5/20/20151 D. Excitable Media. 5/20/20152 Examples of Excitable Media Slime mold amoebas Cardiac tissue (& other muscle tissue) Cortical tissue Certain.

Slides:



Advertisements
Similar presentations
Lecture #9 Regulation.
Advertisements

LECTURE 12 Graded Potentials Action Potential Generation
Neural Modeling Suparat Chuechote. Introduction Nervous system - the main means by which humans and animals coordinate short-term responses to stimuli.
The Role of Calcium in Ischemic Brain Damage: By: Christian Stork.
5/13/20151 C. Slime Mold (Dictyostelium discoideum) “Dicty”
Types of Ion Channels Leak channels
1 Predator-Prey Oscillations in Space (again) Sandi Merchant D-dudes meeting November 21, 2005.
1 3. Spiking neurons and response variability Lecture Notes on Brain and Computation Byoung-Tak Zhang Biointelligence Laboratory School of Computer Science.
GRADED POTENTIAL & ACTION POTENTIAL Dr.Mohammed Sharique Ahmed Quadri Assistant prof. Physiology Al Maarefa College.
5/21/20151 B. Pattern Formation. 5/21/20152 Differentiation & Pattern Formation A central problem in development: How do cells differentiate to fulfill.
Sean J.Kirkpatrick, Ph.D. Department of Biomedical Engineering Michigan Technological University 1400 Townsend Dr. Houghton, MI USA
The Chemotactic Paradox Amie Cubbon Denise Gutermuth.
Homework 4, Problem 3 The Allee Effect. Homework 4, Problem 4a The Ricker Model.
Neuronal excitability from a dynamical systems perspective Mike Famulare CSE/NEUBEH 528 Lecture April 14, 2009.
Phase portrait Fitzugh-Nagumo model Gerstner & Kistler, Figure 3.2 Vertical Horizontal.
Mathematical Biology Summer Workshop DICTYOSTELIUM DISCOIDEUM Social Amoeba –act like either a unicellular or multicellular organism depending on circumstances.
Propagation of the Action Potential The Central Dogma Of Excitable Tissues.
Spiral waves meandering in a model of an excitable medium Presenter: Mike Malatt Phil McNicholas Jianfeng Zhu.
Neural Condition: Synaptic Transmission
The Decisive Commanding Neural Network In the Parietal Cortex By Hsiu-Ming Chang ( 張修明 )
The Integrate and Fire Model Gerstner & Kistler – Figure 4.1 RC circuit Threshold Spike.
Adventures in Multicellularity The social amoeba ( a.k.a. slime molds ) Dictyostelium discoideum.
Modeling Chemotaxis, Cell Adhesion and Cell Sorting. Examples with Dictyostelium Eirikur Pálsson Dept of Biology, Simon Fraser University.
Excitable Membranes. What is an excitable membrane? Any plasma membrane that can hold a charge and propagate electrical signals.
Action Potentials Miss Tagore A2 Biology.
Chapter 5 Lecture 10 Spring Nonlinear Elements 1. A nonlinear resistance 2. A nonlinear reactance 3. A time varying element in you circuit or system.
Basic Models in Theoretical Neuroscience Oren Shriki 2010 Integrate and Fire and Conductance Based Neurons 1.
Analysis of the Rossler system Chiara Mocenni. Considering only the first two equations and ssuming small z The Rossler equations.
Nervous System Neurophysiology.
Defining of “physiology” notion
Biological Modeling of Neural Networks Week 3 – Reducing detail : Two-dimensional neuron models Wulfram Gerstner EPFL, Lausanne, Switzerland 3.1 From Hodgkin-Huxley.
Lukasz Grzegorz Maciak Micheal Alexis
Transmission of Nerve Impulses WALT Neurones transmit impulses as a series of electrical signals A neurone has a resting potential of – 70 mV Depolarisation.
MEMBRANE POTENTIAL DR. ZAHOOR ALI SHAIKH Lecture
Mathematical Modelling of Cancer Invasion of Tissue: The Role of the Urokinase Plasminogen Activation System Mark Chaplain and Georgios Lolas Division.
John Wordsworth, Peter Ashwin, Gabor Orosz, Stuart Townley Mathematics Research Institute University of Exeter.
Sarah Gillies Ultrasound Sarah Gillies
Physiology as the science. Defining of “physiology” notion Physiology is the science about the regularities of organisms‘ vital activity in connection.
Simplified Models of Single Neuron Baktash Babadi Fall 2004, IPM, SCS, Tehran, Iran
Spiral and Scroll Waves in Excitable Media: Cardiological Applications A Calculus III Honors Project by David Hausner And Katrina McAlpin.
Membranes & Receptors Chemical compounds & electricity are the medium through which, cells communicate! Analogous with information sent via electrical.
Physiology as the science. Bioelectrical phenomena in nerve cells
Biological Modeling of Neural Networks: Week 12 – Decision models: Competitive dynamics Wulfram Gerstner EPFL, Lausanne, Switzerland 12.1 Review: Population.
Nervous System.
Synchronization in complex network topologies
Circadian Rhythms 안용열 ( 물리학과 ). Index Intro - What is the circadian rhythm? Mechanism in reality How can we understand it?  Nonlinear dynamics –Limit.
1 Introduction to Biological Modeling Steve Andrews Brent lab, Basic Sciences Division, FHCRC Lecture 2: Modeling dynamics Sept. 29, 2010.
Muscle Physiology: Cellular Mechanisms of Muscle Contraction Review of Membrane Permeability Resting Potential of Muscle Cells Local Membrane Potentials.
The role of the bidomain model of cardiac tissue in the dynamics of phase singularities Jianfeng Lv and Sima Setayeshgar Department of Physics, Indiana.
ACTION POTENTIALS Chapter 11 Part 2 HONORS ANATOMY & PHYSIOLOGY.
Biological Modeling of Neural Networks Week 4 – Reducing detail - Adding detail Wulfram Gerstner EPFL, Lausanne, Switzerland 3.1 From Hodgkin-Huxley to.
Pattern formation in nonlinear reaction-diffusion systems.
Neural Synchronization via Potassium Signaling. Neurons Interactions Neurons can communicate with each other via chemical or electrical synapses. Chemical.
Analysis of the Rossler system Chiara Mocenni. Considering only the first two equations and assuming small z, we have: The Rossler equations.
Nerve Impulse Generation & Conduction
Arthur Straube PATTERNS IN CHAOTICALLY MIXING FLUID FLOWS Department of Physics, University of Potsdam, Germany COLLABORATION: A. Pikovsky, M. Abel URL:
Spatiotemporal Networks in Addressable Excitable Media International Workshop on Bio-Inspired Complex Networks in Science and Technology Max Planck Institute.
Biological Modeling of Neural Networks Week 4 Reducing detail: Analysis of 2D models Wulfram Gerstner EPFL, Lausanne, Switzerland 3.1 From Hodgkin-Huxley.
1 Computational Vision CSCI 363, Fall 2012 Lecture 32 Biological Heading, Color.
Emergence of Homochirality in Chemical Systems Department of Applied Chemistry Faculty of Science & Technology Keio University Kouichi Asakura.
Ryan Woodard (Univ. of Alaska - Fairbanks)
Monte Carlo methods 10/20/11.
Biointelligence Laboratory, Seoul National University
Instability Analysis of Nerve Cell Dynamics in the FitzHugh-Nagumo Model Nasrin Sultana*, Sampad Das and M. Osman Gani** Department of Mathematics, Jahangirnagar.
Electrochemical Impulse
Reaction & Diffusion system
Critical Timing without a Timer for Embryonic Development
Volume 97, Issue 9, Pages (November 2009)
Transmission of Nerve Signals
Presentation transcript:

5/20/20151 D. Excitable Media

5/20/20152 Examples of Excitable Media Slime mold amoebas Cardiac tissue (& other muscle tissue) Cortical tissue Certain chemical systems (e.g., BZ reaction) Hodgepodge machine

5/20/20153 Characteristics of Excitable Media Local spread of excitation –for signal propagation Refractory period –for unidirectional propagation Decay of signal –avoid saturation of medium

5/20/20154 Behavior of Excitable Media

5/20/20155 Stimulation

5/20/20156 Relay (Spreading Excitation)

5/20/20157 Continued Spreading

5/20/20158 Recovery

5/20/20159 Restimulation

5/20/ Circular & Spiral Waves Observed in: Slime mold aggregation Chemical systems (e.g., BZ reaction) Neural tissue Retina of the eye Heart muscle Intracellular calcium flows Mitochondrial activity in oocytes

5/20/ Cause of Concentric Circular Waves Excitability is not enough But at certain developmental stages, cells can operate as pacemakers When stimulated by cAMP, they begin emitting regular pulses of cAMP

5/20/ Spiral Waves Persistence & propagation of spiral waves explained analytically (Tyson & Murray, 1989) Rotate around a small core of of non- excitable cells Propagate at higher frequency than circular Therefore they dominate circular in collisions But how do the spirals form initially?

5/20/ Some Explanations of Spiral Formation “the origin of spiral waves remains obscure” (1997) Traveling wave meets obstacle and is broken Desynchronization of cells in their developmental path Random pulse behind advancing wave front

5/20/ Step 0: Passing Wave Front

5/20/ Step 1: Random Excitation

5/20/ Step 2: Beginning of Spiral

5/20/ Step 3

5/20/ Step 4

5/20/ Step 5

5/20/ Step 6: Rejoining & Reinitiation

5/20/ Step 7: Beginning of New Spiral

5/20/ Step 8

5/20/ Formation of Double Spiral from Pálsson & Cox (1996)

5/20/ NetLogo Simulation Of Spiral Formation Amoebas are immobile at timescale of wave movement A fraction of patches are inert (grey) A fraction of patches has initial concentration of cAMP At each time step: –chemical diffuses –each patch responds to local concentration

5/20/ Response of Patch if patch is not refractory (brown) then if local chemical > threshold then set refractory period produce pulse of chemical (red) else decrement refractory period degrade chemical in local area

5/20/ Demonstration of NetLogo Simulation of Spiral Formation Run SlimeSpiral.nlogo

5/20/ Observations Excitable media can support circular and spiral waves Spiral formation can be triggered in a variety of ways All seem to involve inhomogeneities (broken symmetries): –in space –in time –in activity Amplification of random fluctuations Circles & spirals are to be expected

5/20/ NetLogo Simulation of Streaming Aggregation 1. chemical diffuses 2. if cell is refractory (yellow) 3. then chemical degrades 4. else (it’s excitable, colored white) 1. if chemical > movement threshold then take step up chemical gradient 2. else if chemical > relay threshold then produce more chemical (red) become refractory 3. else wait

5/20/ Demonstration of NetLogo Simulation of Streaming Run SlimeStream.nlogo

5/20/ Typical Equations for Excitable Medium (ignoring diffusion) Excitation variable: Recovery variable:

5/20/ Nullclines

5/20/ Local Linearization

5/20/ Fixed Points & Eigenvalues stable fixed point unstable fixed point saddle point real parts of eigenvalues are negative real parts of eigenvalues are positive one positive real & one negative real eigenvalue

5/20/ FitzHugh-Nagumo Model A simplified model of action potential generation in neurons The neuronal membrane is an excitable medium B is the input bias:

5/20/ NetLogo Simulation of Excitable Medium in 2D Phase Space (EM-Phase-Plane.nlogo)

5/20/ Elevated Thresholds During Recovery

5/20/ Type II Model Soft threshold with critical regime Bias can destabilize fixed point fig. < Gerstner & Kistler

5/20/ Poincaré-Bendixson Theorem

5/20/ Type I Model stable manifold

5/20/ Type I Model (Elevated Bias)

5/20/ Type I Model (Elevated Bias 2)

5/20/ Type I vs. Type II Continuous vs. threshold behavior of frequency Slow-spiking vs. fast-spiking neurons fig. < Gerstner & Kistler

5/20/ Modified Martiel & Goldbeter Model for Dicty Signalling Variables (functions of x, y, t):  = intracellular concentration of cAMP  = extracellular concentration of cAMP  = fraction of receptors in active state   

5/20/ Equations

5/20/ Positive Feedback Loop Extracellular cAMP increases (  increases)  Rate of synthesis of intracellular cAMP increases (  increases)  Intracellular cAMP increases (  increases)  Rate of secretion of cAMP increases (  Extracellular cAMP increases) See Equations

5/20/ Negative Feedback Loop Extracellular cAMP increases (  increases)  cAMP receptors desensitize (f 1 increases, f 2 decreases,  decreases)  Rate of synthesis of intracellular cAMP decreases (  decreases)  Intracellular cAMP decreases (  decreases)  Rate of secretion of cAMP decreases  Extracellular cAMP decreases (  decreases) See Equations

5/20/ Dynamics of Model Unperturbed  cAMP concentration reaches steady state Small perturbation in extracellular cAMP  returns to steady state Perturbation > threshold  large transient in cAMP, then return to steady state Or oscillation (depending on model parameters)

5/20/ Additional Bibliography 1.Kessin, R. H. Dictyostelium: Evolution, Cell Biology, and the Development of Multicellularity. Cambridge, Gerhardt, M., Schuster, H., & Tyson, J. J. “A Cellular Automaton Model of Excitable Media Including Curvature and Dispersion,” Science 247 (1990): Tyson, J. J., & Keener, J. P. “Singular Perturbation Theory of Traveling Waves in Excitable Media (A Review),” Physica D 32 (1988): Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G.,& Bonabeau, E. Self-Organization in Biological Systems. Princeton, Pálsson, E., & Cox, E. C. “Origin and Evolution of Circular Waves and Spiral in Dictyostelium discoideum Territories,” Proc. Natl. Acad. Sci. USA: 93 (1996): Solé, R., & Goodwin, B. Signs of Life: How Complexity Pervades Biology. Basic Books, continue to “Part III”