Dr. Sanjeeva Srivastava IIT Bombay Proteomics Systems Biology IIT Bombay 2 1
DNA Genomics RNA Transcriptomics Global Cellular Protein Proteomics Global Cellular Metabolite Metabolomics Global Cellular IIT Bombay 3 IIT Bombay 4 2
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 IIT Bombay 5 IIT Bombay 6 3
Gel- based methods Gel- free MS Mass Spectrometry Interactomics Structural Proteomics IIT Bombay 7 Mass Spectrometry Tissue Biopsy Blood Sample Cell P P P P P P 2DE P P P P Microarray Chip Pattern Recognition, Learning Algorithm Proteomic Image Monitor therapy Early disease diagnosis IIT Bombay 8 4
IIT Bombay 9 Examination of a biological entity as an integrated system, rather than study of its individual characteristic reactions and components IIT Bombay 10 5
System level understanding of biological networks Common elements of systems biology Networks Modeling Computation Dynamic properties DNA Protein Systems Human RNA IIT Bombay 11 DNA RNA Other Biomolecules Proteins Networks Cells IIT Bombay 12 6
Protein-protein interaction networks Gene regulatory networks Protein-DNA interaction networks Protein-lipid interactions Metabolic networks IIT Bombay 13 Cell Genome Protein Profile & mrna profile Transcriptional Network Signalling Networks Metabolic Networks Protein Localisation IIT Bombay 14 7
Respiratory Intercellular Intracellular Molecular Nervous Intercellular Intracellular Molecular Gastrointestinal Intercellular Intracellular Molecular Cardiovascular Intercellular Intracellular Molecular Molecular Intercellular Intracellular Reproductive Development Immune Intercellular Intracellular Molecular IIT Bombay 15 IPG strip Intracellular signalling Transcrip;on Factors Nucleus Gene4c Regulatory Network Cis sites mrna Receptors Transla;on + Processing Cytoplasm Ligands Ion Channels Electrophysiology Extracellular Space IIT Bombay 16 8
Understanding biology in holistic rather than the reductionist approach Quantitate the qualitative biological data To make biology a predictive science IIT Bombay 17 Systems biology involves: Experimental data set collection Mathematical models IIT Bombay 18 9
IPG strip Model Analysis Experiment Model Construction IIT Bombay 19 IIT Bombay 20 10
Systems Biology Model-based Prior information implemented Computation modeling and simulation tools Data-based Finding new phenomena Datasets ( omic ) IIT Bombay 21 IPG strip DNA Protein Product DNA binding protein Lipoprotein IIT Bombay 22 11
Reductionist Approach: Disintegrating the system into its component parts and studying them SYSTEMS PARTS Reductionist Approach IIT Bombay 23 Integrative Approach: Integrating the study of individual components to form conclusions about system SYSTEMS Integrative Approach PARTS IIT Bombay 24 12
IIT Bombay 25 IPG strip Large Scale Organization Functional Modules Regulatory Motifs Metabolic Pathways Genes mrna Proteins Metabolites Information Storage Processing Execution IIT Bombay 26 13
IPG strip Genomic Sequences Cytosol Nucleus Intracellular signals TFs RNA Proteins Cellular processes Cis Binding Activities Expression Profiles IIT Bombay 27 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 IIT Bombay 28 14
EXPERIMENT COMPUTATIONAL MODELLING a12 a23 TECHNOLOGY x1 x2 x3 y1 a21 y2 y3 THEORY IIT Bombay 29 Wet-lab experiments or bioinformatics Propose a model Model Validation a12 a23 x1 x2 x3 y1 a21 y2 y3 a12 a23 x1 x2 x3 y1 a21 y2 y3 IIT Bombay 30 15
IPG strip IIT Bombay 31 Systems Sciences Systems Study Life Sciences Information Sciences IIT Bombay 32 16
IIT Bombay 33 HT-DNA sequencing, SNPs Microarrays, SAGE, RNA-Seq MS, 2D-PAGE, Protein chips, Yeast-2-hybrid NMR, X-ray IIT Bombay 34 17
IPG strip Phenome Metabolome Proteome Transcriptome Genome IIT Bombay 35 IIT Bombay 36 18
REAL SYSTEM BIOINFORMATICS PARTS LIST SYSTEMS BIOLOGY SYSTEM/SUBSYSTEM MODEL SYSTEMS BIOLOGY Closing the Loop SYSTEM MODEL ANALYSIS IIT Bombay 37 Level 1 Level 4 Nucleus Gene DNA RNA Metabolism Protein Level 2 Level 3 38 19
Curation of InterPro & Member Databases Recursive database search Initial Alignment Expanded Alignment Predictive Model a12 a23 x1 x2 x3 Protein sequence Method Scan Protein Domain Classification Ran BP1 InterProScan Annotation Look-Up EVH1 Literature Curation GO terms User Query Cached Results y1 a21 y2 y3 InterPro entry IIT Bombay 39 Quantitative Models Systematic Experiments Model Reaction Models Mechanistic Models Statistical Models Stochastic Models Modify Molecular Genetics Chemical Genetics Cell Engineering Mine BioInformatics Databases Data Semantics Measure Arrays Spectroscopy Imaging Microfluidics IIT Bombay 40 20
Biological System Model Experiment Simulation Experimental data Simulated data Experimental data intermediate statistics Simulated data intermediate statistics COMPARISON IIT Bombay 41 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 IIT Bombay 42 21
Dr. Sarath Chandra Janga Indiana University & Purdue University Indianapolis (IUPUI) IIT Bombay 43 An example 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 arac AraC AraC AraC CRP AraC AraC σs arabad melr MelR MelR MelR CRP MelR MelR σs melab 22
Networks in Biology Network Protein Interaction Metabolic Transcriptional Nodes Proteins Metabolites Transcription factor Target genes Links Physical Interaction Enzymatic conversion Transcriptional Interaction Interaction Protein-Protein Protein-Metabolite Protein-DNA A A A A B B B B 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 Graphs can be directed or undirected Undirected Directed 23
Graph Parameters Degree or connectivity Path length Clustering coefficient Degree of a node in a graph Undirected Degree or connectivity Degree = 2 Directed Degree of a node tells us how many links the node has to other nodes in a graph In-degree = 1 Out-degree = 3 24
Path length between two nodes in a graph Path length Undirected Path length = 1 Directed Path length is the shortest number of links needed to connect two nodes in a graph Path length = 2 Clustering coefficient of a node in a graph Clustering coefficient CC = 2 x M N x (N 1) N, neighbors of a node M, links between neighbors of a node CC = Clustering coefficient tells us how interconnected are the neighbors of a give node # links among neighbors # all possible links among neighbors N=3, M=0 CC=0 N=3, M=2 CC=0.66 N=3, M=3 CC=1 25
Transcriptional networks are scale-free Scale-free structure Presence of few nodes with many links and many nodes with few links Power law distribution N (k) α 1 k γ Scale free structure provides robustness to the system 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 26
INPUT signal A Genomic View- Gene Regulatory Network INPUT signal B Sensor proteins Sensor proteins Inactive TF A active TF A Inactive TF B Confirmational change active TF B Feedback OUTPUT OUTPUT mrna protein DNA gene B Legend, Transcription Factors (TFs) RNA polymerase, TF binding sites Transcription Start Site +1 Cis-regulatory DNA sequence elements target gene A Janga & Collado-Vides 2007, Res. in Microbiology Structure of the transcriptional regulatory network Transcription factor Target gene Basic unit (Components) transcriptional interaction Motifs (Local level) patterns of Interconnections Scale free hierarchical network (Global level) all transcriptional interactions in a cell Janga & Collado-Vides 2007, Res. in Microbiology 27
Gene regulation beyond transcription Gene regula4on is a highly regulated and complex process Gene regula4on takes place at several steps Transcrip;onal Post- transcrip;onal Diverse functions of RNA-Binding Proteins (RBPs) 4 40S 60S RNA stability RBP RBP Transla;onal control 5 Degrada;on Protein 2 Cytoplasm Transport 3 Localiza;on RBP 1 RBP Splicing Gene Transcrip;on RBP Nucleus Pre mrna Intron mrna Mitochondria Mittal et al., 2009 PNAS 28
A network of RBPs in human diseases Change in the expression dynamics of RBPs is associated with several diseases Networks in drug discovery settings Healthy state Disease state Drug space Network Pharmacology Magic Bullet LeChatelier s Principle in Network Pharmacology Cellular Interactome Janga SC. & Tzakos A. Molecular Biosystems 2009 29
Integration of data for understanding system-wide perturbations Janga SC. & Tzakos A. Molecular Biosystems 2009 What network pharmacology offers? Target protein Drug molecule Drugs sharing targets are linked Targets sharing drugs are linked Diseases sharing drugs are linked Diseases sharing targets are linked Disease Janga SC. & Tzakos A. Molecular Biosystems 2009 30
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 highthroughput sequencing and other technologies. IIT Bombay 62 31
Genomics Proteomics Systems Biology 1990 1995 2000 2005 2010 2015 2020 IIT Bombay 63 Genome- wide data sets Expression Protein Interac;on Validated Networks, Therapeu;c Targets Experimental Valida;on IIT Bombay 64 32
IPG strip Biology Medicine SYSTEMS BIOLOGY Engineering Computer Science IIT Bombay 65 IPG strip Systems Biology Physiology and medicine IIT Bombay 66 33
Systems biology is extremely challenging Understanding dynamics of biological networks requires modeling, simulation and understanding of biology Requires mathematical & statistical approaches IIT Bombay 67 Proteomics is useful to understand complex signaling networks in biological systems Proteomics is indispensable for systems biology IIT Bombay 68 34
Proteomics Systems biology IIT Bombay 69 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. IIT Bombay 35
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 IIT Bombay Dr. Sarath Chandra Janga for stimulating discussion and presentation on Systems approaches for studying biological networks: from post-transcriptional control to drug discovery. IIT Bombay 72 36