Information Driven Biomedicine. Prof. Santosh K. Mishra Executive Director, BII CIAPR IV Shanghai, May

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1 Information Driven Biomedicine Prof. Santosh K. Mishra Executive Director, BII CIAPR IV Shanghai, May

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 protein:dna, protein:rna and protein:protein 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, EBI s Ewan Birney, MIT s Chris Burge, and GlaxoSmithKline s 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 : 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 protein:dna, protein:rna and protein:protein 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, EBI s Ewan Birney, MIT s Chris Burge, and GlaxoSmithKline s 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 Cell Circuitry Will Be Key Tissues Organs Organisms Disease Physiology

11 Hartwell et al (1999) A useful theory must : 1. Provide realistic, accurate, predictive simulations of complex biochemical networks, and 2. Reveal general principles by which proteins control the adaptive behavior of cells

12 Application Integration Modeling, Simulation, Hypothesis Generation Map data to molecules, bio-chemical process, and diseases Systems Biology Technology Integration Annotation, Functionation, License, Literature, Visualization System diagram; Courtesy

13 What do we understand? Biological chemistry, Transmission of genetic information What we don t 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 Revise Our Modeling strategy Biological knowledge Conceptual Model Analytical Model Rate Equations Constraints Guess missing parameters Add lots of assumptions! Computer simulation Match in-silico & in-vivo Validated model Use model for diagnostic purposes Make predictions Explain nonintuitive phenomenon

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 protein:dna, protein:rna and protein:protein 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, EBI s Ewan Birney, MIT s Chris Burge, and GlaxoSmithKline s Jim Fickett gave an impromptu roundup of the future challenges of the field. Burge polished them up for GT:

17 Architectural finesse

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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 Three types of biomarkers: (1) Disease biomarkers - used to monitor and diagnose the progression of a disease (2) Drug efficacy/toxicity biomarkers - used to monitor the efficacy or toxicity of a treatment regime (3) 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 Newtonian 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 leadingedge 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 SBCR Information Science Systems Infra Comp. Biology Systems Biology Medical (Clinical) (Biomarkers) (Cheminformatics) Ph. D. Training Masters Training JC Outreach MOE Teacher NUS (NTU) NG BioImaging Custom Training

31 Bioinformatics Graduate Curriculum in association with the A*STAR Graduate Scholarship (AGS) / NUS Graduate School (NGS) schemes. Courseworkintensive Year 1 Qualifying Exam Research Year 2 Year 3 Year 4 PhD Post-Doc Training (2 years) 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

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