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MIT Jamboree 2007. Talk Outline Background System design Novel reporter system Established modelling techniques Cutting-edge modelling.

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Presentation on theme: "MIT Jamboree 2007. Talk Outline Background System design Novel reporter system Established modelling techniques Cutting-edge modelling."— Presentation transcript:

1 MIT Jamboree 2007

2 Talk Outline Background System design Novel reporter system Established modelling techniques Cutting-edge modelling

3 MIT Jamboree 2007 Phenolic compounds Polycyclic aromatic hydrocarbons (PAH) BTEX compounds The Problem

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

5 MIT Jamboree 2007 Why a Biosensor? Lab-based monitoring Skilled workforce Expensive!

6 MIT Jamboree 2007 operator / promoterreporter gene() XylR toluene What is a Biosensor? Biosensors include a transcriptional activator coupled to a reporter Luciferase gene luciferase luciferin luminescence

7 MIT Jamboree 2007 Our Construct Design Responsive promoter Double terminator BBa_B0015 RBS BBa_J61101 Reporter gene Constitutive promoter Transcriptional activator Double terminator BBa_B0015 RBS

8 MIT Jamboree : 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! Objectives

9 MIT Jamboree 2007 Phenolic compounds Polycyclic aromatic hydrocarbons (PAH) BTEX compounds Our Solution DmpR - phenols DntR - PAHs XylR - toluene

10 MIT Jamboree 2007 Responsive promoter RBS BBa_J61101 Double terminator BBa_B0015 Reporter gene Constitutive promoter Transcriptional activator Double terminator BBa_B0015 RBS LacZ GFP Luciferase DmpR - phenols DntR - PAHs XylR - toluene Our Construct Design

11 MIT Jamboree : 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! Objectives

12 MIT Jamboree 2007 Testing The System XylR - inducible luciferase DntR - inducible LacZ [PAH metabolite] ( M)

13 MIT Jamboree : 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! Objectives

14 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!

15 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

16 MIT Jamboree 2007 Microbial Fuel Cells

17 MIT Jamboree 2007 Pyocyanin From pathogenic Pseudomonas aeruginosa

18 MIT Jamboree 2007 Phz genes – 7 gene operon, pseudomonad specific PhzM and PhzS – P. aeruginosa specific Pyocyanin

19 MIT Jamboree 2007 Our Constructs Constitutive promoter Inducible transcription factor Double terminator RBS Target promoter PhzM coding region RBS PhzS coding region Double terminator RBS

20 MIT Jamboree Pollutant Microbial Fuel Cell Electrical Output xylR RBS Term. PrPr PuPu phz genes Term. PYOCYANIN RBS

21 MIT Jamboree : 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! Objectives

22 MIT Jamboree 2007 Wetlab - Drylab

23 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

24 MIT Jamboree 2007 The Model 24 PhzMPhzS PCA Intermediate compound PYO TF + S TF|S tf phzM phzS TF|S mRNA PhzM mRNA PhzS mRNA TF Merge transcription and translation Merge phzM with phzS (Parsons 2007) TF: Dntr or Xylr S: signal TF|S: complex

25 MIT Jamboree 2007 The Model 25 tf TF + S TF|S phzMS PhzMS PCA PYO TF|S PYO TF: Dntr or Xylr S: signal TF|S: complex Merge transcription and translation Merge phzM with phzS (Parsons 2007)

26 MIT Jamboree 2007 Feedback Loop tf TF + S TF|S phzMS PhzMS PCA PYO TF|S PYO TF: Dntr or Xylr S: signal TF|S: complex tf

27 MIT Jamboree 2007 Modelling framework

28 MIT Jamboree 2007 Modelling framework

29 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

30 MIT Jamboree 2007 Modelling framework

31 MIT Jamboree 2007 Parameters 31 Literature search Experts knowledge

32 MIT Jamboree 2007 Ordinary Differential Equations 32 Available! Created in

33 MIT Jamboree 2007 Parameters 33 Literature search Experts knowledge

34 MIT Jamboree 2007 Model Parameter Refinement 34 Modified MPSA

35 MIT Jamboree 2007 Modelling framework

36 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

37 MIT Jamboree 2007 Chemical Master Equations A set of linear, autonomous ODEs, 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

38 MIT Jamboree 2007 Propensity Functions

39 MIT Jamboree 2007

40

41 Simulink Modelling Environment

42 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

43 MIT Jamboree : 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! Objectives

44 MIT Jamboree 2007 Our Constructs So Far… Native promoter XylR Double Terminator BBa_B0015 Native RBS XylR responsive promoter Double Terminator BBa_B0015 RBS BBa_J61101 Renilla Luciferase BBa_J52008 IRESXylR responsive promoter RBS Renilla Luciferase BBa_J52008 Double Terminator BBa_B0015 IRESXylR

45 MIT Jamboree 2007 Registry Contributions

46 MIT Jamboree 2007 Instructors David Forehand David Gilbert Gary Gray Xu Gu Raya Khanin David Leader Susan Rosser Emma Travis Gabriela Kalna Students Toby Friend Rachael Fulton Christine Harkness Mai-Britt Jensen Karolis Kidykas Martina Marbà Lynsey McLeay Christine Merrick Maija Paakkunainen Scott Ramsay Maciej Trybiło

47 MIT Jamboree 2007 Thank You!


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