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Systems Biology 1 24 / 9 2007 Bodil Nordlander – trained as a molecular biologist

Systems Biology 1 24 / 9 2007 Bodil Nordlander – trained as a molecular biologist. Outline. What is Systems Biology? Why a need for Systems Biology (motivation)? How is Systems Biology conducted? Drivers for Technology Networks versus pathways

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Systems Biology 1 24 / 9 2007 Bodil Nordlander – trained as a molecular biologist

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  1. Systems Biology 1 24 / 9 2007 Bodil Nordlander – trained as a molecular biologist

  2. Outline • What is Systems Biology? • Why a need for Systems Biology (motivation)? • How is Systems Biology conducted? • Drivers for Technology • Networks versus pathways • Examples of systems; signal transduction pathways • metabolic pathways etc

  3. What is Systems biology? • How is systems biology different from ”classical” reductionist biology? • In classical biology the study of isolated parts, such as the function of • a protein, transcriptional control of a gene etc, of systems have been • performed and from these functions hypothesis of different models/hypothesis • have been set-up. It is these hypothesis that are the foundation for our under- • standing of different processes in the cell. • In systems biology, from complex biological phenomenon we try to identify a • hypothesis (this is due to a combined effort between experimentalists and modelers) • which can be used to perform studies of the biological process. The model is used • to test different hypothesis.

  4. What is Systems biology? Central Dogma • The central dogma of information flow in biology: Information flows from DNA to RNA to protein. With other words: the amino acid sequence making up a protein, its structure and function, is determined by the DNA transcription. • “This states that once ‘information’ has passed into protein it cannot get out again. In more detail, the transfer of information from nucleic acid to nucleic acid, or from nucleic acid to protein may be possible, but transfer from protein to protein, or from protein to nucleic acid is impossible. Information means here the precise determination of sequence, either of bases in the nucleic acid or of amino acid residues in the protein.” Francis Crick, On Protein Synthesis, in Symp. Soc. Exp. Biol. XII, 138-167 (1958) TRANSCRIPTION TRANSLATION DNA RNA PROTEIN REPLICATION www.brc.dcs.gla.ac.uk, David Gilbert, Systems Biology (1) Introduction

  5. What is Systems Biology? The information about how a system works does not lie in the genome but rather in how proteins work together in the context of the organ / tissue / cell etc. http://www.zum.de/Faecher/Materialien/beck/bilder/transsri5.jpg ocw.mit.edu/.../0/chp_subtilisinbp.jpg http://www.biochem.northwestern.edu/mayo/Lab%20GIF%20Images/Signaling.gif

  6. What is Systems Biology? 1. Understanding how biomolecules (proteins, metabolites, RNA....) function together (i.e. in a system), rather than in isolation. System-level understanding! 2. Airplane analogy (Hiroaki Kitano) 3. To get a system-level understanding you need to know: the system structure (protein-protein interactions, biochemical pathways etc), System dynamics (how does a system behave over time?) Few systems with this understanding! 4. What is a model? An abstract representation of the process which also can explain properties / features of the process. (E.klipp, Systems biology in practice)

  7. What is a Systems Biologist? Mathematical modelers Experimentalists Systems Biologists Common goal: Is to understand complex systems by combining mathematical modeling and experimental studies. Systems biology offer the chance to predict the outcome of complex processes. How do cells work ? How are cellular processes regulated? How do cells react to environmental pertubations? Etc etc etc etc etc http://pubs.acs.org/cen/coverstory/8120/8120biology.html

  8. What is Systems Biology? Quantitative versus Qualitative?? Qualitative analysis: It tries to answer the questions why and how, it catagorises data into patterns. In biology, qualitative research has provided a huge amount of information which is the basis for today´s and future research. It has been the basis for the reductionist era of molecular biology. Quantitative analysis: It tries to answer the questions what, where and when, relies on the analysis on numerical data which can be quantified, time-series data. In Systems Biology, the temporal and spatial dynamics of each molecular spicies are of interest! (ref: http://en.wikipedia.org) Parameters: Quantities which have a value e.g. Km of an enzyme. These values are normally set in a model whereas variables change.

  9. What is Systems Biology? What is a biological system?: • Consists of components that interact such in order to form a functional unit. • Defined at different hierarchical levels with different extent of detail (enzyme, • glycolysis, cellular, tissue, organ, whole organism, ecosystems). www4.liber.se/kemionline/gymkeb/bilder/12_a.jpg http://biologi.uio.no/plfys/haa/gif/form142.gif http://www.acuhealthzone.com/images/anatomy_of_human_body.gif System biology, Definitions and perspectives, Topics in current Genetics 2005

  10. Why a need for Systems Biology (motivation)? Nucleotide sequence Nucleotide structure Gene expression Protein function Protein sequence Protein-protein interactions (pathways) Cell Cell to cell signalling Tissues Organs Physiology Organism

  11. Why a need for Systems Biology (motivation)? 1. Testing if the biological hypothesis is accurate – is it likely that the experimental data explains the model? 2. Testing quantitative predictions of behaviors. This allows us to minimize the number of experiments and do the critical ones which can give us most information. 3. A model provides the opportunity to address critical scientific questions. 4. Cellular regulation depends on time and space, which a model can address. Input to system A B C Our model D E F H I G Output Function

  12. Example of a model which links together different biological processes taking into account time and space, e.g. the compartments cytosol and nucleus are included.

  13. Why a need for Systems Biology (motivation)? 5. If you have a model you can analyse which parts of the system which contribute most to the desired properties of the model. 6. Signaling networks can interact in multivarious ways which complexity requires a model.

  14. Why a need for Systems Biology (motivation)? 7. Investigate the principles underlying biological robustness. It is an essential property of biological systems (Kitano H, Science v.292, 2002). ”The persistent of a system´s characteristic behaviour under perturbation or conditions of uncertainty” (System modeling in cellular biology, zoltan Szallasi et al, 2006). What design elements are thought to be required to avoid harmful disturbances: 1) redundancy (back- up systems) 2) Feedback control 3) Structure complex systems into modules which have semi-autonomous functions etc etc. A robust system is for instance believed to adapt to environmental stresses, it has slow degradation of a system´s function after damage and parameter insensitivity to specific kinetic parameters. To take into account principles of robustness might provide some guidelines for how we model and analyse model complexity.

  15. THE EQUILIBRIA OF LIFE WATER AVAILABILITY NUTRIENTS TEMPERATURE SURVIVAL OPTIMISATION OF GROWTH RADIATION CHEMICALS COMPETITION WASTE From Marcus Krantz

  16. Why a need for Systems Biology (motivation)? 8. To understand general ”design principles” shaped by evolution; some people believe that there exist functional modules as a critical level of biological organisation (ref. Hartwell L.H. Nature 1999, vol 402, 2 Dec). A module ” a discrete entity whose function is separable from those of other modules”, e.g. a ribosome which synthesizes proteins is spatially isolating its function, signalling pathways etc. What are ”design principles” : e.g. positive or negative feedback-loops, amplifiers, parallel circuits (common terms to engineers)? Are they found in nature? Negative feedback: reduces output Positive feedback: increases output, or Bipolar feedback: Either increase or decrease output.

  17. Hypothetical module A signalling pathway provides the means for the cell to sense aspects of its surroundings and/or condition. It usually consists of: A sensor or receptor able to respond to the environment. One or more cytoplasmic signal transducers, perhaps acting on cytoplasmic targets. A shuttling component able to carry the signal into the nucleus, activating one or more transcription factors. Mechanism of feedback control. Kinases and phosphates are common, using (de)phosphorylation as the signal. PLASMA MEMBRANE CYTOSOL NUCLEAR MEMBRANE NUCLEUS GENE EXPRESSION From Marcus Krantz

  18. How is Systems Biology conducted? • It is also a coordinated study of: • Investigating cellular components • and their interactions. • Experimentation • Computational methods. • Integrated study of: • Experimental data • Data processing • Modeling Input to system A B C Our model D E F H I G Output or funcion E.Klipp, Systems Biology in Practice

  19. How is Systems Biology conducted? How did we do? A signalling pathway In yeast – HOG pathway • The biological knowledge was gathered from literature and own observations. • The structure of the pathway was decided and converted into equations (static). • Static model dynamic model. The model structure was analysed and • parameters optimised. Quantitative experimental data was used to compare • with simulations. • The model was tested by simulations and new experiments –validation! etc etc.

  20. 4. Drivers for Technology • Experimental techniques steadily improves in the direction of Systems Biology • Large Scale studies (-Omics) which produces an enormous amount of data at • different levels of cellular organization. This data can be integrated into mathematical • models and to fill gaps of unknown players. These methods constantely improves and new arise. • Improved conventional methods; better quantification methods, single-cell • analysis methods (e.g. microscopy with microfluidic systems), quantitative measurements of gene expression, protein levels etc. • Increased awareness of studying the favourite system quantitatively instead of qualitatively leading to improved techniques and an increased usage of certain methods. This awareness might lead to better planned experiments if using a • mathematical model. Experimental planning! • To include engineers in biology will lead to improved or new highly sophisticated • techniques. And more statistical analysis!!!

  21. 4. Drivers for Technology • Omics - • Focuses on large scale and holistic data/information to understand life in encapsulated omes • Genomics (the study of genes, regulatory and non-coding sequences ) • Transcriptomics (RNA and gene expression) • Proteomics (Systematic study of protein expression) • Interactomics (studying the interactome, which is the interaction among proteins) • Metabolomics (the study of small-molecule metabolite profiles in cells) • Phenomics (describes the state of an organism as it changes with time) • and so on......

  22. 5. Networks versus pathways • Pathways or Networks (common terms in systems biology)? • Pathways: a more defined system which you analyse and study. • Interactions are shown by arrows and in most cases the nature of • this interaction is known. • Networks: a complex connectivity. You link many proteins together • with arrows to get the general topology. We probably know some biochemical • steps but we do not understand the whole network. Network Pathway http://www1.qiagen.com/literature/qiagennews/weeklyarticle/05_06/e8/images/GeneNetwork.gif http://www.bio.davidson.edu/COURSES/GENOMICS/2002/James/pathway.jpg

  23. 6. Examples of systems JAK-STAT signaling pathway Biology • Hormone (Epo) • Receptor binding Epo • Binding leads to transphosphorylation • of JAK2 and phosphorylation of the • cytoplasmic receptor domains. • -Phosphotyrosine residues 343 and 401 • recruit monomeric STAT-5 (x1), which gets • phosphorylated (x2), it then dimerises (x3), • and migrates to the nucleus (x4). • In nucleus: Stimulated transcription of target • genes. • What happens then? Core model I. Swameye, PNAS, Feb.4, 2003

  24. 6. Examples of systems Data - Simulations A + B : experimental data C + D : testing two hypothesis Time-series measurements

  25. 6. Examples of systems The High Osmolarity Glycerol (HOG) pathway in yeast Edda Klipp et al, Nature Biotechnology 2005, number 8,

  26. 6. Examples of systems Simulations Experimental data A B Gpd1 Gpd1 mRNA mRNA Concentration, relative Concentration, relative Hog1P2 Hog1P2 Ssk1 Time / min Time / min 1.5 D C Glycin Glycin 1 Glycerol, relative Glycerol, relative 0.5 Glycex Glycex 0 0 30 60 90 120 Time / min Time / min F Pi E Pe Pressure /MPa Volume, relative Pt Time / min Time / min Edda Klipp et al, Nature Biotechnology 2005, number 8,

  27. Future perspectives Short term goal • Get answers to questions like: what happens, why does it happen and how • is specificity achieved? • To discover new principles and mechanisms for biological function • - Biotechnology: to get predictive cells • To create a detailed model of cell regulation, focused on signal-transduction • cascades. This could lead to system-level insights into mechanisms which • could be the basis for drug discovery. • To understand cells and eventually tissues and organs • In pharmaceutical industry: to get predictive medicines (to avoid side-effects, • to individualise medicines) Long term goal

  28. Summary – What did we learn? • Systems biology is an approach where mathematical modeling and quantitative • experimental data are combined to get a system-level understanding of your • biological system. • Systems biology offers the chance to predict the outcome of complex processes and • it decreases the number of experiments (experimental planninig). • To take into account principles of robustness might provide some guidelines for how • we model and analyse model complexity. • To conduct systems biology often involves: 1) set up pathway structure based on • previous knowledge (static) 2) Simulating experimental data to determine parameters • 3) Predictions to test model. • Qualitative data and quantitative data are of different types. • It drives technology forward!!!! This might be the bottle-neck today, but when we have • better technologies / methods systems biology could move faster towards a promising • future. • Long term applications: To get better and predictive medicines.

  29. References: Articles Hartwell et al. Nature,V 402,1999, From molecular to modular cell biology Peter K Sorger, Current opinion in Cell Biology 2005, A reductionist´s systems biology Hiroaki Kitano, Science, V 295, 2002 Systems Biology: A Brief Overview Hiroaki Kitano, Nature, V420 2002, Computational Systems biology Books E.Klipp et al, System Biology in Practice, Wiley-vch verlag 2005, ISBN-13 978-3-527-31078-4 L.Alberghina, H.V Westerhoff (Eds.), Systems Biology, Topics in Current Genetics, Springer-verlag 2005, ISBN-13 978-3-540-22968-1 Zoltan Szallasi, Jörg Stelling, Vipul Periwal (Eds), System Modeling in Cellular Biology, A Bradford book 2006, ISBN 0-262-19548-8 Resources on the net: http://en.wikipedia.org/wiki/Main_Page www.brc.dcs.gla.ac.uk, David Gilbert, Systems Biology (1) Introduction

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