1. The document discusses systems biology approaches to modeling the endothelial cell response to fluid shear stress. It describes experimental techniques to apply controlled fluid shear stress and measure downstream cellular responses. 2. Mathematical models are formulated to represent molecular interactions and pathways involved in the shear stress response. Model predictions are compared to experimental data to validate and refine the models. 3. The models can provide insights into critical components, feedback loops, and how external perturbations may influence the cellular response to shear stress. Further experimental validation of model predictions is needed.

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