Every object that biology studies is a system of systems (1974) Francois Jacob (1965 Nobel Prize in Medicine)

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2 Every object that biology studies is a system of systems (1974) Francois Jacob (1965 Nobel Prize in Medicine) 2

3 Reductionism vs Holism (Aristotle, 1946) The whole is something over and above its parts and not just the sum of them all 3

4 Life s Complexity Pyramid Information storage, processing, and execution lie in different levels of organizations 4

5 High-throughput Data 5

6 Systems Medicine A systems approach to health and disease (Hood) A disease is rarely the outcome from one single gene or its product with genetic abnormality, but a cohort of genes/proteins that involve in a complex network Paul O Shea: Future medicine shaped by an interdisciplinary new biology 6

7 A Simple Example Mutation leading to the gene regulatory network malfunctioning (Current Opinion in Biotechnology, 2010, 21: ) 7

8 Systems Medicine Proactive P4 (predictive/preventive/personalized/participatory) medicine: L. Hood and M. Flores, 2012 Provide deep insights into disease mechanisms Make it possible for viewing health and disease for the individual Stratify complex diseases into their distinct subtypes for a match against proper drugs Provide new approaches to drug discovery Generate metrics for assessing wellness 8

9 Outlines Network Constructions Networks and Human Diseases Network properties & diseases Prediction of disease genes Other applications Opportunities and Challenges 9

10 Network Constructions 10

11 Simplified Biological Systems Abstract representations of biological systems Nodes: RNA, gene, proteins, metabolites etc Edges: physical, biochemical and functional interactions Alon, Science 301,

12 Network Types Gene Regulatory Networks Protein-protein Interaction Networks Metabolic Networks Transcriptional Profiling Networks Phenotypic Profiling Networks Others 12

13 Network Constructions High-throughput Experiments Large scale versus accuracy Manual Curation Existing information from literatures Quality of the published data and quantity Computational Methods Large scale, take advantage of the existing knowledge Capable of dealing with noises 13

14 Examples Networks in Cellular Systems Vidal, Cusick, and Barabasi, Cell 144,

15 Gene Regulatory Networks Nodes: TF or DNA regulatory elements Edges: physical (functional) binding (directional) 15

16 Modeling GRN Biological Knowledge 16

17 Modeling GRNs Hypothesis-driven approaches assume the network structure is known (e.g., biochemically driven models, neural network models) Clustering assumes co-expression is caused by coregulation; Hierarchical, k-means, biclustering etc Ordinary differential equations Boolean networks Bayesian networks 17

18 Bayesian Networks Data are noisy The behavior of GRNs at the single-cell level is fundamentally stochastic (e.g., Computational Modeling of GRNs by Hamid Bolouri) Optical readout noise ODE and Boolean Networks inherently deterministic The sound probabilistic semantics allows BNs to deal with the noises BNs can handle missing data and permit the incomplete knowledge about the biological system BNs are capable of integrating the prior biological knowledge into the system 18

19 Challenges High dimensionality Genome-wide structure learning task is NP-hard Need dimensionality reduction Small sample size Several tens to hundreds (typically < 200) Model overfiting Our solution HMM-based model for dimensionality reduction Integration of constraint-based and scoring-based structure learning methods for network reconstruction 19

20 Computational System Dimensionality reduction followed by structure learning 20

21 Constructing an Undirected Network Integration of mutual information and graph theory for complete connectivity (e.g., path matrix based approach) 21

22 Refine the Network Using the d-separation and the Markov independence criterion to evaluate the edges (remove and add edges). For example, C P(A C) P(A B,C) T estafterdeleting A B P(A B) P(A C, B) T estafterdeleting A C A B P(B A) P(B C, A) T estafterdeleting B C A C B D If the conditional independence test fails for all the deletions, it could be that the triangle loop is authentic, or other paths exist from one node to another. For example, consider a true sub-network. Assume that in the UDS, a triangle loop A B C is identified; while in a true network, the edge between C and B (dashed line) does not exist, and another node D is involved in this network. 22

23 Direction Assignment Using graph theory, d-separation, and maximizing BIC score (exhaustive methods on sub-networks, e.g., find loops with four or five nodes), coming edges etc. C A B Computational complexity: O(n^4) + O(mn^2), m: # samples, n: # of nodes 23

24 Structure Learning X. Chen, et al., Improving Bayesian Network Structure Learning with Mutual Information-based Node Ordering in the K2 Algorithm, IEEE Transactions on Knowledge and Data Engineering, vol. 20(5): , X. Chen, et al., An effective structure learning method for constructing gene networks, Bioinformatics, 22(11): ,

25 Experimental Results POL 30 BN built with our algorithm for yeast cell cycle-related genes. A total of 20 nodes (genes) and 34 edges (interactions) were incorporated. This network captured 65% of all currently reported direct and indirect interactions among these genes. CDC 45 RFA 3 MS H6 MS H2 HPR 5 PRI1 POL 2 POL 1 PDS 1 RAD 53 ASF 1 MCD 1 PRI2 RAD 54 POL 12 DPB 2 CLB 5 PMS 1 25 CLB 6

26 Protein-protein Interaction Networks Nodes: proteins Edges: physical interaction, complexes 26

27 Model Organisms Jeong, Mason, Barabasi, and Oltvai - Nature, 411, 3 May, Rual et al., Nature, 437(20), Oct. 2005

28 In Silico Methods Sequence based methods Rosetta Stone Method Co-evolution Method Physicochemical Properties Method Domain based methods Association Method MLE Method Random Forest Method Integrative methods Decision Tree Logistic Regression Bayesian Network 28

29 Domain-based Approaches p 1 d 1 d 2 d 3 d 5 p 2 d 4 d 5 d 5 d 3 d 2 d 4 p 4 p 3 Protein-protein interactions d 2 Domain-domain interaction 29

30 RFD Methods X. Chen and M. Liu, Prediction of Protein-protein Interactions Using Random Decision Forest Framework, Bioinformatics, 21(24): ,

31 Extended Method X. Chen, M. Liu, and R. Ward, Protein Function Assignment through Mining Cross-Species Protein-protein Interactions, PLoS ONE, 3(2): e1562, 2008 X. Chen and J. Jeong, Sequencebased Prediction of Protein Interaction sites with an Integrative Method, Bioinformatics, 25(5): , 2009 M. Liu, X. Chen and R. Jothi, Knowledge-Guided Inference of Domain-Domain Interactions from Incomplete Protein-Protein Interaction Networks, Bioinformatics, 25(19): ,

32 KUPS Structure of KUPS Workflow diagram 32 URL:

33 KUPS X. Chen, J. Jeong, and P. Dermyer, KUPS: Constructing datasets of interacting and non-interacting protein pairs with associated attributes, Nucleic Acids Research, 2011, Jan; 39:D750-4 Purpose Services Providing high-quality interacting protein pairs (IPPs) and noninteracting pairs (NIPs) for researchers IPPs from three manually curated PPI databases (i.e. IntAct, MINT and HPRD) NIPs calculated with four different methods Test set collections Benchmark datasets 33

34 Metabolic Networks Nodes: metabolites Edges: biochemical reactions (or enzyme that catalyzes) Construction: manual curation + computational assist KEGG 34

35 Genotypic Profiling Networks Indirect interactions (functional links): protein (genes) who function together share similar expressions Genes versus microarray, DNA chip, de novo RNA sequencing etc. Correlation + threshold, e.g., Pearson correlation coeff Stuart et al. Science 302, 2003 Gunsale et al. Nature 436,

36 Some Other Methods Clustering algorithms Signature Algorithm (Ihmels, Bergman, et al. 2002, Nature Genet; 2003, Bioinformatice) Supervised Neural Networks (Alvaro et al., PNAS, 2002) Gene Recommender (Owen et al., Genome Research, 2003) Bayesian Network Biclustering (Dhollander et al., Bioinformatics, 2007) Other bi-clustering algorithms Not considering time sequence 36

37 Dimensionality Reduction rand HMM Σ FFR seed HMM p-val Iterations? 37 37

38 HMM-based Approach A. Senf and X. Chen, Identification of Genes Involved in the Same Pathway Using a Hidden Markov Model-based Approach, Bioinformatics, 25(22): ,

39 Synthetic Data According to Signature Algorithm Literature Initial matrix contains all-zeros Add a module by adding ones to the matrix Randomly scale all genes and conditions Add noise Using a trained Hidden Markov Model Initial matrix contains all-zeros Add module by generating observation sequences using a previously trained HMM Add random values at remaining matrix positions Add noise 39

40 Signature Algorithm Data Set 2600 genes, 100 experimental conditions Embedded modules: 250 genes, 40 conditions Low amount of noise High amount of noise 40

41 HMM Data Sets 500 genes, 100 experimental conditions Embedded module: 125 genes, 40 conditions Low amount of noise 41 High amount of noise

42 Cell Cycle Pathway Found new genes functionally related to input genes Same functional annotations overrepresented as in input gene group 42

43 Phenotypic Profiling Networks Nodes: genes Edges: correlated phenotypic profiles (e.g., RNA interference (RNAi), gene knock-out) Giaever et al., 2002, yeast Mohr et al., 2010, c. elegans, human etc. PPNs confer PPIs (evidence in c. elegans DNA damage response etc.) 43

44 Network Integration Overlap all the networks constructed (Gunsalus et al.,nature 2005; Vidal et al., Cell 2011) 44

45 Network Properties & Diseases 45

46 Network Properties The structure and evolution of networks (social, food, biological etc.) share some common principles: Scale free Hubs Essential genes (Jeong et al. 2001) Evolved more slowly (Fraser et al., 2002) Cancer proteins tend to have more interacting partners than non-cancer proteins Motifs Subgraphs (patterns), more frequent than expected Tied to some biological functions 46

47 Disease Networks Diseases are not independent from each other They may be triggered by different perturbations of some highly connected biological networks Nodes: diseases; edges: how the two diseases are linked Constructing a bipartite network Goh et al, 2007: OMIM genes, two diseases share the same disease genes whose mutations are the cause Rzhetsky et al. 2007; Hidalgu et al., 2009: by data mining individuals who were diagnosed for disease A often for disease B as well - comorbidity (e.g., diabetes and obesity) 47

48 Disease Networks Integrative analysis of cellular networks and disease networks (e.g., the molecular defects for diseases A and B in disease networks how will it affect other genes or other diseases? 48 Barabasi et al., Nature 2011 (12)

49 Prediction of disease genes 49

50 Network-based Applications The human interactome network is predictive (Lim et al., Lee et al., 2010) Linkage methods direct interaction partners Disease module-based methods genes in the same topological, functional or disease modules (integrative analysis of both cellular and disease networks) Diffuse-based methods seed genes randomly walk along an identified pathways (closely related to the see genes) 50

51 Disease Modules Nature reviews, vol. 12,

52 Our Recent Work Based on an integrative model OMIM Gene Network Red circle: mitochondrial deficiency related diseases Yellow circle: protocadherin beta related diseases 52

53 Our Recent Work Newly discovered disease genes 53

54 SNPs-disease Associations Epistasis: interactive effect between two or more genetic variants The primary reason for variation in common (or complex) human diseases Detection of epistatic interactions can help to improve pathogenesis, prevention, diagnosis and treatment of complex diseases Challenges of epistatic interaction detection Large size of genotyped data (10 million SNPs) Enormous number of all possible combinations of genetic factors 54

55 General Methods Statistical methods Only can be applied to small-scale analysis due to their computational complexity Machine learning-based methods Might identify a SNP set that produces the highest classification accuracy, but not necessarily has the strongest association with the diseases lack the ability to detect the causal elements Tend to introduce many false positives How many SNPs??? 55

56 POWER POWER POWER POWER BN-based Approaches A new scoring method and search algorithm Age-related Macular Degeneration (AMD) dataset Contains 116,204 SNPs genotyped with 96 cases and 50 controls. Three associated SNPs were found: Rs Found with a significant association with AMD BN-BnB DASSO-MB BEAM SVM MDR Model1 (=0.3 r 2 =1) MAF Model3 (=0.6 r 2 =1) BN-BnB DASSO-MB BEAM SVM MDR MAF BN-BnB DASSO-MB BEAM SVM MDR Model2 (=0.3 r 2 =1) MAF BN-BnB DASSO-MB BEAM SVM MDR Model4 (=7 r 2 =1) MAF 56

57 Other Applications Network Pharmacology systems level method for drug design: reduce the search for therapeutic agents; identify potential side effects; multi-target drug strategy an essential part for drug development Network Perturbation Disease Classification Personalized Medicine Reformed healthcare (predictive/preventive) 57

58 Opportunities and Challenges Big picture: personalized medicine/healthcare, preventative medicine Whole genome information available Data Sources: large scale + heterogeneous (BIG Data) still need creative algorithms and systematic approached for data analysis (genome wide etc.) Perturbation and time series analyses Integration: a (weighted) complete, reliable human interactome Current Opinion Pharmacology, 2012, 12:1-6 58

59 Thank You 59