1.Introduction to System biology1Dr. Etienne Z. GNIMPIEBA605 677 6064Etienne.gnimpieba@usd.edu

2. Who ? Im Biotechnologist/ Bioinformatician/Clinical Data Manager You are For you, what does SB look like? How would you expected to use SB inyour work or career path?2 3. Aims My job:Help you to aimer SB.Provide a simple view of complex Systems Biologyworld.Your job:Listen question - read .3 4. Content Quiz What is SB? Why SB? Who can do SB? How does SB work ? Modeling Integration Systems view of Lifescience Systems Biology uses case Gene and target therapy Disease geneidentification Systems Biology case study Metabolic modeling Protein modeling Gene regulatory network(GRN) Cancer tumor growth Epidemiology: HIVspread Virtual biology QuizSession 1 (SB I):Session 2 (SB II)4 5. Assignments Synopses of the literature: proposed list Exercise1: Quiz design, think as a teacher Exercise 2: Molecular modeling tools used5 6. Session 1 (SB I)6 Quiz What is SB? Why SB? Who can do SB? How does SB work? Modeling Integration Systems view of Lifescience 7. Systems BiologyBios=lifelogos= study /scienceBIOLOGICAL SYSTEMSScience is built up of facts, as a house is with stones. But a collection of facts is no morea science than a heap of stones is a house (H. Poincar) 7What is Systems Biology? 8. Systems biology is concerned with the study of biological functions andmechanisms, underpinning inter- and intra-cellular dynamical networks, by means ofsignal- and system-oriented approaches. (Cosentino, 2008)Systems biology is an approach by which a system of interacting entities is analyzedas a whole rather than by analyzing its individual constituent entities separately.(Nahleh, 2011)Systems biology is an approach in biomedical research to understanding the largerpicturebe it at the level of the organism, tissue, or cellby putting its piecestogether. Its in stark contrast to decades of reductionist biology, which involvestaking the pieces apart. (NIH, 2013)Systems biology: The study of biological systems taking into account the interactionsof the key elements such as DNA, RNA, proteins, and cells with respect to oneanother. The integration of this information may be by computer. (MedicalDictionary, 2013)Systems biology is the research endeavor that provides the scientific foundation forsuccessful synthetic biology. (Breitling, 2010)8What is Systems Biology? 9. Components /elements Interrelatedcomponents Boundary Purpose Environment Interfaces Input Output Constrain9What is Systems Biology? 10. Scientific Research Reduce experimental cost (virtual screening in CADD) Improved biological knowledge New experimental techniques (in silico) Classical mathematical modeling of biological processes Computer power for simulation of complex systems Storage and retrieval capability in large databases and datamining techniques Internet as the medium for the widespread availability frommultiple sources of knowledge EducationWhy?10 11. Elucidate network properties Check the reliability of basic assumptions Uncover lack of knowledge and requirements forclarification Create large repository of current knowledge, formalizedin a non-ambiguous way and including quantitative dataModels are not Real, though Reality can be Modeled Interactions in cell are too complex to handle by pen andpaper With high throughput tools, biology shifts from descriptive topredictive Computers are required to store, processing, assemble, andmodel all high-throughput data into networksWhy?11 12. Why?Toward personalized medicine12 13. Cloud Databank Database Data designer Information manipulation Create/collect information Statistic analysis Date inference, learning Model from data Model from SB Large scale modelModeling & learning SBInformaticsData manipulationBio/lifeWho can do system biology?13 14. How does SB work? Modeling Biological systems modeling Process modeling Case study modeling Integration Biological System integration Data integration (big data before Big dataconcept ) Tools integration (software, material) Process integration 14 15. Integration Aspects of SBProcess of combining two or more parts.15 16. Integration in Systems BiologyProcess of combining two or more parts.DatabasesToolsExperimentsKnowledgeScientistDecision makerStudentsOtherQuestionIntegratedanswer16 17. System integrationLink publication, gene and protein17 18. System integrationLink publication, gene and protein18 19. Data integration (why?) Observation of biological phenomena is restricted tothe granularity and precision of the availableexperimental techniques A strong impulse to the development of a systematicapproach in the last years has been given by the newhigh-throughput biotechnologies Sequencing of human and other genomes (genomics) Monitoring genome expression (transcriptomics) Discovering protein-protein and -DNA interactions(proteomics)19 20. Data integration (why?) Different types of information need to be integrated Data representation and storage: (too) Manydatabases (GO, KEGG, PDB, Reactome) XML-like annotation languages (SBML, CellML) Information retrieval Tools for retrieving information from multiple remoteDBs Data correlation Find the correlation between phenotypes andgenomic/proteomic profiles Statistics, data mining, pattern analysis, clustering,PCA, 20 21. Data integrationStructural Biology Knowledge Basehttp://sbkb.org/ 21 22. 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 Knowledge base SBKB22 23. Modeling aspect of SB23Biological Systems look as Systems with sub-systems 24. 24What is a Model? 25. Model in systems biology: generic workflow25 26. Life science levels26 27. ADNEADNARNmEDgradationDgradationTraductionTranscriptionRpression duGneS PCatalyseKey concept: molecular biology dogma27 28. Key concept: Lactose Operon (lac)28Genes and its binding sitesIn the "induced" state, the lac repressoris NOT bound to the operator siteIn the "repressed" state, the repressor ISbound to the operator. 29. Executable biology vs. experimental biology29 30. Time and space in SB modeling30 31. System Biology Model vocabulary State (steady state, ) Parameters Variables Constants Behavior Scope Statements31 32. SB Model characterization Structural / functional model Qualitative / quantitative model Deterministic / non deterministic Nature (continuous / discreet /hybrid) Reversibility / irreversibility /periodicity32 33. Models development33 34. Three Approaches for System design Bottom-up: Construct a network and predict itsbehavior starting with a collection of experimental data Top-down: Starts from observed behavior and thenfills in the components and interactions required togenerate these observations by iterative experimentalresults and simulations Middle-out: Starts at any point for which data areavailable, as long as it is supported by a hypothesis, andthen expand either up or down in terms of bothresolution and coverage34 35. Models development tasks Formulation of the problem:Identify the specific questions that shall be answered,along with background, problem and hypotheses. Available Knowledge:Check and collect quantitative and structural knowledgeComponents of the systemInteraction map and kind of interactionsExperimental results with respect to phenotypicresponses against different stimuli (gene knockout,RNAi, environmental conditions)Essentially, all models are wrong, but some are useful. - George E.P. Box35 36. Models development tasks Selection of model structure:Level of description (atomistic, molecular,cellular, physiological)Deterministic or stochastic modelDiscrete or continuous variablesStatic, dynamical, spatio-temporaldynamicalEssentially, all models are wrong, but some are useful. - George E.P. Box36 37. Models development tasksEssentially, all models are wrong, but some are useful. - George E.P. Box Robustness/Sensitivity Analysis: Test the dependence of thesystem behavior on changes of theparameters Numerical simulations Bifurcation analysis37 38. Models development tasksEssentially, all models are wrong, but some are useful. - George E.P. BoxExperimental TestsHypotheses drivenChoice of parameters to be measured,different types of experiments, number ofsamples and repetitions, Assessment of the agreement and divergencesbetween experimental results and modelbehavior Iterative refinement of the hypotheses (and ofthe model) 38 39. Models development39 40. Different Mathematical Formulations Differential Equations Linear (ordinary) Partial Stochastic S-Systems Power-law formulation Captures complicate dynamics Parameter estimation iscomputation intensive Game theory Multi-agent systems Graph theory tools Logic (binary, fuzzy, )40 41. Model development from data Methods: Bayesian Inferences Machine learning (clustering, classification) Fuzzy logic S PObs Cond1Cond2Cond31 2 3 Microarray gene expression patterns: Up-regulated/ down-regulated Gene expression profiles under different conditions:Tumor/normal, cell cycle, drug treatment, 41 42. Life science systems representation42 43. disiDegilliSyntikikiKKVVdtdm-Ligne1-Ligne2Init. F rponseSpcification logique temporelleTrace erreurModel CheckerModle tatsfinisAlgo.RechercheRF-R1-R2-R3-.c1 c2 c3 c4g1 L M M Hg2 L H H Mg3 M M M Mg4 L H H Mg5 L L H MAlgo. CS & prog. logiqueSmirij(Eij,Vij)mjrji(Eji,Vji)rii(Eii,Vii)ParameterValueBRBFLOGICPROGRAMMINGCODINGBCKINETICCHARACTERIZATIONBiologicalKnowladge(Litterature)MATHEMATICALFORMALISMModelExperimental ConditionsC-C1-C2--.c1 c2 c3 c4g1 120.9 81.8 116.8 66.6g2 1.6 1.5 1.4 1.1g3 7.5 9.2 7.4 7.9g4 0.6 0.7 0.8 0.7g5 80.5 77.9 103.4 75.24Ens. Floues & T-normesDNA0%100%65%20%Prob(%)130%HypermethylationADNHCY0%100%85%30%Prob(%)175%HyperhomocysteinemieHypohomocysteinemieg6g5g1 g2g4g3m5m4m1m3m2E1E3E2 E5E4FRRFCMicro nutrientMetabolic networkGenetic networkbiologicalknowledgeExperimentalData (ADN)DATAMicroarray dataBiological levels ofabstractionSpecies and biologicalinteractionsStructurerepresentationTransportMetabolicGeneticDataeg6eg5eg2eg4eg3Epigenetic FactorsEpigeneticLife science systems focus43 44. Session 2 (SB II)44 Systems Biology uses case Gene and target therapy Disease gene identification Systems Biology case study Metabolic modeling Protein modeling Gene regulatory network (GRN) Cancer tumor growth Epidemiology: HIV spread Virtual biology Quiz 45. Targeted therapy Using antibody againstbiomarkers (cancer orother infectious agents) Require priorknowledge of patientresponse (through labtests or biochips)Gene therapy Replace or inhibitgenes in patients Vectors Adenovirus (AAV) Silencing the diseasegene RNAi microRNA45 46. Disease Gene Identification From networks From literature From microarray Quantitative Trait Loci (QTL) Genome-Wide Association Study (GWAS) Endeavour Systems biology (integrated) approaches?46 47. Gene identification from network Nodes Hubs Edges (interactions) Define critical genes from connected edges? Shortest path, alternative path? Weights Metabolic pathways as well47 48. Systems Biology ModelingCase study48 49. Metabolic modeling49 50. Metabolic Pathways50 51. DesignMethodsm1m2 m4m3m5Metabolic level E4E4E4E4E4E4Metabolic networkDefinition: A biological network (metabolic, genetic, protein, ) is a directed graphwhose nodes are labeled biological species, reactions and edges labels are enzymes thatcatalyze these reactions.Modeling using network in biologyContinuousDifferentialEquation /Algebraic EquationDiscretLogic theoryHybridContinuousfunction interval51 52. Formalization of the model of metabolic networksSmirij(Eij,Vij)mjrji(Eji,Vji)rii(Eii,Vii)),,( ijijij Pmtfv))()),,(,(),()(),( 00tVPPtmtVdtPtdmPmPtmrcdisiDegilliSyntikikiKKVVdtdmExampleneHomocysteikdtMethioninedneHomocysteikdtneHomocysteidcc..MethionineneHomocystei ckContinuous model52 53. Folates metabolism (folic acid or Vitamin B9) and pathogenesis53 54. 0 5 10 15 20 25024681012141618Time(Hours)Concentration(m)CH3_5_THFe0.20.40.60.811.21.41.61.82Concentration(m)FR:CH3_5_THFeRFC:CH3_5_THFe0 5 10 15 20 25024681012141618Time(Hours)Concentration(m)CH3_5_THFe0 5 10 15 20 2500.20.40.60.811.21.41.61.82Time(Hours)Concentration(m)FR:CH3_5_THFeRFC:CH3_5_THFeContinuous model analysis54 55. Protein modelingProtein structure modelingprotein-protein interaction55 56. Protein structure modeling56>TARGETQGQEPPPEPRITLTVGGQPVTFLVDTGAQHSVLTQNPGPLSDRSAWVQGATGGKRYRWTTRKVHLATGKVTHSFLHVPDCPYPLLGRDLLTKLKAQI; 57. Protein structure modeling57 58. Protein-Protein Interaction Network58 59. Gene regulatory network (GRN)59 60. Can be Complex60 61. Gene regulation61 62. Boolean modeling of GRN62 63. ODE modeling of GRN63 64. FBA modeling of GRNFlux Balanced Analysis: about equilibrium and steady state64 65. Cancer tumor growthBiological species as agents65 66. CancerTumorDevelopment66 67. Epidemiology: HIV spread67 68. HIV spread10 Northwestern, 201068 69. Virtual biology:Body browserVirtual Cellvirtual brain69 70. Body browserGoogle Body browser7 Google, 201170 71. Virtual CellVirtual Cell project71http://www.vcell.org/vcell_software/login.html 72. Virtual CellVirtual Cell72http://www.vcell.org/vcell_software/login.html 73. Virtual brainVirtual Brain73 74. QUIZ74