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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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Talk outline l.jpg

Talk Outline

  • Background

  • System design

  • Novel reporter system

  • Established modelling techniques

  • Cutting-edge modelling


Slide3 l.jpg

The Problem

Phenolic compounds

Polycyclic aromatic

hydrocarbons (PAH)

BTEX compounds


Slide4 l.jpg

Objectives

  • 1: Design modular sensor construct

  • 2: Create the construct

  • 3: Test the system

  • 4: Development into a machine

  • 5: Model and predict outcomes!


Why a biosensor l.jpg

Why a Biosensor?

  • Lab-based monitoring

  • Skilled workforce

  • Expensive!


What is a biosensor l.jpg

luminescence

luciferin

luciferase

operator / promoter

reporter gene()

Luciferase gene

What is a Biosensor?

  • Biosensors include a transcriptional activator coupled to a reporter

toluene

XylR


Slide7 l.jpg

Transcriptional

activator

Double

terminator

BBa_B0015

Constitutive

promoter

RBS

RBS

BBa_J61101

Reporter

gene

Responsive

promoter

Our Construct Design

Double

terminator

BBa_B0015


Slide8 l.jpg

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!


  • Slide9 l.jpg

    Our Solution

    Phenolic compounds

    DmpR - phenols

    Polycyclic aromatic hydrocarbons (PAH)

    DntR - PAHs

    BTEX compounds

    XylR - toluene


    Slide10 l.jpg

    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


    Slide11 l.jpg

    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!


  • Testing the system l.jpg

    XylR - inducible luciferase

    DntR - inducible LacZ

    [PAH metabolite] (M)

    Testing The System


    Slide13 l.jpg

    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!


  • Unique reporter system l.jpg

    Unique Reporter System

    • Conventional biosensors use conventional reporter genes

      • e.g. LacZ, GFP, luciferase…

  • Lengthy and expensive procedures

  • Need a novel idea!


  • Microbial fuel cells l.jpg

    Microbial Fuel Cells

    • Clean, renewable

    • & autonomous

    • Electrons from metabolism

    • harvested at anode

    • Versatile, long-lasting, varied carbon sources

    • Advantage over conventional power sources


    Slide16 l.jpg

    Microbial Fuel Cells


    Pyocyanin l.jpg

    Pyocyanin

    From pathogenic Pseudomonas aeruginosa


    Slide18 l.jpg

    Pyocyanin

    • Phz genes – 7 gene operon, pseudomonad specific

    • PhzM and PhzS – P. aeruginosa specific


    Slide19 l.jpg

    Inducible

    transcription

    factor

    Double

    terminator

    Constitutive

    promoter

    RBS

    RBS

    RBS

    Target

    promoter

    PhzM

    coding

    region

    PhzS

    coding

    region

    Double

    terminator

    Our Constructs


    Slide20 l.jpg

    -

    +

    Pollutant

    Electrical Output

    Microbial Fuel Cell

    Term.

    Term.

    RBS

    phz genes

    RBS

    Term.

    Term.

    xylR

    Pr

    Pu

    PYOCYANIN


    Slide21 l.jpg

    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!


  • Wetlab drylab l.jpg

    Wetlab - Drylab


    Computational modelling of the biosensor l.jpg

    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


    The model l.jpg

    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


    The model25 l.jpg

    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


    Feedback loop l.jpg

    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


    Modelling framework l.jpg

    Modelling framework


    Modelling framework28 l.jpg

    Modelling framework


    Qualitative petri net modelling analysis l.jpg

    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


    Modelling framework30 l.jpg

    Modelling framework


    Parameters l.jpg

    Parameters

    • Literature search

    • Experts’ knowledge


    Ordinary differential equations l.jpg

    Ordinary Differential Equations

    Available!

    Created in


    Parameters33 l.jpg

    Parameters

    • Literature search

    • Experts’ knowledge


    Model parameter refinement l.jpg

    Model Parameter Refinement

    • Modified MPSA


    Modelling framework35 l.jpg

    Modelling framework


    Advantages and disadvantages of stochastic modelling l.jpg

    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


    Chemical master equations l.jpg

    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


    Propensity functions l.jpg

    Propensity Functions


    Simulink modelling environment l.jpg

    Simulink Modelling Environment


    In the end l.jpg

    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


    Slide43 l.jpg

    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!


  • Slide44 l.jpg

    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…


    Slide45 l.jpg

    Registry Contributions


    Slide46 l.jpg

    • 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


    Slide47 l.jpg

    Thank You!


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