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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  • Hi, Welcome to this SB course.
  • I’m Etienne, I have a bachelor in Mathematics and informatics, a master in Computer science, a Master Degree in computer science and mathematics for integrative biology, and a PhD in Biotechnology and bioinformatics.During this course, we can name informatician persons who have a background in information systems like a computer scientist. We can also name biology persons who have a background in the life sciences like biology, ecology, agriculture, and so on …Who are you?For you, how SB look like?How would you expected to use SB in your work or career path? (begin and end of the course)?
  • I don’t know if it is “love or like”, but in French we say aimer.Other way,my job consist to provide to you a simple …….Yours, if you accepted it, consist to listen, question and read relate docs Are we agree, or someone would like to add something in the contract
  • We will answer several questions and talk about systems biology and talk about some uses or study cases.Don’t try to retain the slide content. The aim of this talk is to give you the overview of SB.At the end of the talk, you will be able to indicate what role SB plays in the whole biotechnology and bioinformatics area, some studies and uses cases. It is an interactive talk, if you have any question, don’t hesitate to interrupt me. We’ll open and close the course with interactive Quiz.
  • You have three assignments to complete
  • Let’s get back to QuizSBas Bioinformatics depend on the focus public as well.
  • What are we talking about??Du systems and / or biology?Du systems biology?Du system for biology?Systems in biology ?Systems with biology?….None of those!!!!!!!Or all of thoseWe are talking about Systems BiologyA complete, complex and independent entities / Discipline / Domain / Language / Rules / ….Even if… Even if it fundamentals are build using Systems theory tools and biology toolsA domain of science where biology is define as a system with subsystems called BIOLOGICAL SYSTEM as many new domain, the definition is not already standardized. We have many definition. Like…
  • as many complex domain, the definition is not already standardized. We have many definition. Like…Nowadays, we have many different philosophies about SB. This could generate many definitions as…In Breif…..
  • By the way, The only thing I could say clearly today is that, when you hear Systems Biology, look to identify;- components- Interactions between components....And finally the relate constraints
  • With the growth of tools, databases and experimental protocols, biological knowledge users are more confused.Questions answers are then more oriented toward the intersection of several diferents answers.Whether issue decision, research, student, or other person interested in the life sciences.These differences may be that relate:By the nature of the data (heavydata sources, many exp. cond. ...), it is necessary to set the number of bases and give talks about data integrationIn the complexity of the biological system of interest, involving several sub system (studying a metabolic pathway, we need more information on Enzymes Involved, then the gene code this enzyme bring us to integrate the gene regulatory network)....
  • Protein analysis using InterProScan. Curators of member databases of InterPro identify proteins that share a known functional domain and manually identified sequences can be supplemented through iterative searching of the public databases until a comprehensive set is found. The sequences are aligned and a classifying function is built from the alignment, which is designed to recognise other proteins that posses the same domain. Many of these classifiers are built using hidden Markov models. Alternative classifying functions judged (by curators) to identify the same domain are grouped into a single entry in the InterPro database, and annotated with relevant information (expressed both in free text and in the terms of the GO controlled vocabulary). InterProScan is a programme that allows users to characterize a sequence by applying these classifying functions. Performance can be improved by precomputing the results for known sequences.
  • A schematic representation of a typical workflow for bioinformatics analysis. The figure shows a typical workflow for bioinformatics analysis, progressing from sequence to functional annotation, protein structure and literature. The complete analysis may be carried out entirely within a bioinformatics warehousing system (such as SRS), or as a sequence of separate operations performed in different environments. Starting with the sequence of a gene or protein, identical and/or similar sequences are identified in the public databases. The database records describing these sequences also contain general information about the sequence, curated links to structural information and relevant scientific literature. Protein sequence itself can also be directly analysed to predict its domain composition and structure. These may provide a more reliable indication of protein function than overall sequence similarity. It is often useful to express the results of these analyses in standard controlled vocabularies (such as GO), to allow the comparison and correspondence of information derived from different resources.
  • This is an image from the SBKB help area entitled “Tour of the PSI SBKB” which illustrates the breath and goals of the SBKB. The circles at the top of the image represent various structural resources offered by the PSI. We will be examining many of the PSI resources, and how to access them in this tutorial. At the base of the image, you can see individual biological resources, many of which you probably recognize, such as PubMed and other NCBI resources, Reactome, KEGG, UniProt, model organism databases, and more. Information from these individual resources has been incorporated into the SBKB. The PSI Structural Biology Knowledgebase acts a bridge to connect the individual genomic, structural, and functional information about proteins into one knowledgebase, all of which is searchable to allow easy access to this vast wealth of information in a way that will enable a broad research community to attain an understanding of living systems and disease more quickly.
  • We have spoken about biological species and interactions between them. the main issue is how mathematician can understand these interactions? Biologists propose two key tools in this direction. the lactose operon to detail the mechanism of gene expression, and the central dogma of molecular biology for interactions.The gene regions of the DNA in the nucleus of the cell is copied (transcribed) into the RNA andRNA travels to protein production sites and is translated into proteins. In short, DNA , RNA Proteins, is the Central Dogma of Molecular Biology. Imagine, there are trillions of cells in your body, the DNA of each of them is churning out thousands of RNAs which in turn cause thousands of proteins to be produced, every moment. One of them is making your hair strong, another giving the glitter in your eyes, another one carrying oxygen to different parts, and yet another one helping in the making of proteins themselves!No wonder that famous life scientist Russel Doolittle exclaimed: “We are our proteins”
  • With the study of the lactose operon, François Jacob, André Lwoff and Jacques Monod were the first scientists to describe a system for regulating gene transcription. They propose the existence of two classes of genes that differ in their function: the structural genes and regulatory genes. It is from this work was born the concept of gene regulation. (Nobel Prize for Physiology or Medicine in 1965).
  • The state of a system is a snapshot of the system at a given time.The state is described by the set of variables that must be kept track of in a model.Model scope✤ Models consist of mathematical elements (variables, parameters, constants)✤ A model describe certain aspects of the system, and simplifies/neglects all othersModel statements✤ Statements and equations describe facts about the model elements✤ Examples include ODEs, inequalities, probabilistic statements, etc.Model behavior✤ Two fundamental factors that determine the behavior of a system are influences from the environment (input), and processes within the system.✤ Measurements of the system output often do not suffice to choose between alternative models, as different system structures may still produce similar system behavior.
  • A structural or qualitative model specifies the interactions among model elements. A quantitative model assigns values to the elements and to their interactions.In a deterministic model, the system evolution through all following states can be predicted from the knowledge of the current state. Stochastic descriptions give instead a probability distribution for the successive states. The nature of values that time, state, or space may assume distinguishes a discrete model (where values are taken from a discrete set) from a continuous model (where values belong to a continuum).Reversible processes can proceed in a forward and backward direction.✤ Irreversibility means that only one direction is possible. ✤ Periodicity indicates that the system assumes a series of states in the time interval {t,t+∆t} and again in the time interval {t+i∆t,t+(i+1)∆t} for i=1,2,...
  • I bioinformatics, model could design from data or from system biologyFrom data, model are design to learn some knowledge from data using some inference or learning algoritmFrom biological system, models are design to make prediction (function) or visualization (structure).In all case, all model have support by an biological context.For example,
  • In the other hand, we can observe bioinformatics like an integrate based tool, process and databank according to the aims of our work.In that case, we have
  • Herewe have thefolatemetabolic network and the implicateddiseaseslike cancer, …...Biologistwant to simulate the behaviour of eachmatabolite (node)Atthisstep, the designing are beginingbecause the system biologyisrepresent as a network using the graph theory.The nextstepconsisty to takeeachmetabolite and built the equationformalizeit variationNEXT
  • These models can also be made to show how a cancer tumor resists chemical treatment. A tumor consists of two kinds of cells: stem cells (in blue) and transitory cells (all other colors).During mitosis, a stem cell can divide either asymmetrically or symmetrically. In asymmetric mitosis, one of the two daughter cells remains a stem cell, replacing its parent. So a stem cell effectively never dies.The transitory cells stop dividing at a certain age and change color from red to white to black, eventually dying.A stem cell may also divide symmetrically into two stem cells.In this example the second stem cell moves to the right. This activity, in which the cell advances into distant sites and creates another tumor colony, is called metastasis. The created model incorporates all of these relationships into a viewable and predictive system.
  • With this model we can simulate the spread of the human immunodeficiency virus (HIV), via sexual transmission, through a small isolated human population. It therefore illustrates the effects of certain sexual practices across a population. Green people represent uninfected humans, red are infected, and blue are unknown. When activated, this model will show red people infecting other individuals. Other factors can be changed within the model such as relation periods and population size. This is the end of our course. Thank you for listening to USD Bioinformatics on Bioinformatics Applications
  • 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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