Session ii g2 overview chemical modeling mmc
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The document provides an introduction to systems biology. It begins with an overview of what systems biology is, including definitions that emphasize studying biological functions and mechanisms through signal and system-oriented approaches. It then discusses why systems biology is used, including to better understand biological systems as a whole rather than individual parts, and to aid in areas like drug development. The document also covers common techniques in systems biology, such as modeling biological networks and integrating different types of data. It concludes by listing some examples of case studies where systems biology has been applied, including for metabolic and gene regulatory network modeling.
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Session ii g2 overview chemical modeling mmc
- 1. Introduction to System biology 1 Dr. Etienne Z. GNIMPIEBA 605 677 6064 Etienne.gnimpieba@usd.edu
- 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 in your work or career path? 2
- 3. Aims My job: Help you to “aimer” SB. Provide a simple view of complex Systems Biology world. 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 Life science • 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 Session 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 used 5
- 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 Life science
- 7. Systems Biology Bios=life logos= study /science BIOLOGICAL SYSTEMS “Science is built up of facts, as a house is with stones. But a collection of facts is no more a science than a heap of stones is a house ” (H. Poincaré) 7 What is Systems Biology?
- 8. Systems biology is concerned with the study of biological functions and mechanisms, underpinning inter- and intra-cellular dynamical networks, by means of signal- and system-oriented approaches. (Cosentino, 2008) Systems biology is an approach by which a system of interacting entities is analyzed as a whole rather than by analyzing its individual constituent entities separately. (Nahleh, 2011) Systems biology is an approach in biomedical research to understanding the larger picture—be it at the level of the organism, tissue, or cell—by putting its pieces together. It’s in stark contrast to decades of reductionist biology, which involves taking the pieces apart. (NIH, 2013) Systems biology: The study of biological systems taking into account the interactions of the key elements such as DNA, RNA, proteins, and cells with respect to one another. The integration of this information may be by computer. (Medical Dictionary, 2013) Systems biology is the research endeavor that provides the scientific foundation for successful synthetic biology. (Breitling, 2010) 8 What is Systems Biology?
- 9. Components / elements Interrelated components Boundary Purpose Environment Interfaces Input Output Constrain 9 What 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 data mining techniques • Internet as the medium for the widespread availability from multiple sources of knowledge • Education Why? 10
- 11. • Elucidate network properties • Check the reliability of basic assumptions • Uncover lack of knowledge and requirements for clarification • Create large repository of current knowledge, formalized in a non-ambiguous way and including quantitative data Models are not Real, though Reality can be Modeled • Interactions in cell are too complex to handle by pen and paper • With high throughput tools, biology shifts from descriptive to predictive • Computers are required to store, processing, assemble, and model all high-throughput data into networks Why? 11
- 13. • Cloud • Databank • Database • Data designer • Information manipulation • Create/collect information • Statistic analysis • Date inference, learning • Model from data • Model from SB • Large scale model Modeling & learning SB Informatics Data manipulation Bio/life Who 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 data concept ) ◦ Tools integration (software, material) ◦ Process integration ◦ … 14
- 15. Integration Aspects of SB Process of combining two or more parts. 15
- 16. Integration in Systems Biology Process of combining two or more parts. Databases Tools Experiments Knowledge Scientist Decision maker Students Other… Question Integratedanswer 16
- 17. System integration Link publication, gene and protein 17
- 18. System integration Link publication, gene and protein 18
- 19. Data integration (why?) Observation of biological phenomena is restricted to the granularity and precision of the available experimental techniques A strong impulse to the development of a systematic approach in the last years has been given by the new high-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) Many databases (GO, KEGG, PDB, Reactome…) XML-like annotation languages (SBML, CellML) Information retrieval Tools for retrieving information from multiple remote DBs Data correlation Find the correlation between phenotypes and genomic/proteomic profiles Statistics, data mining, pattern analysis, clustering, PCA, … 20
- 21. Data integration Structural Biology Knowledge Base http://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 SBKB 22
- 23. Modeling aspect of SB 23 Biological Systems look as Systems with sub-systems
- 24. 24 What is a Model?
- 25. Model in systems biology: generic workflow 25
- 27. ADN E ADN ARNm E Dégrada tion Dégrada tion Traducti on Transcrip tion Répressi on du Gène S P Cataly se Key concept: molecular biology dogma 27
- 28. Key concept: Lactose Operon (lac) 28 Genes and its binding sites In the "induced" state, the lac repressor is NOT bound to the operator site In the "repressed" state, the repressor IS bound to the operator.
- 29. Executable biology vs. experimental biology 29
- 30. Time and space in SB modeling 30
- 31. System Biology Model vocabulary State (steady state, ) Parameters Variables Constants Behavior Scope Statements 31
- 32. SB Model characterization Structural / functional model Qualitative / quantitative model Deterministic / non deterministic Nature (continuous / discreet / hybrid) Reversibility / irreversibility / periodicity 32
- 34. Three Approaches for System design Bottom-up: Construct a network and predict its behavior starting with a collection of experimental data Top-down: Starts from observed behavior and then fills in the components and interactions required to generate these observations by iterative experimental results and simulations Middle-out: Starts at any point for which data are available, as long as it is supported by a hypothesis, and then expand either up or down in terms of both resolution and coverage 34
- 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 phenotypic responses against different stimuli (gene knockout, RNAi, environmental conditions) “Essentially, all models are wrong, but some are useful.” - George E.P. Box 35
- 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-temporal dynamical “Essentially, all models are wrong, but some are useful.” - George E.P. Box 36
- 37. Models development tasks “Essentially, all models are wrong, but some are useful.” - George E.P. Box Robustness/Sensitivity Analysis: Test the dependence of the system behavior on changes of the parameters Numerical simulations Bifurcation analysis 37
- 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 of samples and repetitions, … • Assessment of the agreement and divergences between experimental results and model behavior • Iterative refinement of the hypotheses (and of the model) 38
- 40. Different Mathematical Formulations Differential Equations ◦ Linear (ordinary) ◦ Partial ◦ Stochastic S-Systems ◦ Power-law formulation ◦ Captures complicate dynamics ◦ Parameter estimation is computation 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 P Obs Cond 1 Cond 2 Cond 3 1 … … … 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 representation 42
- 43. disi Degil li Syntik ik i KKVV dt dm -Ligne1 ……… -Ligne2 Init. F réponse Spécification logique temporelle Trace erreur Model Checker Modèle à états finis Algo. Recherche RF -R1 -R2 -R3 -………. c1 c2 c3 c4 g1 L M M H g2 L H H M g3 M M M M g4 L H H M g5 L L H M Algo. CS & prog. logique S mi rij(Eij,Vij) mj rji(Eji,Vji) rii(Eii,Vii) Parameter Value BR BF LOGICPROGRAMMING CODING BC KINETIC CHARACTERIZATION Biological Knowladge (Litterature) MATHEMATICAL FORMALISM Model Experimental Conditions C -C1 -C2 --…. c1 c2 c3 c4 g1 120.9 81.8 116.8 66.6 g2 1.6 1.5 1.4 1.1 g3 7.5 9.2 7.4 7.9 g4 0.6 0.7 0.8 0.7 g5 80.5 77.9 103.4 75.24 Ens. Floues & T-normes DNA 0% 100% 65%20% Prob(%) 130% HypermethylationADN HCY 0% 100% 85%30% Prob(%) 175% Hyperhomocysteinemie Hypohomocysteinemie g6 g5 g1 g2 g4 g3 m5 m 4 m 1 m 3 m 2 E 1 E 3 E 2 E 5 E4 FR RFC Micro nutrient Metabolic network Genetic network biological knowledge Experimental Data (ADN) DATA Microarray data Biological levels of abstraction Species and biological interactions Structure representation Transport Metabolic Genetic Data eg6 eg5 eg2 eg4 eg3 Epigenetic Factors Epigenetic Life science systems focus 43
- 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 against biomarkers (cancer or other infectious agents) Require prior knowledge of patient response (through lab tests or biochips) Gene therapy Replace or inhibit genes in patients Vectors ◦ Adenovirus (AAV) Silencing the disease gene ◦ RNAi ◦ microRNA 45
- 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 well 47
- 48. Systems Biology Modeling Case study 48
- 51. Design Methods m1 m2 m4 m3 m5 Metabolic level E4 E4 E4 E4 E4 E4 Metabolic network Definition: A biological network (metabolic, genetic, protein, …) is a directed graph whose nodes are labeled biological species, reactions and edges labels are enzymes that catalyze these reactions. Modeling using network in biology Continuous Differential Equation / Algebraic Equation Discret Logic theory Hybrid Continuous function interval 51
- 52. Formalization of the model of metabolic networks S mi rij(Eij,Vij) mj rji(Eji,Vji) rii(Eii,Vii) ),,( ijijij Pmtfv ))()),,(,( ),( )(),( 00 tVPPtmtV dt Ptdm PmPtm rc disi Degil li Syntik ik i KKVV dt dm Example neHomocysteik dt Methionined neHomocysteik dt neHomocysteid c c . . MethionineneHomocystei ck Continuous model 52
- 53. Folates metabolism (folic acid or Vitamin B9) and pathogenesis 53
- 54. 0 5 10 15 20 25 0 2 4 6 8 10 12 14 16 18 Time(Hours)Concentration(µm) CH3_5_THFe 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Concentration(µm) FR:CH3_5_THFe RFC:CH3_5_THFe 0 5 10 15 20 25 0 2 4 6 8 10 12 14 16 18 Time(Hours) Concentration(µm) CH3_5_THFe 0 5 10 15 20 25 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Time(Hours) Concentration(µm) FR:CH3_5_THFe RFC:CH3_5_THFe Continuous model analysis 54
- 55. Protein modeling Protein structure modeling protein-protein interaction 55
- 59. Gene regulatory network (GRN) 59
- 62. Boolean modeling of GRN 62
- 63. ODE modeling of GRN 63
- 64. FBA modeling of GRN Flux Balanced Analysis: about equilibrium and steady state 64
- 65. Cancer tumor growth Biological species as agents 65
- 68. HIV spread 10 Northwestern, 2010 68
- 69. Virtual biology: Body browser Virtual Cell virtual brain 69
- 70. Body browser Google Body browser 7 Google, 2011 70
- 71. Virtual Cell Virtual Cell project 71 http://www.vcell.org/vcell_software/login. html
- 74. QUIZ 74
Editor's Notes
- 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