Cellular signaling is fundamentally dynamic.

Slides:



Advertisements
Similar presentations
How will you know what your experimental data means? Student’s Lab Notebook.
Advertisements

The Effects of Molecular Noise and Size Control on Variability in the Budding Yeast Cell Cycle  Talia et al, Nature, 23 August 2007  William Morejón.
Lessons Learned from Measuring Cell Response by Quantitative Automated Microscopy FDA Workshop, Potency Measurements for Cellular and Gene Therapy Products,
Accurate Predictions of Genetic Circuit Behavior from Part Characterization and Modular Composition Jacob Beal, Noah Davidsohn, Aaron Adler, Fusun Yaman,
Author: Gene Yu Co-Authors: Dr. AlexBlake Dr. David Eddington July 29, 2010 NSF Research Experiences for Undergraduates (REU) in Novel Advanced Materials.
Signal Processing in Single Cells Tony 03/30/2005.
Hana El-Samad, PhD Grace Boyer Jr. Endowed Chair Biochemistry and Biophysics California Institute for Quantitative Biosciences (QB3) University of California,
Monolithic Microfabricated Valves and Pumps by Multilayer Soft Lithography EECE 491C Unger et al. (Quake group, CALTECH) Science, 2000.
Greg Carter Galitski Lab Institute for Systems Biology (Seattle) Maximal Extraction of Biological Information from Genetic Interaction Data.
. Gene Expression and Signaling Pathways in Yeast.
Structure Learning for Inferring a Biological Pathway Charles Vaske Stuart Lab.
Lorena Postiglione, M. Biomedical Eng. Tutor: Dr. Diego di Bernardo XXIX Cycle – 1 st year presentation Towards Microfluidics-based Automatic Control of.
Functionality of microbial phenotypic heterogeneity in bioprocessing conditions: an analysis based on the use of on/off-line flow cytometry Delepierre.
BEH.109: Laboratory Fundamentals in Biological Engineering. MODULE 3 Eukaryotic Cells as Phenotypic Indicators: The use of RNAi to modulate gene expression.
Multiple signaling pathways control the cellular response to O 2 levels Stephen D. Willis 2 and Mark J. Hickman 1,2 Departments of 1 Biological Sciences.
Schematic of TIR signalling Cells as computational devices Contains 1 copy of the genome Contains ca protein molecules in a volume of.
: iPSC-CM were treated up to 4 weeks with T3 (3nm and 30nm). Gene expression of TH target genes and/or are key markers of cardiac maturation were assessed.
Shankar Subramaniam University of California at San Diego Data to Biology.
Nan Hao, Erin K O’Shea. + How is an environmental stimuli transmitted into a cell? + How a cell respond to a specific signal? – Here the signal can be.
Alexander van Oudenaarden Lab Final Presentation Mashaal Sohail
Reconstruction of Transcriptional Regulatory Networks
A road map for cell biology: Why studying large protein complexes is crucial at this time David Drubin, UC Berkeley.
Systems Biology ___ Toward System-level Understanding of Biological Systems Hou-Haifeng.
IGEM: Measurement Techniques for Pathway Output Noah Helman Lim Lab May 2007.
NY Times Molecular Sciences Institute Started in 1996 by Dr. Syndey Brenner (2002 Nobel Prize winner). Opened in Berkeley in Roger Brent,
A Nanoliter-Scale Nucleic Acid Processor with Parallel Architecture Jong Wook Hong, Vincent Studer, Giao Hang, W French Andreson, Stephen R Quake presented.
Magnetic Tweezer System Development Jason Sherfey Senior BME, Vanderbilt University Probing mechanical properties across multiple scales Advisor: Dr. Franz.
전통적인 신약 개발 과정.
Single-cell NF-κB dynamics reveal digital activation and analogue information processing Savas Tay, Jacob J. Hughey, Timothy K. Lee, Tomasz Lipniacki,
Page 1 Assay kitAssay kit in Creative BioMart —by Creative BioMart.
EQTLs.
Naomi Ziv, Mark Siegal and David Gresham
BISC 220 Lab—Series 2 Protein Transport through the Secretory Pathway
Biological System – Yeast Pheromone Signal Transduction Pathway
University of California at San Diego
Naomi Ziv, Mark Siegal and David Gresham
Mapping variation in growth in response to glucose concentration
Parimal Samir1, Rahul2, James C. Slaughter3, Andrew J. Link1,4,5, *
GFP‐Sed5p localization defect in sgt2Δ.
Distributed computation: the new wave of synthetic biology devices
University of California at San Diego
Masahiro Ueda, Tatsuo Shibata  Biophysical Journal 
Anders S. Hansen, Erin K. O’Shea  Current Biology 
Tineke L. Lenstra, Antoine Coulon, Carson C. Chow, Daniel R. Larson 
Distinct Interactions Select and Maintain a Specific Cell Fate
Visual vocab part 3.
A mathematical model for transcription factor‐activated gene expression allows clustering of promoters and detailed quantitative characterization. A mathematical.
Analyzing Time Series Gene Expression Data
Dynamic Response Diversity of NFAT Isoforms in Individual Living Cells
Schedule-dependent interaction between anticancer treatments
Volume 23, Issue 2, Pages (January 2013)
Fuqing Wu, David J. Menn, Xiao Wang  Chemistry & Biology 
Approaches to signal transduction:
Yeast Replicator: A High-Throughput Multiplexed Microfluidics Platform for Automated Measurements of Single-Cell Aging  Ping Liu, Thomas Z. Young, Murat.
Volume 49, Issue 1, Pages (January 2013)
Author: Gene Yu Co-Authors: Dr. AlexBlake Dr. David Eddington
Tineke L. Lenstra, Antoine Coulon, Carson C. Chow, Daniel R. Larson 
Distinct Interactions Select and Maintain a Specific Cell Fate
Volume 16, Issue 1, Pages (June 2016)
Gene Regulation: Hacking the Network on a Sugar High
Volume 3, Issue 6, Pages (June 2013)
Wenfeng Qian, Di Ma, Che Xiao, Zhi Wang, Jianzhi Zhang  Cell Reports 
Volume 55, Issue 1, Pages (July 2014)
Quantitative Image Restoration in Bright Field Optical Microscopy
Daniel Schultz, Adam C. Palmer, Roy Kishony  Cell Systems 
Cellular Decision Making by Non-Integrative Processing of TLR Inputs
Yeast lab By Amrith Bhaskaran.
Single cell analysis 2019.
Volume 10, Issue 7, Pages (February 2015)
Presentation transcript:

High-Throughput Microfluidic Technologies for Systems Studies of Protein Signaling in Yeast

Cellular signaling is fundamentally dynamic. Cells exist in dynamically changing environments. www.ambion.com

Prototypical Signaling Pathway http://fig.cox.miami.edu Haploid (1N) Diploid (2N)

Experimental System for Quantitative Dynamic Cellular Signaling Modulate the chemical environment precisely over time. Resolve clonal population heterogeneity. Track and measure individual cells over time. Gene expression & phenotype High-throughput to obtain a system level understanding. Microfluidic System for Live Cell Fluorescent Microscopy

µFluidics - Multilayer Soft Lithography BLUE – cell flow lines RED – control lines 8 strains x 32 experimental conditions x time

Non-Adherent Cell Trapping Microchamber Sieve Valve

Fully Automated Live Cell Microscopy

Genetic Perturbations X Kinetic Stimuli Genetic Perturbations PRE::GFP INPUT OUTPUT

a-factor concentration dependent cell response Δh time Single step function 32 different a-factor concentrations in range 0nM to 100nM Exponential increments (ci=1.16i), i is the row number

a-factor concentration dependent cell response

a-factor concentration dependent cell response

a-factor concentration dependent cell response WT increasing a-factor

Over 3,000 Live Cell Imaging Experiments Δw Single pulse function 4 different a-factor concentrations 8 different pulse widths Δh time Δw Repeated pulse function 4 different a-factor concentrations 4 different pulse delays Δh time

Acknowledgements: Paper: Didier Falconnet Timothy Galitski Carl Hansen Antti Niemisto Susi Prinz Stephen Ramsey Ilya Shmulevich Galitski Lab: Greg Carter, Song Li Hansen Lab: Milenko Despotovic, Mark Homenuke Hieter Lab: Phil Hieter, Kirk McManus, Ben Montpetit, Jan Stoepel, Karen Yuen