Pathways, Networks and Systems Biology OR “what do I do with my gene list?” BMI 705 Kun Huang Department of Biomedical Informatics Ohio State University

Gene Enrichment Analysis Gene Ontology / Pathways / Networks Databases and Resources Gene Regulation (cis-)Networks Challenges in system biology New computation and modeling methods Kinetics vs. dynamics Scale-Free Network and Network Motifs

Where do I get the gene list? Comparative study  e.g., microarray experiments between two types of samples or two disease states (can also be from RT-PCA, proteomics, …) Clustering / classification of genes  e.g., co-expressed genes Homologue analysis  e.g., genes from BLAST Other sources

What do I do with the gene list? Find commonality among the gene  Common biological functions  Common molecular processes  Common cellular components  Common pathways  Interact with common genes  Common sequences / molecular structures  Regulated by common Transcription Factors  Involved in the same disease  … Generate new hypothesis based on the commonality

How do I find commonality from my gene list? Using a priori knowledge (e.g., gene ontology, pathway, annotation, etc.) Fisher’s exact test (chi-square based) Other statistical method Good news – most of the time you can use software to do it How significant is the intersection?

What softwares are available? Many DAVID ( Cytoscape GOTerm BiNGO GSEA GenMapp (Free) Pathway Architect (Commercial) Pathway Studio (Commercial) Ingenuity Pathway Analysis (Commercial) Manually curated On-demand computation

Genes Functions, pathways and networks

Pathway – What’s out there? 240

Ingenuity Pathway Analysis (IPA)

Gene Enrichment Analysis Gene Ontology / Pathways / Networks Databases and Resources Gene Regulation (cis-)Networks Challenges in system biology New computation and modeling methods Kinetics vs. dynamics Scale-Free Network and Network Motifs

Transcription in higher eukaryotes Adapted from Wesserman & Sandelin, 2004, Nature Rev. Genetics TFBS: Transcription Factor Binding Sites proximal promoter region distal promoter region Gene Expression 1.Chromatin structure 2.Initiation of transcription 3.Processing of transcripts 4.Transport to cytoplasm 5.mRNA translation 6.mRNA stability 7.Protein activity stability

Characterization of transcriptional regulation  Annotating regulatory regions (TSS and Promoter)  Identifying cis-regulatory modules  Deciphering logic of regulatory networks

Transcriptional regulatory module cis-regulatory elements are sequence-specific regions transcription factors bind AGGCTA CGGTTAAG GCTAACGC TFs combinatorially associate with each other to form modules and regulate their target genes

Gene regulatory network

Identify Cis-Regulatory Element TFs bind to cis-acting regulator elements (CAREs). CAREs are DNA motifs of length 5 – 20 (e.g., 5’ CGGnnnnnnnnnnnCCG 3’, the binding site for yeast TF, Gal4). Most CAREs are in the 5’ vicinity of the gene (promoter), but some have been identified downstream. Algorithms focus on identify common motifs. Words count. Probabilistic methods (weight matrix, combined with EM search). Phylogenetic footprinting. Other features: CpG island.

Example – from JASPAR Database AGL3 A [ ] C [ ] G [ ] T [ ]

Example Workflow i candidate Motifs Screen against TRANSFAC n final known and novel Motifs Gene list Ab Initio Motifs Discovery Programs (Weeder and MEME) Question : How do you extract upstream sequences for genes? Extract promoter sequences Multiple sequence alignment Manual selection

ChIPMotifs (from Dr. Victor Jin) m final statistical significant candidate Motifs Bootstrap re-sampling approach to determine optimal cutoff of Motifs and screen against non- enrichment sequences i candidate Motifs Screen against TRANSFAC n final known and novel Motifs k>i>m>n k Top Level Sequences Ab Initio Motifs Discovery Programs (Weeder and MEME) Question : How do you extract upstream sequences for genes?

Gene Enrichment Analysis Gene Ontology / Pathways / Networks Databases and Resources Gene Regulation (cis-)Networks Challenges in system biology New computation and modeling methods Kinetics vs. dynamics Scale-Free Network and Network Motifs

Biology Domain knowledge Hypothesis testing Experimental work Genetic manipulation Quantitative measurement Validation System Sciences Theory Analysis Modeling Synthesis/prediction Simulation Hypothesis generation Informatics Data management Database Computational infrastructure Modeling tools High performance computing Visualization System Biology Understanding! Prediction!

“A key element of the GTL program is an integrated computing and technology infrastructure, which is essential for timely and affordable progress in research and in the development of biotechnological solutions. In fact, the new era of biology is as much about computing as it is about biology. Because of this synergism, GTL is a partnership between our two offices within DOE’s Office of Science— the Offices of Biological and Environmental Research and Advanced Scientific Computing Research. Only with sophisticated computational power and information management can we apply new technologies and the wealth of emerging data to a comprehensive analysis of the intricacies and interactions that underlie biology. Genome sequences furnish the blueprints, technologies can produce the data, and computing can relate enormous data sets to models linking genome sequence to biological processes and function.”

Taniguchi et al. Nature Reviews Molecular Cell Biology 7, 85–96 (February 2006) | doi: /nrm1837

Challenges in system biology Large data Kinetics vs. dynamics Multiple (temporal) scale New computation and modeling methods New mathematics or new physics laws

AB Oscillation Maeda et al., Science, 304(5672): , 2004

Simple Two Nodes Pattern Bistable dynamics in a two-gene system with cross-regulation. A. Gene regulatory circuit diagram. Blunt arrows indicate mutual inhibition of genes X and Y. Dashed arrows indicate a basal synthesis (affected by the inhibition) and an independent first-order degradation of the factors. B. Two-dimensional XY phase plane representing the typical dynamics of the circuit. Every point (X, Y) represents a momentary state defined by the values of the pair X, Y. Red arrows are gradient vectors indicating the direction and extent that the system will move to within a unit time at each of the (X, Y) positions. Collectively, the vector field gives rise to a "potential landscape", visualized by the colored contour lines (numerical approximation). In this "epigenetic landscape", the stable states (attractors) are in the lowest points in the valleys: a (X>>Y) and b (Y>>X) (gray dots). C. Schematic representation of the epigenetic landscape as a section through a and b in which every red dot represents a cell. Experimentally, this bistability is manifested as a bimodal distribution in flow cytometry histograms in which the stable states a and b appear as peaks at the respective level of marker expression (e.g., Y). Chang et al., Multistable and multistep dynamics in neutrophil differentiation, BMC Cell Biology 2006, 7:11

Marlovits et.al., Biophysical Chemistry, Vol:72, p

Pomerening et.al., Cell, Vol:122(4), p

New system biology Kinetics vs. Dynamics Compartmentalization (Spatial and Temporal) Hybrid Systems and System Abstraction Hierarchical/multiscale description Discrete Event System New System Theory Graph Theory and Network Theory / New Mathematics and New Physics

Gene Enrichment Analysis Gene Ontology / Pathways / Networks Databases and Resources Gene Regulation (cis-)Networks Challenges in system biology New computation and modeling methods Kinetics vs. dynamics Scale-Free Network and Network Motifs

A Tale of Two Groups A.-L. Barabasi at University of Notre Dame Ten Most Cited Publications: Albert-László Barabási and Réka Albert, Emergence of scaling in random networks, Science 286, (1999). [ PDF ] [ cond-mat/ ]PDFcond-mat/ Réka Albert and Albert-László Barabási, Statistical mechanics of complex networks Review of Modern Physics 74, (2002). [ PDF ] [cond-mat/ ]PDFcond-mat/ H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.-L. Barabási, The large-scale organization of metabolic networks, Nature 407, (2000). [ PDF ] [ cond-mat/ ]PDFcond-mat/ R. Albert, H. Jeong, and A.-L. Barabási, Error and attack tolerance in complex networks Nature 406, 378 (2000). [ PDF ] [ cond-mat/ ]PDFcond-mat/ R. Albert, H. Jeong, and A.-L. Barabási, Diameter of the World Wide Web Nature 401, (1999). [ PDF ] [ cond-mat/ ]PDFcond-mat/ H. Jeong, S. Mason, A.-L. Barabási and Zoltan N. Oltvai, Lethality and centrality in protein networks Nature 411, (2001). [ PDF ] [ Supplementary Materials 1, 2 ] PDF 1, 2 E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, and A.-L. Barabási, Hierarchical organization of modularity in metabolic networks, Science 297, (2002). [ PDF ] [ cond-mat/ ] [ Supplementary Material ]PDFcond-mat/ Supplementary Material A.-L. Barabási, R. Albert, and H. Jeong, Mean-field theory for scale-free random networks Physica A 272, (1999). [ PDF ] [ cond-mat/ ]PDFcond-mat/ Réka Albert and Albert-László Barabási, Topology of evolving networks: Local events and universality Physical Review Letters 85, 5234 (2000). [ PDF ] [ cond-mat/ ]PDFcond-mat/ Albert-László Barabási and Zoltán N. Oltvai, Network Biology: Understanding the cells's functional organization, Nature Reviews Genetics 5, (2004). [ PDF ]PDF

A Tale of Two Groups Uri Alon at Weissman Institute Selected Publications: R Milo, S Itzkovitz, N Kashtan, R Levitt, S Shen-Orr, I Ayzenshtat, M Sheffer & U Alon, Superfamilies of designed and evolved networks, Science, 303: (2004). Pdf.Pdf R Milo, S Shen-Orr, S Itzkovitz, N Kashtan, D Chklovskii & U Alon, Network Motifs: Simple Building Blocks of Complex Networks, Science, 298: (2002). Pdf.Pdf S Shen-Orr, R Milo, S Mangan & U Alon, Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics, 31:64-68 (2002). Pdf.Pdf S. Mangan, S. Itzkovitz, A. Zaslaver and U. Alon, The Incoherent Feed-forward Loop Accelerates the Response-time of the gal System of Escherichia coli. JMB, Vol 356 pp (2006). Pdf. S Mangan & U Alon, Structure and function of the feed-forward loop network motif. PNAS, 100: (2003). Pdf. S. Mangan, A. Zaslaver and U. Alon, The Coherent Feedforward Loop Serves as a Sign-sensitive Delay Element in Transcription Networks. JMB, Vol 334/2 pp (2003). Pdf.Pdf Guy Shinar, Erez Dekel, Tsvi Tlusty & Uri Alon, Rules for biological regulation based on error minimization, PNSA. 103(11), (2006). Pdf.Pdf Alon Zaslaver, Avi E Mayo, Revital Rosenberg, Pnina Bashkin, Hila Sberro, Miri Tsalyuk, Michael G Surette & Uri Alon, Just-in-time transcription program in metabolic pathways, Nature Genetics 36, (2004). Pdf.Pdf U. Alon, M.G. Surette, N. Barkai, S. Leibler, Robustness in Bacterial Chemotaxis, Nature 397, (1999). PdfPdf M Ronen, R Rosenberg, B Shraiman & U Alon, Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics. PNAS, 99:10555–10560 (2002). Pdf.Pdf N Rosenfeld, M Elowitz & U Alon, Negative Autoregulation Speeds the Response Times of Transcription Networks, JMB, 323: (2002). Pdf. N Rosenfeld & U Alon, Response Delays and the Structure of Transcription Networks, JMB, 329:645–654 (2003). Pdf. S. Kalir, J. McClure, K. Pabbaraju, C. Southward, M. Ronen, S. Leibler, M.G. Surette, U. Alon, Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria. Science, 292: (2001). PdfPdf Y. Setty, A. E. Mayo, M. G. Surette, and U. Alon, Detailed map of a cis-regulatory input function, PNAS, 100: (2003). Pdf. Shiraz Kalir and Uri Alon, Using a Quantitative Blueprint to Reprogram the Dynamics of the Flagella Gene Network, Cell, 117:713–720, (2004). Pdf.Pdf

Small world phenomena ( P(k) ~ k -  Found R. Albert, H. Jeong, A-L Barabasi, Nature, (1999). Expected

Other Observations: Scientific citations Paper coauthorship/collaboration Organization structure Social structure Actor joint casting in movies Online communities Websites linkage … Protein networks Gene networks Cell function networks …

Scale-Free Networks

Metabolic network Organisms from all three domains of life are scale-free networks! H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature, (2000) ArchaeaBacteriaEukaryotes

Power Law Small World Rich Get Richer (preferential attachment) Self-similarity HUBS!

Preferential attachment in protein Interaction networks  k vs. k : increase in the No. of links in a unit time No PA:  k is independent of k PA:  k ~k Eisenberg E, Levanon EY, Phys. Rev. Lett Jeong, Neda, A.-L.B, Europhys. Lett. 2003

Nature Biotechnology 18, (2000) doi: /82360 A network of protein−protein interactions in yeast Benno Schwikowski, Peter Uetz & Stanley Fields

Nature Biotechnology 18, (2000) doi: /82360 A network of protein−protein interactions in yeast Benno Schwikowski, Peter Uetz & Stanley Fields

C. Elegans Li et al. Science 2004 Drosophila M. Giot et al. Science 2003

Nature (2000) … “One way to understand the p53 network is to compare it to the Internet. The cell, like the Internet, appears to be a ‘scale-free network’.” Consequence 1 : Hubs and Robustness

Complex systems maintain their basic functions even under errors and failures (cell  mutations; Internet  router breakdowns) node failure fcfc 01 Fraction of removed nodes, f 1 S

Hubs and Robustness Complex systems maintain their basic functions even under errors and failures (cell  mutations; Internet  router breakdowns) R. Albert, H. Jeong, A.L. Barabasi, Nature (2000)

Achilles’ Heel of complex networks Internet failure attack R. Albert, H. Jeong, A.L. Barabasi, Nature (2000)

Yeast protein network - lethality and topological position Highly connected proteins are more essential (lethal)... H. Jeong, S.P. Mason, A.-L. Barabasi, Z.N. Oltvai, Nature 411, (2001)

Subgraphs Subgraph: a connected graph consisting of a subset of the nodes and links of a network Subgraph properties: n: number of nodes m: number of links (n=3,m=3) (n=3,m=2) (n=4,m=4) (n=4,m=5).

R Milo et al., Science 298, (2002).

System biology Integration Computation Theory Prediction!!!