Hormones as Tokens of Selection addresses deep questions in biology: How are biological systems controlled? How can one formulate general theories of homeostasis and control and instantiate such theories in mathematical models? How can one use evolutionary arguments to guide our answers to these questions, recognising that the control mechanisms themselves are a product of evolution? Biological systems are exceptionally varied and extremely difficult to understand, because they are complex and experimentation remains limited relative to the challenges at hand. Moreover, biological phenomena occur at a wide range of temporal and spatial scales. Such a deeply convoluted subject calls for a unifying and coherent theoretical foundation - one which recognises and departs from the primary importance of mathematical modelling and key physicochemical principles to theory formation in the life sciences. This Focus monograph proposes and outlines such a foundation, departing from the deceptively simple proposition that hormones are tokens of evolutionary pressures. Features Provides a coherent and unified approach to a multifaceted problem Pays close attention to both the biological and mathematical modelling aspects of the subject matter, exploring the philosophical background where appropriate Written in a concise and innovative style

Written for researchers and postgraduate students with a background in physics or applied mathematics and a desire to apply their skills to problems in the life sciences, this beautifully illustrated and stimulating book develops an understanding of the gene-to-trait problem in the context of evolutionary dynamics, from the modern perspective of integrative biology. The gene-to-trait problem resides at the heart of a great many questions in biology. The author presents both elementary and advanced material in a way that brings out how this gene-to-trait problem is treated in the contexts of bioinformatics and evolutionary dynamics. Key ideas and techniques that underlie some of the most-used bioinformatics methods are discussed in an integrative context and a wide range of examples of mathematical models of living things is developed in an evolutionary framework.