Advanced Aspects of Chemotactic Mechanisms:

Slides:



Advertisements
Similar presentations
Lecture #9 Regulation.
Advertisements

An Intro To Systems Biology: Design Principles of Biological Circuits Uri Alon Presented by: Sharon Harel.
Simulation of Prokaryotic Genetic Circuits Jonny Wells and Jimmy Bai.
Receptor clustering and signal processing in E.coli Chemotaxis Ref.1----TRENDS in Microbiology Vol.12 No.12 December 2004 Ref.2----PNAS Vol. 102 No. 48.
Chemotaxis and Motility in E. coli Examples of Biochemical and Genetic Networks Background Chemotaxis- signal transduction network Flagella gene expression.
Signal transduction in bacterial chemotaxis Lengeler et al. pp
Remote Control of Bacterial Chemotaxis UCSF iGEM Team 2006 Patrick Visperas Matthew Eames Eli Groban Ala Trusina Christopher Voigt, Tanja Kortemme, Chao.
Sept 25 Biochemical Networks Chemotaxis and Motility in E. coli Examples of Biochemical and Genetic Networks Background Chemotaxis- signal transduction.
Signal Processing in Single Cells Tony 03/30/2005.
Marcus Tindall Centre for Mathematical Biology Mathematical Institute St Giles’ Oxford. PESB, Manchester, 2007.
Chemotaxis: Another go Chrisantha Fernando Systems Biology Centre Birmingham University Chrisantha Fernando Systems Biology Centre Birmingham University.
Modeling the chemosensing system of E. coli
Bacterial Chemotaxis Dr. Chrisantha Fernando Systems Biology Centre University of Birmingham, UK March 2007 Dr. Chrisantha Fernando Systems Biology Centre.
Systems Biology Ophelia Venturelli CS374 December 6, 2005.
Protein Networks Week 5. Linear Response A simple example of protein dynamics: protein synthesis and degradation Using the law of mass action, we can.
BIOC3800 Sensory Transducers Dr. J.A. Illingworth.
FRET(Fluorescent Resonance Energy Transfer)
Single molecule pull-down Jain et al, Nature 473:484 (2011) Main points to cover fluorescence TIRF microscopy main advantage evanescent field depth single-fluor.
Bacterial chemotaxis lecture 2 Manipulation & Modeling Genetic manipulation of the system to test the robustness model Explaining Ultrasensitivity and.
1 Introduction to Biological Modeling Steve Andrews Brent lab, Basic Sciences Division, FHCRC Lecture 1: Introduction Sept. 22, 2010.
Overview of next five lectures: How is directional motility accomplished at the single cell level? An emphasis on experimental approaches for testing models.
Adaptation Essential for sensory perception Nearly universal Salmonella How to move towards unseen food?
Robustness in protein circuits: adaptation in bacterial chemotaxis 1 Information in Biology 2008 Oren Shoval.
Jean chamoun biophysical tools April 20, 2004
March 8, 2007March APS Meeting, Denver, CO1 Near-Perfect Adaptation in Bacterial Chemotaxis Yang Yang and Sima Setayeshgar Department of Physics Indiana.
Fig 7.1. Fig 7.2 The bacteria flagella motor [source: Berg HC, Ann. Rev. Biochem 2003] Fig 7.3.
Stochastic Models of Microdomain Formation in Biological Membranes Anne Kenworthy Lab: Maria Byrne, Kimberly Drake, Shawn Goodwin, Minchul Kang, Carl Rogers.
L17. Robustness in bacterial chemotaxis response
Statistical Mechanics of Sloppy Models Bacterial Cell-Cell Communication and More Josh Waterfall, Jim Sethna, Steve Winans.
1/2/2016Yang Yang, Candidacy Seminar1 Near-Perfect Adaptation in Bacterial Chemotaxis Yang Yang and Sima Setayeshgar Department of Physics Indiana University,
Optimal Strategy in E. coli Chemotaxis: An Information Theoretic Approach Lin Wang and Sima Setayeshgar Department of Physics, Indiana University, Bloomington,
Lin Wang Advisor: Sima Setayeshgar. Motivation: Information Processing in Biological Systems Chemical signaling cascade is the most fundamental information.
In-silico Implementation of Bacterial Chemotaxis Lin Wang Advisor: Sima Setayeshgar.
Approach…  START with a fine-tuned model of chemotaxis network that:  reproduces key features of experiments (adaptation times to small and large ramps,
The chemotaxis network is able to extract as much as information possible once the input signal varies slower relative to the response time of the chemotaxis.
Cmpe 588- Modeling of Internet Emergence of Scale-Free Network with Chaotic Units Pulin Gong, Cees van Leeuwen by Oya Ünlü Instructor: Haluk Bingöl.
Measurement Methods in Systems Biology
Agrobacterium tumefaciens
BT8118 – Adv. Topics in Systems Biology
Conformational Change in Proteins
Adaptation as a nonequilibrium paradigm
Dr. habil. Kőhidai László
Near-Perfect Adaptation in Bacterial Chemotaxis
Bacterial Chemotaxis Bacteria swim toward attractants and away from repellents. Their motion is a biased random walk due to control of tumbling frequency.
The chemotaxis network of E. coli
Module 2: Robustness in Biochemical Signaling Networks
Xianfeng Song Lin Wang Sima Setayeshgar
Bacterial Chemotaxis Bacteria swim toward attractants and away from repellents. Their motion is a biased random walk due to control of tumbling frequency.
Masahiro Ueda, Tatsuo Shibata  Biophysical Journal 
Nutrient-Sensing Mechanisms across Evolution
Dr. habil. Kőhidai László
Precision and Variability in Bacterial Temperature Sensing
Förster Resonance Energy Transfer (FRET)
Near-Perfect Adaptation in Bacterial Chemotaxis
Yang Yang & Sima Setayeshgar
Fundamental Constraints on the Abundances of Chemotaxis Proteins
Andreas Hilfinger, Thomas M. Norman, Johan Paulsson  Cell Systems 
A Solution to Limited Genomic Capacity: Using Adaptable Binding Surfaces to Assemble the Functional HIV Rev Oligomer on RNA  Matthew D. Daugherty, Iván.
Mechanism of chemotaxis in E
Thermal Robustness of Signaling in Bacterial Chemotaxis
Rasika M Harshey, Ikuro Kawagishi, Janine Maddock, Linda J Kenney 
Bacterial chemotaxis: The five sensors of a bacterium
Near-Perfect Adaptation in Bacterial Chemotaxis
Covalent Modification Regulates Ligand Binding to Receptor Complexes in the Chemosensory System of Escherichia coli  Guoyong Li, Robert M. Weis  Cell 
Toshinori Namba, Masatoshi Nishikawa, Tatsuo Shibata 
Effects of Receptor Interaction in Bacterial Chemotaxis
Near-Perfect Adaptation in Bacterial Chemotaxis
Simulating cell biology
Bacterial chemotaxis Current Biology
Presentation transcript:

Advanced Aspects of Chemotactic Mechanisms: Chemotaxis II Advanced Aspects of Chemotactic Mechanisms: Clustering of receptors Dynamic range of the response to chemoattractors Allostery among receptors Robustness of the control network Sensitivity to chemoattractors Integration of signals Theory vs. experiment Connection to other processes Jason Kahn: Chemotaxis II

How does chemotaxis work so well? Chemotaxis operates rapidly, with terrific sensitivity to chemoattractors, over a wide range of attractor concentration, and in response to multiple signals simultaneously. It has been difficult to understand how the process can work so well based on fundamental physical principles. Clustering and communication among receptors provides some answers. Theoretical approaches expand the concept of allostery to large aggregates of receptors. Jason Kahn: Chemotaxis II

Jason Kahn: Chemotaxis II Review of the Basics Source: Sourjik (2004), Trends in Microbiology CheA is the central control nexus, forming a dimer and interacting with CheW, receptors, CheB, and CheY Jason Kahn: Chemotaxis II

Chemotaxis as Engineering Chemotaxis is often discussed using the languages of electrical engineering or computer science Defaults for simple binding interactions vs. actual values: Amplification (Gain): 1 vs. 100 Hill coefficient: 1 vs. 10 Dynamic range: ~ 10 fold vs 100,000-fold This puzzle has engaged many brilliant people Noise and cell-cell fluctuations have recently emerged as important as well. Sourjik, 2004 Jason Kahn: Chemotaxis II

Jason Kahn: Chemotaxis II The Issue of Gain Sourjik and Berg, PNAS 2002, with accompanying commentary by Bray, PNAS, 2002. Gain = (% change in Bias/% change in receptor occupancy) A change in occupancy of only ~0.1% can be detected, corresponding to 1-2 molecules bound per cell, over a wide concentration range. Simple kinetic models fail to account for this. Gain occurs at two stages: (1) The fractional change in the level of CheY~P changes ~35x more than the fractional change in receptor occupancy for small changes in occupnacy (see below). (2) The Hill coefficient for CheY~P control of the motor bias is about 10, as measured in single cells. Experiment: Fluorescence resonance energy transfer (FRET) was measured between CheY-YFP and CheZ-CFP. YFP and CFP are two variants of Green Fluorescent Protein. When they come into close proximity, excitation of CFP is transferred to YFP and detected as YFP fluorescence. Since the substrate of CheZ is CheY~P, FRET reports on the abundance of CheY~P, which in turn is a measure of the activity of CheA. The apparent Kd for Asp can be measured from the concentration-dependence of the response to methyl-Asp (not metabolized). This value is used to calculate change in receptor occupancy; there’s very little apparent cooperativity. Then the FRET response can be compared to the calculated change in receptor occupancy to give the gain (see next page). This doesn’t explain how gain comes about! Could be at the receptor level or something to do with methylation. Jason Kahn: Chemotaxis II

Jason Kahn: Chemotaxis II Evidence for gain Sourjik and Berg’s demonstration of gain at the receptor to CheA step Receptor occupancy is calculated from Kd’s derived from response curves for receptor variants in cheR-cheB mutant cells Slope = -36 Removal Addition Jason Kahn: Chemotaxis II

Gain and Integration via Clustering Cooperative interactions among clustered receptors (MCP’s) can result in both gain and also integration of different signals, because mixed receptor complexes can affect each others’ activity. This means that the effects of low-abundance Trg, Aer, and Tap receptors are amplified by the high-abundance Tsr and Tar receptors. Both MWC and more complex models (Shimizu et al., JMB 2003) for cooperativity have been developed/applied. Jason Kahn: Chemotaxis II

Methylation provides dynamic range Levit and Stock, JBC 2002 Receptor methylation has minor effects on receptor occupancy. The response (CheA inactivation) is markedly different. Jason Kahn: Chemotaxis II

Connection to Engineering “Integral control” is a robust control mechanism whereby the integral of the error in a response is fed back in to the system in order to control it. Thermostats work this way. Chemotaxis is an example of integral control, as long as CheB demethylates only active receptors (those that are stimulating CheA to cause tumbling) and a few other constraints on rate constants are met. Application of engineering principles makes testable predictions for how the system should behave in order to exhibit “exact adaptation,” which is the idea that as long as the chemoattractant concentration is constant, no matter what the actual value of the concentration is, the bug swims randomly. Yi et al., PNAS 2000 Jason Kahn: Chemotaxis II

Connection to Quorum Sensing So far we have thought only about single bacterial cells seeking out nutrients. Starving cells are attracted to each other, due to nutrient leakage or pheromones. Alteration of behavior in response to neighbors is called “quorum sensing.” It may be adaptive in allowing e.g. biofilm formation, virulence gene expression, or plasmid sharing. Thus chemotaxis is an important mechanism for quorum sensing, and tends to direct cells to migrate to small enclosed areas. Park et al. (Stock lab), Science 2003, and see PNAS 2003 Jason Kahn: Chemotaxis II