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Towards Intelligent Probiotics. Chris Brasseaux , EE David Golynskiy , Bio/ Crim Tyler Guinn, BioChem /EE Sameer Sant , Bio/Econ Mitu Bhattatiry , Biomed (Columbia) Nimi Bhattatiry (High School) Jose Alfredo Flores (Monterey Mexico). Towards Intelligent Probiotics. Introduction

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towards intelligent probiotics

Towards Intelligent Probiotics

Chris Brasseaux, EE

DavidGolynskiy, Bio/Crim

Tyler Guinn, BioChem/EE

SameerSant, Bio/Econ

  • MituBhattatiry, Biomed (Columbia)

NimiBhattatiry (High School)

Jose Alfredo Flores (Monterey Mexico)

towards intelligent probiotics1
Towards Intelligent Probiotics
  • Introduction
  • Immunobot: sensor-taxis
  • Killbot: population control
  • More
towards intelligent probiotics2
Towards Intelligent Probiotics
  • Introduction
  • Immunobot: sensor-taxis
  • Killbot: population control
  • More
towards intelligent probiotics3
Towards Intelligent Probiotics
  • Live microorganisms that give benefit to host (e.g. live cultures in yogurt)
  • May be used as therapeutic tool
  • Address disorders afflicting the intestine
  • May result in problematic tissue damage
towards intelligent probiotics4
Towards Intelligent Probiotics
  • Human bowel symbionts represent engineering platform
  • Intelligent probiotics are user-controlled and confer health benefits to host
  • Stanford 2009: regulated lymphocytes to control inflammation
  • Goals
      • Interface with immune system to produce localization at damage site
      • Enable population control
towards intelligent probiotics5
Towards Intelligent Probiotics
  • “Device 1”: Immunobot
    • Immune interface
    • Drug delivery
  • “Device 2”: Killbot
    • System control
towards intelligent probiotics6
Towards Intelligent Probiotics
  • Introduction
  • Immunobot: sensor-taxis
  • Killbot: population control
  • More
signal detection
Signal Detection
  • Fibroblasts play important role in wound healing
  • Fibroblast growth factors (FGFs) are paracrine heparin-binding proteins that trigger fibroblast differentiation
  • FGFs can represent wound signals
fgf receptor interface
FGF Receptor Interface
  • Kolmer et al., 1994 achieved a similar effect with two constructs:
    • Chimeric receptor: maltose receptor fused to ToxR transcription factor
    • CTX cloned upstream of lacZ genes

Image from Kolmer, 1994

experiments
Experiments

1. Transformed E.Coli Dh5a

2. Incubated with nitrates for production of the receptor

3. Introduced heparin and FGF

4. Performed fluorescent microscopy measurements

chemotaxis
Chemotaxis

Image from Roland Institute at Harvard

Image from Roland McGraw Hill

modeling
Modeling

The signaling network from the input of external ligand signal to the output of the tumbling state of a E coli cell can be quantitatively described by a modular model.

The model is formulated based on the law of mass action and Michaelis-Menten mechanism and contains four relatively independent modules.Module 1: Activation of ToxR receptor

Module 2: Transcription/translation of CheZModule 3:CheYdephosphorylation by the CheZ proteinModule 4: The tumbling activity of E coli is characterized by the so-called “bias”, which is defined as the ratio of the time of directed movement and the total time. It is experimentally measured that the bias is a Hill function dependent on the concentration of phosphorylatedCheY (Cluzel, Surette et al. 2000).

towards intelligent probiotics7
Towards Intelligent Probiotics
  • Introduction
  • Immunobot: sensor-taxis
  • Killbot: population control
  • More
killbot a suicide mechanism
Killbot: A suicide mechanism
  • This mechanism uses two plasmids:
  • PcstA*-RBS-LuxI-double terminator (Berkeley 2006)
  • AHL Inducible Colicin E2 with GFP (Calgary 2008)
a glucose repressible killbot
A Glucose Repressible Killbot

Delivery/Glucose

Time

killbot experiments
Killbot Experiments
  • BL21 colicin sensitive cells (used same OD)
  • Population 1: AHL inducible colicinE2 with GFP
  • Population 2: glucose-repressible AHL producer

Population 1 (Immunobot)

Population 1 (Immunobot) + Population 2 (Killbot)

Population 1 (Immunobot) + more Population 2 (Killbot)

killbot experiments1
Killbot Experiments

The killbots eliminate the majority of the cells

killbot experiments2
Killbot Experiments

The addition of glucose in the medium increases the population two-fold

towards intelligent probiotics8
Towards Intelligent Probiotics
  • Introduction
  • Immunobot: sensor-taxis
  • Killbot: population control
  • More
accomplishments
Accomplishments

Built new BioBricks relating to wound sensing and chemotactic abilities

Demonstrated that some of them, particularly the killbot, seem to work as expected.

Improved the characterization existing BioBricks, and included our experience in the appropriate Registry page.

Qualifying for MIT will allow us to test the different promoter-receptor constructs with longer induction times, different ligand concentrations, and the chemotaxis experiments with controlled gradient settings.

slide34

Dr. LeonidasBleris

Dr. Hyun-Joo Nam

Dr. Lan Ma

NehaKashyap

LagnajeetPradhan

Kristina Ehrhardt