Friday, November 12, 2010


One feature of the 4th edition of Intermediate Physics for Medicine and Biology that distinguishes it from many other medical or biological textbooks is its focus on analyzing biomedical topics quantitatively. This point of view is also promoted at the BIONUMB3R5 (bionumbers) website, established by researchers in the systems biology department at Harvard. There is also a BIONUMB3R5 wiki where many researchers are coming together to provide new insights into key numbers in biology.

I particularly like the “Bionumber of the Month” feature. The March 2010 entry (“What are the Time Scales for Diffusion in Cells”) could easily be made into a homework problem for Chapter 4 of Intermediate Physics for Medicine and Biology. The January 2010 entry (“What is Faster, Transcription or Translation?”) is fascinating.
Transcription, the synthesis of mRNA from DNA, and translation, the synthesis of protein from mRNA, are the main pillars of the central dogma of molecular biology. How do the speeds of these two processes compare? …

Transcription of RNA by RNA polymerase in E. coli cells proceeds at a maximal speed of about 40–80 bp/sec… Translation by the ribosome in E. coli proceeds at a maximal speed of about 20 aa/sec… Interestingly, since every 3 base pairs code for one amino acid, the rates of the two processes are quite similar…
The “collection of fundamental numbers in molecular biology” found at the bionumbers website has the same tone as the first section of Chapter 1 in Intermediate Physics for Medicine and Biology, in which Russ Hobbie and I look at the relative size of biological objects. The collection contains this gem: “concentration of 1 nM in a cell the volume of E. coli is ~ 1 molecule/cell.”

The bionumbers website arose from an article by Rob Phillips and Ron Milo in the Proceedings of the National Academy of Sciences (Volume 106, pages 21465–21471, 2009), “A Feeling for the Numbers in Biology.” The abstract of their paper is given below:
Although the quantitative description of biological systems has been going on for centuries, recent advances in the measurement of phenomena ranging from metabolism to gene expression to signal transduction have resulted in a new emphasis on biological numeracy. This article describes the confluence of two different approaches to biological numbers. First, an impressive array of quantitative measurements make it possible to develop intuition about biological numbers ranging from how many gigatons of atmospheric carbon are fixed every year in the process of photosynthesis to the number of membrane transporters needed to provide sugars to rapidly dividing Escherichia coli cells. As a result of the vast array of such quantitative data, the BioNumbers web site has recently been developed as a repository for biology by the numbers. Second, a complementary and powerful tradition of numerical estimates familiar from the physical sciences and canonized in the so-called “Fermi problems” calls for efforts to estimate key biological quantities on the basis of a few foundational facts and simple ideas from physics and chemistry. In this article, we describe these two approaches and illustrate their synergism in several particularly appealing case studies. These case studies reveal the impact that an emphasis on numbers can have on important biological questions.
Russ and I introduce similar order-of-magnitude estimates (Fermi problems) in Chapter 1 of our book (for example, see homework problems 1–4, which are new in the 4th edition). One of my favorite Fermi problems, which I first encountered in the book Air and Water by Mark Denny, is to calculate the concentration of oxygen molecules in blood and in air, and compare them. Not too surprisingly, they are nearly the same (about 8 mM). I suspect the bionumbers folks would enjoy Air and Water. (I hope they would enjoy Intermediate Physics for Medicine and Biology, too.)

For those of you who find all of this interesting but prefer video over text, see the bionumbers video on YouTube.

Bionumbers: The data base of useful biological numbers. 

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