Quantifying Life

The computational, mathematical and statistical sciences have a continually increasing role in the successful investigation and understanding of phenomena and processes of the greatest interest to researchers in the life sciences. Specifically, advanced techniques from computing, applied mathematics, and statistics are being employed by researchers to model, simulate, and analyze data derived from experimental investigations in the biological and biomedical sciences. Moreover, computation and modeling play a fundamental role in synthesizing experimental data into coherent and robust theories that push the life sciences to the cutting edge of knowledge. Largely this is carried out via interdisciplinary collaborations among scientists and researchers spread across a variety of disciplines. I am not at all surprised to see papers with authors from four or five different departments published in journals like PLoS Computation Biology.

The current interdisciplinary research efforts in biomedical research and the role that computing, mathematics and statistics is playing in biology have been noticed by educators in these fields. Despite the relatively common occurrence of courses and textbooks with titles like Biocalculus and Biostatistics, however, it seems like students with majors or concentrations in Biology, Pre-Health, and Neuroscience are still not uniformly exposed to sufficient doses of computing, mathematics, or statistics during their undergraduate studies. Perhaps this is most true with regard to computer programming. I believe the result is that many undergraduate students in the health and life sciences are at least denied exposure to many of the most exciting discoveries in biology, and at worst put at a disadvantage in terms of being prepared for employment, graduate study, and research in their chosen field.

Taking one or two semester of calculus is of great benefit to students of Biology and Pre-Health. Adding to this a semester of statistics is even better. But I do not believe that this is enough, because taking one or two mathematics or quantitative courses at a nonspecific time in the curriculum puts the math too far removed from what biology students do in their studies generally. Furthermore, they may not see the mathematics early or regularly enough, and they may not see any computing, which is equally if not more important. The University of Chicago has apparently implemented a course for first-year biology students that addresses many of the points I have argued. Quantifying Life by Dmitry Kondrashov is the textbook for this course.

Quantifying Life teaches the student of biology several essential aspects of mathematical and quantitative modeling that are currently most relevant to the life sciences. Some of the topics covered include mathematical functions, probability, regression and data fitting, Markov models, and basic discrete and continuous dynamical systems. Furthermore, the author guides readers through implementing the mathematical and statistical models in practice on the computer, using the R programming language. Of course the contents of the text will not make mathematicians or statisticians out of biology students (or vice versa). I do believe, however, that this book (and course) will help build the quantitative confidence of students of the life sciences. Furthermore, readers that carefully work their way through this text will gain valuable experience in R programming.

Dmitry Kondrashov has written a wonderful and highly readable text that has the potential to have a great impact on the early training of biology students. Of course, there are other very well-done textbooks that are great resources for student of biology. A Biologist’s Guide to Mathematical Modeling in Ecology and Evolution, by Otto and Day, and Mathematics for the Life Sciences, by Bodine, Lenhart and Gross, are both noteworthy texts and can be used by biology students to their great benefit. Mathematics for the Life Sciences has the additional benefit that it introduces readers to Matlab for computing and model implementation. In my opinion, however, Quantifying Life is the best source for biology students to begin their study of quantitative modeling, with the books of Otto and Day, and Bodine, Lenhart and Gross as excellent followups or supplements.

The strengths of Quantifying Life are its size, choice of content, accessibility, price, and perhaps above all its approach to computing. I am also very excited about the use of the R language over other software or programming languages. Besides being open source, R is most likely the preferred computing platform for a working biologist. If you are a serious student of biology, then I strongly recommend to you to read Quantifying Life. I also believe that this book is a valuable resource for mathematicians and other quantitative scientists that find themselves working with biologists. It may prove very helpful in communicating with their collaborators in the life sciences.

Jason M. Graham is an assistant professor in the department of mathematics at the University of Scranton, Scranton, Pennsylvania. His current professional interests are in teaching applied mathematics and mathematical biology, and collaborating with biologists specializing in the collective behavior of groups of organisms.