proteomics systems biology - nptel

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Dr. Sanjeeva Srivastava IIT Bombay

IIT Bombay Proteomics Course NPTEL

•  Proteomics

•  Systems Biology

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IIT Bombay Proteomics Course NPTEL 3

Global

Global

Global

DNA

Protein

Metabolite

Genomics

Transcriptomics

Proteomics

Metabolomics

Cellular

Cellular

Cellular

RNA


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•  Proteome: set of all the PROTEins expressed by a genOME

•  Proteomics: study of full set of proteins encoded by a genome for their expression, localization, interaction and post-translational modifications

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Gel-‐based methods

Gel-‐free MS

Mass Spectrometry Interactomics Structural

Proteomics


Tissue Biopsy

Cell

Microarray Chip

Proteomic Image

Mass Spectrometry

Pattern Recognition,

Learning Algorithm

Early disease diagnosis Monitor therapy

Blood Sample

P

P

P

P

P P

P

P

P

P

2DE

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•  Examination of a biological entity as an integrated system, rather than study of its individual characteristic reactions and components

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•  System level understanding of biological networks •  Common elements of systems biology

–  Networks –  Modeling –  Computation –  Dynamic properties

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DNA Protein Systems Human RNA


DNA

RNA

Proteins

Other Biomolecules

Networks

Cells

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•  Protein-protein interaction networks

•  Gene regulatory networks

•  Protein-DNA interaction networks

•  Protein-lipid interactions

•  Metabolic networks

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Cell

Signalling Networks

Transcriptional Network

Protein Profile &

mRNA profile

Genome

Protein Localisation

Metabolic Networks

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Respiratory

Reproductive Development

Nervous

Cardiovascular Gastrointestinal

Intercellular

Intercellular

Intercellular Intercellular

Intercellular

Intercellular

Intracellular

Intracellular

Intracellular

Intracellular

Intracellular

Intracellular

Immune

Molecular

Molecular

Molecular Molecular

Molecular

Molecular


IPG strip

Extracellular Space

Cytoplasm

Intracellular signalling

Ion Channels

Receptors

Ligands

Electrophysiology

Gene4c Regulatory Network

Cis sites

mRNA

Nucleus

Transcrip;on Factors

Transla;on + Processing

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•  Understanding biology in “holistic” rather than the “reductionist” approach

•  Quantitate the qualitative biological data

•  To make biology a predictive science

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•  Systems biology involves:

•  Experimental data set collection

•  Mathematical models

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IPG strip

Model

Construction

Experiment Model Analysis


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Prior information implemented

Finding new phenomena

Systems Biology

Model-based Data-based

Computation modeling and simulation tools

Datasets (“omic” )


IPG strip

DNA binding protein

DNA Protein Product

Lipoprotein

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SYSTEMS

PARTS Reductionist Approach

Reductionist Approach: Disintegrating the system into its component parts and studying them


SYSTEMS

PARTS Integrative Approach

Integrative Approach: Integrating the study of individual components to form conclusions about system

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IPG strip

Information Storage Processing Execution

Large Scale Organization

Functional Modules

Metabolic Pathways

Regulatory Motifs

Genes mRNA Proteins Metabolites

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IPG strip

Genomic Sequences

Nucleus

Cis Binding Activities

Expression Profiles

Cytosol

TFs RNA Proteins Intracellular signals

Cellular processes


•  Extraction and mining of complex & quantitative biological data

•  Integration and analysis of data for development of mechanistic, mathematical & computational models

•  Validation of models by refining and re-testing

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EXPERIMENT

TECHNOLOGY COMPUTATIONAL MODELLING

THEORY

a12 a23

x1 x3

y3 y1 y2 a21

x2


Propose a model

Model Validation

Wet-lab experiments or bioinformatics

a12 a23

x1 x3

y3 y1 y2 a21

x2

a12 a23

x1 x3

y3 y1 y2 a21

x2

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IPG strip


Systems Study

Syste

ms Sc

ience

s Life Sciences

Information Sciences

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•  HT-DNA sequencing, SNPs

•  Microarrays, SAGE, RNA-Seq

•  MS, 2D-PAGE, Protein chips, Yeast-2-hybrid

•  NMR, X-ray

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IPG strip

Phenome

Metabolome

Proteome

Transcriptome

Genome


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REAL SYSTEM PARTS LIST

SYSTEM/SUBSYSTEM MODEL

SYSTEM MODEL ANALYSIS

BIOINFORMATICS

SYSTEMS BIOLOGY

SYSTEMS BIOLOGY

Closing the Loop

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Level 1

Level 3

Level 4

Level 2

Gene

Protein

DNA

RNA Metabolism

Nucleus

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Curation of InterPro & Member

Databases

Expanded Alignment

Initial Alignment

Protein sequence Protein Domain Classification

User Query

Cached Results

Method Scan

Recursive database

search

Annotation Look-Up

Literature Curation GO terms

InterProScan

InterPro entry

Ran BP1 EVH1

Predictive Model

x1 x2 x3

y3 y1 y2

a12

a21

a23


Quantitative Models Systematic Experiments

Model

Reaction Models Mechanistic Models Statistical Models Stochastic Models

Mine

BioInformatics Databases

Data Semantics

Measure

Arrays Spectroscopy

Imaging Microfluidics

Modify

Molecular Genetics Chemical Genetics Cell Engineering

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Biological System

Experiment

Experimental data

Experimental data intermediate statistics

Model

Simulated data

Simulation

Simulated data intermediate statistics

COMPARISON


•  Ordinary Differential Equations (ODE): mathematical relation that can be used for modeling biological systems

•  Stochiometric model: modeling a biological network based on its stochiometric coefficients, reaction rates and metabolite concentrations

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Dr. Sarath Chandra Janga

•  Indiana University & Purdue University Indianapolis (IUPUI)

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Central Dogma of Molecular Biology

Transcription is regulated by a class of

proteins called transcription factors that

bind DNA and affect expression of the nearby

genes

σS araBAD araC AraC AraC AraC AraC AraC CRP

MelR σS CRP

MelR MelR MelR MelR melAB melR

An example

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Networks in Biology

Nodes

Links

Interaction

A

B

Network

Proteins

Physical Interaction

Protein-Protein

A

B

Protein Interaction

Metabolites

Enzymatic conversion

Protein-Metabolite

A

B

Metabolic

Transcription factor Target genes

Transcriptional Interaction

Protein-DNA

A

B

Transcriptional

Graphs are objects, which are made of nodes and edges

Graph – a collection of nodes and links

Nodes represent entities

Links represent interactions between entities

Undirected Directed

Graphs can be directed or undirected

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Graph Parameters

Degree or connectivity

Path length

Clustering coefficient

Degree or connectivity

Degree of a node tells us how many links the node

has to other nodes in a graph

Undirected

Directed

Degree = 2

In-degree = 1 Out-degree = 3

Degree of a node in a graph

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Path length

Path length is the shortest number of links needed to

connect two nodes in a graph

Undirected

Directed

Path length = 1

Path length between two nodes in a graph

Path length = 2

Clustering coefficient of a node in a graph

Clustering coefficient

Clustering coefficient tells us how interconnected are the neighbors of a give node

# links among neighbors

# all possible links among neighbors

CC =

N=3, M=0 CC=0

N=3, M=2 CC=0.66

N=3, M=3 CC=1

2 x M

N x (N – 1) CC =

N, neighbors of a node M, links between neighbors of a node

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N (k) α k γ

1

Scale-free structure

Presence of few nodes with many links and many

nodes with few links

Transcriptional networks are scale-free

Scale free structure provides robustness to the system

Power law distribution

Scale-free networks exhibit robustness

Robustness – The ability of complex systems to maintain their function even when the structure of the system changes significantly

Tolerant to random removal of nodes (mutations)

Vulnerable to targeted attack of hubs (mutations) – Drug targets

Hubs are crucial components in such networks

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Genomic View- Gene Regulatory Network

OUTPUT

protein

Cis-regulatory DNA sequence elements

target gene A gene B +1

INPUT

signal B

Sensor proteins

Inactive TF B

active TF B

INPUT

signal A

Sensor proteins

Inactive TF A

active TF A

Confirmational change

OUTPUT

mRNA

Feed

back

DNA

TF binding sites

Legend Transcription Factors (TFs) RNA polymerase

Transcription Start Site ,

,

Janga & Collado-Vides 2007, Res. in Microbiology

Structure of the transcriptional regulatory network

Scale free hierarchical network (Global level)

all transcriptional interactions in a cell

Motifs (Local level)

patterns of Interconnections

Basic unit (Components) transcriptional

interaction

Transcription factor

Target gene

Janga & Collado-Vides 2007, Res. in Microbiology

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 Gene regula4on is a highly regulated and complex process

 Gene regula4on takes place at several steps

Post-‐transcrip;onal

Transcrip;onal

Gene regulation beyond transcription

Transcrip;on

Pre mRNA

Splicing mRNA Intron

Transport

Nucleus

Cytoplasm

Gene Transla;onal control

RNA stability

Localiza;on

60S

40S

Protein

Degrada;on

2

3

4

1

5

RBP

RBP

RBP

RBP

RBP

Mitochondria

Diverse functions of RNA-Binding Proteins (RBPs)

Mittal et al., 2009 PNAS

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A network of RBPs in human diseases

Change in the expression dynamics of RBPs is associated with several diseases

Networks in drug discovery settings

Janga SC. & Tzakos A. Molecular Biosystems 2009

Healthy state Disease state

“Magic Bullet” Netw

ork

Pharmacology

LeChatelier’s Principle in Network Pharmacology

Drug space

Cellular Interactome

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Integration of data for understanding ��system-wide perturbations


What network pharmacology offers ?


Target protein

Drug molecule

Drugs sharing

targets are linked

Disease

Targets sharing

drugs are linked

Diseases sharing

drugs are linked Diseases

sharin

g

targets are

linked

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Conclusion   Network-based approaches are essential for

dissecting the design principles of biological systems

  They play an important role in biomarker identification and elucidation of key players responsible for the disease phenotype.

  Systems medicine can lead to the development of personalized medical treatment options in years to come with developments in high-throughput sequencing and other technologies.


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1990 1995 2005 2000 2015 2010 2020

Genomics

Proteomics

Systems Biology


Genome-‐wide data sets

Expression Protein Interac;on

Validated Networks, Therapeu;c Targets

Experimental Valida;on

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IPG strip

Biology

Computer Science

Engineering

Med

icin

e SYSTEMS BIOLOGY


IPG strip

Systems Biology

Physiology and medicine

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•  Systems biology is extremely challenging

•  Understanding dynamics of biological networks requires modeling, simulation and understanding of biology

•  Requires mathematical & statistical approaches

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•  Proteomics is useful to understand complex signaling networks in biological systems

•  Proteomics is indispensable for systems biology

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•  Proteomics

•  Systems biology


•  Systems Biology: A Brief Overview, Hiroaki Kitano,, 1662 (2002); 295 Science •  Simpson R. J. Proteins and Proteomics: a laboratory manual ed., Cold Spring Harbor laboratory press, 2002. ISBN: 0879695544. •  Trey Ideker, L. Raimond Winslow, A. Douglas Lauffenburger. Bioengineering and Systems Biology. Annals of Biomedical Engineering. February 2006, Volume 34, Issue 2, pp 257-264 •  Trey Ideker, et al., Integrated Genomic and Proteomic Analyses of a systematically Perturbed Metabolic Network, Science, 2001. •  Sarath Chandra Janga and Julio Collado-Vides. Structure and evolution of gene regulatory networks in microbial genomes. Research in Microbiology , 2007 158(10):787-94.

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•  Mittal N, Roy N, Babu MM, Janga SC. Dissecting the expression dynamics of RNA-binding proteins in posttranscriptional regulatory networks. Proc Natl Acad Sci U S A. 2009 Dec 1;106(48):20300-5. •  Sarath Chandra Janga and Andreas Tzakos. Structure and organization of drug-target networks : Insights from genomic approaches for drug discovery. Molecular Biosystems , 2009, 5(12):1536-48 •  Ernesto Perez-Rueda, Sarath Chandra Janga * and Agustino-Martinez-Antonio. Scaling relationship in the gene content of transcriptional machinery in bacteria. Molecular Biosystems, 2009, 5(12):1494-501


•  Dr. Sarath Chandra Janga for stimulating discussion and presentation on Systems approaches for studying biological networks: from post-transcriptional control to drug discovery.

proteomics systems biology - nptel

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