Stripe formation In an expanding bacterial colony with density-suppressed motility The 5 th KIAS Conference on Statistical Physics: Nonequilibrium Statistical.

Slides:



Advertisements
Similar presentations
How does the ParABC system segregate low copy number plasmids in bacteria? Martin HowardDept of Systems Biology John Innes Centre Norwich, UK.
Advertisements

A SYNTHETIC GENE- METABOLIC OSCILLATOR Reviewed by Fei Chen.
An Intro To Systems Biology: Design Principles of Biological Circuits Uri Alon Presented by: Sharon Harel.
Movements of Molecular Motors: Random Walks and Traffic Phenomena Theo Nieuwenhuizen Stefan Klumpp Reinhard Lipowsky.
Self-propelled motion of a fluid droplet under chemical reaction Shunsuke Yabunaka 1, Takao Ohta 1, Natsuhiko Yoshinaga 2 1)Department of physics, Kyoto.
Modelling Cell Signalling and Pattern Formation Nick Monk Department of Computer Science Collaboration: Erik Plahte & Siren Veflingstad Agricultural University.
Stochastic Analysis of Bi-stability in Mixed Feedback Loops Yishai Shimoni, Hebrew University CCS Open Day Sep 18 th 2008.
Systems Biology of Pattern Formation, Canalization and Transcription in the Drosophila Blastoderm John Reinitz STAT Applied Math Retreat Gleacher Center.
Programmed population control by cell-cell communication and regulated killing Lingchong You, Robert Sidney Cox III, Ron Weiss & Frances H. Arnold Programmed.
BZ and the Turing Instability Tamas Bansagi BZ Boot Brandeis.
T HE ROLE OF MOTILITY AND NUTRIENTS IN BACTERIAL COLONY FORMATION AND COMPETITION 1 Silogini Thanarajah Guest Lecture.
1 Predator-Prey Oscillations in Space (again) Sandi Merchant D-dudes meeting November 21, 2005.
Two continuum models for the spreading of myxobacteria swarms Angela Gallegos1, Barbara Mazzag2, Alex Mogilner1¤ 1 Department of Mathematics, University.
U N C L A S S I F I E D Operated by the Los Alamos National Security, LLC for the DOE/NNSA Suppression of Spin Diffusion: Modeling and Simulations Gennady.
Characterization of Al-Humic Complexation and Coagulation Mechanism Removal of natural organic matter (NOM) by coagulation using metal coagulants (aluminium.
Marcus Tindall Centre for Mathematical Biology Mathematical Institute St Giles’ Oxford. PESB, Manchester, 2007.
Strategies to synchronize biological synthetic networks Giovanni Russo Ph.D. Student University of Naples FEDERICO II Department of Systems and Computer.
Stochasticity in molecular systems biology
SCB : 1 Department of Computer Science Simulation and Complexity SCB : Simulating Complex Biosystems Susan Stepney Department of Computer Science Leo Caves.
A reaction-advection-diffusion equation from chaotic chemical mixing Junping Shi 史峻平 Department of Mathematics College of William and Mary Williamsburg,
Wouter-Jan Rappel UCSD Establishing direction during chemotaxis in eukaryotic cells: What can theoretical models tell us? Collaborators: Herbert Levine.
Mathematical Modelling of Phage Dynamics: Applications in STEC studies Tom Evans.
Modelling Flow Distributed Oscillations In The CDIMA Reaction Jonathan R Bamforth, Serafim Kalliadasis, John H Merkin, Stephen K Scott School of Chemistry,
Pattern Formation Patrick Lucey.
The tail of Listeria monocytogenes : Lessons learned from a bacterial pathogen (cont.) 1. How do Listeria make tails Nucleation, growth 2. Role of ABPs.
Demetris Kennes. Contents Aims Method(The Model) Genetic Component Cellular Component Evolution Test and results Conclusion Questions?
CompuCell Software Current capabilities and Research Plan Rajiv Chaturvedi Jesús A. Izaguirre With Patrick M. Virtue.
Power-law performance ranking relationship in exponentially growing populations Chunhui Cai 1, Da-Liang Li 2, Qi Ouyang 2, Lei-Han Tang 1,3, Yuhai Tu 2,4.
Morgan Haskell Coby Turner Dan Karkos. Jeff Hasty and team  University of California in San Diego Biological synchronized clocks ○ Flash to keep time.
What does non- dimensionalization tell us about the spreading of Myxococcus xanthus? Angela Gallegos University of California at Davis, Occidental College.
Metabolic pathway alteration, regulation and control (5) -- Simulation of metabolic network Xi Wang 02/07/2013 Spring 2013 BsysE 595 Biosystems Engineering.
E. coli exhibits an important behavioral response known as chemotaxis - motion toward desirable chemicals (usually nutrients) and away from harmful ones.
Heat Equation and its applications in imaging processing and mathematical biology Yongzhi Xu Department of Mathematics University of Louisville Louisville,
The problem of development Collegium BudapestEötvös University Budapest Eörs Szathmáry (Alpbach 2005)
Nonlinear Dynamics in Mesoscopic Chemical Systems Zhonghuai Hou ( 侯中怀 ) Department of Chemical Physics Hefei National Lab of Physical Science at Microscale.
Post-trancriptional Regulation by microRNA’s Herbert Levine Center for Theoretical Biological Physics, UCSD with: E. Levine, P. Mchale, and E. Ben Jacob.
Introduction to Self-Organization
Robustness in protein circuits: adaptation in bacterial chemotaxis 1 Information in Biology 2008 Oren Shoval.
Systems Biology ___ Toward System-level Understanding of Biological Systems Hou-Haifeng.
Turn Me On, Lactones! New Tools For Self-Organized Pattern Formation University of Cambridge iGEM 2006.
Nigel Clarke Department of Chemistry Durham University Effect of Shear Flow on Polymer-Polymer Miscibility: Theoretical Advances and Challenges With.
1 Computational Modeling in Quantitative Cancer Imaging Biomedical Science and Engineering Conference 18 March 2009 Tom Yankeelov, Nkiruka Atuegwu, John.
March 8, 2007March APS Meeting, Denver, CO1 Near-Perfect Adaptation in Bacterial Chemotaxis Yang Yang and Sima Setayeshgar Department of Physics Indiana.
Optimal Strategy in E. coli Chemotaxis: An Information Theoretic Approach Lin Wang and Sima Setayeshgar Department of Physics, Indiana University, Bloomington,
L17. Robustness in bacterial chemotaxis response
Engineering a Molecular Predation Oscillator Thanks to staff from Imperial College for the support over.
Optimal Strategy in E. coli Chemotaxis: An Information Theoretic Approach Lin Wang and Sima Setayeshgar Department of Physics, Indiana University, Bloomington,
Harvard iGEM 2005: Team BioWire Orr Ashenberg, Patrick Bradley, Connie Cheng, Kang-Xing Jin, Danny Popper, Sasha Rush.
Pattern formation in nonlinear reaction-diffusion systems.
Programmed population control by cell-cell communication and regulated killing Lingchong You, Robert Sidney Cox III, Ron Weiss & Frances H. Arnold Programmed.
In-silico Implementation of Bacterial Chemotaxis Lin Wang Advisor: Sima Setayeshgar.
Active Walker Model for Bacterial Colonies: Pattern Formation and Growth Competition Shane Stafford Yan Li.
Arthur Straube PATTERNS IN CHAOTICALLY MIXING FLUID FLOWS Department of Physics, University of Potsdam, Germany COLLABORATION: A. Pikovsky, M. Abel URL:
Information Theoretic Projection of Cytoskeleton Dynamics onto Surrogate Cellular Motility Models Sorin Mitran 1 1 Department of Mathematics, University.
Approach…  START with a fine-tuned model of chemotaxis network that:  reproduces key features of experiments (adaptation times to small and large ramps,
General Microbiology (MICR300) Lecture 6 Microbial Physiology (Text Chapters: 3; 4.14; 4.16 and )
Dynamic of Networks at the Edge of Chaos
IGEM 2005: Team BioWire and BioLoserz!!! LOL Orr Ashenberg, Patrick Bradley, Connie Cheng, Kang-Xing Jin, Danny Popper, Sasha Rush.
Pattern Formation in Tissues Walter de Back, Fabian Rost, Lutz Brusch ZIH,TU Dresden Kondo and Miura 2010, Science 329, 1616.
Adaptation as a nonequilibrium paradigm
Two talks this week and next on morphogenesis
quorum sensing & biofilms
Distributed computation: the new wave of synthetic biology devices
Physical Mechanisms for Chemotactic Pattern Formation by Bacteria
Complex Systems in Biology
Reaction & Diffusion system
Expansion-Repression Mechanism for Scaling the Dpp Activation Gradient in Drosophila Wing Imaginal Discs  Danny Ben-Zvi, George Pyrowolakis, Naama Barkai,
Computational Biology
Model of Bacterial Band Formation in Aerotaxis
Morpheus Unbound: Reimagining the Morphogen Gradient
Presentation transcript:

Stripe formation In an expanding bacterial colony with density-suppressed motility The 5 th KIAS Conference on Statistical Physics: Nonequilibrium Statistical Physics of Complex Systems 3-6 July 2012, Seoul, Korea Synthetic biology Phenotype (structure and spatiotemporal dynamics) Molecular mechanisms (players and their interactions) Traditional biological research (painstaking) GENETICSBIOCHEMISTRY discovery of novel mechanisms and function Lei-Han Tang Beijing Computational Science Research Center and Hong Kong Baptist U

Chenli Liu (Biochem) Xiongfei Fu (physics) Dr Jiandong Huang (Biochem) The Team HKU UCSD: Terry Hwa Marburg: Peter Lenz C. Liu et al, Science 334, 238 (2011); X. Fu et al., Phys Rev Lett 108, (2012) HKBU Xuefei Li Lei-Han Tang

Periodic stripe patterns in biology dicty fruit fly embryo snake

Morphogenesis in biology: two competing scenarios Morphogen gradient (Wolpert 1969) –Positional information laid out externally –Cells respond passively (gene expression and movement) Reaction-diffusion (Turing 1952) –Pattern formation autonomous –Typically involve mutual signaling and movement Reaction-Diffusion Model as a Framework for Understanding Biological Pattern Formation, S Kondo and T Miura, Science 329, 1616 (2010)

Cells have complex physiology and behavior Growth Sensing/Signaling Movement Differentiation All play a role in the observed pattern at the population level Components characterization challenging in the native context Synthetic molecular circuit inserted into well-characterized cells (E. coli)

Experiment

Swimming bacteria (Howard Berg)

Bacterial motility 1.0: Run-and-tumble motion ~10 body length in 1 sec cheZ needed for running Extended run along attractant gradient => chemotaxis CheY-P low CheY-P high

Couple cell density to cell motility High density Low density cheZ expression normal cheZ expression suppressed

Genetic Circuits CheZ luxR luxI Plac/ara-1 cI PluxI CI LuxR LuxI cheZ Pλ(R-O12) AHL Quorum sensing module Motility control module

200 min300 min400 min 500 min 600 min WT control Experiments done at HKU Seeded at plate center at t = 0 min 300 min700 min900 min1400 min1100 min engr strain Colony size expands three times slower Nearly perfect rings at fixed positions once formed!

Phase diagram Simulation Experiments at different aTc (cI inducer) concentrations Increase basal cI expression => decrease cheZ expression => reduction of overall bacterial motility many rings => few rings => no ring

How patterns form? Anything new in this pattern formation process? Robustness? Qualitative and quantitative issues

How patterns form Initial low cell density, motile population Growth => high density region => Immotile zone Expansion of immotile region via growth and aggregation Appearance of a depletion zone Same story repeats itself? Sequential stripe formation

Modeling and analysis

Front propagation in bacteria growth Fisher/Keller-Segel: Logistic growth + diffusion  x ρsρs c Traveling wave solution Exponential front No stripes!

Growth equations for engineered bacteria 3-component model Bacteria (activator) AHL (repressor) Nutrient AHL-dependent motility nutrient-limited growth

Sequential stripe formation from numerical solution of the equations front propagation Band formation propagating front unperturbed aggregation behind the front

Analytic solution: 2-component model Kh-εKh-ε μ(h)μ(h) hKhKh 0 motile Non- motile Bacteria AHL random walkimmotile high density/AHLlow density/AHL Growth rate Degradation rate

Moving frame, z = x - ct Steady travelling wave solution (no stripes) Solution strategy i)Identify dimensionless parameters ii)Exact solution in the linear case iii)Perturbative treatment for growth with saturation Solution of the  -eqn in two regions Solution of the h-eqn using Green’s fn Stability limit Motile front Cell depletion zone

“Phase Diagram” from the stability limit Characteristic lengths Cell density profile AHL diffusion Stability boundary: L h /L ρ 

Key parameters governing the stability of the solution Bacteria profile AHL profile i)AHL profile follows the cell density profile most of the time. ii)In the depletion zone, AHL profile is smoothened compared to the cell density profile. The degree of smoothening determines if AHL density can exceed threshold value in the motile zone. iii)If the latter occurs, nucleation of high density/immotile band takes place periodically => formation of stripes

Discussion

The mathematics of biological pattern formation

Debate: cells are much more complex than small molecules => Deciphering necessary ingredients in the native context challenging Resort to synthetic biology (E. coli) –Minimal ingredients: cell growth, movement, signaling, all well characterized –Defined interaction: motility inhibited by cell density (aggregation)  Formation of sequential periodic stripes on semi-solid agar  Genetically tunable  Stripe formation in open geometry (new physics)  Theoretical analysis deepens understanding of the experimental system in various parameter regimes

Open issues Period of stripes analysis of the immotile band formation in the motile zone Robustness of the pattern formation scheme Residual chemotaxis Inhomogeneous cell population Cell-based modeling Sharpness of the zones Multiscale treatment (cell => population)

Biology goes quantitative New problems for statistical physicists Close collaboration key to success Life is complex! Biological game: precise control of pattern through molecular circuits Population: pattern formation 5mm Cell: reaction-diffusion dynamics 5m5m This work

Acknowledgements: The RGC of the HKSAR Collaborative Research Grant HKU1/CRF/10 HKBU Strategic Development Fund

Thank you for your attention!

Turing patterns The Chemical Basis of Morphogenesis A. M. Turing Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 237, (1952) Ingredients: two diffusing species, one activating, one repressing S Kondo and T Miura, Science 329, 1616 (2010) Pattern formation (concentration modulation) requires i)Slow diffusion of the active species (short-range positive feedback) ii)Fast diffusion of the repressive species (long- range negative feedback) control circuit (reaction)