Making genome edits in mammalian cellsChris Thorne
Looking at the kind of modifications that can be made in mammalian cells, and how at Horizon moving to a haploid model system has significantly improved efficiency of both editing and validation
Making genome edits in mammalian cellsChris Thorne
Looking at the kind of modifications that can be made in mammalian cells, and how at Horizon moving to a haploid model system has significantly improved efficiency of both editing and validation
In this research paper from the Spring 2015 semester, I described my analysis of certain genome scaffolds, or gaps within the Malaclemys terrapin genome. I examined seven of these scaffolds and determined their approximate sizes through Polymerase Chain Reaction (PCR) and Gel Electrophoresis. The DNA was then prepped to be sent for sequencing by an external source. The resulting chromatograms gave inconclusive results on the exact sequences of these scaffolds.
We previously reported a CRISPR-mediated knock-in strategy into introns of Drosophila genes, generating an attP-FRT-SA T2A-GAL4-polyA-3XP3-EGFP-FRT-attP transgenic library for multiple uses (Lee et al., 2018a). The method relied on double stranded DNA (dsDNA) homology donors with ~1 kb homology arms. Here, we describe three new simpler ways to edit genes in flies. We create single stranded DNA (ssDNA) donors using PCR and add 100 nt of homology on each side of an integration cassette, followed by enzymatic removal of one strand. Using this method, we generated GFP-tagged proteins that mark organelles in S2 cells. We then describe two dsDNA methods using cheap synthesized donors flanked by 100 nt homology arms and gRNA target sites cloned into a plasmid. Upon injection, donor DNA (1 to 5 kb) is released from the plasmid by Cas9. The cassette integrates efficiently and precisely in vivo. The approach is fast, cheap, and scalable.
In this research paper from the Spring 2015 semester, I described my analysis of certain genome scaffolds, or gaps within the Malaclemys terrapin genome. I examined seven of these scaffolds and determined their approximate sizes through Polymerase Chain Reaction (PCR) and Gel Electrophoresis. The DNA was then prepped to be sent for sequencing by an external source. The resulting chromatograms gave inconclusive results on the exact sequences of these scaffolds.
We previously reported a CRISPR-mediated knock-in strategy into introns of Drosophila genes, generating an attP-FRT-SA T2A-GAL4-polyA-3XP3-EGFP-FRT-attP transgenic library for multiple uses (Lee et al., 2018a). The method relied on double stranded DNA (dsDNA) homology donors with ~1 kb homology arms. Here, we describe three new simpler ways to edit genes in flies. We create single stranded DNA (ssDNA) donors using PCR and add 100 nt of homology on each side of an integration cassette, followed by enzymatic removal of one strand. Using this method, we generated GFP-tagged proteins that mark organelles in S2 cells. We then describe two dsDNA methods using cheap synthesized donors flanked by 100 nt homology arms and gRNA target sites cloned into a plasmid. Upon injection, donor DNA (1 to 5 kb) is released from the plasmid by Cas9. The cassette integrates efficiently and precisely in vivo. The approach is fast, cheap, and scalable.
Endocytosis and Endosome Trafficking: Roles in Coronavirus Uptake and Cell Si...InsideScientific
To learn more and watch the webinar, visit:
https://insidescientific.com/webinar/endocytosis-endosome-trafficking-coronavirus-uptake-cell-signaling-aps
Endocytosis and Endosome Trafficking: Roles in Coronavirus Uptake and Cell Signaling
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ON DEMAND
Ole Petersen, Roop Mallik and Erwin Neher share late-breaking research looking at endocytosis and calcium signaling in the context of SARS-CoV-2, organelle transport and calcium imaging. This webinar is brought to you by APS’ new journal, Function, and part of their Physiology in Focus learning series.
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During this exclusive live webinar, Ole Petersen, Roop Mallik and Erwin Neher discuss how the COVID-19 virus uses receptor-mediated endocytosis to gain entry into host cells, how motor proteins guide endosomes and phagosomes from the cell surface to lysosomes, and how intracellular calcium buffering can be used to modulate cell signaling and calcium imaging.
Endocytic Uptake of SARS-CoV-2: The Critical Roles of pH, Ca2+ and NAADP
Ole Petersen, CBE, FRS
Very recent work shows that SARS-CoV-2 enters our cells through receptor-mediated endocytosis, dependent on an endosomal bafilomycin-sensitive proton pump as well as two-pore channels (TPCs). Physiological intracellular Ca2+ signals, mediated by the messenger nicotinic acid adenine dinucleotide phosphate (NAADP), depend on the very same proton pump and TPCs. Two hitherto completely separate research fields, namely molecular virology and cellular Ca2+ signaling physiology are now coming together, creating exciting new research opportunities.
Trafficking of Endosomes and Phagosomes: Geometry, Force and Cholesterol
Roop Mallik, PhD
Uptake of material from the external world by endocytosis/phagocytosis supplies nutrients to cells, and is also critical for cell signaling. The journey of endosomes/phagosomes begins at the cell periphery and ends at lysosomes near the cell center. I will discuss how the balance of forces generated by antagonistic motor proteins guides this journey, and how lipids are emerging as a master-controller of this balance.
Calcium Buffering in Endo- and Exocytosis Studies
Erwin Neher, FRS
Researchers use calcium-chelators (buffers) to manipulate levels of free intracellular calcium concentration ([Ca2+]i) and to shape Calcium signals. Unlike pH buffers, which are used to strictly control pH levels, calcium buffers inside a living cell may not influence the steady-state level of [Ca2+]i at all, but rather slow-down [Ca2+]i-changes induced either endogenously or by the experimenter. Such properties and their consequences on Ca2+-imaging will be discussed.
Kupffer Cells Mediate Leptin-Induced Liver Fibrosis.
GASTROENTEROLOGY 2009;137:713–723
JIANHUA WANG,* ISABELLE LECLERCQ,‡ JOANNE M. BRYMORA,* NING XU,* MEHDI RAMEZANI–MOGHADAM,* ROSLYN M. LONDON,* DAVID BRIGSTOCK,§ and JACOB GEORGE*
*Storr Liver Unit, Westmead Millennium Institute, University of Sydney and Westmead Hospital, Westmead, Australia; ‡Laboratory of Gastroenterology, Faculty of
Medicine, Université Catholique de Louvain, Brussels, Belgium; and §Center for Cell and Vascular Biology, Children’s Research Institute, Columbus, Ohio
瘦素(Leptin)是一由脂肪細胞(Adipocyte)所分泌之荷爾蒙,是調控體重及新陳代謝之重要因子。過去研究發現病態肥胖(Obese)、脂肪肝(Nonalcoholic steatohepatitis)及酒精性肝炎(Alcoholic liver disease)等病患之血液循環中,Leptin量有明顯增加。而近期研究報告指出leptin具有促進肝臟纖維化(Liver fibrosis)之能力,當中分子機理並未明確。
在肝纖維化過程中,肝臟星狀細胞(HSC)會被活化增生及促進胞外基質(ECM)產生,而鄰近之Kupffer細胞(KC)則已知可透過促發炎因子(Proinflammatory factor)和促纖維化因子(Profibrogenic factors)例如TGF-β1和ROS影響HSC表現。雖然HSC是肝纖維化過程中重要角色,前人研究卻發現leptin似對HSC無任何調控作用。故本篇作者針對Leptin是否透過間接作用於HSC鄰近之KC,刺激其產生促纖維化因子,以活化HSC。
為探討leptin直接或間接影響HSC之分子機理,本篇作者透過RT-PCR、Immunoblot等分子生物學方法,分別測定leptin刺激後HSC及KC中Collagen I、TIMP1等促纖維化因子基因及蛋白表現,發現leptin雖可促使HSC增生,但對其纖維化能力之影響甚微。而leptin可刺激KC中TGF-β1及CTGF/CCN2等肝纖維化中重要之cytokines表現。另發現Leptin-treated KC-conditioned培養液可刺激HSC增生及增加其中Collagen I、TIMP1等表現,得出了leptin是透過刺激KC來活化HSC之推論。作者亦於後續實驗中,透過磷酸化測定、EMSA等方法探討leptin訊號傳遞作用,發現leptin可活化KC中STAT3、ERK1/2、AKT等路徑,及下游因子AP-1、NF-κB,而此兩種蛋白具有增強TGF-β1及CTGF/CCN2基因表現之能力。
Severe Long QT Phenotypes Associated with Novel Mutation of I313K at the Centre of KCNQ1 Potassium Channel Pore, (Authors:
Taruna Ikrar
Division of Multidisciplinary of Neuroscience, School of Medicine, Universality of California, Irvine, CA 92617, USA
Division of Cardiology, First Department of Internal Medicine, School of Medical Sciences, Niigata, Japan)
Presentation for the Nature paper: Bergles, et al. Glutamatergic synapses on
oligodendrocyte precursor
cells in the hippocampus, request download for animations
Tumor suppression and inflammation: controlling the senescence associated se...adamfreund
This is the powerpoint presentation from a talk I gave at a conference in October, 2009. It will be hard to follow without the spoken part, but it will hopefully give anyone who is interested a brief introduction to my thesis research.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
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.
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.
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.
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.
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
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?
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.
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
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.
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
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
Editor's Notes
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.