Introduction to Systems Biology

國立台灣大學資訊工程系博士後研究員 詹鎮熊

What is a system?

Features of a system Components Interrelated components Boundary Purpose Environment Interfaces Input Output Constrain

Examples of Systems

Life‘s Complexity Pyramid

Components

Buildingblocks

Functionalmodules

System

Z. N. Oltvai and A.-L. Barabási, Science 298, 763 (2002)

生態體系

社區

族群

個體

器官系統

個體

組織

細胞

分子

原子

生物圈

個體 – 細胞 – 胞器 – 分子Organism – Cell – Organelle – Molecules

人體由上兆個細胞組成每個細胞具有：46 條染色體2 米長的 DNA30 億個鹼基 (A, T, G, C)2~3 萬個基因

The Central

Dogma

Bottom-up

From genes to phenotypes

If the genome sequence can be fully sequenced, can we resolve all the secrets hidden in the DNA?

The -omics (-ome) era

Genomics (Genome) Human Genome Project Other Genome Projects

Mouse Fly Dog Worm Bacteria … Most recently … Cat

Human genome project

Sequence the whole genome sequence of several individuals

Competition between Celera and NIH Took over a decade Draft in 2000, complete in 2003

The next stage: HapMap HapMap is a catalog of common genetic

variants that occur in human beings It describes:

what these variants are where they occur in our DNA and how they are distributed among people

within populations and among populations in different parts of the world

Single Nucleotide Polymorphism (SNP)

Personalized genome James Watson (454 Life Science) Craig Venter (Venter Institute)

23andme (backed by Google, focus on social/family relationships)

Navigenics (focus on medical conditions) Personal Genome Project (PGP, Harvard)

Proteomics (Proteome)

Categorize all proteins (and their relationships) in a temporal-spatial confined system Identities of these proteins Quantities Variants of these proteins

Alternative splicing forms Post-translational modifications

(Phosphorylation, Methylation, Ubiqutination, …)

Proteomics

Mass Spectrometry

Fluorescence Resonance Energy Transfer (FRET)

Co-localization (interaction) between protein-protein, protein-DNA pairs

Transcriptome Identify all transcription factors (TF) func

tioning in a specific temporal-spatial confined system

Identify all genes regulated by specific TFs

ChIP-chip TransFac database

Chromatin Immuno-Precipitation (ChIP) a well-established

procedure used to investigate interactions between DNA-binding proteins and DNA in vivo

ChIP-chip

Transcription Factor Binding Motifs

Interactome

Categorized all interactions (protein-protein or protein-DNA) within an organism Yeast Two-Hybrid Immuno-coprecipitation (co-IP) Mass Spectrometry FRET …

Yeast Two-hybrid

Metabolomics (Metabolome) “systematic study of the unique

chemical fingerprints that specific cellular processes leave behind”

Collection of all metabolites in a biological organism

Analytical methods for metabolomics Separation

Gas Chromatography (GC) High performance liquid chromatography

(HPLC) Capillary electrophoresis (CE)

Detection Mass Spectrometry Nuclear magnetic resonance (NMR)

spectroscopy

Glycomics Oligosaccharide Glycoprotein/Proteoglycan

Proteins attached to oligosaccharides Important to cell recognition

Cancer targeting Influenza

Model Organisms

Yeast (S. cerevisiae) Worm (C. elegans) Fruit Fly (D. melanogaster) Mouse (M. musculus)

Monitoring the System

High throughput monitoring of gene expression Microarray Protein microarray GC/HPLC/MASS/Tandem MASS

Phenotype/Disease

Microarray

Protein Microarray

Phenotypes

Lethality Synthetic lethal

Developmental Morphological Behavioral Diseases

Genotypes and Phenotypes

genotype + environment → phenotype genotype + environment + random-variation → phenotype

Importance of Computer Models

Interactions in cell are too complex to handle by pen-and-paper

With high-throughput tools, biology shifts from descriptive to predictive

Computers are required to store, processing, assemble, and model all high-throughput data into networks

Types of Computer Models Chemical Kinetic Model

Defined by concentrations of different molecular species in the cell

Represented with a number of equations Some processes may be stochastic

Simplified Discrete Circuit Network with nodes and arrows Nodes represent quantity or other attributes Directed edges represent effect of nodes on

other nodes

Different Mathematical Formulations Differential Equations

Linear (ordinary) Partial Stochastic

S-Systems Power-law

formulation Captures complicate

dynamics Parameter estimation

is computation intensive

Model details Selection of genes, gene products, and ot

her molecules to be included Cellular compartments: nucleus, golgi, or

other organelles Too much details may lead to more noises Minimal model able to predict system pro

perties (mRNA level, growth rate, etc) is sufficient

Construct Model from Global Patterns

Microarray gene expression patterns: Up-regulated/down-regulated

Gene expression profiles under different conditions: Tumor/normal, cell cycle, drug treatment, …

Methods: Bayesian Inferences Machine learning (clustering, classification) …

Framework for Systems Biology

Tools for Simulation

E-cell Cell Illustrator Virtual Cell

Standardizing efforts: BioJake SBML (systems biology markup language) Facilitate the exchange of models

E-Cell System

A software to construct object models equivalent to a cell system or a part of the cell system

Employing Structured Variable-Process model (previously called the Substance-Reactor model, or SRM)

Objects: Variables, Processes, Systems

Cell Illustrator

Computational Databases Protein-protein interaction

DIP, BIND, MIPS, MINT, IntAct, POINT, BioGRID Protein-DNA interaction

TRANSFAC, SCPD Metabolic pathways

KEGG, EcoCyc, WIT, Reactome Gene Expression

GEO, ArrayExpress, GNF, NCI60, commercial Gene Ontology

Network Biology

The entities within a system form intertwined complex networks Genes Proteins Metabolites External factors…

Gene (Transcription) Regulatory Network

Protein-Protein Interaction Network

Metabolic Pathways

KEGGmetabolicpathway

Gene Ontology

The Gene Ontology project provides a controlled vocabulary to describe gene and gene product attributes in any organism

Annotations Molecular Function Cellular Components Biological Processes

Challenges of Databases

Provide information other than simple entries (e.g. PPI with functional annotation or binding strength)

Data maintenance – update Integration with other databases

Applications

Target identification and drug discovery

Disease Gene Identification From networks From literature From microarray Quantitative Trait Loci (QTL) Genome-Wide Association Study (GWAS) Endeavour Systems biology (integrated) approaches?

Drug Targets

Gene identification from network

Nodes Hubs

Edges (interactions) Define critical genes from connected

edges? Shortest path, alternative path? Weights

Metabolic pathways as well

Gene identification from literature

OMIM (Online Mendelian Inheritance in Men) Single gene disease Complex disease

Defects identified, target for drugs and cures

Gene identification from microarray Up-regulated genes Down-regulated genes Too many? Cluster of genes Regulator (transcription factors) for

the important clusters

Quantitative Trait Loci (QTL)

Region of DNA that is associated with a particular phenotypic trait

Phenotypic characteristic varies in degree and attributes to interaction between two or more genes

QTL may not be gene itself, but as a sequence of DNA, is closely linked with the target gene

Quantitative Trait Loci

LOD (log odd ratio): how likely to observe a locus for a group with specific trait (phenotype)

Expression QTL (e-QTL): combine microarray for gene expression (identify transcription regulatory elements as QTL)

cM: centimorgan, 1,000,000 bases in chromosome

Genome-Wide Association Studies (GWAS)

Genome-wide association studies (GWAS) rely on newly available research tools and technologies to rapidly and cost-effectively analyze genetic differences between people with specific illnesses, such as diabetes or heart disease, compared to healthy individuals.

Keys to success of GWAS

Population Resource Large sample size required for significant det

ection SNP Map and Genotyping

High-throughput genotyping IT and Analysis Tool

Storage and analysis (1000 microarrays for billions of data points)

What have GWAS found?

Genes associated with risks of: type 2 diabetes Parkinson's disease heart disorders Obesity prostate cancer …

An integrated approach: Endeavour

Genes can obtain various scores regarding their association with disease

These scores include those mentioned above

The various ranks of these genes according to different scores are determined

With a consensus scoring scheme (data fusion), the resulting prediction accuracy could be improved

Aerts, et al. (2006)

Toward personalized medicine

Targeted therapy

Using antibody against biomarkers (cancer or other infectious agents)

Require prior knowledge of patient response (through lab tests or biochips)

Gene therapy

Replace or inhibit genes in patients Vectors

Adenovirus (AAV) Silencing the disease gene

RNAi microRNA

RNA interference

Putting All Together

Network of Networks

Gene regulation (protein-DNA) Protein-protein interaction Metabolic pathway

How…?

Questions?