Quantitative Elements of General Biology: A Dynamical Systems Approach

This monograph sketches out a broad spectrum of problems (from evolution and metabolism to morphogenesis and biogeographical dynamics) whose solution has been impacted by mathematical models. Each of the selected examples has led to the recognition―and set direction to further study―of certain fundamental but unintuitive properties of biological systems, such as the making and breaking of specific symmetries that underlie morphogenesis. Whether they are long-established or only recently accepted, these models are selected for being thought-provoking and illuminating both the achievements and the gaps in our current understanding of the given area of biology. The selection of models is also meant to bring to the fore the existing degree of unity in the quantitative approach to diverse general-biological questions and in the systems-level properties that are discovered across the levels of biological organization. It is the thesis of this book that further cultivation of such unity is away forward as we progress toward a general theory of living matter.

This is an ideal book for students (in the broadest sense) of biology who wish to learn from this attempt to present the exemplary models, their methodological lessons, and the outline of a unified theory of living matter that is now beginning to emerge. In addition to a doctoral student preparing for quantitative biology research, this reader could also be an interdisciplinary scientist transitioning to biology. The latter―for example, a physicist or an engineer―may be comfortable with the mathematical apparatus and prepared to quickly enter the intended area of work, but desires a broader foundation in biology from the quantitative perspective.

Ivan Maly earned his PhD in the Life Sciences from Northwestern University. After a research stint in the Biological Engineering Division at MIT, he became a founding member of the Department of Computational and Systems Biology at the University of Pittsburgh School of Medicine. Now at the State University of New York, he continues his experimental and computational cell-biological research while maintaining a broad interest in quantitative biology and problems of knowledge integration. His earlier books are Systems Biology (Springer, 2009) and Systems Biomechanics of the Cell (Springer, 2013).