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 1.Introduction 2.Immunobot: sensor-taxis 3.Killbot: population control 4.More

Towards Intelligent Probiotics 1.Introduction 2.Immunobot: sensor-taxis 3.Killbot: population control 4.More

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 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 Probiotics “Device 1”: Immunobot –Immune interface –Drug delivery “Device 2”: Killbot –System control

Towards Intelligent Probiotics 1.Introduction 2.Immunobot: sensor-taxis 3.Killbot: population control 4.More

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 (FGFR)

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

Connection to Chemotaxis

Immunobot System

Cloning…

More Cloning

…and more cloning

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

Experiments

Interface to Chemotaxis

Chemotaxis Image from Roland Institute at Harvard Image from Roland McGraw Hill

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 CheZ Module 3: CheY dephosphorylation by the CheZ protein Module 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 phosphorylated CheY (Cluzel, Surette et al. 2000).

Towards Intelligent Probiotics 1.Introduction 2.Immunobot: sensor-taxis 3.Killbot: population control 4.More

Killbot: A suicide mechanism This mechanism uses two plasmids: 1.PcstA*-RBS-LuxI-double terminator (Berkeley 2006) 2.AHL Inducible Colicin E2 with GFP (Calgary 2008)

A Glucose Repressible Killbot Time Delivery/Glucose

Killbot Experiments Population 1 (Immunobot)Population 1 (Immunobot) + more Population 2 (Killbot) Population 1 (Immunobot) + Population 2 (Killbot) BL21 colicin sensitive cells (used same OD) Population 1: AHL inducible colicin E2 with GFP Population 2: glucose-repressible AHL producer

Killbot Experiments The killbots eliminate the majority of the cells

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

Towards Intelligent Probiotics 1.Introduction 2.Immunobot: sensor-taxis 3.Killbot: population control 4.More

Android Apps

Biobricks NameTypeDescriptionDesignerLength BBa_K569001CompositePcstA+RBS+LuxI+double terminatorMitu Bhattatiry937 BBa_K569013CompositePyeaR+ToxR+FGFRDavid Golynskiy & Tyler Guinn1536 BBa_K569014CompositeSCP+ToxR+FGFRDavid Golynskiy & Tyler Guinn1471 NameTypeDescriptionDesignerLength BBa_K569000IntermediateRBS+LuxI+dbltermMitu Bhattatiry798 BBa_K569003CompositePhototaxis Receptor+eYFPJose Alfredo Flores2918 BBa_K569004CompositePhototaxis Receptor+GFPJose Alfredo Flores2916 BBa_K569005CompositeCtx-CheZDavid Golynskiy & Tyler Guinn790 BBa_K569005CompositeCtx-CheZDavid Golynskiy & Tyler Guinn790 BBa_K569006CompositeCtx-CheYDavid Golynskiy & Tyler Guinn571 BBa_K569007CodingCheZ mutantDavid Golynskiy & Tyler Guinn664 BBa_K569010Compositectx+CheZ mutantDavid Golynskiy & Tyler Guinn1701 BBa_K569011CodingFGFRDavid Golynskiy & Tyler Guinn792 BBa_K569012CompositeToxR+FGFRDavid Golynskiy & Tyler Guinn1430 BBa_K569017CodingCheYDavid Golynskiy & Tyler Guinn418

Accomplishments 1.Built new BioBricks relating to wound sensing and chemotactic abilities 2.Demonstrated that some of them, particularly the killbot, seem to work as expected. 3.Improved the characterization existing BioBricks, and included our experience in the appropriate Registry page. 4.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.

Dr. Leonidas Bleris Dr. Hyun-Joo Nam Dr. Lan Ma Neha Kashyap Lagnajeet Pradhan Kristina Ehrhardt