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Talk Outline

Talk Outline. Background System design Novel reporter system Established modelling techniques Cutting-edge modelling. The Problem. Phenolic compounds. Polycyclic aromatic hydrocarbons (PAH). BTEX compounds. Objectives. 1: Design modular sensor construct 2: Create the construct

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Talk Outline

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  1. Talk Outline • Background • System design • Novel reporter system • Established modelling techniques • Cutting-edge modelling

  2. The Problem Phenolic compounds Polycyclic aromatic hydrocarbons (PAH) BTEX compounds

  3. Objectives • 1: Design modular sensor construct • 2: Create the construct • 3: Test the system • 4: Development into a machine • 5: Model and predict outcomes!

  4. Why a Biosensor? • Lab-based monitoring • Skilled workforce • Expensive!

  5. luminescence luciferin luciferase operator / promoter reporter gene() Luciferase gene What is a Biosensor? • Biosensors include a transcriptional activator coupled to a reporter toluene XylR

  6. Transcriptional activator Double terminator BBa_B0015 Constitutive promoter RBS RBS BBa_J61101 Reporter gene Responsive promoter Our Construct Design Double terminator BBa_B0015

  7. Objectives • 1: Design modular sensor construct • Switch on reporter in presence of pollutants • 2: Create the construct • 3: Test the system • 4: Development into a machine • 5: Model and predict outcomes!

  8. Our Solution Phenolic compounds DmpR - phenols Polycyclic aromatic hydrocarbons (PAH) DntR - PAHs BTEX compounds XylR - toluene

  9. Transcriptional activator DmpR - phenols DntR - PAHs XylR - toluene Double terminator BBa_B0015 Constitutive promoter RBS RBS BBa_J61101 Responsive promoter Reporter gene Double terminator BBa_B0015 LacZ GFP Luciferase Our Construct Design

  10. Objectives • 1: Design modular sensor construct • Switch on reporter in presence of pollutants • 2: Create the construct • Use 3 different sensors to express luciferase or LacZ • 3: Test the system • 4: Development into a machine • 5: Model and predict outcomes!

  11. XylR - inducible luciferase DntR - inducible LacZ [PAH metabolite] (M) Testing The System

  12. Objectives • 1: Design sensor/reporter construct • Switch on reporter in presence of pollutants • 2: Create the construct • Use 3 different sensors to express luciferase or LacZ • 3: Test the system • PAH-metabolite and xylene sensors successful • 4: Development into a machine • 5: Model and predict outcomes!

  13. Unique Reporter System • Conventional biosensors use conventional reporter genes • e.g. LacZ, GFP, luciferase… • Lengthy and expensive procedures • Need a novel idea!

  14. Microbial Fuel Cells • Clean, renewable • & autonomous • Electrons from metabolism • harvested at anode • Versatile, long-lasting, varied carbon sources • Advantage over conventional power sources

  15. Microbial Fuel Cells

  16. Pyocyanin From pathogenic Pseudomonas aeruginosa

  17. Pyocyanin • Phz genes – 7 gene operon, pseudomonad specific • PhzM and PhzS – P. aeruginosa specific

  18. Inducible transcription factor Double terminator Constitutive promoter RBS RBS RBS Target promoter PhzM coding region PhzS coding region Double terminator Our Constructs

  19. - + Pollutant Electrical Output Microbial Fuel Cell Term. Term. RBS phz genes RBS Term. Term. xylR Pr Pu PYOCYANIN

  20. Objectives • 1: Design sensor/reporter construct • Switch on reporter in presence of pollutants • 2: Create the construct • Use 3 different sensors to express luciferase or LacZ • 3: Test the system • PAH-metabolite and xylene sensors successful • 4: Development into a machine • Use Pseudomonas aeruginosa to power a fuel cell which generates a remote signal sent to base station • 5: Model and predict outcomes!

  21. Wetlab - Drylab

  22. Computational Modelling of the Biosensor • Aims • Guide biologists for the better design of synthetic networks • Use different computational approaches to model and analyze the systems • Simple biosensor • Positive feedback within the biosensor • Test and Validate the hypothesis proposed by the biologists

  23. mRNATF mRNAPhzS mRNAPhzM The Model tf • Merge transcription and translation • Merge phzM with phzS (Parsons 2007) TF|S TF + S TF|S phzS phzM TF: Dntr or Xylr S: signal TF|S: complex PhzM PhzS Intermediate compound PYO PCA

  24. The Model • Merge transcription and translation • Merge phzM with phzS (Parsons 2007) tf TF|S TF + S TF|S phzMS TF: Dntr or Xylr S: signal TF|S: complex PhzMS PCA PYO PYO PYO

  25. tf Feedback Loop tf TF|S TF + S TF|S phzMS TF: Dntr or Xylr S: signal TF|S: complex PhzMS PCA PYO PYO PYO

  26. Modelling framework

  27. Modelling framework

  28. Qualitative Petri-Net Modelling & Analysis • Graphical representation--Snoopy • Graphical representation--Snoopy • Qualitative analysis Charlie • T invariants (cyclic behavior in pink) • P invariants • (constant amount of output) • Quantitative Analysis by continuous Petri Net • ODE Simulation

  29. Modelling framework

  30. Parameters • Literature search • Experts’ knowledge

  31. Ordinary Differential Equations Available! Created in

  32. Parameters • Literature search • Experts’ knowledge

  33. Model Parameter Refinement • Modified MPSA

  34. Modelling framework

  35. Advantages and disadvantages of stochastic modelling • Living systems are intrinsically stochastic due to low numbers of molecules that participate in reactions • Gives a better prediction of the model on a cellular level • Allows random variation in one or more inputs over time • Slow simulation time

  36. Chemical Master Equations A set of linear, autonomous ODE’s, one ODE for each possible state of the system. The system may be written: • Ф→TF -production of TF • TF→ Ф -degradation of TF • TF+S→TFS -association of TFS • TFS →TF+S -dissociation of TFS • TFS→ Ф -degradation of TFS • Ф→PhzMS -production of PhzMS • PhzMS→Ф -degradation of PhzMS • PhzMS→ PYO-production of pyocyanin • PYO → Ф -degradation of pyocyanin

  37. Propensity Functions

  38. Simulink Modelling Environment

  39. In the end… Our Contributions: • standard SBML models of the systems • new biobricks with mathematical description • Practical comparison of modelling apporaches – qualitative, continuous, stochastic, based on sound theoretical framework • Tools to support synthetic biology (Code available) : • Minicap: multi-parametric sensitivity analysis of dynamic systems • Simulink environment

  40. Objectives • 1: Design sensor/reporter construct • Switch on reporter in presence of pollutants • 2: Create the construct • Use 3 different sensors to express luciferase or LacZ • 3: Test the system • PAH-metabolite and xylene sensors successful • 4: Development into a machine • Use Pseudomonas aeruginosa to power a fuel cell which generates a remote signal sent to base station • 5: Model and predict outcomes!

  41. Double Terminator BBa_B0015 Native RBS Native promoter XylR Double Terminator BBa_B0015 Double Terminator BBa_B0015 Renilla Luciferase BBa_J52008 Renilla Luciferase BBa_J52008 XylR responsive promoter XylR responsive promoter RBS BBa_J61101 RBS IRES XylR IRES XylR Our Constructs So Far…

  42. Registry Contributions

  43. Students • Toby Friend • Rachael Fulton • Christine Harkness • Mai-Britt Jensen • Karolis Kidykas • Martina Marbà • Lynsey McLeay • Christine Merrick • Maija Paakkunainen • Scott Ramsay • Maciej Trybiło Instructors • David Forehand • David Gilbert • Gary Gray • Xu Gu • Raya Khanin • David Leader • Susan Rosser • Emma Travis • Gabriela Kalna

  44. Thank You!

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