Prof' Santosh K' Mishra

1
Information Driven Biomedicine

• Prof. Santosh K. Mishra
• Executive Director, BII
• CIAPR IV Shanghai, May 21 2004

2
What/How
RNA
3
Complexity of Data
Information
The Genetic Code
DNA
RNA
Proteins
Pathways
Complexity
4
Bioinformaticians Will Be Busy Bees

• Precise, predictive model of transcription
  initiation and termination ability to predict
  where and when transcription will occur in a
  genome
• Precise, predictive model of RNA
  splicing/alternative splicing ability to predict
  the splicing pattern of any primary transcript in
  any tissue
• Determining effective proteinDNA, proteinRNA
  and proteinprotein recognition codes
• Precise, quantitative models of signal
  transduction pathways ability to predict
  cellular responses to external stimuli
• Accurate ab initio protein structure prediction

At a bioinformatics conference last fall, EBIs
Ewan Birney, MITs Chris Burge, and
GlaxoSmithKlines Jim Fickett gave an impromptu
roundup of the future challenges of the field.
Burge polished them up for GT
5
Human Genes by General Function

• Incomplete List of parts
• No assembly instructions

Science Feb 16 2001 1304-1351
Unknown Genes
6
Bioinformaticians Will Be Busy Bees

• Precise, predictive model of transcription
  initiation and termination ability to predict
  where and when transcription will occur in a
  genome
• Precise, predictive model of RNA
  splicing/alternative splicing ability to predict
  the splicing pattern of any primary transcript in
  any tissue
• Determining effective proteinDNA, proteinRNA
  and proteinprotein recognition codes
• Precise, quantitative models of signal
  transduction pathways ability to predict
  cellular responses to external stimuli
• Accurate ab initio protein structure prediction

At a bioinformatics conference last fall, EBIs
Ewan Birney, MITs Chris Burge, and
GlaxoSmithKlines Jim Fickett gave an impromptu
roundup of the future challenges of the field.
Burge polished them up for GT
7
The Evolution Of High Resolution Biology
8
Pioneers

• Hartwell et al (1999) Nature 402, C47-C52
• We need to develop simplifying, higher level
  models and find general principles that will
  allow us to grasp and manipulate the function of
  biochemical networks

9
Genes to Targets to Pathways to Systemic
Physiology
10
The Hierarchy Of Biological Organization The
Post Genome Initiative Era
Genes All The
Genes Will Be Identified
Proteins
The Proteome Will Be The Focus
Organelles Cells
Tissues Organs
Organisms
Disease Physiology
11
Hartwell et al (1999)

• A useful theory must
• Provide realistic, accurate, predictive
  simulations of complex biochemical networks, and
• Reveal general principles by which proteins
  control the adaptive behavior of cells

12
Systems Biology

• Application Integration
• Modeling, Simulation, Hypothesis Generation
• Map data to molecules, bio-chemical process,
• and diseases

Technology Integration Annotation, Functionation,
License, Literature, Visualization
13

• What do we understand ?
• Biological chemistry, Transmission of genetic
  information

What we dont understand ? Biological
complexity The best non-living equivalent of life
(for in-silico modeling) Emergent phenomena
14

• Why in-silico modeling ?
• What-if questions ?
• Essential vs. redundant
• Rejection of false hypothesis
• Prediction of future systems behavior
• Perform experiments at will !

Why mathematical modeling?

• Advantages, limitations, problems
• Quantitative vs. qualitative

15
Our Modeling strategy
Conceptual Model
Analytical Model
Computer simulation
Match in-silico in-vivo
16
Bioinformaticians Will Be Busy Bees

• Precise, predictive model of transcription
  initiation and termination ability to predict
  where and when transcription will occur in a
  genome
• Precise, predictive model of RNA
  splicing/alternative splicing ability to predict
  the splicing pattern of any primary transcript in
  any tissue
• Determining effective proteinDNA, proteinRNA
  and proteinprotein recognition codes
• Precise, quantitative models of signal
  transduction pathways ability to predict
  cellular responses to external stimuli
• Accurate ab initio protein structure prediction

At a bioinformatics conference last fall, EBIs
Ewan Birney, MITs Chris Burge, and
GlaxoSmithKlines Jim Fickett gave an impromptu
roundup of the future challenges of the field.
Burge polished them up for GT
17
Architectural finesse
18
(No Transcript)
19
Bioinformaticians Will Be Busy Bees

• Biomarkers discovery
• Rational design of small molecule inhibitors of
  proteins
• Mechanistic understanding of protein evolution
  understanding exactly how new protein functions
  evolve
• Mechanistic understanding of speciation
  molecular details of how speciation occurs
• Continued development of effective gene
  ontologies systematic ways to describe the
  functions of gene or protein
• Education development of appropriate
  bioinformatics curricula for secondary,
  undergraduate, and graduate education

20
Biomarkers

• A characteristics that is objectively
  measured and evaluated as an indicator of normal
  biological processes, pathogenic processes, or
  pharmacologic response(s) to a therapeutic
  intervention
• NIH/FDA Biomarkers Definitions Working
  Group in 1999.
• Three types of biomarkers
• Disease biomarkers - used to monitor and diagnose
  the progression of a disease
• Drug efficacy/toxicity biomarkers - used to
  monitor the efficacy or toxicity of a treatment
  regime
• PD marker for pharmacologic response

21
Biomarkers

• A biomarker needs to be linked with a clinical
  endpoint
• Clinical endpoint is defined as how patient
  feels, functions or survives
• Biomarkers needs to be validated for sensitivity,
  specificity, and reproducibility
• Biomarker can be any anatomical, histological,
  physiological, molecular measurements such as a
  gene, protein, metabolite, SNP, brain image, cell
  count, etc.
• Can even be a mathematical equation
• It is very rare for a single marker to have both
  sensitivity and specificity linked to an end point

22
Biomarkers

• A dynamic relationship between effector gene and
  gene, protein and protein, metabolite and
  metabolite as described by mathematical equations
  is a better biomarker, albeit with great
  challenge in experimental design and explanation.
  
• It is well-documented that even for the same
  class of drug one can have different surrogate
  markers for different clinical endpoint.

23
Bioinformaticians Will Be Busy Bees

• Biomarkers discovery
• Rational design of small molecule inhibitors of
  proteins
• Mechanistic understanding of protein evolution
  understanding exactly how new protein functions
  evolve
• Mechanistic understanding of speciation
  molecular details of how speciation occurs
• Continued development of effective gene
  ontologies systematic ways to describe the
  functions of gene or protein
• Education development of appropriate
  bioinformatics curricula for secondary,
  undergraduate, and graduate education

24
Atomic level enquiry Modelling/simulations Newtoni
an Quantum Brownian Imaginary!!
Links to Cheminformatics
25
Bioinformaticians Will Be Busy Bees

• Biomarkers discovery
• Rational design of small molecule inhibitors of
  proteins
• Mechanistic understanding of protein evolution
  understanding exactly how new protein functions
  evolve
• Mechanistic understanding of speciation
  molecular details of how speciation occurs
• Continued development of effective gene
  ontologies systematic ways to describe the
  functions of gene or protein
• Education development of appropriate
  bioinformatics curricula for secondary,
  undergraduate, and graduate education

26
Bioinformaticians Will Be Busy Bees

• Biomarkers discovery
• Rational design of small molecule inhibitors of
  proteins
• Mechanistic understanding of protein evolution
  understanding exactly how new protein functions
  evolve
• Mechanistic understanding of speciation
  molecular details of how speciation occurs
• Continued development of effective gene
  ontologies systematic ways to describe the
  functions of gene or protein
• Education development of appropriate
  bioinformatics curricula for secondary,
  undergraduate, and graduate education

27
Bioinformaticians Will Be Busy Bees

• Biomarkers discovery
• Rational design of small molecule inhibitors of
  proteins
• Mechanistic understanding of protein evolution
  understanding exactly how new protein functions
  evolve
• Mechanistic understanding of speciation
  molecular details of how speciation occurs
• Continued development of effective gene
  ontologies systematic ways to describe the
  functions of gene or protein
• Education development of appropriate
  bioinformatics curricula for secondary,
  undergraduate, and graduate education

28
BII Singapore - Vision

• To be a premier International BioInformatics
  Institute by fostering and conducting
  leading-edge informatics research, development,
  and high quality training, to generate knowledge
  from large diverse volumes of Biology and
  Chemistry data

29
BII Singapore - Mission

• Human Capital
• To foster high quality, innovative, and
  multi-disciplinary research and post-graduate
  training in BioInformatics
• Intellectual Capital
• To create knowledge base and tools to manage, and
  understand large, diverse biological and
  chemistry datasets
• To create Intellectual Property
• Industrial Capital
• To play an active role in Knowledge and
  Technology transfer - Drug target
  identification/validation, BioMarkers, etc.

30
BII Focus Areas
Bioinformatics Institute
Information Science Systems
Research Development
Education
Comp. Biology
Ph. D. Training
NUS (NTU)
Systems Biology
SBCR
Masters Training
Medical (Clinical)
Information Science
JC Outreach
(Biomarkers)
MOE Teacher
Systems Infra
(Cheminformatics)
Custom Training
BioImaging
NG
31
Bioinformatics Graduate Curriculum

• in association with the ASTAR Graduate
  Scholarship (AGS) / NUS Graduate School (NGS)
  schemes.

Coursework- intensive
Research
Post-Doc Training (2 years)
PhD
Year 1
Year 2
Year 3
Year 4
Qualifying Exam
Coursework components 12 modules (4MC each)

• Computational Biology 1 (BII)
• Computational Biology 2 (BII)
• Protein Classification Structure Prediction
  (BII)
• Systems Biology (BII)
• Mathematical Biology (BII)
• Research Ethics and Integrity I II (NUS)
• 6 Electives (NUS)

Electives in
Life Sciences Mathematics Probability
Statistics Computing I.T.
32
SBCR
33
BII
The Biopolis_at_one north
34
(No Transcript)
35
Our Home

• www.bii.a-star.edu.sg