Fractals overview

Slide1 : Introduction to Fractals Larry S. Liebovitch Florida Atlantic University Center for Complex Systems and Brain Sciences Center for Molecular Biology and Biotechnology Department of Psychology Department of Biomedical Sciences Lina A. Shehadeh Copyright 2003 by Larry S. Liebovitch


How fractals CHANGE the most basic ways we analyze and understand experimental data. : How fractals CHANGE the most basic ways we analyze and understand experimental data.


Slide3 : Non-Fractal


Slide4 : Fractal


Slide5 : Non - Fractal Size of Features 1 cm 1 characteristic scale


Slide6 : Fractal Size of Features 2 cm 1 cm 1/2 cm 1/4 cm many different scales


Slide7 : Fractals Self-Similarity


Slide8 : Water Land Water Land Water Land Self-Similarity Pieces resemble the whole.


Slide9 : Sierpinski Triangle


Slide10 : Branching Patterns blood vessels Family, Masters, and Platt 1989 Physica D38:98-103 Mainster 1990 Eye 4:235-241 in the retina air ways in the lungs West and Goldberger 1987 Am. Sci. 75:354-365


Slide11 : Blood Vessels in the Retina


Slide12 : PDF - Probability Density Function HOW OFTEN there is THIS SIZE Straight line on log-log plot = Power Law


Slide13 : Statistical Self-Similarity The statistics of the big pieces is the same as the statistics of the small pieces.


Slide14 : Currents Through Ion Channels


Slide15 : Currents Through Ion Channels


Slide16 : Currents Through Ion Channels ATP sensitive potassium channel in cell from the pancreas Gilles, Falke, and Misler (Liebovitch 1990 Ann. N.Y. Acad. Sci. 591:375-391) 5 sec 5 msec 5 pA FC = 10 Hz FC = 1k Hz


Slide17 : Closed Time Histograms potassium channel in the corneal endothelium Number of closed Times per Time Bin in the Record Liebovitch et al. 1987 Math. Biosci. 84:37-68 Closed Time in ms


Slide18 : Closed Time Histograms potassium channel in the corneal endothelium Number of closed Times per Time Bin in the Record Liebovitch et al. 1987 Math. Biosci. 84:37-68 Closed Time in ms


Slide19 : Closed Time in ms Number of closed Times per Time Bin in the Record Closed Time Histograms potassium channel in the corneal endothelium Liebovitch et al. 1987 Math. Biosci. 84:37-68


Slide20 : Closed Time Histograms potassium channel in the corneal endothelium Number of closed Times per Time Bin in the Record Liebovitch et al. 1987 Math. Biosci. 84:37-68 Closed Time in ms


Slide21 : Fractals Scaling


Slide22 : Scaling The value measured depends on the resolution used to do the measurement.


Slide23 : How Long is the Coastline of Britain? Richardson 1961 The problem of contiguity: An Appendix to Statistics of Deadly Quarrels General Systems Yearbook 6:139-187 Log10 (Total Length in Km) AUSTRIALIAN COAST CIRCLE SOUTH AFRICAN COAST GERMAN LAND-FRONTIER, 1900 WEST COAST OF BRITIAN LAND-FRONTIER OF PORTUGAL 4.0 3.5 3.0 1.0 1.5 2.0 2.5 3.0 3.5 LOG10 (Length of Line Segments in Km)


Slide24 : Genetic Mosaics in the Liver P. M. Iannaccone. 1990. FASEB J. 4:1508-1512. Y.-K. Ng and P. M. Iannaccone. 1992. Devel. Biol. 151:419-430.


Fractal Kinetics : Kinetic Rate Constant: k = Prob. to change states in the next ∆t. Effective Kinetic Rate Constant: keff = Prob. to change states in the next ∆t, given that we have already remained in the state for a time keff. k = Pr ( T=t, t+∆t | T > t ) / ∆t eff eff age-specific failure rate = – d dt ln P(t) P(t) = cumulative dwell time distribution Fractal Kinetics


70 pS K+ ChannelCorneal Endothelium : 70 pS K+ Channel Corneal Endothelium Liebovitch et al. 1987 Math. Biosci. 84:37-68. effective time scale t eff in msec 100 1000 10 1 1 10 100 1000 k eff = A t eff 1-D


Fractal Approach : Fractal Approach New viewpoint: Analyze how a property, the effective kinetic rate constant, keff, depends on the effective time scale, teff, at which it is measured. This Scaling Relationship: We are using this to learn about the structure and motions in the ion channel protein.


Slide28 : one measurement: not so interesting slope Scaling Logarithm of the measuremnt Logarithm of the measuremnt one value Logarithm of the resolution used to make the measurement Logarithm of the resolution used to make the measurement scaling relationship: much more interesting


Slide29 : Fractals Statistics


Slide30 : Not Fractal


Slide31 : Not Fractal


Slide32 : Gaussian Bell Curve “Normal Distribution”


Slide33 : Fractal


Slide34 : Fractal


Slide35 : Mean Non - Fractal More Data pop


Slide36 : The Average Depends on the Amount of Data Analyzed


Slide37 : The Average Depends on the Amount of Data Analyzed each piece


Slide38 : Ordinary Coin Toss Toss a coin. If it is tails win $0, If it is heads win $1. The average winnings are: 2-1.1 = 0.5 1/2 Non-Fractal


Slide39 : Ordinary Coin Toss


Slide40 : Ordinary Coin Toss


Slide41 : St. Petersburg Game (Niklaus Bernoulli) Toss a coin. If it is heads win $2, if not, keep tossing it until it falls heads. If this occurs on the N-th toss we win $2N. With probability 2-N we win $2N. H $2 TH $4 TTH $8 TTTH $16 The average winnings are: 2-121 + 2-222 + 2-323 + . . . = 1 + 1 + 1 + . . . = Fractal


Slide42 : St. Petersburg Game (Niklaus Bernoulli)


Slide43 : St. Petersburg Game (Niklaus Bernoulli)


Slide44 : Non-Fractal Log avg density within radius r Log radius r


Slide45 : Fractal Log avg density within radius r Log radius r .5 -1.0 -2.0 -1.5 .5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 0 -2.5 0 Meakin 1986 In On Growthand Form: Fractal and Non-Fractal Patterns in Physics Ed. Stanley & Ostrowsky, Martinus Nijoff Pub., pp. 111-135


Slide46 : Electrical Activity of Auditory Nerve Cells Teich, Jonson, Kumar, and Turcott 1990 Hearing Res. 46:41-52 voltage time action potentials


Slide47 : Electrical Activity of Auditory Nerve Cells Teich, Jonson, Kumar, and Turcott 1990 Hearing Res. 46:41-52 2 Count the number of action potentials in each window: 6 3 1 5 1 Firing Rate = 2, 6, 3, 1, 5,1 Divide the record into time windows:


Slide48 : Electrical Activity of Auditory Nerve Cells Teich, Johnson, Kumar, and Turcott 1990 Hearing Res. 46:41-52 Repeat for different lengths of time windows: 8 4 6 Firing Rate = 8, 4, 6


Slide49 : Electrical Activity of Auditory Nerve Cells Teich, Jonson, Kumar, and Turcott 1990 Hearing Res. 46:41-52 0 The variation in the firing rate does not decrease at longer time windows. 4 8 12 16 20 24 28 70 60 80 90 100 120 130 140 110 150 T = 50.0 sec T = 5.0 sec T = 0.5 sec FIRING RATE SAMPLE NUMBER (each of duration T sec)


Slide50 : Fractals Power Law PDFs


Heart Rhythms : Heart Rhythms


Inter-event Times : Inter-event Times Episodes of Ventricular Tachycardia (v-tach) t 1 t 2 t 3 t 4 t 5 time -> Cardioverter Defibrillator


Patient #33 : Interval (in days) Relative Frequency Patient #33


Patient #53 : Interval (in days) Relative Frequency Relative Frequency = (3.2545) Interval-1.3664 10 3 10 2 10 1 10 0 10 -1 10 -2 10 -3 10 -4 10 -5 10 -4 10 -3 10 -2 10 -1 10 0 10 1 10 2 10 3 10 4 10 5 10 6 Patient #53


6 Patients : 6 Patients Liebovitch et al. 1999 Phys. Rev. E59:3312-3319.


Inter-arrival Times of E-mail Viruses : Inter-arrival Times of E-mail Viruses t 1 t 2 t 3 t 4 t 5 time -> Liebovitch and Schwartz 2003 Phys. Rev. E68:017101. AnnaKournikova "Hi: Check This!” AnnaKournikova.jpg vbs. Magistr Subject, body, attachment from other files: erase disk, cmos/bios. Klez E-mail from its own phrases: infect by just viewing in Outlook Express. Sircam “I send you this file in order to have your advice.”


E-mail Viruses : E-mail Viruses 20,884 viruses 153,519 viruses


E-mail Viruses : E-mail Viruses 413,183 viruses 781,626 viruses


Determining the PDFfrom a Histogram : Determining the PDF from a Histogram Bins ∆t Small Good at small t. BAD at large t. Bins ∆t Large BAD at small t. Good at large t.


Determining the PDF : Determining the PDF Liebovitch et al. 1999 Phys. Rev. E59:3312-3319. Solution: Make ONE PDF From SEVERAL Histograms of DIFFERENT Bin Size Choose ∆t = 1, 2, 4, 8, 16 … seconds ∆t = bin size


Determiningthe PDF : Determining the PDF New multi-histogram Standard fixed ∆t


Slide62 : Fractals Summary


Summary of Fractal Properties : Summary of Fractal Properties Self-Simialrity Pieces resemble the whole.


Summary of Fractal Properties : Summary of Fractal Properties Scaling The value measured depends on the resolution.


Summary of Fractal Properties : Summary of Fractal Properties Statistical Properties Moments may be zero or infinite.


Slide66 : 400 years ago: Gambling Problems Probability Theory 200 years ago: Statistics How we do experiments. 100 years ago: Student’s t-test, F-test, ANOVA Now: Still changing Statistics is NOT a dead science.


Fractals CHANGE the most basic ways we analyze and understand experimental data. : Fractals CHANGE the most basic ways we analyze and understand experimental data. Fractals Measurements over many scales. What is real is not one number, but how the measured values change with the scale at which they are measured (fractal dimension). No Bell Curves No Moments No mean ± s.e.m.


References: : References: Fractals and Chaos and Simplified for the Life Sciences Larry S. Liebovitch Oxford Univ. Press, 1998 The Mathematics and Science of Fractals Larry S. Liebovitch and Lina Shehadeh www.ccs.fau.edu/~liebovitch/larry.html CD ROM NSF DUE-9752226 DUE-9980715