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MIT Jamboree 2007 Talk Outline • Background • System design • Novel reporter system • Established modelling techniques • Cutting-edge modelling MIT Jamboree 2007 The Problem Phenolic compounds Polycyclic aromatic hydrocarbons (PAH) BTEX compounds MIT Jamboree 2007 Objectives • 1: Design modular sensor construct • 2: Create the construct • 3: Test the system • 4: Development into a machine • 5: Model and predict outcomes! MIT Jamboree 2007 Why a Biosensor? • Lab-based monitoring • Skilled workforce • Expensive! MIT Jamboree 2007 What is a Biosensor? • Biosensors include a transcriptional activator coupled to a reporter toluene luciferin XylR operator / promoter luminescence luciferase Luciferase reporter gene() gene MIT Jamboree 2007 Our Construct Design Constitutive promoter Transcriptional activator RBS RBS Responsive promoter BBa_J61101 Reporter gene MIT Jamboree 2007 Double terminator BBa_B0015 Double terminator BBa_B0015 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! MIT Jamboree 2007 Our Solution Phenolic compounds DmpR - phenols Polycyclic aromatic hydrocarbons (PAH) DntR - PAHs BTEX compounds XylR - toluene MIT Jamboree 2007 Our Construct Design Transcriptional activator Constitutive promoter DmpR - phenols RBS DntR - PAHs XylR - toluene Reporter gene RBS Responsive promoter BBa_J61101 LacZ GFP Luciferase MIT Jamboree 2007 Double terminator BBa_B0015 Double terminator BBa_B0015 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! MIT Jamboree 2007 Testing The System DntR - inducible LacZ [PAH metabolite] (M) MIT Jamboree 2007 XylR - inducible luciferase 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! MIT Jamboree 2007 Unique Reporter System • Conventional biosensors use conventional reporter genes – e.g. LacZ, GFP, luciferase… • Lengthy and expensive procedures • Need a novel idea! MIT Jamboree 2007 Microbial Fuel Cells • Clean, renewable & autonomous • Electrons from metabolism harvested at anode • Versatile, long-lasting, varied carbon sources • Advantage over conventional power sources MIT Jamboree 2007 Microbial Fuel Cells MIT Jamboree 2007 Pyocyanin • From pathogenic Pseudomonas aeruginosa MIT Jamboree 2007 Pyocyanin • Phz genes – 7 gene operon, pseudomonad specific • PhzM and PhzS – P. aeruginosa specific MIT Jamboree 2007 Our Constructs Constitutive promoter Target RBS promoter Inducible transcription factor RBS PhzM coding region RBS MIT Jamboree 2007 PhzS coding region Double terminator Double terminator - + Pollutant Electrical Output Microbial Fuel Cell Pr RBS xylR Term. Term. Pu RBS phz genes Term. Term. PYOCYANIN MIT Jamboree 2007 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! MIT Jamboree 2007 Wetlab - Drylab MIT Jamboree 2007 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 o Simple biosensor o Positive feedback within the biosensor • Test and Validate the hypothesis proposed by the biologists MIT Jamboree 2007 The Model tf • Merge transcription and translation • Merge phzM with phzS (Parsons 2007) mRNA TF TF + S TF|S TF|S phzS phzM TF: Dntr or Xylr S: signal mRNA PhzM mRNA PhzS PhzM PhzS TF|S: complex PCA Intermediate compound MIT Jamboree 2007 PYO 24 The Model • Merge transcription and translation • Merge phzM with phzS (Parsons 2007) tf TF + S TF|S TF|S TF: Dntr or Xylr phzMS S: signal PhzMS TF|S: complex PCA MIT Jamboree 2007 PYO PYO 2 Feedback Loop tf TF + S TF|S TF|S TF: Dntr or Xylr phzMS tf S: signal PhzMS TF|S: complex PCA MIT Jamboree 2007 PYO PYO Modelling framework MIT Jamboree 2007 Modelling framework MIT Jamboree 2007 Qualitative Petri-Net Modelling & Analysis • 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 MIT Jamboree 2007 Modelling framework MIT Jamboree 2007 Parameters • Literature search • Experts’ knowledge MIT Jamboree 2007 3 Ordinary Differential Equations Available! Created in MIT Jamboree 2007 3 Parameters • Literature search • Experts’ knowledge MIT Jamboree 2007 3 Model Parameter Refinement • Modified MPSA MIT Jamboree 2007 3 Modelling framework MIT Jamboree 2007 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 MIT Jamboree 2007 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 TF → Ф TF+S → TFS TFS → TF+S TFS → Ф Ф → PhzMS PhzMS → Ф PhzMS → PYO PYO → Ф - production of TF - degradation of TF - association of TFS - dissociation of TFS - degradation of TFS - production of PhzMS - degradation of PhzMS - production of pyocyanin - degradation of pyocyanin MIT Jamboree 2007 Propensity Functions MIT Jamboree 2007 MIT Jamboree 2007 MIT Jamboree 2007 Simulink Modelling Environment MIT Jamboree 2007 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 MIT Jamboree 2007 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! MIT Jamboree 2007 Our Constructs So Far… Native promoter Native RBS XylR Double Terminator BBa_B0015 Double Double Renilla Renilla XylR XylR RBS Terminator Terminator Luciferase Luciferase responsive responsive RBS BBa_J61101 IRES XylR BBa_B0015 BBa_B0015 BBa_J52008 BBa_J52008 promoter promoter IRES MIT Jamboree 2007 XylR Registry Contributions Number 1 2 3 4 5 6 7 8 9 10 BioBrick Number BBa_I723032 BBa_I723029 BBa_I723023 BBa_I723031 BBa_I723024 BBa_I723025 BBa_I723026 BBa_I723027 Bba_I7230 30 BBa_I723020 Description Xylene-sensitive promoter Xylene-sensitive promoter plus RBS Xylene-inducible luciferase Inducible luciferase PhzM PhzS PhzM plus terminator PhzS plus terminator Salicylate-inducible transcription factor Salicylate-sensitive promoter MIT Jamboree 2007 Students • • • • • • • • • • Instructors • • • • • • • • • Toby Friend Rachael Fulton Christine Harkness Mai-Britt Jensen Karolis Kidykas Martina Marbà Lynsey McLeay Christine Merrick Maija Paakkunainen Scott Ramsay • Maciej Trybiło MIT Jamboree 2007 David Forehand David Gilbert Gary Gray Xu Gu Raya Khanin David Leader Susan Rosser Emma Travis Gabriela Kalna Thank You! MIT Jamboree 2007