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What is Bioinformatics?

Bio molecular biology

Informatics computer science

Bioinformatics solving problems arising frombiology using methodology from computer

science

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Human Genome Project

Identify the approximate20,000-25,000 genes inhuman DNA.

Store this information indatabases..

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Some Facts

97% of DNA in the humangenome has no known functions

Over 2.000 article on

endocrine per month

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Bio- & Medical Informatics

Differences - Gaps

Medicine Genomics

Informatics

Medicine + Informatics =

Medical Informatics

Genomics + Informatics =

Bioinformatics

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What is Biomedical Informatics (BMI) ?Medicine Genomics

Informatics

Medical Informatics +

Bioinformatics =

Biomedical Informatics

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Gene Bank

Med Reference

Drug Activities

REACTOME

Pathway : diseases

mechanism drug data bases

chemical data bases

SNP data bases

Mutant Data bases

Metabolomic

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Structure search in SciFinder

Retrieved 4000 papers

(refine search only MS and MALDI)

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Applications of Bioinformatics

Medical science

Drug design

Gene therapy

Sequence analysis of proteins and DNA

Analysis of microarrays Proteomics

Genomics

Molecular structure

Analysis and simulation of metabolic networks

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DRUG VACCINE DIAGNOSTIC

DESIGN Drug target Identification

Virtual Screening

Epitope Mapping

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Applications of Bioinformatics :

Gene Therapy

RNAi Target Prediction

Antisense Target

Prediction

Specific Target for GeneInsertion

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Applications of Bioinformatics :Sequence analysis of proteins and DNA

Mutation analysis

Structure analysis

3D Aligment: Structural

change due to Mutation

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Applications of Bioinformatics :Analysis of microarrays

Gene expression levelanalysis

Sequencing by hybridization

Mutation detection

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Systems Biology

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Genomics, Proteomics & Systems

Biology

1990 1995 2000 2005 2010 2015 2020

Genomics

Proteomics

Systems Biology

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What is Systems Biology?

Systems Biology - Systems biology is thestudy of an organism, viewed as an integrated

and interacting networkof genes, proteins and

biochemical reactions. The integration ofgenomics, proteomics, metabolomics & modeling

The Goal: Predictive, Preventative andPersonalized Medicine -- Leroy Hood

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Whats it good for?

Basic Science/Understanding Life

Predicting Phenotype from Genotype

Understanding/Predicting Metabolism

Understanding Cellular Networks

Predicting Disease Outcome/Prognosis

Understanding Pathogenicity/Toxicity

Predicting Adverse Drug Reactions

Improving Medical Efficiency/Efficacy

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Systems Biology is Multidisciplinary

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Going From Omics to Metrics

Genomics Genometrics

Proteomics Proteometrics

Metabolomics Metabometrics

Phenomics Phenometrics

Bioinformatics Biosimulation Quantify, quantify, quantify

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Going From Networks in vivo to

Networks in silico

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Going From Model Systems to

Medicine

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Systems Biology in Medicine

Still in its infancy

Ethnopharmacology & traditional meds

Integrated biodiagnostics (combined microarray, ICAT-

MS and metabolite profiles multicomponent

biomarkers) Adverse drug response prediction and monitoring

(personalized medicine)

Understanding complex metabolic diseases (cachexia,

obesity, diabetes)

Clinical Lab Medic ine Leads the Way

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The Pyramid of Life

25,000 Genes

2500 Enzymes

1400

Chemicals

Metabolomics

Proteomics

Genomics

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Limitation of Genome Project

Genomic theory:

1) Discovery of the gene related with a specific disease

2) Discovery of the protein related with the gene

Unsuccessful !!!!

Reason : fundamental failure to understand biological complexity

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Why ?

Problem 1: the function of a gene is NOTspecified in the DNA languageProblem 2: each gene plays roles in MULTIPLEfunctionsProblem 3: each function arises from co-operation ofMANYgenesProblem 4: function also depends on important properties NOTspecified by

genes - properties of water, lipids, self-assembly etc

Problem 5: nature has built-in fail-safe redundancy - this ONLYemerges atthe functional level

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Pre-Genomic

Reductionist (DNA or RNA or Protein)

Observational/ phenomenological

Generally qualitative and non-numeric

Hypothesis driven

Organism

Cellular

Molecular

Post-Genomic

Global/ holistic

Systems approach (DNA and RNA and Protein)

Quantitative and highly numeric

Not only hypothesis driven but also data driven Organism

Cellular

Molecular