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  • CHECK: COCHRANE LIBRARY Good afternoon everybody. I will be presenting on the systems biology of the shear stress response. This is a minimised version of my talk, reduced by 20 slides, but I hope to still show the main points of my work.
  • Results obtained in Anne Ridley’s lab, demonstrated distinct effects of biomechanical force on cell morphology of cultured human endothelial cells. Cells undergo cytoskeletal rearrangements as a result of differential activation of Rho proteins and the subsequent protein/protein interactions of pathway components. These rearrangements cause contraction of the cell body, forward extension of the front end of the cell, alignment of cell in the direction of flow and loss of old adhesions at the back end of the cell.
  • Focal contacts (focal adhesions) are associated with the ends of actin filament bundles (stress fibers) and are formed from focal complexes. The focal complex formation starts with integrin clustering upon presence of ligand. When ligand binds, induces conformational changes that allows further association of molecules. FCs are composed to over 40 distinct molecules that have been reported including protein tyrosine kinases, serine-threonine kinases, protein phosphatases, adapter proteins: and proteins with SH2/SH3 binding domains.
  • The target of this project is to aid the understanding of the disease and provide such solutions. We aim to identify the sequence of events and interactions that take place within the cell under shear stress stimuli and assume enough knowledge and predictive power to use this for diagnosis and control over the risk of cardiovascular disease. To achieve this we have to analyse the complete system, starting from the extracellular input and shear stress and how this regulates signalling, and then indentify all interactions and molecule interrelationships of all the pathways that are known to be involved. This should continue to the point of being able to predict the activation levels and regulation control of critical transcription factors. In such we can use this knowledge as the magnifying glass that pinpoints which are the key regulator components and in which way can be manipulated in order to suppress and inhibit disease causing genes without suppressing any of the good guys. In terms of diagnosis for example, ultimately I want to have all my pathways complete and be able to predict the expression levels of the protein troponin under different conditions and in different times. That could be extremely important since troponin is the number one risk predictor of heart attacks and can be measured by a simple blood test. Ultimately, the aim to identify the key regulators of the endothelial response that can be manipulated in order to bypass the destructive effects of the shear stress fluctuations. We are not just there yet, but we are not too far away either.
  • NOTE: SAY LESS HERE The 20 th century has been dominated by the field of molecular biology, which as the terms implies investigates biology at the molecular level. This is essentially a reductionistic approach where the aim to elucidate the function and structure of individual components within the cell at the molecular level. The applied methods always target to brake down the system into its individual components whether one is using DNA arrays, proteomics or antibody techniques. Although this approach has been vastly successful, it has become obvious in the past two decades that it is not possible to understand how a cell functions in whole by investigation of individual components alone. For this reason a new field has recently emerged, called systems biology which aims to do things in the opposite direction. To integrate all data produced by molecular biology and other disciplines in order to examine and understand the properties of the cell and its function at the system level.
  • The general method we apply to this problem is mathematical modelling, which is the most powerful so far method in computational systems biology. The way we go about this is to create a comprehensive model of the system all molecule and interactions and with ability to test the potential effects of perturbations. The way to formulate a mechanistic pathway model is easier than it seems at first glance. All on has to do is translate each biochemical formula representing an interaction into an ordinary differential equations. These equations are solved using parameters that have been already collected for the specific interactions such as rate constants or concentrations.
  • Nevertheless, the aim is not to simply create a model, but to create an accurate one. This is achieved by systematic and iterative formulation and collapse. The first step is to define the model system. This involves the construction of a connection map (i.e. pathway diagram) based on available biological data. Once the reaction schemes are set up, parameter information is collected from the existing biochemical literature and experimental data that constrain the model. Parameters may also be estimated using data-fitting analysis software. Simply, model parameters can include things like protein concentrations or kinetic constants. Once all the parameters are obtained, a model is developed and various simulations created. These simulations will be used to give rise to predictions that can be compared with experimental data. If the predictions match the experimental data, they can then be used to guide future experiments that will test hypotheses and answer interesting behavioural questions. If not, the model needs to be reviewed.
  • Now, the effects of Shear stress are measured experimentally by a setup designed to mimic blood flow in vascular tissue. Endothelial cells are spread on fibronectin coated surface and placed in parallel flow enclosed chambers. The cells are bound to fibronectin and form adherent junctions to the surface. Laminar medium Flow of a certain velocity is then sent in the chambers mimicking the blood flow pumping in the vascular tissue. We believe that there are three ways in which fluid flow application leads to intracellular signaling with subsequent cytoskeletal rearrangements and gene expression changes. 1. Mass transfer of molecules from fluid to the apical membrane surface. This is the result of movement of molecules by convection/diffusion, which is movement by fluid flow and random diffusion. Binding of ligand to membrane receptors initiates signalling. However, the available ligand mass is different at all geometrical points of the chambers and is not easily calculated. 2. Increased calcium influx and intracellular calcium levels. Shear stress changes the friction gradient in the membrane which leads to changes in its electric potential and subsequent changes in membrane permeabiliy. Channels open up and calicum gets in the cell. And we all know that calcium is a major mediator of intracellular signalling. 3. Deformation of membrane receptors by shear force that has direct effects in molecular signaling. We believe that direct force from shear stress deforms the certain receptor molecules. One a threshold of critical deformation has taken place is sufficient to induce certain conformational changes that differentiate interactions and rates of signalling, either by increasing affinity rate for certain molecules or revealing cryptic binding sites and inducing new reactions.
  • The mass transfer has now been calculated. This involves solving the convection diffusion equation using the finite element method a method usually applied in chemical engineering problems. I wont go into the mathematics and numerical solutions as I am sure you don’t want me to, but we are now able to calculate the amount of available ligand on the surface of the cells that binds and activated the receptors such as thrombin and integrin receptors. These plots show ligand concentration within the tube that spreads towards the walls and the endothelial surface. We are able to calculate the same for any molecules included in the medium provided that we know their molecular weights. Figure. Time intervals at 0.5 seconds. Levels of concentration are shown only qualitatively and not quantitatively. The shapes reproduce the velocity profile of the fluid while the areas in red indicate the level of the ligand concentration spread. These graphs have been produced for the purpose of visually demonstrating how concentration spreads within the experimental medium chambers.
  • For the second part we have build a module that calculate the calcium dynamics affected by synergistic agonist stimulation and direct force from shear stress, including AtTPase synthase, internal production of calcium from IP3 receptors, the stretch activated calcium channels I talked about and most of the known ion pumps and channels has been that contribute to overall calcium dynamics. In the model, while the production of calcium coming from ligand binding and IP3 production contributes to the initial rise of calcium it is mostly the strech calcium channels that lead to the rise of intracellular calcium which later on gones down as the pumps attempt to balance the calcium concentration difference and as the membrane adapts to the force applied. The model predicts a biphasic character with a slighty delayed decay of calcium concentration to basal levels which is extremely pleasant since it is exactly what we expect from literature reports and experimental results. The Y scale is micromoles. The profile has been generated with application of Shear Stress equal to 12dynes/cm2.
  • Force effects. This is a slightly trickier part, not to its mathematics but the idea behind it. We hypothesize that the length of the protein actually changes according to force, and the transmembrane receptors such as pecam and integrins are characterised by natural viscoelasticity. Application of force from shear stress leads to physical deformation of the proteins. However the application of this is only meaningful is deformation has an effect on signaling by the receptor proteins. The hypothesis supports that reaction rates are of the receptors are altered once a threshold of critical deformation has been exceeded and sufficient conformational changes take place. The reaction rates are altered either by enhancing the affinity of the receptor for certain molecules or by revealing and activating cryptic binding sites, such as in the case of PECAM-1 as shown in the figure. For the case of PECAM-1 this has been more or less proven experimentally by experiments using ATF and optical tweezers. They observed that once they stretch the receptors using magnetic beads, there is a highly increased level of phosphorylation of the receptors by tyrosine kinases. How we actually came to make a hypothesis like this I will talk about later.
  • We have used elastic parameters of the molecules derived by the experiments of these researchers and used them to calculate the deformation level and subsequently altered reaction rates. The result of such simulation is shown in the figure on the bottom right. We mathematically prove that laminar fluid flow and constant shear stress results in the activation of the receptors by deformation, however we also prove that this is not the case if the flow is turbulent. In such case turbulent force, the oscillatory force is not sufficient to activate the receptors and the signal goes down. These results predict a loosening of cell to cell adhesions that would most certainly lead to endothelial damage and accumulation of fats and lipids. This result agrees with biopsies from patients, where there is a high correlation between presence of atherosclerotic plaques and bifurcation points of arteries where there is turbulent flow and shear stress fluctuations. To us this is extremely important because as far as I know, this is the first time an biological and a mathematical explanation are coupled and provided together for such phenomenon.
  • Now all these constitute the extra cellular part of the model. Having modelled the above mathematically, we integrate them with the complete network of molecular interactions under one single large mathematical model. This is just a miniature of the model showing roughly 150 molecular reactions and only parts of the pathways. (excluding PI3K pathways, PECAM-1 pathways and we are at the moment adding VEGFR associated signalling). And is used mostly for qualitative understanding. The model underneath is a lot more detailed and predicts the concentration levels of each protein at different times at all their possible forms, active inactive, phopshorlated with 1-5 tyrosines, bound to another or two or three or molecules and more. In other words the model includes combinatorial complexity. So far, about 45 molecules are explicitly modelled with an average of 5 distinct functional states for each molecule and a total of more than 700 molecular interactions. This takes the form of 220 coupled ordinary differential equations at this stage but we keep expanding it every day. The more information the model accumulates and expands the more defined and constrained it becomes, making the predictions more accurate. Just to give an example, only the module in orange exists in the model as something like this. We certainly tend to disagree with protocols supporting model simplification and levels of abstraction. 10 molecules can have more than 200 possible interactions and all these have to considered. As a result, the orange module there depicting the focal complex formation is modeled mathematically as something like this: NOTE: MAKE SURE YOU LET THEM KNOW THAT EXPANDING THE MODEL MAKES IT MORE SPECIFIC AND INFORMATION COMPLETE, NOT MORE DIFFICULT TO PREDICT. ARGUMENT MOL…..remember seattle, make sure you convince them, its not chaotic. NOTE: CUT THE JDESIGNER OUT When we run the simulations we get the dynamics for each molecular component of the pathways, which at the moment is 160 graph such as these per simulation run. This is a detailed overview of the system and one of the 15 model versions constructed. The connection map can be divided in 6 distinct modules. The module in orange includes the assembly and disassembly of focal contacts which are proteolytically cleaved by Calpain. The module in green involves the activation of thrombin receptors and downstream g-protein signaling. The modules in blue and purple show the regulation of Rac and Rho GTPases respectively and how they crosstalk. Finally the modules in yellow and dark pink include the regulation of myosin light chain and f-actin respectively. Formed F-actin feeds back into the system by assisting focal complex formation. These modules can be modelled individually as to their biochemical properties and later on be connected to account for the complete physiological response to shear stress. The current system is built based on Biological knowledge and experimental observations with more than 300 published papers and 20 activity profiles used just for the network design and parameter collection. This is a dynamic diagram, with equations and parameters hidden behind every reaction and component. However even the design of the interaction network is not straightforward itself. To give you an example, consider the focal adhesion formation. Based on the papers I read, where proteins can actually interact in more than one ways, I chose one that seems logical to me, where talin binds to integrin, then vinculin etc. However, the perception of how a pathway works can be very misleading since it is always biased towards what we read, which is usually a very linear scenario. And I was initially assumed that the construction of the network was to be based primarily on biological knowledge and then dynamic behaviour, and never consider a trade of as a guide. As a result, the problem with this design was that it lacked all the kinetic parameters and it really bugged for some time. Only then I actually realised that the order and combination that these proteins bind to each other is not that important and this is the reason that they actually have more than one ways of interacting in nature. What it is really important is that they all contribute to focal adhesions formation by binding to actin filaments, something that usually escapes focus in literature. So the problem was solved by creating a network of interactions where proteins actually binds to f-actin, and for this parameters were available, showing actually that binding is specific and fast. This design calculates how fast a focal adhesion is formed from all components based on the available concentrations of these proteins and does not concentrate on the actual sequence of events. Anyhow, this is more or less how one can come around the lack of parameters by modifying the network to be still biologically valid but to a from where it can also be modelled. This is an example of one of the many tricks that systems biology threw back at me after a lot of unproductive days. Anyhow, to proceed, the schemes were first built in autonomous modules and their dynamics investigated and were then integrated for final assembly of the network. Pathway ends of each module were defined based on availability of data and local elasticity analysis. What this means, is the answer to the question, how did you know where to draw the end barriers and stop adding more regulatory components. Proteins are mostly dynamically controlled with positive and negative feedback loops to avoid linearity.
  • What are the problems arising in the development of a model of shear stress? Well as I said, the mechanisms by which the cells identify and respond to shear stress are still unclear. We certainly know that there is no single mechanosensor protein. Since we want understand and elucidate the shear stress response we have to first obtain all information that is already out there. This automatically implies that nothing can be left out, nothing can be missed. I need to put together all the information that there is out there and make sense out of it. And that requires lots of reading, thought, and hypothesising. To make sure that the information is not lost I am forced to keep an archive of all the processes going on. For this reason we have created a knowledgebase of interactions and dependencies that holds all the information taken from the literature and multiple data source that can be accessed at anytime. The model evolved slowly by adding more and more things and everything was built in a modular way. We had to integrate the information held in data sets from biochemistry experiments, immunoprecipitation and western blotting, microarray data sets, optical tweezers experiments and many other sources (Talk more about the different types of data, how did I integrate them?) . Now as you can imagine we came across inconsistencies, disagreements and contradicting data in the literature and so we spent a great deal of time in deciding which information to use (How did I go about that problem?). In terms of understanding and to be able to process everything in parallel we also draw maps of processes such as the one shown which is about the 1/10 th of all the processes our model contains so far, from fluid dynamics, to cytosolic signalling and to later nuclear events.
  • Now as I said, at the moment we produce about 220 graphs of the dynamics of the components included in the model. Before we actually start analysing anything, we have to assess the predictive power of the model and make sure the model is valid and we can be allowed to believe the results. So we take the results and we compare them with experimental results such as protein activity profiles that have been produced by western blots after application of shear stress.
  • Remember to say that: Rap1 activity has not been measured in ednothelial cells and under Shear Stress conditions. It is a hypothesi, however, Rap1 is know in general to activate Integrins receptors but by unknown yet mechanisms…
  • NOTE: MAK CLEAR AND ADD SPECIFIC EXAMPLED FOR ALL THESE. NEGATIVE FEEDBACK, FAK and CALCIUM, RPTPa, PECAM-1, MATCHES….etc.. SHOW GRAPHS AND FIGURES for each. THESE ARE THE MAIN RESULTS, SPLIT INTO 2 SLIDES IF NECESSARY. How has it really helped biology. What are the real results and specifically how I am personally going to experimentally verify each one. Getting the profiles of all these different molecules is not easy. Each has a different time of peaked activation and regulation by multiple directions, it is only possible to start matching things
  • Now all this investigation has improved our understanding of the biology of focal complex formation and signalling and revealed aspects not visible by simple qualitative examination. The question is however how can all this be used to identify drug targets? How do we go about it? Once we investigate the dynamics in such manner and form our conclusions and hypotheses that, then we are then interested to identify the contribution of each component and module to the overall dynamic signature of the system, and that we cannot do by eye. 5) STATISTICAL & SENSITIVITY ANALYSIS On the data produced from the simulations we perfom statistical and sensitivity analysis, to identify key molecules that control and balance the system. The components that show the biggest contribution to the overall dynamic signature of the system are the ones we are really interested in The dynamics of the system have been examined by multivariate statistical analysis (Principal Component Analysis, Partial Least Squares) to identify the contribution of each module and each molecular component of the model to the overall dynamic signature of the model. The insights and information obtained (modules that most balance/imbalance the network) were used to perform and one-by-one (perturb one input parameter and run simulation while keeping the rest stable) and combinatorial sensitivity analysis (monte-carlo sampling – random multiple perturbations) to identify the level of global robustness and local parametric sensitivities. The aim of sensitivity analysis is to estimate the rate of change in the output of a model with respect to changes in model inputs. Such knowledge is important for (a) evaluating the applicability of the model, (b) determining parameters for which it is important to have more accurate values, and (c) understanding the behaviour of the system being modelled.
  • Hmmm…are we really validating model? I’m not sure, one way or the other…. Maybe add: Come up with way to use typical structures (helices, beta-sheets) as guide for protein elasticity. Also, need to test non-load bearing molecules Between
  • Modelling as an applied method can depend on perspective.
  • Why have I chosen this approach and why is it the best. Why is the best that fits my problem and what are the real advantages. It was the best choice considering the available data I had in hand and the phenomenon I am investigating.
  • Correlative: Geom. Said of propositions, figures, etc. reciprocally related so that to a point in either corresponds (in solid geometry) a plane , or (in plane geometry) a straight line in the other. Biol. Of variations of structure, etc.: Mutually related so that the one is normally associated with the other Interpolation is having the effect of interpolation= The process of inserting in a series an intermediate number or quantity ascertained by calculation from those already known. extrapolation = The action or method of finding by a calculation based on the known terms of a series, other terms outside of them, whether preceding or following. Hence transf. , the drawing of a conclusion about some future or hypothetical situation based on observed tendencies; the inference resulting from such a process. If you extrapolate from known facts, you use them as a basis for general statements about a situation or about what is likely to happen in the future. (FORMAL) Extrapolating from his American findings, he reckons about 80% of these deaths might be attributed to smoking... It is unhelpful to extrapolate general trends from one case. His estimate of half a million HIV positive cases was based on an extrapolation of the known incidence of the virus.
  • This is an example of fluorescence images showing the changes in cell morphology upon shear stress.
  • NOTE: TAKE THIS OUT AND KEEP IT FOR POSSIBLE QUESTIONS Now as I said this is tricky and so far I have included deformation of a single molecule. However, in the case of focal adhesion where force might be transferred from molecule to molecule and where the direction and orientation of the force applied is variable, things get more complicated. And this is excluding the structural networks of actin filaments and microtubules. Force nevertheless affects rates of signalling at significant levels and we cannot disregard it, even if we have to start very simple. This is under continuous thought and investigation.
  • NOTE: TAKE THIS OUT AND KEEP IT FOR POSSIBLE QUESTIONS In the future we might add global mechanical effects and response of large protein complexes to force.
  • AFM & Optical tweezers : Spatial resolution  1 A Questions to be asked in biomechanics, when using optical tweezers or the AFM: How long does it take for a protein to refold after being stretched? How does the force vary with amino acid composition. Can alpha helices and beta sheets be roughly classified according to their elasticity? What is the force required for conformational change? Are the effects of force on protein temperature sensitive? (Brownian motion  (thermal vibration))
  • Focal contacts (focal adhesions) are associated with the ends of actin filament bundles (stress fibers) and are formed from focal complexes. The focal complex formation starts with integrin clustering upon presence of ligand. When ligand binds, induces conformational changes that allows further association of molecules. FCs are composed to over 40 distinct molecules that have been reported including protein tyrosine kinases, serine-threonine kinases, protein phosphatases, adapter proteins: and proteins with SH2/SH3 binding domains.
  • NOTE: CHANGE TITLE – USE IMPROVED UPDATED DATABASE DIAGRAM FROM BORISAS. TALK ABOUT DATA AND NEED FOR DATABASE AND INTEGRATION.
  • Anyhow there is also another project I’m working on apart from mathematical modelling. It was immediately apparent that the amount of data that was involved in all this was overwhelming to handle with just spreadsheets. Such data is best exploited when you have the flexibility to ask complex questions of it. For this reason, it was decided to design a database where this pathway data and also other published models could be stored and manipulated. The data model was carefully designed to support a) SBML model information b) CellML model information and c) reusable pathway components and multiple model versions per pathway. Having started with this data model, we are now building and online platform to serve as a general all-purpose online pathway model database and modelling system. To briefly take you through it, first an administration java tool is built based on the designed relational model and is used to insert/edit pathway-model data into the database. Specialised SBML parsers will be used to automatically insert existing SBML models into the database. An HTML client front end with embedded java applets is used to contact and query the database. The connection is made via a series of java servlets that are running continuously on the web server. The servlets are using a connection pool broker to establish the physical connections to the database server. This allows fast concurrent database queries. The use of applets on the client side allows the use of object serialisation for data transfer. The data is actually compressed on the fly to reduce data transfer size. Note: < Evaluation of the system from a developing point of view was satisfactory since the system delivers up to 2000 rows of data (5 columns each) in about 1 second. > This architecture was chosen after comparing with equivalent desings involving java server pages and java web start. The system will be complemented with a connection to a simulator engine. The user could potentially query pathway model data, change parameter values and create simulation models on the fly. An algorithm is also under development for automatic generation of animated pathways based on the produced simulation dynamics. The boxes in green show the completed parts. I expect both the modelling and computing projects to be completed within the next 6 months.

Transcript

  • 1. SYSTEMS BIOLOGY OF THE SHEAR STRESS RESPONSE Kostas Lykostratis
  • 2. Flow mediated mechanostransduction
    • Endothelial cells:
      • Form monolayer between blood and arterial wall
      • Hemodynamic forces regulate cells via flow mediated signal transduction
    Fluid Shear stress Intimal Layer Medial Layer Adventitial Layer Smooth Muscle cell Endothelial cell Fibroblast
  • 3. Physiological responses to altered pressure Endothelial cells and shear stress FLUID FLOW Cell Movement 3. Cell aligns in direction of flow 2. Front of cell extends forwards 4. Old adhesions lost 1. Cell body contracts
  • 4. Formation of focal complexes Crk P Talin Paxillin SRC CAS Rac Talin Paxillin FAK SRC CAS Crk Rac GAP P Vin Vin FAK GAP
  • 5. The target: the endothelial response Fn FAK GRB2 RAF RAS MEK1 ERK1/2 ELK-1 SRF c-fos c-jun AP-1 c-jun c-fos SRC RAC RHO ROCK mDIA LIMK F-actin G-actin MALi FORCE SS Troponin BLOOD GCK MEKK1 JNK egr-1 EGR-1 PAK p38 CBP/p300 Genes PP2A SP-1 Genes PKC IKKa NF-kB CALCIUM NF-kB STAT3 STAT3 STAT3 STAT3 STAT3 PI3K JAK2 MALa
  • 6. Biological Research Approach Systems Biology, (modelling/simulation based analysis) Integrative approach Molecular Level Systems Level Gene/protein structure and function is studied at the molecular level. Interactions of components in the biological system are studied – cells, tissues etc Genome Sequencing, DNA arrays, Mass Spec, Data mining Reductionistic approach
  • 7. Mechanistic pathway models formulation
    • Represent molecular interactions with mathematics
    • Translate biochemical formulas representing an interaction to ODEs.
    rate constant concentrations  k f [A][B]  k r [C] is described by dC dt Forward reaction rate Reverse reaction rate A  B  k r k f C
  • 8. Modelling - Steps Define Model System Collect Parameters Model Parameter Estimation Experimental Data (literature & in-house) Data Fitting Analysis Experimental approach Data Simulation Predictions Match No ? Yes ?
  • 9. Experimental shear stress stimulus
    • Generate fluid flow in microchannels via automated applied force
    y u Laminar parabolic velocity profile Q Endothelial cell monolayer Coupled Convection/ diffusion of ligand Fibronectin coated surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Calcium Influx 3 Molecular Deformation Shear Stress on EC surface
  • 10. Mass Transfer of molecules Methods and Results (1) Finite Element Method: c[x, y, z, 0]=0 c[x, y, 0, t]=c o Convection – Diffusion Equation Boundary Conditions: Element Node 1 Y X 2 2 2 2 y x z t        D  c  c   v y c   y=+b  x=+a y c    0 y c   y=-b  K on R u C s - K off R b SD N A Progression of [x] spread towards the tube walls (fibronectin surface)
  • 11. Experimental shear stress stimulus
    • Generate fluid flow in microchannels via automated applied force
    y u Laminar parabolic velocity profile Q Endothelial cell monolayer Coupled Convection/ diffusion of ligand Fibronectin coated surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Calcium Influx 3 Molecular Deformation Shear Stress on EC surface
  • 12. Calcium dynamics – Methods and Results (2) Na/Ca Exchanger R G PLC PIP2 DAG IP3 IP3R ER agonist Ca 2+ Buffer ATPase Calcium channel Capacitative Calcium entry PKC Shear Stress = 12 dynes/cm μ M
  • 13. Experimental shear stress stimulus
    • Generate fluid flow in microchannels via automated applied force
    y u Laminar parabolic velocity profile Q Endothelial cell monolayer Coupled Convection/ diffusion of ligand Fibronectin coated surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Calcium Influx 3 Molecular Deformation Shear Stress on EC surface
  • 14. Direct Force Effects Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω TK β - catenin Actin Filament PECAM-1 Signalling Initiation by molecular deformation Ω Ω Ω Ω Ω Ω Ω Ω Y P Y P TK Tyrosine Posphorylation Mechanical Stimulation Ω Ω Ω Ω Ω Ω Force Force SHP2 Gab1 RAS – RAF – MEK - ERK Nucleus Gene Expression
  • 15. Molecular Deformation – Methods and Results (3) 1. Relative deformation x 2. [x] of deformed receptors Laminar Flow – Stable Force Turbulent Flow - Oscillatory Force 3. Rates of Deformation 0 F F  ) cos( 0 t F F             T K W K x LS F K B r r exp )) ( 1 ( ) ( 0          T K W K x LS F K B f f exp ) ( ) ( 0 dt dF k m F x k dt dx k k m 1 1 1 2 1 1 1             kf A kr kf dt A d     ] )[ ( ] [ * * opposite response
  • 16. Focal Complex Rac Cycle MLC G-protein Rho Cycle Actin Filaments Network Constuction – Methods (4a)
  • 17. IP 3 DAG Ca s Capacitative calcium entry q cc Ca c Calcium channel Na + -Ca + exchanger q ex Ca 2+ -ATPase q in +/- q res - ER + + L L PKCi PKC-Ca PKC-Ca-DAG PKCbasal IKKi IKKa * PKCa GRB2 G RPTPa RPTPaP SRC SRCi-p CSK Fn Fn Calpain i Calpain a Calpastatin degradation SRC * TalinInt TalinClv Talin Talin Calpain a Calpastatin Calpain a TalinInt GRB2 RPTPa IKKi PKCa NF-kB IkB NF-kB NF-kB IkBα t IkBβ t IkBε t IkB NF-kB IkBα IkBε IkBβ IkB nucleus PIPKIγ661 PIPKIγ661 * Talin PIPKIγ661 * Paxillin Fn Talin Paxillin FAKi FAKi Paxillin Fn Talin Talin FAKi Paxillin Fn Talin PTP_PEST PTP_PEST Paxillin* FAK * SRC Fn Talin SRC FAK * Paxillin * Fn Talin FAK ** Paxillin ** Fn Talin SRC * PTP_SRC Fn Talin Paxillin** FAK ** Shear Stress FAK ** Paxillin ** Fn Talin CAS CAS SRC * PTP_PEST FAK ** Paxillin ** Fn Talin CAS * CRK FAK ** Paxillin ** Fn Talin CAS * CRK FAK ** Paxillin ** Fn Talin CAS * CRK CRK DOCK180 FAK ** Paxillin ** Fn Talin CAS * CRK CRK DOCK180 DOCK180 FAK ** Paxillin ** Fn Talin FAK ** Paxillin ** Fn Talin CRK CRK DOCK180 DOCK180 FAK ** Paxillin ** Fn Talin CRK DOCK180 CAS GRB2 SOS GRB2 SOS SOS GRB2 RasGDP RasGDP RasGTP RAF RasGTPp SOS GRB2 RasGTPp RAFp PTP_1 MEK MEKp MEKpp ERK ERKp ERKpp PTP_2 PTP_3 C-fos C-jun K.Lykostratis Shear Stress Response LEGEND PubMed hits for “endothelial shear stress” = 1754 Ra PLC R R PIP 2 Paxillin* FAK * SRC FAK * Paxillin * Fn Talin Paxillin* FAK * Paxillin FAKi Fn Talin FAK * Paxillin * Fn Talin Paxillin* FAK * PTP_PEST FAK * Paxillin * Fn Talin Fn Talin FAK * Paxillin * Fn Talin Paxillin* FAK * SRC * Paxillin** FAK ** FAK ** Paxillin ** Fn Talin FAK ** Paxillin ** Fn Talin SOS GRB2 FAK ** Paxillin ** Fn Talin RasGTP RAF FAK ** Paxillin ** Fn Talin = Affinity = Catalysis = Nuclear = Enzyme = GTPase GEF = GTPase = Adaptor = Phosphatase = Kinase = TF
  • 18. Assessment of Accuracy Compare model dynamics with experimental protein activity profiles Integrins FAK SRC Pink: Experiment Protein activities Blue: Simulation
  • 19. Dynamic Analysis of molecular interactions – Results (4a) Song Li et al , JBC, 1997 Tzima et al , EMBO, 2001 Integrins
  • 20. Looking for an answer Fn Fn PECAM1 RAP1 Talin Resting ~85% Pre-active (Talin bound) Active ~10% Talin FAK SRC CAS CRK C3G Ca++
  • 21. Dynamic Analysis of molecular interactions – Results (4a) Steady state – NO shear stress Applied shear stress (12dyn/cm ) 2 Basal Level, 10% of total Rap1 contribution ?? Activation boost after 5 minutes. …Why ?..How?
  • 22. Hypothesising, Predicting, Proving Results (4b)
    • Model predictions that agree with published results
      • Concentration profile of intracellular calcium
      • Disruptive effects of turbulent flow (oscillatory shear stress)
      • So far 7 published protein activity profiles partly or fully matched including:
        • avb3 Integrins, PECAM-1, FAK, SRC, CAS, PKC, CRK
    • Predictions rising from disagreement with current views and knowledge
      • FAK model dynamics fail to match published activity profile.
      • We predict that its activation is indirectly affected by increased intracellular calcium levels and directly by deformation of Intergrin R.
        • Recent research supports our hypothesis. Giannone et al , JBC, 2004
    • Model predictions supporting hypotheses not yet verified experimentally
      • We predict distinct negative feedback loop that lowers global signal.
      • SRC kinase phosphorylates Integrin receptor reducing its affinity for downstream targets.
        • Scientific evidence supports our prediction. Frame MC, JCS, 2004
  • 23. Multivariate Statistical/Sensitivity Analysis Methods (5) NL-PLS, VIP CA PCA Clustering
  • 24. ??
    • What are the components the system can’t survive without?
    • What is missing from the model?
    • Are there interactions identified by the model that potentially exist but are currently unknown?
    • Which components control and balance the system most and which pathway modules are the more robust or sensitive to changes?
    • Can we control and influence the system externally by addition of chemicals?
    • And how can this be done without any undesired side effects.
  • 25. Acknowledgements
    • Anne Ridley and lab (LICR – UCL, London, UK)
    • Marketa Zvelebil and lab (LICR – UCL, London, UK)
    • Mike Waterfield (LICR – UCL, London, UK)
    • Christine Orengo (UCL, London, UK)
    • Hamid Bolouri and lab (ISB, Seattle, US)
    • Daehee Hwang and Leroy Hood (ISB, Seattle, US)
    • Douglas Lauffenburger (MIT, Boston, US)
    • Theodore Wiesner (Texas University, Texas, US)
    • Herbert Sauro (KECK Graduate Institute, CA, US)
    • SBML/SBW Group (CALTECH, CA, US)
    • And a whole bunch of other fellow researchers who helped me one time or another
  • 26. Future directions: Model expansion and Experimental Validation
    • Western blots following fluid flow stimulation of cells to obtain first hand protein activity profiles to aid in the validation of the model.
    • Assess validity of mathematical model – pinpoint modules/molecules of interest and examine regulatory loops. Identify the most critical perturbations to be performed.
    • Apply time shifts and shear stress fluctuations (turbulence) in combined signals for synergistic effect predictions. Identify whether calcium or force is most critical in the shear stress response.
  • 27. Modeling approach can depend on perspective
    • Bottom-Up
      • “ Data-driven”, “Hypothesis-neutral”
      • Gather maximally comprehensive set of components, see what behaviours arise
        • Example: DNA microarrays – ‘Cluster analysis’, etc
    • Integrative, but inefficient for generating understanding of predictive power
      • Useful devises are not productively constructed by gathering random components to see what operations might result
        • Example: Computer programs themselves
  • 28. Modeling approach can depend on perspective
    • Top-Down
      • “ Behaviour-driven”, “Hypothesis-central”
      • Start from observed system phenomena, determine components and interactions and require to generate observations
        • Example: Virtual heart
    • Builds effectively on reductionist mechanism information base, integrating components and interactions up into system behaviour
    • Efficient for generating understanding, predictive power, and rational manipulation
  • 29. Modeling approach can depend on perspective
    • Middle-Out – special case of Top-down
      • Still “Behaviour-driven” and “Hypothesis-central”
      • Cells as the fundamental operational unit in biological systems – Integrative approach.
      • Data is collected from both molecular experiments (mechanistic modelling) and system-level observations (phenomenological modelling).
        • Model cell biology in terms of molecular mechanisms
        • Example: Current Project
    • Most efficient for generating understanding, predictive power, and rational manipulation
  • 30. Modeling approach can depend on Type of Information Focus
    • Descriptive models - easy
      • Correlative rather than mechanistic
        • Example: Mathematical metaphor
      • Useful for interpolative prediction, but not for extrapolative prediction
    • Mechanistic models - difficult
      • Physico-chemical (molecular, cellular) interactions in space & time . Can be ‘modular in framework’
        • Example: Cell cycle signalling (John Tyson - Virginia Tech), Virtual Heart Project (Denis Noble - Oxford)
        • Example: Current Project
  • 31. Fluorescence microscopy Your endothelium looks like this. Cultured Endothelium Flow (cells elongated) No Flow
  • 32. k m F k m F F F Adherent Free
  • 33. Viscoelastic Model of the FAC β α P T V A k    k    k T  T k P  P k V  V k A  A x F(t) F 1 F 2
  • 34. Single-molecule approaches to study structure and dynamics J. Zlatanova et al., 2000 mirror AFM tip photodiode position detector cantilever laser imaging surface sample Atomic Force Microscope Optical Tweezers laser beam trap F external F optical trap force balances the external force objective focus of optical trap
  • 35. Modular Modeling of Cell Systems
      • Modules in molecular networks enable core cell operations to be regulated by surrounding control loops
    • Viewpoint: Molecular networks are complex to ensure simple operation
      • Dynamic properties of module models are governed by parameters that depend on physico-chemical properties of regulatory molecular interactions
    • Implication: Molecular networks may be modelled in terms of core operation modules
  • 36. Formation of focal complexes Crk P Talin Paxillin SRC CAS Rac Talin Paxillin FAK SRC CAS Crk Rac GAP P Vin Vin FAK GAP
  • 37. Differential Equations Boolean Models Bayesian Networks Statistical Mining mechanisms Marcov Chains influences relationships (including structure) SPECIFIED ABSTRACTED Computational Modeling Approaches – Diverse Spectrum
  • 38. Shear stress and calcium influx dynamics Relationship between strain energy density and applied shear stress Fraction of channels in the open state Relationship for Membrane Potential Relationship for Membrane Permeability Balance equation for cytosolic-free calcium ions kTN - f e W( τ ) 1+ α *exp f o ( τ ) = 1 W( τ ) = (1- ε ) τ L + 16 μ 2 + τ 2 L 2 ( ε 2 -2 ε +1) - 4 μ ] 2 16 μ 2 + τ 2 L 2 ( ε 2 -2 ε +1) [ ] (1- ε ) τ L + [ 8  q s +q in -q b -q out dCa c dt f 0 (0)+tanh [f 0 ( τ )- f 0 (0)] P max P(t, τ ) = { } π t t f [ ] Δφ (t, τ ) = -E r - Δ E m ( τ ) 1-e -t/t φ
  • 39. Laminar and Turbulent Flows Laminar (molecular action) accelerate when molecules moving upward, slow down when moving downward Produce drag (shear) Turbulent (Random 3D eddies) molecular action still present, random eddies increase transport Enhanced mixing (turbulent ) u = u(y) average velocity ) ( y u u 
  • 40. Bioengineering – Potential for new technology
    • Potential for developing novel technology producing biological response with mechanical stimuli
    • Technology I: Flow mediated Gene Expression
      • Cell expresses a gene to a level based upon shear stress
      • Possibilities in gene therapy
    • Technology II: Spatially variable expression in single tissues via multiple laminar flow streams over tissue
      • Uses laminar flow to stream flows upon a tissue at different stresses
      • Flow induces on/off gene expression in each of the cells of the tissue
  • 41. Engineer a new measurement tool Induced Shear Stress Controlled velocity Microfluidic channel endothelial cell tissue Controlled protein X produced as a function of initial applied velocity
  • 42. Additional research: Database for dynamic models Database Schema Primary keys are shown in red. Secondary in blue. DNA DNA_ID Genbank_ID LocusLinkSNP_ID GeneCard_ID OMIM_ID DNA_Name DNA_Location GenInfo Compound Compound_ID Compound_Name Compound_Type GenInfo ProtParameters ProtParam_ID Protein_ID EntityType_ID Concentration C_Units Stoichiometry CompLocationVol CompLoc_Relation Shape_n_Structure S_n_S_metrics ConformatChange ComplexWith Direct_Bind_To Relation_Metrics CompParameters CompParam_ID Compound_ID EntityType_ID Concentration C_Units InteractionInfo Interaction_ID ProtParam_ID CompParam_ID DNA_ID EntityType EntityType_ID Entity_Name Protein Protein_ID Protein_Name Protein_Type GenInfo PDB_ID PFAM_ID SwissProt_ID EntrezProtein_ID Genbank_ID LocusLinkSNP_ID GeneCard_ID OMIM_ID InteractionType InteractionType_ID Interaction_Name Interaction Interaction_ID SubPath_ID InteractionType_ID KineticLaw Math_equation Interaction_expression Direction Reversible SubPath SubPath_ID Pathway_ID Interaction_ID Pathway_Name Pathway_ID Pathway_Name SubPath_Name SubPath_ID SubPath_Name RateVariable RateV_ID Interaction_ID RateVType RateValue R_Units Events Event_ID Interaction_ID EventTrigger TimeDelay TimeUnits Delay_Math EventExec
  • 43. Additional Research: Distributed computing Web Browser Host A Web Server SBW broker Database Server Host B SBW broker SBW simulation module Simulation Results (Graph Plots) Pathway diagrams Database Queries Online database/modelling platform SBW broker Servlets Database Connection pool broker Oracle Database Admin Tool SBML Client HTML/Applet SBW broker SBW simulation module