Scaling in Biology How do properties of living systems change as their size is varied? Example: How does basal metabolic rate (heat radiation) vary as a function of an animal s body mass? Mouse Hamster Radius = 2 Mouse radius Mass 8 Mouse radius Surface area 4 Mouse radius 4 3 Volume of a sphere: π r 3 Surface area of a sphere: 2 4π r Hypothesis 1: metabolic rate body mass Hippo Radius = 50 Mouse radius Mass 125,000 Mouse radius Surface area 2,500 Mouse radius Problem: mass is proportional to volume of animal but heat can radiate only from surface of animal 1
Volume of a sphere: 4 π r 3 3 Volume of a sphere scales as the radius cubed Surface area of a sphere: 2 4π r Surface area of a sphere scales as the radius squared Surface area scales with volume to the 2/3 power. Hypothesis 2: metabolic rate mass 2/3 mouse hamster (8 mouse mass) hippo (125,000 mouse mass) Metabolic Rate Metabolic rate: the process by which energy and materials are transformed within an organism and exchanged between the organism and its environment. Metabolic rate = R i i i.e., the sum of energy production (R i ) of all metabolic chemical reactions. This rate depends on rate of supply of substrates to cells, and rate of removal of products of reactions. The higher the metabolic rate as a function of body mass, the more efficient the organism. 2
Metabolic scaling Surface hypothesis: Body is made of cells, in which metabolic reactions take place. Can approximate body mass by a sphere of cells with radius r. Can approximate metabolic rate by surface area Body mass r 3 Metabolic rate r 2 r Thus, metabolic rate (r 3 ) 2/3 body mass 2/3 y= x 2/3 metabolic rate body mass 3
y= x 2/3 log (metabolic rate) log (body mass) On log-log plot, a power law is a straight line whose slope is the exponent of power law: log (metabolic rate) log (body mass 2/3 ) =2/3 log(body mass) Actual data: Kleiber's law : metabolic ratef body 3/4 mass For sixty years, no explanation 4
Kleiber s law extended over 21 orders of magnitude y= x 2/3 y= x 3/4 metabolic rate body mass 5
Other Observed Biological Scaling Laws Heart rate body mass 1/4 Blood circulation time body mass 1/4 Life span body mass 1/4 Growth rate body mass 1/4 Heights of trees tree mass 1/4 Sap circulation time in trees tree mass 1/4 West, Brown, and Enquist s Theory (1990s) General idea: metabolic scaling rates (and other biological rates) are limited not by surface area but by rates at which energy and materials can be distributed between surfaces where they are exchanged and the tissues where they are used. How are energy and materials distributed? 6
Distribution systems 7
West, Brown, and Enquist s Theory (1990s) Assumptions about distribution network: branches to reach all parts of three-dimensional organism (i.e., needs to be as space-filling as possible) has terminal units (e.g., capillaries) that do not vary with size among organisms evolved to minimize total energy required to distribution resources 8
Prediction: Distribution network will have fractal branching structure, and will be similar in all / most organisms (i.e., evolution did not optimize distribution networks of each species independently) Therefore, Euclidean geometry is the wrong way to view scaling; one should use fractal geometry instead! With detailed mathematical model using three assumptions, they derive metabolic rate body mass 3/4 9
Their interpretation of their model Metabolic rate scales with body mass like surface area scales with volume... but in four dimensions. Circle: circumference = 2πr area = πr 2 circumference scales as area to the??? power Sphere: surfacearea = 4πr 2 volume = 4/3 πr 3 surface area scales as volume to the??? power? Hypersphere: surfacevolume (hyperarea) = hypervolume = surface area scales as volume to the??? power 10
Although living things occupy a three-dimensional space, their internal physiology and anatomy operate as if they were four-dimensional... Fractal geometry has literally given life an added dimension. West, Brown, and Enquist Critiques of their model Lots! See textbook for examples. Bottom line: Interesting, elegant theory, but still needs further testing. 11
Other interesting scaling laws Power law scaling in the Web: Probability that a web page will have k in-links scales as k -1/2 Zipf s law 1: In English text, word frequency scales as (word rank) -1 Seems to work for other languages also! Implies 2 nd ranked word will be??times as frequent as 1 st rank word, 3 rd ranked word will be??times as frequent as 1 st ranked word, etc. Word frequencies from 423 Time Magazine articles Word frequencies from 46,449 Wall Street Journal articles 12
Zipf s law 2: The population of cities in a country scales as (city rank) -1 Zipf in numbers, America City proper Urban agglomeration Country City Pop. (x1000) Rank Ln rank Ln size Pop. (x1000) Rank Ln rank Ln size Index United States, 1994 New York 7333 1 0.0 15.8 19796 1 0.0 16.8 Los Angeles 3449 2 0.7 15.1 15302 2 0.7 16.5 Chicago 2732 3 1.1 14.8 8527 3 1.1 16.0 Houston 1702 4 1.4 14.3 4099 10 2.3 15.2 Philadelphia 1524 5 1.6 14.2 5959 6 1.8 15.6 San Diego 1152 6 1.8 14.0 Phoenix 1049 7 1.9 13.9 2473 17 2.8 14.7 Dallas 1023 8 2.1 13.8 4362 9 2.2 15.3 San Antonio 999 9 2.2 13.8 1437 28 3.3 14.2 Detroit 992 10 2.3 13.8 5256 8 2.1 15.5 Indianapolis 752 11 2.4 13.5 1462 27 3.3 14.2 San Francisco 735 12 2.5 13.5 6513 5 1.6 15.7 Baltimore 703 13 2.6 13.5 Jacksonville (Fl.) 665 14 2.6 13.4 972 45 3.8 13.8 Columbus (Oh.) 636 15 2.7 13.4 1423 29 3.4 14.2 Milwaukee 617 16 2.8 13.3 1637 24 3.2 14.3 San Jose 617 17 2.8 13.3 Memphis 614 18 2.9 13.3 1056 41 3.7 13.9 El Paso 579 19 2.9 13.3 665 57 4.0 13.4 WASHINGTON D.C. 567 20 3.0 13.2 7051 4 1.4 15.8 Boston 548 21 3.0 13.2 5497 7 1.9 15.5 Seattle 521 22 3.1 13.2 3226 13 2.6 15.0 Austin 514 23 3.1 13.2 964 46 3.8 13.8 Nashville-Davidson 505 24 3.2 13.1 1070 40 3.7 13.9 Denver 494 25 3.2 13.1 2190 19 2.9 14.6 Cleveland 493 26 3.3 13.1 2899 14 2.6 14.9 New Orleans 484 27 3.3 13.1 1309 31 3.4 14.1 Oklahoma City 463 28 3.3 13.0 1007 42 3.7 13.8 Fort Worth 452 29 3.4 13.0 Portland (Or.) 451 30 3.4 13.0 1982 21 3.0 14.5 Pareto distribution Originally stated as Number of people owning a fraction k of the total wealth in a country Also 80-20 rule : 20% of the population owns 80% of the wealth 13
Benford s law: in lists of numbers from many real-life sources of data, the probability of the leading digit being d (0 9) is Example Benford s law: Heights (in feet) of 60 tallest buildings (leading digit) 14
More examples 15