and sulfur molecules that are critical for the function of many proteins, including those needed for energy production. Mutations in the FXN gene cause Friedreich ataxia. Friedreich ataxia is a genetic lethal neurodegenerative disease, how
condition that affects the nervous system and causes movement problems. Most people with Friedreich ataxia begin to experience the signs and symptoms of the disorder around puberty. about liver cancer?
0. Specification & aims Resolution process
Aim: T1. Genome exploration:
The purpose of this experiment is to initiate online Objective: used of Ensembl online tools to localize the FXN on the human genome and
biological exploration tools of the human genome. We identify the genes implicate in pancreatic cancer disease. After, getting an appropriate
simulated the application (FXN gene and pancreatic data (sequence) on FASTA and Blast format.
cancer). Now we can understand how a researcher can
come to identify cross biological knowledge available T1.1. Locate a given gene on human genome
in data banks. T1.2. Get a genomic sequence from NCBI
T1.3. Get the protein information and sequence from EBI
Keywords:
T1.4. Save the export sequences data in data folder
Bio: FXN, Frataxin, pancreatic cancer, CDKN4
Math: HMM,
Informatics: programing, bioinformatics tools, getting T2. Sequences manipulation
and exporting data Frataxin molecule structure
Objective: Find similar sequence using BLAST tools and make an alignment on given
FXN on chromosome
9
(pymol) sequences.
T2.1. Find similar sequences using BLAST tool
T2.2. Align generated sequences with ClustalW tool
T1.3. Visualized result using phylogenic tree on Jalview
Biological DB
?
T2. Bioextract server
Objective: used server tool to optimized data manipulation process, apply on Bioextract server.
Tools
T3.1. Server Initialization
T3.2. Pancreatic cancer & Frataxin (FXN)
T3.3. Mapping, Alignment
Pancreas anatomy Pancreatic cancer T3.4. Workflow save & reused
Acquired skills
Online and server tools:
- Query biological DB (fasta, Html, txt, figure formats) Conclusion: ?
- Sequence tools (protein and gene)
Mapping (tmap)
Alignment (clustalw2)
- Manage data result (select, keep, map, export)
- Built and reuse workflow
16 Korean Bioinformation Center, 2010 29

30. Bioinformatics Applications
Example 2
Gene expression data: Microarray, NGS &
qRT-PCR
Biostatistics: gene expression data analysis
[1] Saffroy & al., 2004
[2] Chango & al., 2008 30

31. Bioinformatics Applications
Biostatistics: gene expression data analysis
Gene expression data (microarray, NGS) analysis process
Biological question
Differentially expressed genes
Sample class prediction etc.
Experimental design
Microarray experiment
Image analysis
Normalization
Estimation Testing Clustering Discrimination
Biological verification
and interpretation
31

32. Bioinformatics Applications
Example 3
Model design
Mathematical modeling of molecular nutrition
From food to molecule: folate absorption,
metabolism, and distribution
32

33. Bioinformatics Applications
Model design: Molecular nutrition and nutrigenomic 17
17 Achuthsankar S. Nair, 2007

34. Bioinformatics Applications
Example 2
Model design
Mathematical modeling of Biological systems
Folate mediate one carbon metabolism: MTHFR
(gene) mutation and cancer genesis
34

35. cle
105
4
10
Bioinformatics Applications 15
15
10
100
5
0 0.2 0.4 0.6 0.8 1 1.2 1.4
10 2
5
5 Mathematical modeling of Biological systems
Time(Hours)
18
0 0.2 0.4 0.6 0.8 1 1.2 1.4
0 0.2 0.4 0.6 0.8 1 1.2 1.4
Time(Hours) 00 0.5 1 1.5 2 2.5 3
Time(Hours) 0 0.2 0.4 0.6 0.8 1 1.2 1.4
Time(hours)
Folate metabolism (folic acid or Vitamin B9) and pathogenesis
Time(Hours)
45
140 AdoMet/AdoHc 40
60
135 AdoMet 35 y
Formalization of the model of metabolic networks
50
130
30
Unit AdoMet/AdoHcy
40
125 25 S dm ( t , P )
AdoHcy(µM)
AdoMet(µM)
Unit
rij(Eij,Vij)
Transmethylation pathway
Vc ( t , m ( t , P ),P ) Vr ( t ))
UM
120 20 30
dt
15
115
m
20 m rii(Eii,Vii) m ( t0 , P ) m0 ( P )
110
10
105
5
i
10
rji(Eji,Vji)
j
vij f (t , mij , Pij )
0
100 0 0.20 0.4 0.6 0.8 1 1.2 1.4
0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0.2 0.4 0.6
Time(Hours) 0.8 1 1.2 1.4
Time(Hours) IntraCellCp.DNA Time(Hours)
States versus Time IntraCellCp.DNA_CH3
States versus Time
kc
18
10
20.02
Homocystei ne Methionine 10.18
20 10.16
16 45 0.7
DNA 9
19.98 DNA-CH3 10.14
40
d Homocystei ne
14
0.6
35
8
19.96 10.12
kc . Homocystei ne
dUMP(µM)
dTMP(µM)
12
0.5
19.94 10.1
Unit
Unit
7
30
dt
AdoHcy/AdoMet
AdoMet/AdoHcy
UM
UM Amount (µM)
Amount (µM)
10
25 6
19.92 0.4 10.08
8
20 5
19.9
0.3 d Methionine 10.06
6 15 19.88 kc . Homocystei ne 10.04
4
10
4
19.86
0.2
dt 10.02
3
0.1
5 19.84 10
2 0 5 10 15 0
2
0 0 Time(Hours)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0.2 0.4 0.6 0.8 1 1.2 1.4
0
Time(Hours) 1
0 1 2 3 4
Time(Hours) 5 6
1 2 3 4 5 6 Time (Hours)
Time (Hours)
20.02 2.01 10.18 0.514
Uracile methylation
20
dUMP 10.16
0.512
19.98
2
10.14
dUMP/dTMP
0.51
19.96 1.99 10.12
Unit
Unit dUMP/dTMP
dTMP/dUMP
dUMP(µM)
dTMP(µM)
19.94 10.1 0.508
UM
1.98
19.92 10.08 0.506
19.9 1.97 10.06
0.504
19.88 10.04
1.96 0.502
19.86 10.02
19.84 1.95 10 0.5
0 5 10 15 0 0 5 5 10 10 15 15 0
Time(Hours) Time(Hours)
Time(Hours)
18 J. M.
2.01 Scott, 1994 0.514 35

36. Bioinformatics Applications
Example 4
Model design
Drug-DNA interaction
[1] Saffroy & al., 2004
[2] Chango & al., 2008 36

37. Bioinformatics Applications
Model design: drug-DNA interaction 19
Protein/DNA
Ligand (drug molecule)
Evaluate the uploaded molecule Predict the possible target protein
through the Lipinski's Rule of Five allosteric site Target Protein ready for Docking
Target Protein ready for Docking Docking & Scoring
[1] Saffroy & al., 2004 37
19 B. Jayaram, 2011 [2] Chango & al., 2008 37

38. Bioinformatics Applications
Example 5
Model design
3D Modeling /simulation in biology
[1] Saffroy & al., 2004
[2] Chango & al., 2008 38

39. Bioinformatics Applications
Model design: 3D Modeling 20, 21
Google Body browser E-cell project
20 Google, 2011 21 E-Cell.org, 2011 39

40. Bioinformatics Applications
Example 6
Model design
Cancer tumor model
[1] Saffroy & al., 2004
[2] Chango & al., 2008 40

41. Bioinformatics Applications
Model design: cancer tumor development 22
22 Northwestern, 2010 41

42. Bioinformatics Applications
Example 7
Model design
Epidemiology: HIV spread
42

43. Bioinformatics Applications
Model design: HIV spread 23
23 Northwestern, 2010 43

44. THANKS.
44