Introduction to System biology1Dr. Etienne Z. GNIMPIEBA605 677 6064Etienne.gnimpieba@usd.edu
Who …? I’m Biotechnologist/ Bioinformatician/Clinical Data Manager… You are … For you, what does SB look like? How wou...
Aims My job:Help you to “aimer” SB.Provide a simple view of complex Systems Biologyworld.Your job:Listen – question - read...
Content Quiz What is SB? Why SB? Who can do SB? How does SB work ?◦ Modeling◦ Integration Systems view of Lifescienc...
Assignments Synopses of the literature: proposed list Exercise1: Quiz design, think as a teacher Exercise 2: Molecular ...
Session 1 (SB I)6 Quiz What is SB? Why SB? Who can do SB? How does SB work?◦ Modeling◦ Integration Systems view of L...
Systems BiologyBios=lifelogos= study /scienceBIOLOGICAL SYSTEMS“Science is built up of facts, as a house is with stones. B...
Systems biology is concerned with the study of biological functions andmechanisms, underpinning inter- and intra-cellular ...
 Components /elements Interrelatedcomponents Boundary Purpose Environment Interfaces Input Output Constrain9What ...
Scientific Research• Reduce experimental cost (virtual screening in CADD)• Improved biological knowledge• New experimental...
• Elucidate network properties• Check the reliability of basic assumptions• Uncover lack of knowledge and requirements for...
Why?Toward personalized medicine12
• Cloud• Databank• Database• Data designer• Information manipulation• Create/collect information• Statistic analysis• Date...
How does SB work? Modeling◦ Biological systems modeling◦ Process modeling◦ Case study modeling◦ … Integration◦ Biologica...
Integration Aspects of SBProcess of combining two or more parts.15
Integration in Systems BiologyProcess of combining two or more parts.DatabasesToolsExperimentsKnowledgeScientistDecision m...
System integrationLink publication, gene and protein17
System integrationLink publication, gene and protein18
Data integration (why?) Observation of biological phenomena is restricted tothe granularity and precision of the availabl...
Data integration (why?) Different types of information need to be integrated Data representation and storage: (too) Many...
Data integrationStructural Biology Knowledge Basehttp://sbkb.org/ 21
Computational Databases Protein-protein interaction◦ DIP, BIND, MIPS, MINT, IntAct, POINT, BioGRID Protein-DNA interacti...
Modeling aspect of SB23Biological Systems look as Systems with sub-systems
24What is a Model?
Model in systems biology: generic workflow25
Life science levels26
ADNEADNARNmEDégradationDégradationTraductionTranscriptionRépression duGèneS PCatalyseKey concept: molecular biology dogma27
Key concept: Lactose Operon (lac)28Genes and its binding sitesIn the "induced" state, the lac repressoris NOT bound to the...
Executable biology vs. experimental biology29
Time and space in SB modeling30
System Biology Model vocabulary State (steady state, ) Parameters Variables Constants Behavior Scope Statements31
SB Model characterization Structural / functional model Qualitative / quantitative model Deterministic / non determinis...
Models development33
Three Approaches for System design Bottom-up: Construct a network and predict itsbehavior starting with a collection of e...
Models development tasks Formulation of the problem:Identify the specific questions that shall be answered,along with bac...
Models development tasks• Selection of model structure:•Level of description (atomistic, molecular,cellular, physiological...
Models development tasks“Essentially, all models are wrong, but some are useful.” - George E.P. Box Robustness/Sensitivit...
Models development tasks“Essentially, all models are wrong, but some are useful.” - George E.P. Box•Experimental Tests•Hyp...
Models development39
Different Mathematical Formulations Differential Equations◦ Linear (ordinary)◦ Partial◦ Stochastic S-Systems◦ Power-law ...
Model development from data Methods:◦ Bayesian Inferences◦ Machine learning (clustering, classification)◦ Fuzzy logic◦ …S...
Life science systems representation42
disiDegilliSyntikikiKKVVdtdm-Ligne1………-Ligne2Init.  F réponseSpécification logique temporelleTrace erreurModel CheckerM...
Session 2 (SB II)44• Systems Biology uses case– Gene and target therapy– Disease gene identification• Systems Biology case...
Targeted therapy Using antibody againstbiomarkers (cancer orother infectious agents) Require priorknowledge of patientre...
Disease Gene Identification From networks From literature From microarray Quantitative Trait Loci (QTL) Genome-Wide A...
Gene identification from network Nodes◦ Hubs Edges (interactions)◦ Define critical genes from connected edges?◦ Shortest...
Systems Biology ModelingCase study48
Metabolic modeling49
Metabolic Pathways50
DesignMethodsm1m2 m4m3m5Metabolic level E4E4E4E4E4E4Metabolic networkDefinition: A biological network (metabolic, genetic,...
Formalization of the model of metabolic networksSmirij(Eij,Vij)mjrji(Eji,Vji)rii(Eii,Vii)),,( ijijij Pmtfv))()),,(,(),()()...
Folates metabolism (folic acid or Vitamin B9) and pathogenesis53
0 5 10 15 20 25024681012141618Time(Hours)Concentration(µm)CH3_5_THFe0.20.40.60.811.21.41.61.82Concentration(µm)FR:CH3_5_TH...
Protein modelingProtein structure modelingprotein-protein interaction55
Protein structure modeling56>TARGETQGQEPPPEPRITLTVGGQPVTFLVDTGAQHSVLTQNPGPLSDRSAWVQGATGGKRYRWTTRKVHLATGKVTHSFLHVPDCPYPLLGR...
Protein structure modeling57
Protein-Protein Interaction Network58
Gene regulatory network (GRN)59
Can be Complex60
Gene regulation61
Boolean modeling of GRN62
ODE modeling of GRN63
FBA modeling of GRNFlux Balanced Analysis: about equilibrium and steady state64
Cancer tumor growthBiological species as agents65
CancerTumorDevelopment66
Epidemiology: HIV spread67
HIV spread10 Northwestern, 201068
Virtual biology:Body browserVirtual Cellvirtual brain69
Body browserGoogle Body browser7 Google, 201170
Virtual CellVirtual Cell project71http://www.vcell.org/vcell_software/login.html
Virtual CellVirtual Cell72http://www.vcell.org/vcell_software/login.html
Virtual brainVirtual Brain73
QUIZ74
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Session ii g2 overview chemical modeling mmc

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Session ii g2 overview chemical modeling mmc

  1. 1. Introduction to System biology1Dr. Etienne Z. GNIMPIEBA605 677 6064Etienne.gnimpieba@usd.edu
  2. 2. Who …? I’m 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. 3. Aims My job:Help you to “aimer” SB.Provide a simple view of complex Systems Biologyworld.Your job:Listen – question - read .3
  4. 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. 5. Assignments Synopses of the literature: proposed list Exercise1: Quiz design, think as a teacher Exercise 2: Molecular modeling tools used5
  6. 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. 7. Systems BiologyBios=lifelogos= study /scienceBIOLOGICAL SYSTEMS“Science 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. Poincaré) 7What is Systems Biology?
  8. 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 largerpicture—be it at the level of the organism, tissue, or cell—by putting its piecestogether. It’s 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. 9.  Components /elements Interrelatedcomponents Boundary Purpose Environment Interfaces Input Output Constrain9What is Systems Biology?
  10. 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. 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. 12. Why?Toward personalized medicine12
  13. 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. 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. 15. Integration Aspects of SBProcess of combining two or more parts.15
  16. 16. Integration in Systems BiologyProcess of combining two or more parts.DatabasesToolsExperimentsKnowledgeScientistDecision makerStudentsOther…QuestionIntegratedanswer16
  17. 17. System integrationLink publication, gene and protein17
  18. 18. System integrationLink publication, gene and protein18
  19. 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. 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. 21. Data integrationStructural Biology Knowledge Basehttp://sbkb.org/ 21
  22. 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. 23. Modeling aspect of SB23Biological Systems look as Systems with sub-systems
  24. 24. 24What is a Model?
  25. 25. Model in systems biology: generic workflow25
  26. 26. Life science levels26
  27. 27. ADNEADNARNmEDégradationDégradationTraductionTranscriptionRépression duGèneS PCatalyseKey concept: molecular biology dogma27
  28. 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. 29. Executable biology vs. experimental biology29
  30. 30. Time and space in SB modeling30
  31. 31. System Biology Model vocabulary State (steady state, ) Parameters Variables Constants Behavior Scope Statements31
  32. 32. SB Model characterization Structural / functional model Qualitative / quantitative model Deterministic / non deterministic Nature (continuous / discreet /hybrid) Reversibility / irreversibility /periodicity32
  33. 33. Models development33
  34. 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. 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 knowledge◦Components of the system◦Interaction map and kind of interactions◦Experimental 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. 36. Models development tasks• Selection of model structure:•Level of description (atomistic, molecular,cellular, physiological)•Deterministic or stochastic model•Discrete or continuous variables•Static, dynamical, spatio-temporaldynamical“Essentially, all models are wrong, but some are useful.” - George E.P. Box36
  37. 37. Models development tasks“Essentially, 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. 38. Models development tasks“Essentially, all models are wrong, but some are useful.” - George E.P. Box•Experimental Tests•Hypotheses driven•Choice 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. 39. Models development39
  40. 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. 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. 42. Life science systems representation42
  43. 43. disiDegilliSyntikikiKKVVdtdm-Ligne1………-Ligne2Init.  F réponseSpécification logique temporelleTrace erreurModel CheckerModèle à étatsfinisAlgo.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. 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. 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. 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. 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. 48. Systems Biology ModelingCase study48
  49. 49. Metabolic modeling49
  50. 50. Metabolic Pathways50
  51. 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. 52. Formalization of the model of metabolic networksSmirij(Eij,Vij)mjrji(Eji,Vji)rii(Eii,Vii)),,( ijijij Pmtfv))()),,(,(),()(),( 00tVPPtmtVdtPtdmPmPtmrcdisiDegilliSyntikikiKKVVdtdmExampleneHomocysteikdtMethioninedneHomocysteikdtneHomocysteidcc..MethionineneHomocystei ckContinuous model52
  53. 53. Folates metabolism (folic acid or Vitamin B9) and pathogenesis53
  54. 54. 0 5 10 15 20 25024681012141618Time(Hours)Concentration(µm)CH3_5_THFe0.20.40.60.811.21.41.61.82Concentration(µm)FR:CH3_5_THFeRFC:CH3_5_THFe0 5 10 15 20 25024681012141618Time(Hours)Concentration(µm)CH3_5_THFe0 5 10 15 20 2500.20.40.60.811.21.41.61.82Time(Hours)Concentration(µm)FR:CH3_5_THFeRFC:CH3_5_THFeContinuous model analysis54
  55. 55. Protein modelingProtein structure modelingprotein-protein interaction55
  56. 56. Protein structure modeling56>TARGETQGQEPPPEPRITLTVGGQPVTFLVDTGAQHSVLTQNPGPLSDRSAWVQGATGGKRYRWTTRKVHLATGKVTHSFLHVPDCPYPLLGRDLLTKLKAQI;
  57. 57. Protein structure modeling57
  58. 58. Protein-Protein Interaction Network58
  59. 59. Gene regulatory network (GRN)59
  60. 60. Can be Complex60
  61. 61. Gene regulation61
  62. 62. Boolean modeling of GRN62
  63. 63. ODE modeling of GRN63
  64. 64. FBA modeling of GRNFlux Balanced Analysis: about equilibrium and steady state64
  65. 65. Cancer tumor growthBiological species as agents65
  66. 66. CancerTumorDevelopment66
  67. 67. Epidemiology: HIV spread67
  68. 68. HIV spread10 Northwestern, 201068
  69. 69. Virtual biology:Body browserVirtual Cellvirtual brain69
  70. 70. Body browserGoogle Body browser7 Google, 201170
  71. 71. Virtual CellVirtual Cell project71http://www.vcell.org/vcell_software/login.html
  72. 72. Virtual CellVirtual Cell72http://www.vcell.org/vcell_software/login.html
  73. 73. Virtual brainVirtual Brain73
  74. 74. QUIZ74

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