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Technical Comments
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(1) |
denotes proportional variation.
These laws refer to changes in dimensions and number of capillary blood vessels accompanying change in mammal size, for example, from the mouse to the human and on to the elephant. The scaling laws were derived by considering basic restrictions associated with similar designs of the cardiovascular system of all mammals. They apply both to the actual beds of capillaries in the individual organs of mammals, as well as to the capillaries in representative single systemic and pulmonary beds. West et al. assume that rc and lc do not vary with mammal weight and that nc varies with mammal weight raised to the 3/4 power.
With regard to Eqs. 1 and the basic design of the cardiovascular
system, we may consider, for example, the total volume of blood
Vc in the systemic or pulmonary capillaries.
This volume is proportional to the product of their characteristic
number nc and their characteristic individual
volume
rc2lc.
Thus, we have the proportional relation
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(2) |
With regard to Eqs. 1 and physiological processes, we may
consider, for example, the exchange of oxygen between capillaries and
surrounding cells. This matter is of fundamental importance in
understanding the scaling relations for capillary dimensions and
number. Rate of oxygen transfer is determined by a diffusion operation
that is, insofar as geometry is concerned, directly proportional to the
product of characteristic number nc and
characteristic surface area
2
rclc of the
capillaries and inversely proportional to their characteristic wall
thickness, hc. Oxygen transfer also depends on
the driving force, as measured by the oxygen partial pressure. Thus, if
Po denotes difference between oxygen partial pressure inside and outside a capillary vessel, the following proportional relation applies for the oxygen transfer rate
Qo from a representative systemic capillary bed
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(3) |
P0 be proportional to
P0, the oxygen partial pressure in the blood
itself. Thus, we have the relation
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(4) |
The rate of oxygen transfer from the capillaries of mammals must, of course, equal the rate of utilization of oxygen by their cells. To describe the latter, we may use the concept of an average body cell (2), with a characteristic number of such cells, ns, assumed to be proportional to characteristic number of capillaries, nc, as mammal size is varied. The volume of an average body cell is then proportional to the ratio W/nc, and the characteristic length, ls, defining the volume (or other external quantity such as surface area) is given by the cube root of this ratio. Thus, using the third of Eq. 1, we see that the scaling relations for average body cells are described by
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(5) |
o expressible as
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(6) |

o denotes the
difference between oxygen partial pressure outside and inside the
cells, and hs denotes membrane (wall) thickness
of the cells.
If 
o is assumed independent of mammal
size and hs assumed to vary in the same manner
as ls, as in (2), then the scaling
Eqs. 5, when combined with Eq. 6, provide the prediction that oxygen
utilization rate must vary with mammal weight raised to the 3/4 power,
as required from measurement and from oxygen transfer rate.
Alternatively, if 
o is assumed to vary
with mammal weight raised to the negative 1/12 power, as in the
capillary exchange process described earlier, the same result can be
predicted, provided that cell-membrane thickness scales with mammal
weight raised to the 1/24 power. In either case, we see that, with
hs
ls
o, the
definition of average body cell as based on the capillary number of
Eqs. 1 provides consistent results between oxygen transfer and oxygen
utilization.
With respect to Eq. 6, we may also observe that the concept of an
average body cell applies directly to cardiac cells because heart
weight varies directly with mammal weight (2). The rate of
operation of average body cells, say
, can therefore be assumed to
be proportional to the heart rate. The amount of oxygen per unit cell
volume that is utilized per cell cycle is then expressible from Eq. 6, with hs
ls
o, as
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(7) |
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(8) |
, and
hence heart rate, is predicted to vary with mammal weight raised to the
negative 1/4 power. This is in agreement with extensive measurements of
heart rate (2, 5) and provides further evidence for the
validity of the scaling laws of Eqs. 1.
Thomas H. Dawson
Department of Naval Architecture,
Ocean and Marine Engineering,
U.S. Naval Academy,
Annapolis, MD 21402, USA
Response: The comments of Kurz and Sandau allow us to address some issues that were omitted from our report because of space constraints. We agree with several of the points that they make. In presenting our general geometric and hydrodynamic model for allometric scaling, we explicitly recognized that the branching architectures of biological distribution networks are not perfect fractals in two respects: (i) there is a fixed size of the terminal branches, and (ii) the scaling changes from area-preserving in major arteries to area-increasing in peripheral vessels.
We were also aware that the branching of real circulatory systems and other biological networks is not perfectly symmetrical, as our zeroeth order model assumes. We explored the sensitivity of our results to variations in architecture, including asymmetrical branching and anastomoses. So long as the deviations are not extreme, the scaling exponents do not change. The crucial features are that the branching be predominantly area-preserving and volume-filling. This is reminiscent of phase transitions and scaling exponents in physical systems as derived from normalization group arguments. Other recent studies also show that our model is robust. Zamir (1) has made detailed anatomical and physiological measurements of cardiac arteries, which typically exhibit asymmetrical and anastomosing branching. He finds that the quantitative predictions of our model are still upheld. Turcotte et al. (2) analyze mathematically and by computer simulation the properties of area- and volume-filling networks with non-fractal "side-branches," and they draw three-dimensional representations. They also find that the deviations from perfect fractal geometries do not appreciably change the predictions and applications our model. Therefore, we are confident that our zeroeth order model is relatively insensitive to details of the branching architecture.
We do not agree with several other points made by Kurz and Sandau. First, although the mechanism of formation of cardiovascular and other biological distribution networks during early embryonic development is fascinating, it may be of limited relevance to the geometric and functional properties of well-developed functional networks during later stages of the life history. The cardiovascular systems of mammals and the vessel networks of plants appear to have different ontogenetic mechanisms, but still exhibit similar structural and functional properties. Second, we do not agree that our model has been "fine tuned to predict the famous scaling law of metabolism." We developed a general model for fractal-like biological distribution networks, based on a simple branching geometry, basic physical principles, and a few well-known facts about mammalian anatomy and physiology.
Without any "fine tuning," the model predicts a self-similar fractal-like network, 3/4-power allometric scaling of metabolic rate, and many other scaling relationships for structural and functional properties of mammalian cardiovascular and respiratory systems. Basically the same model, modified to incorporate known features of plant biology, predicts many features of the architecture, anatomy, and physiology of vascular plants.
Finally, we are not sure how one would perform the "controlled experiment" or make the comparisons to "nonfractal" models that Kurz and Sandau allude to. It is hard to imagine a system that would economically supply billions of cells without some kind of continuously branching network. We did mention that gasoline engines and electric motors, two examples of energy transforming systems without fractal-like resource supply, exhibit simple geometric scaling. Our model is difficult to compare with others that we are aware of, including those cited by Kurz and Sandau, because it considers the geometric structure and hydrodynamic function of an entire network as a single integrated system. The other models either consider the properties of isolated parts of networks, or are much more "fine tuned" by explicitly incorporating some of the empirical allometric scaling relationships that emerge as predictions of our model.
We see two problems with Dawson's approach. First, in Eqs. 1 he
assumes scaling relationships for the radius, length, and number of
mammalian capillaries. The justification for the exponents is unclear
to us, except that they appear to be required for the consistency of
his argument about how the design of the mammalian cardiovascular
system changes with body size. We question the empirical support for
these values. In particular, while the data indicate that the radii of
capillaries and of the red blood cells that travel through them do not
vary with body size (3), Dawson assumes that capillary
radius scales as M to the 1/12 power. Further, in expression (5)
Dawson assumes that the linear dimensions of body cells increase
with body size, as M to the 1/8. This relation would require that, when
comparing a shrew weighing 2 g with a whale weighing
200,000,000 g, one observes the radii of capillaries and red
corpuscles to increase by approximately 4.5 and those of somatic cells
by 10. Such a difference has not been reported by mammalian anatomists
and physiologists to our knowledge. In contrast, our model assumes that
the dimensions of capillaries are invariant with respect to mammalian
body size, and it predicts (not assumes, as Dawson seems to imply) that
the number of capillaries varies as M to the 3/4. Therefore, our model
predicts that the density of capillaries (number per cross-sectional
area of tissue) should scale as M to the
1/12 =
0.083. Empirical
measurements of four muscles (3) give an average value of
0.095, which we take as support for our model.
Second, this difference in the treatment of capillary anatomy highlights a fundamental difference in approach. In his comment and book (4), Dawson does not appear to treat the mammalian cardiovascular system with a complete model that demands internally consistent values of all parameters. Instead, he seems to make ad hoc assumptions about scaling exponents and other parameters. More fundamentally, Dawson does not explicitly treat the branching architecture of the entire circulatory system, although such an analysis is essential to derive the scaling relationships for such critical parameters as total blood volume, circulation time, and change in pressure and velocity from heart to capillary. In contrast, our zeroeth-order model specifies both the geometry and hydrodynamics of the entire fractal-like branching network. Consequently, it is able to make a priori, testable predictions of all the relevant parameters. Whenever our predictions or assumptions differ from Dawson's, we believe that the best available data support our model.
James H. Brown
Brian J. Enquist
Department of Biology,
University of New Mexico,
Albuquerque, NM 87131,
and Santa Fe Institute,
1399 Hyde Park Road,
Santa Fe, NM 87501 USA
Geoffrey B. West
Theoretical Division,
T-8, MS B285,
Los Alamos National Laboratory,
Los Alamos, NM 87545
and Santa Fe Institute
Science. ISSN 0036-8075 (print), 1095-9203 (online)