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As scientists debated the nature of life in the nineteenth century, two theories predominated: vitalism, which suggested that living things contained a "vital spark," and mechanism, the idea that animals and humans differed from nonliving things only in their degree of complexity. Erik Peterson tells the forgotten story of the pursuit of a Third Way in biology, known by many names, including "the organic philosophy," which gave rise to C. H. Waddington's work in the subfield of epigenetics: an alternative to standard genetics and evolutionary biology that captured the attention of notable scientists from Francis Crick to Stephen Jay Gould. The Life Organic chronicles the influential biologists, mathematicians, philosophers, and biochemists from both sides of the Atlantic who formed Joseph Needham's Theoretical Biology Club, defined and refined Third-Way thinking through the 1930s, and laid the groundwork for some of the most cutting-edge achievements in biology today. By tracing the persistence of organicism into the twenty-first century, this book also raises significant questions about how we should model the development of the discipline of biology going forward.
Drawing on recent advances in evolutionary biology, prominent scholars return to the question posed in a pathbreaking book: how evolution itself evolved. In 1995, John Maynard Smith and Eörs Szathmáry published their influential book The Major Transitions in Evolution. The "transitions" that Maynard Smith and Szathmáry chose to describe all constituted major changes in the kinds of organisms that existed but, most important, these events also transformed the evolutionary process itself. The evolution of new levels of biological organization, such as chromosomes, cells, multicelled organisms, and complex social groups radically changed the kinds of individuals natural selection could act upon. Many of these events also produced revolutionary changes in the process of inheritance, by expanding the range and fidelity of transmission, establishing new inheritance channels, and developing more open-ended sources of variation. Maynard Smith and Szathmáry had planned a major revision of their work, but the death of Maynard Smith in 2004 prevented this. In this volume, prominent scholars (including Szathmáry himself) reconsider and extend the earlier book's themes in light of recent developments in evolutionary biology. The contributors discuss different frameworks for understanding macroevolution, prokaryote evolution (the study of which has been aided by developments in molecular biology), and the complex evolution of multicellularity.
"This book addresses basic and advanced questions surrounding the idea of levels or organization in the biological sciences"--
This self-contained introduction to the fast-growing field of Mathematical Biology is written for students with a mathematical background. It sets the subject in a historical context and guides the reader towards questions of current research interest. A broad range of topics is covered including: Population dynamics, Infectious diseases, Population genetics and evolution, Dispersal, Molecular and cellular biology, Pattern formation, and Cancer modelling. Particular attention is paid to situations where the simple assumptions of homogenity made in early models break down and the process of mathematical modelling is seen in action.
Experts examine new modeling strategies for the interpretation of biological data and their integration into the conceptual framework of theoretical biology, detailing approaches that focus on morphology, development, behavior, or evolution. Abstract and conceptual models have become an indispensable tool for analyzing the flood of highly detailed empirical data generated in recent years by advanced techniques in the biosciences. Scientists are developing new modeling strategies for analyzing data, integrating results into the conceptual framework of theoretical biology, and formulating new hypotheses. In Modeling Biology, leading scholars investigate new modeling strategies in the domains of morphology, development, behavior, and evolution. The emphasis on models in the biological sciences has been accompanied by a new focus on conceptual issues and a more complex understanding of epistemological concepts. Contributors to Modeling Biology discuss models and modeling strategies from the perspectives of philosophy, history, and applied mathematics. Individual chapters discuss specific approaches to modeling in such domains as biological form, development, and behavior. Finally, the book addresses the modeling of these properties in the context of evolution, with a particular emphasis on the emerging field of evolutionary developmental biology (or evo-devo). Contributors Giorgio A. Ascoli, Chandrajit Bajaj, James P. Collins, Luciano da Fontoura Costa, Kerstin Dautenhahn, Nigel R. Franks, Scott Gilbert, Marta Ibañes Miguez, Juan Carlos Izpisúa-Belmonte, Alexander S. Klyubin, Thomas J. Koehnle, Manfred D. Laubichler, Sabina Leonelli, James A. R. Marshall, George R. McGhee Jr., Gerd B. Müller, Chrystopher L. Nehaniv, Karl J. Niklas, Lars Olsson, Eirikur Palsson, Daniel Polani, Diego Rasskin Gutman, Hans-Jörg Rheinberger, Alexei V. Samsonovich, Jeffrey C. Schank, Harry B. M. Uylings, Jaap van Pelt, Iain Werry
Since Darwin, Biology has been framed on the idea of evolution by natural selection, which has profoundly influenced the scientific and philosophical comprehension of biological phenomena and of our place in Nature. This book argues that contemporary biology should progress towards and revolve around an even more fundamental idea, that of autonomy. Biological autonomy describes living organisms as organised systems, which are able to self-produce and self-maintain as integrated entities, to establish their own goals and norms, and to promote the conditions of their existence through their interactions with the environment. Topics covered in this book include organisation and biological emergence, organisms, agency, levels of autonomy, cognition, and a look at the historical dimension of autonomy. The current development of scientific investigations on autonomous organisation calls for a theoretical and philosophical analysis. This can contribute to the elaboration of an original understanding of life - including human life - on Earth, opening new perspectives and enabling fecund interactions with other existing theories and approaches. This book takes up the challenge.
An introduction to the quantitative modeling of biological processes, presenting modeling approaches, methodology, practical algorithms, software tools, and examples of current research. The quantitative modeling of biological processes promises to expand biological research from a science of observation and discovery to one of rigorous prediction and quantitative analysis. The rapidly growing field of quantitative biology seeks to use biology's emerging technological and computational capabilities to model biological processes. This textbook offers an introduction to the theory, methods, and tools of quantitative biology. The book first introduces the foundations of biological modeling, focusing on some of the most widely used formalisms. It then presents essential methodology for model-guided analyses of biological data, covering such methods as network reconstruction, uncertainty quantification, and experimental design; practical algorithms and software packages for modeling biological systems; and specific examples of current quantitative biology research and related specialized methods. Most chapters offer problems, progressing from simple to complex, that test the reader's mastery of such key techniques as deterministic and stochastic simulations and data analysis. Many chapters include snippets of code that can be used to recreate analyses and generate figures related to the text. Examples are presented in the three popular computing languages: Matlab, R, and Python. A variety of online resources supplement the the text. The editors are long-time organizers of the Annual q-bio Summer School, which was founded in 2007. Through the school, the editors have helped to train more than 400 visiting students in Los Alamos, NM, Santa Fe, NM, San Diego, CA, Albuquerque, NM, and Fort Collins, CO. This book is inspired by the school's curricula, and most of the contributors have participated in the school as students, lecturers, or both. Contributors John H. Abel, Roberto Bertolusso, Daniela Besozzi, Michael L. Blinov, Clive G. Bowsher, Fiona A. Chandra, Paolo Cazzaniga, Bryan C. Daniels, Bernie J. Daigle, Jr., Maciej Dobrzynski, Jonathan P. Doye, Brian Drawert, Sean Fancer, Gareth W. Fearnley, Dirk Fey, Zachary Fox, Ramon Grima, Andreas Hellander, Stefan Hellander, David Hofmann, Damian Hernandez, William S. Hlavacek, Jianjun Huang, Tomasz Jetka, Dongya Jia, Mohit Kumar Jolly, Boris N. Kholodenko, Markek Kimmel, Michał Komorowski, Ganhui Lan, Heeseob Lee, Herbert Levine, Leslie M Loew, Jason G. Lomnitz, Ard A. Louis, Grant Lythe, Carmen Molina-París, Ion I. Moraru, Andrew Mugler, Brian Munsky, Joe Natale, Ilya Nemenman, Karol Nienałtowski, Marco S. Nobile, Maria Nowicka, Sarah Olson, Alan S. Perelson, Linda R. Petzold, Sreenivasan Ponnambalam, Arya Pourzanjani, Ruy M. Ribeiro, William Raymond, William Raymond, Herbert M. Sauro, Michael A. Savageau, Abhyudai Singh, James C. Schaff, Boris M. Slepchenko, Thomas R. Sokolowski, Petr Šulc, Andrea Tangherloni, Pieter Rein ten Wolde, Philipp Thomas, Karen Tkach Tuzman, Lev S. Tsimring, Dan Vasilescu, Margaritis Voliotis, Lisa Weber
This beautifully crafted book collects images, which were created during the process of research in all fields of theoretical biology. Data analysis, numerical treatment of a model, or simulation results yield stunning images, which represent pieces of art just by themselves. The approach of the book is to present for each piece of visualization a lucid synopsis of the scientific background as well as an outline of the artistic vision.
This text presents mathematical biology as a field with a unity of its own, rather than only the intrusion of one science into another. The book focuses on problems of contemporary interest, such as cancer, genetics, and the rapidly growing field of genomics.
In recent decades, there has been a major shift in the way researchers process and understand scientific data. Digital access to data has revolutionized ways of doing science in the biological and biomedical fields, leading to a data-intensive approach to research that uses innovative methods to produce, store, distribute, and interpret huge amounts of data. In Data-Centric Biology, Sabina Leonelli probes the implications of these advancements and confronts the questions they pose. Are we witnessing the rise of an entirely new scientific epistemology? If so, how does that alter the way we study and understand life—including ourselves? Leonelli is the first scholar to use a study of contemporary data-intensive science to provide a philosophical analysis of the epistemology of data. In analyzing the rise, internal dynamics, and potential impact of data-centric biology, she draws on scholarship across diverse fields of science and the humanities—as well as her own original empirical material—to pinpoint the conditions under which digitally available data can further our understanding of life. Bridging the divide between historians, sociologists, and philosophers of science, Data-Centric Biology offers a nuanced account of an issue that is of fundamental importance to our understanding of contemporary scientific practices.