What is Systems Biology?

Transcription

1 What is Systems Biology? 1

2 ICBS More than 1000 participants!! 2

3 Outline 1. What is Systems Biology? 2. Why a need for Systems Biology (motivation)? 3. Biological data suitable for conducting Systems Biology 4. Using a mathematical model in biological research. 5. Examples of systems; signal transduction pathways metabolic pathways etc 3

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, (1958) REPLICATION TRANSCRIPTION TRANSLATION DNA RNA PROTEIN 4 David Gilbert, Systems Biology (1) Introduction

5 What is Systems Biology? It is an approach where scientists investigate biology at a system level!! What does this mean? Have we not always done that? Well, to some extent! Theoretical biologist have done that for a long time, but with little supporting experimental data. Up to now we have prepared a cellular map of proteins involved in different pathways and biological processes. This is a rather static map. What we do not know to a large extent is the dynamics of these pathways and how different processes are linked and dependent on each other in a time and space-dependent manner. We need to move from qualitative descriptions to more quantitative descriptions. Integrative Biology! 5

6 What is Systems Biology? An approach to study biological systems in an integrative way 1. Often biologists who use large-scale data (global view) in order to cluster genes / proteins involved in the same functional group. Proteins with unknown function can be functionally linked to a biological process. These biologists or bioinfomaticians look at biological systems with a more holistic view. Science 14 December 2001: Vol no. 5550, pp

7 What is Systems Biology? 2. Biologist who focuses on particular biological systems and tries to understand system properties such as feedback loops, amplification, cross-talk etc. They could also integrate biological events at different levels. This often requires quantitative analysis of biological processes at different levels. Phosphorelay module high osmolarity v TCS? v TCS 2 1 Sln1 Asp P Sln1 His P Sln1 ADP ATP v TCS 3 Ypd1 Ypd1 His P TCS v 4 Ssk1 Asp P Ssk1 v TCS 5 ATP ADP Ssk2 Ssk2P v -1 ATP ADP ATP ADP Pbs2 Pbs2P Pbs2P 2 v -2 v -3 v 1 v 2 Ssk1 v 3 ATP ADP ATP ADP Hog1 Hog1P Hog1P 2 v -4 v -5 v 4 kinase cascade module v 5 Osmotic stress Signal pathway Sln1 Phospho relay Ssk1 system Plasma membrane Π e Glucose kinase Hog1 cascade DHAP Metabolism Gpd1 cytosol Hog1 G3P nucleus Gpp2 Glycerol Transcription Translation GPD1, GPP2,. Gpd1, Gpp2,. Fps1 Gene expression Hog1P 2 Hog1 v trans v trans2 cytosol v trans1 Ptp2 Hog1P 2nuc Hog1 v nuc nucleus dephos Internal osmotic pressure v pd Π i Proteins v ex v mrna nuc mrna rd v cyt ts v tl Osmotic stress Gene expression module External osmotic pressure Glycerol extern v 1 Glucose Glk1 v 2 v 10 synthesis Gluc-6-P v ADP ATP 3 Glucose uptake ATP ADP ATP ADP Glycerol, ex Fps1 Fruc-1,6-BP Glycerol v 4 Gpd1 v 12 Gpp2 v 5 v GAP DHAP 11 NAD G3P 2 ADP 2 ATP v 6 NADH NAD ATP ADP NADH v v 16 Pyruvate 9 NADH NAD synthesis v 14 4 NAD NADH v 7 v 4 NADH 8 NAD v 15 3 CO Ethanol ADP ATP 2 Biophysical changes Metabolism module Π i = f(glycerol) Waterflow over membrane = f(π i, Π e, Π t ) Volume change = f(waterflow) (see text) v 13 Edda Klipp et al, Nature Biotechnology 2005, number 8, 7

8 What is Systems Biology? 3. Theoretical biologist or computational biologist who studies biological systems using and/or develop computational tools. Often they develop mathematical models of biological systems. Differential equations d dt Ssk2 = v1 + v 1 Ssk2 Vratio Ssk2 d dt Pbs2P = v2 v 2 v3 + v 3 Pbs2P Vratio Pbs2 d dt Pbs2 = v2 + v 2 Pbs2 Vratio 8

9 What is a Systems Biologist and who is one? Mathematical modelers Experimentalists This might be the most commonly described systems biologist Systems Biologists Goal 1: To define e.g. proteins networks. Often genome-wide data are used. To group proteins of unknown function to functional groups. Filling gaps of knowledge! Goal 2: Is to understand complex systems by combining mathematical modeling and experimental studies. Systems biology offers 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 Goal 3: To understand dynamic properties of biological systems by pure experimental techniques. 9

10 What is Systems Biology? The information about how a system works demands studies of how proteins work together in the context of the organ / tissue / cell etc ocw.mit.edu/.../0/chp_subtilisinbp.jpg

11 What is Systems Biology? What is a biological system?: 1. Consists of components that interact such in order to form a functional unit. 2. 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 System biology, Definitions and perspectives, Topics in current Genetics

12 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: 12

13 What is Systems Biology? Pieces into systems.. 1. Understanding how biomolecules (proteins, metabolites, RNA...) function together (i.e. in a system), rather than in isolation. System-level understanding! 2. In order to explain function, therefore, systems biology argues that we should take a more holistic view of biological phenomena, one in which function emerges at a higher-than-molecular level. This is the level of systems, whose behaviour is described by topological, not physico-chemical, laws. (Hiroaki Kitano) 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! 13

14 Why a need for Systems Biology (motivation)? Nucleotide sequence Nucleotide structure Gene expression Protein sequence Protein function Protein-protein interactions (pathways) Cell Cell to cell signalling Tissues Organs Physiology 14 Organism

15 Why a need for Systems Biology (motivation)? 1. Genome-wide data sets (transcriptomics, proteomics, mass-spec based analysis etc) provide the opportunity to start integrative research. 2. Genome-wide data sets allow identification of large-protein networks thus filling gaps of knowledge. 3. From a biological point of view thi s is a natural step to take as we have a rather large base of knowledge of many pathways. Mathematical modeling: 3. Testing if the biological hypothesis is accurate is it likely that the experimental data explains the model? 4. 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. Experimental planning!! 5. A model provides the opportunity to address critical scientific questions. 6. Cellular regulation depends on time and space, which a model can address. 15

16 Why a need for Systems Biology (motivation)? 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. Phosphorelay module Sln1 Asp P high osmolarity? v 2 TCS Ypd1 Ssk1 Asp P Sln1 His P ADP ATP v TCS 3 v 4 TCS v TCS 5 ATP ADP Ssk2 Ssk2P v 1 TCS Ypd1 His P Ssk1 Sln1 ATP ADP ATP ADP Pbs2 Pbs2P Pbs2P 2 v 1 v 2 Ssk1 v -1 v -2 v 3 v -3 ATP ADP ATP ADP Hog1 Hog1P Hog1P 2 v 4 v -4 kinase cascade module v -5 v 5 Osmotic stress Signal pathway Sln1 Phospho relay Ssk1 system Plasma membrane Π e Glucose kinase Hog1 cascade DHAP Metabolism Gpd1 cytosol Hog1 G3P nucleus Gpp2 Glycerol Transcription Translation GPD1, GPP2,. Gpd1, Gpp2,. Fps1 Gene expression cytosol Hog1P 2 v trans Hog1P 2nuc nucleus v trans1 Ptp2 v dephos Hog1 v trans2 Hog1 nuc Internal osmotic pressure v pd Π i Proteins v ex v mrna nuc mrna rd v cyt ts v tl Osmotic stress Gene expression module External osmotic pressure Glycerol extern synthesis v 10 ADP ATP Glk1 v 1 Glucose v 2 Gluc-6-P v 3 Glucose uptake ATP ADP ATP ADP Glycerol, ex Fps1 Fruc-1,6-BP Glycerol v 4 Gpd1 v 12 Gpp2 v 5 v GAP DHAP 11 NAD G3P 2 ADP 2 ATP v 6 NADH NAD ATP ADP NADH v v 16 Pyruvate 9 NADH NAD synthesis v 14 4 NAD NADH v 7 v 4 NADH 8 NAD v 3 CO Ethanol ADP 15 ATP 2 Biophysical changes Metabolism module Π i = f(glycerol) Waterflow over membrane = f(π i, Π e, Π t ) Volume change = f(waterflow) v (see text)

17 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. 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. 17

18 THE EQUILIBRIA OF LIFE WATER AVAILABILITY NUTRIENTS TEMPERATURE RADIATION SURVIVAL OPTIMISATION OF GROWTH CHEMICALS COMPETITION From Marcus Krantz WASTE 18

19 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. What is important for mathematical modeling is Quantitative experimental data!!!! 19

20 Hypothetical module CYTOSOL PLASMA MEMBRANE 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. NUCLEAR MEMBRANE A shuttling component able to carry the signal into the nucleus, activating one or more transcription factors. NUCLEUS GENE EXPRESSION Mechanism of feedback control. Kinases and phosphates are common, using (de)phosphorylation as the signal. 20 From Marcus Krantz

21 Biological data suitable for conducting Systems Biology According to the interpretation of System Biology as the ability to obtain, integrate and analyze complex data from multiple experimental sources using interdisciplinary tools 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 / or analysed computationally 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

22 Biological data suitable for conducting Systems Biology 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

23 Using a mathematical model in biological research. A mathematical model: By using mathematical language you can describe a system; can be found in many disciplines such as in engineering, economics and meteorology. It is a descriptive model of a system as a hypothesis of how the system could work It consists of a set of variables and a set of equations that establish relationships between the variables. Example of a Meteorological model Examples of Economic models Black-Scholes option pricing model Heckscher-Ohlin model International Futures IS/LM model Keynesian cross model An example of 500 mbar geopotential height prediction from a numerical weather prediction model

24 Different steps in the modeling procedure: Start with a problem of interest Make reasonable simplifying assumptions Translate the problem from words to mathematically/physically realistic statements. Use experimentally derived data; one training data set and one set of verification data. The training data set should be used to estimate model parameters and the verification data set should be used to test the system. The better fit: the better model. Simulation: Imitation of real biology scenarios. Predictions: Statements or claim that a particular event will occur 24

25 Simulation Experimental data: training set Concentration, relative A Gpd1 mrna Hog1P 2 Ssk Time / min Concentration, relative B ò Ÿ Ÿ Ÿ Ÿ Ÿ ò Gpd1 Ÿ ò ò Hog1P Ÿ 2 ò mrna ò ò ò òÿ Ÿ Time / min Limitations: 1. Not necessarily a correct model 2. Unrealistic models may fit data very well leading to incorrect conclusions 3. Simple models are easy to manage, but complexity is often required 4. Realistic simulations require a large number of hard to obtain parameters 5. Models are not explanations and can never alone provide a complete solution to a biological problem. 25

26 Examples of systems K-signalling pathway Metabolism Yeast Nov;24(11):

27 Examples of systems JAK-STAT signaling pathway Biology -Hormone (Epo) Core model -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. I. Swameye, PNAS, Feb.4, 2003 What happens then? 27

28 Examples of systems Data - Simulations A + B : experimental data Time-series measurements C + D : testing two hypothesis 28

29 How is Systems Biology conducted? How did we do? A signalling pathway In yeast HOG pathway 1. The biological knowledge was gathered from literature and own observations. 2. The structure of the pathway was decided and converted into equations (static). 3. Static model dynamic model. The model structure was analysed and parameters optimised. Quantitative experimental data was used to compare with simulations. 4. The model was tested by simulations and new experiments validation! etc etc. 29

30 Examples of systems The High Osmolarity Glycerol (HOG) pathway in yeast Phosphorelay module Sln1 Asp P high osmolarity? v 2 TCS Ypd1 Ssk1 Asp P Sln1 His P ADP ATP v TCS 3 v 4 TCS v TCS 5 ATP ADP Ssk2 Ssk2P v 1 TCS Ypd1 His P Ssk1 Sln1 ATP ADP ATP ADP Pbs2 Pbs2P Pbs2P 2 v 1 v 2 Ssk1 v -1 v -2 v 3 v -3 ATP ADP ATP ADP Hog1 Hog1P Hog1P 2 v 4 v -4 kinase cascade module v -5 v 5 Osmotic stress Signal pathway Sln1 Phospho relay Ssk1 system Plasma membrane Π e Glucose kinase Hog1 cascade DHAP Metabolism Gpd1 cytosol Hog1 G3P nucleus Gpp2 Glycerol Transcription Translation GPD1, GPP2,. Gpd1, Gpp2,. Fps1 Gene expression cytosol Hog1P 2 v trans Hog1P 2nuc nucleus v trans1 Ptp2 v dephos Hog1 v trans2 Hog1 nuc v pd Π i Proteins v ex v mrna nuc mrna rd v cyt ts Edda Klipp et al, Nature Biotechnology 2005, number 8, Internal osmotic pressure v tl Osmotic stress Gene expression module External osmotic pressure Glycerol extern synthesis v 10 ADP ATP Glk1 v 1 Glucose v 2 Gluc-6-P v 3 Glucose uptake ATP ADP ATP ADP Glycerol, ex Fps1 Fruc-1,6-BP Glycerol v 4 Gpd1 v 12 Gpp2 v 5 v GAP DHAP 11 NAD G3P 2 ADP 2 ATP v 6 NADH NAD ATP ADP NADH v v 16 Pyruvate 9 NADH NAD synthesis v 14 4 NAD NADH v 7 v 4 NADH 8 NAD v 3 CO Ethanol ADP 15 ATP 2 Biophysical changes Metabolism module Π i = f(glycerol) Waterflow over membrane = f(π i, Π e, Π t ) Volume change = f(waterflow) v 13 (see text) 30

31 Examples of systems Volume, relative Glycerol, relative Concentration, relative A C E Simulations Ssk Time / min Glyc in mrna Hog1P 2 Gpd1 Glyc ex Time / min Time / min Edda Klipp et al, Nature Biotechnology 2005, number 8, Concentration, relative Glycerol, relative Pressure /MPa B D F Experimental data Ÿ ò ò ò ò Ÿ ò Ÿ ò ò ò ò Time / min é Glyc in é mrna Hog1P 2 é Π e Time / min é Glyc ex Π i Gpd Time / min Π t é 31

32 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 32

33 Summary What did we learn? - Systems biology (SB) is a scientific approach which aims at integrating biological processes into a unit. It is the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system. - SB is the study of biological systems (at a genome-wide scale or detailed-scale) - SB is often connected to mathematical models where theoretical models and quantitative experimental data are combined to get a system-level understanding of your biological system. - SB offers the chance to predict the outcome of complex processes and it decreases the number of experiments (experimental planninig). - To conduct systems biologyinvolving mathematical modeling: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. SB 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 33 future.