A Systems Biology View to Diabetes Jia-Rui Wu Shanghai - - PowerPoint PPT Presentation

A Systems Biology View to Diabetes

Jia-Rui Wu 吴 家 睿

Shanghai Institutes for Biological Sciences, CAS Department of Systems Biology, USTC

New Trends in Life Science in 21th Century:

Systems Biology

Part I

Scientific Revolution in Life Science in 21th

Normal Sciences

1950 1990

Central Dogma Molecular Biology

Time

Scientific Revolution Normal Sciences Scientific Revolution

Human Genome Project

Scientific Knowledge

Systems Biology

First Scientific Revolution

Change the View of Life

Philosophy for Biologists in 20th Century: Reductionism

Gene Protein Function

Central Dogma

2005，7， 1167

Research limited in individual gene or protein

One protein One gene One function

Classic View of Life: Simple System

Classic View of Life: Simple System

Research based on linear interaction

Real Picture of Life

Genes of Human

~ 300 00

Proteins of Human

Millions

Real Picture of Life

Signaling Network

Protein Interaction Network

Human Genome Project

23 Chromosome ( 3.3X109 bp ) Genes： ~ 30, 000 ~1.5% for coding protein

DNA Books

Fish Arabidopsis Thermophile Rice

Post-Genome Era

• Omics

Genomics Transcriptomics Proteomics Metabolomics

Novel View of Life in 21th Century: Holism

p53 network

Novel View of Life : Complex System

ErbB signaling

Traditional Biology Classical Biology Systems Biology

Systems Biology View of Life

Input Output Black Box

A (gene/

protein)

B (gene/

protein)

Input Output Simple System Output Input Complex System

Second Scientific Revolution

Change the View of Life Science

Process of Biological Knowledge Discovery

Wet Lab. Data Personal-based Knowledge Wet Lab. Researcher

20th Century

Dry Lab.

Information-based Knowledge

Wet Lab. Researcher

Wet Lab. Data

21th Century

Huge amount of Experimental Date

Complexity of Experimental Date

Cell signaling

Protein interaction network

Computing capability decide understanding biological complexity

Life is information：Digital

DNA RNA Protein Protein interactive network Cell Tissue Organism

Information Flow in Biological System

Central Dogma

Computing in Biological System

Traditional Biology Era

Life is vitalism

Molecular Biology Era

Life is machine

Post-Genome Era

Life is information

Novel View of Life Science in 21th Century

Systems Biology

Integration Science

Integration of All Kinds of Molecules

Genome Transcriptome Proteome

Citric acid cycle

Metabolome

Integration of Different Levels

Gene / Protein Cell Gene / Protein Network Organism

Integration of Small Science and Big Science

Systems Biology

1， 2， 3， n “Omics” （Discovery Science） Gene （Protein）

Gene clone Gene expression Gene mutation Protein structure Protein interaction

Experimental Biology （Hypothesis-driven Science）

Molecular Biology Cell Biology Genomics Proteomics Metabolomics Wet Laboratory Computing Informatics Modeling Mathematics Dry Laboratory

Integration of Wet Lab. and Dry Lab.

Systems Biology

New Way against Diabetes:

Systems Biology

Part II

Diabetes in the World

2006-12-20, UN decided Nov. 14th of every year “World Diabetes Day” to be

“United Nations Day”

150 millions diabetes patients / 2000

Nature Reviews Drug Discovery, 2005, 4:367

1970 1970 1975 1975 1980 1980 1985 1985 1990 1990 1995 1995 2000 2000 2005 2005

2487 2487 2487 2517 2517 2517 3498 3498 3498 4566 4566 4566 6101 6101 6101 6955 6955 6955 9315 9315 9315

16795 16795 16795

Answer Question or Solve Question ?

Challenge One

How to study molecular network

Tradition Way for Analyzing Diabetes

Signaling (13) PTP1B Meg1/Grb10 IR Aging galectin-3 Insulin2 PAI-1 Socs7 SOCS1 IGF-I CD IRS1 IRS2 Gas6 Endocrinology (14) COX-2 NOS NPY ACC2 apo E p75(NTR) p27(Kip1) HSL USF1 USF2 Ifng Orexin SHIP2 HO-1 Beta cell (9) Wfs1 TCF2 Pdx1 OGG-1 ARNT Stat4 cyclin D1 cyclin D2 P58(IPK) Insulin resistance (10) MC4R AR GK L-PGDS GLUT4 PTEN Alms1 ORP Ikbkb Acc2 Insulin secretion (8) HNF-4alpha SGK1 P35 HNF-4alpha Fem1b PKClambda IA-2 MafA

2005

Knockout-mice used for diabetes research

• Annu. Rev. Physiol. 2003. 65:313

Tissue-specific knockout insulin receptor

Nature Rev. Mol. Cell Biol. 2006, 7:85

Diabetes: a disease involving complex network

Insulin signaling network

IRS Family

Challenge Two

How to study complexity of systems

Linear deduction

Tradition Way for Studying Diseases

Gene Gene Gene Gene

Metabolism is system work

Cell 1998, 92:593

Diabetes: dynamic development of systems

“when it comes to the control of intermediary metabolism and plasma glucose levels, there may sometimes be important differences between mice and humans.” Science，2005， 307：370 “when it comes to the control of intermediary metabolism and plasma glucose levels, there may sometimes be important differences between mice and humans.” Science，2005， 307：370 Mouse Mouse Human Human

MODY gene mutation MODY gene mutation

Hyperglycemia Hyperglycemia

MODY gene knockout MODY gene knockout

Normal Normal

PPAR γ -Pro467Leu mut PPAR γ -Pro467Leu mut PPARγ-Pro467Leu mut PPARγ-Pro467Leu mut

Insulin resistance Insulin resistance Normal Normal

Animal System is different from Human System

Nature 2003, 423,599

Environ. factors Environ. factors Genetic factors Genetic factors

Diabetes: Personalized disease

Genetic factors Environment factors

1980 1980 2002 2002

4X! 4X!

2.6% 2.6% 0.67% 0.67% 3.74% 3.74% 1.83% 1.83% 6.07% 6.07% Rural Rural Small city Small city Big city Big city

Factors involved in Chinese diabetes population

Solution against diabetes

Systems Biology

http://www.bair.org.uk/bair_organisation.html

Biological Atlas of Insulin Resistance - BAIR

Type 2 diabetes of Chinese clinic / population Systems for analysis Methodology

• Mol. biology

Cell biology Genetics Genomics Proteomics Metobolomics Bioinformation

Genetic factors Environment factors

Clinic/population

Strategy of diabetes project in China

Animal model Cell model

Scientific questions Study

What is life?

Systems Biologist

Components (Nodes) Interactions (Links)

Life is a statistical complex system rather than a determined simple system

Thank You!