Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model—in other words, to answer specific questions about the underlying mechanisms of a biological system—in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks.
The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
Contributors: Florence d'Alch e-Buc, John Angus, Matthew J. Beal, Nicholas Brunel, Ben Calderhead, Pei Gao, Mark Girolami, Andrew Golightly, Dirk Husmeier, Johannes Jaeger, Neil D. Lawrence, Juan Li, Kuang Lin, Pedro Mendes, Nicholas A. M. Monk, Eric Mjolsness, Manfred Opper, Claudia Rangel, Magnus Rattray, Andreas Ruttor, Guido Sanguinetti, Michalis Titsias, Vladislav Vyshemirsky, David L. Wild, Darren Wilkinson, Guy Yosiphon
Issues of regulation and control are central to the study of biological and biochemical systems. Thus it is not surprising that the tools of feedback control theory--engineering techniques developed to design and analyze self-regulating systems--have proven useful in the study of these biological mechanisms. Such interdisciplinary work requires knowledge of the results, tools and techniques of another discipline, as well as an understanding of the culture of an unfamiliar research community. This volume attempts to bridge the gap between disciplines by presenting applications of systems and control theory to cell biology that range from surveys of established material to descriptions of new developments in the field. The first chapter offers a primer on concepts from dynamical systems and control theory, which allows the life scientist with no background in control theory to understand the concepts presented in the rest of the book. Following the introduction of ordinary differential equation-based modeling in the first chapter, the second and third chapters discuss alternative modeling frameworks. The remaining chapters sample a variety of applications, considering such topics as quantitative measures of dynamic behavior, modularity, stoichiometry, robust control techniques, and network identification. ContributorsDavid Angeli, Declan G. Bates, Eric Bullinger, Peter S. Chang, Domitilla Del Vecchio, Francis J. Doyle III, Hana El-Samad, Dirk Fey, Rolf Findeisen, Simone Frey, Jorge Gonçalves, Pablo A. Iglesias, Brian P. Ingalls, Elling W. Jacobsen, Mustafa Khammash, Jongrae Kim, Eric Klavins, Eric C. Kwei, Thomas Millat, Jason E. Shoemaker, Eduardo D. Sontag, Stephanie R. Taylor, David Thorsley, Camilla Trané, Sean Warnick, Olaf Wolkenhauer
From one cell to another, from one individual to another, and from one species to another, the content of DNA molecules is often similar. The organization of these molecules, however, differs dramatically, and the mutations that affect this organization are known as genome rearrangements. Combinatorial methods are used to reconstruct putative rearrangement scenarios in order to explain the evolutionary history of a set of species, often formalizing the evolutionary events that can explain the multiple combinations of observed genomes as combinatorial optimization problems. This book offers the first comprehensive survey of this rapidly expanding application of combinatorial optimization. It can be used as a reference for experienced researchers or as an introductory text for a broader audience. Genome rearrangement problems have proved so interesting from a combinatorial point of view that the field now belongs as much to mathematics as to biology. This book takes a mathematically oriented approach, but provides biological background when necessary. It presents a series of models, beginning with the simplest (which is progressively extended by dropping restrictions), each constructing a genome rearrangement problem. The book also discusses an important generalization of the basic problem known as the median problem, surveys attempts to reconstruct the relationships between genomes with phylogenetic trees, and offers a collection of summaries and appendixes with useful additional information.
Molecular biologist Elizabeth Blackburn--one of Time magazine’s 100 “Most Influential People in the World” in 2007--made headlines in 2004 when she was dismissed from the President’s Council on Bioethics after objecting to the council’s call for a moratorium on stem cell research and protesting the suppression of relevant scientific evidence in its final report. But it is Blackburn’s groundbreaking work on telomeric DNA, which launched the field of telomere research, that will have the more profound and long-lasting effect on science and society. In this compelling biography, Catherine Brady tells the story of Elizabeth Blackburn’s life and work and the emergence of a new field of scientific research on the specialized ends of chromosomes and the enzyme, telomerase, that extends them. In the early stages of telomere research, telomerase, heralded as a potential cure for cancer and diseases related to aging, attracted the voracious interest of biotech companies. The surrounding hype succeeded in confusing the role of telemorase in extending the life of a cell with a mechanism that might extend the lifespan of an entire organism. In Brady’s hands, Blackburn’s story reveals much about the tension between pure and applied science, the politicking that makes research science such a competitive field, and the resourceful opportunism that characterizes the best scientific thinking. Brady describes the science accessibly and compellingly. She explores Blackburn’s struggle to break down barriers in an elite, male-dominated profession, her role as a mentor to other women scientists (many of whom have made their mark in telomere research), and the collaborative nature of scientific work. This book gives us a vivid portrait of an exceptional woman and a new understanding of the combination of curiosity, imaginative speculation, and aesthetic delight that powers scientific discovery.Catherine Brady is Assistant Professor in the MFA in Writing Program at the University of San Francisco. She is the author of two collections of short stories, The End of the Class War and Curled in the Bed of Love (a winner of the 2002 Flannery O’Connor Award for Short Fiction).
Protocells offers a comprehensive resource on current attempts to create simple forms of life from scratch in the laboratory. These minimal versions of cells, known as protocells, are entities with lifelike properties created from nonliving materials, and the book provides in-depth investigations of processes at the interface between nonliving and living matter. Chapters by experts in the field put this state-of-the-art research in the context of theory, laboratory work, and computer simulations on the components and properties of protocells. The book also provides perspectives on research in related areas and such broader societal issues as commercial applications and ethical considerations.
The book covers all major scientific approaches to creating minimal life, both in the laboratory and in simulation. It emphasizes the bottom-up view of physicists, chemists, and material scientists but also includes the molecular biologists' top-down approach and the origin-of-life perspective. The capacity to engineer living technology could have an enormous socioeconomic impact and could bring both good and ill. Protocells promises to be the essential reference for research on bottom-up assembly of life and living technology for years to come. It is written to be both resource and inspiration for scientists working in this exciting and important field and a definitive text for the interested layman.
There are many excellent computational biology resources now available for learning about methods that have been developed to address specific biological systems, but comparatively little attention has been paid to training aspiring computational biologists to handle new and unanticipated problems. This text is intended to fill that gap by teaching students how to reason about developing formal mathematical models of biological systems that are amenable to computational analysis. It collects in one place a selection of broadly useful models, algorithms, and theoretical analysis tools normally found scattered among many other disciplines. It thereby gives studentshte tools that will serve them well in modeling problems drawn from numerous subfields of biology. These techniques are taught from the perspective of what the practitioner needs to know to use them effectively, supplemented with references for further reading on more advanced use of each method covered.
The text covers models for optimization, simulation and sampling, and parameter tuning. These topics provide a general framework for learning how to formulate mathematical models of biological systems, what techniques are available to work with these models, and how to fit the models to particular systems. Their application is illustrated by many examples drawn from a variety of biological disciplines and several extended case studies that show how the methods described have been applied to real problems in biology.
Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These collaborations, necessary because of the enormous breadth of background needed for research in this field, can be hindered by differing understandings of the limitations and applicability of techniques and concerns from different disciplines. This comprehensive introduction and overview of system modeling in biology makes the relevant background material from all pertinent fields accessible to researchers with different backgrounds.
The emerging area of systems level modeling in cellular biology has lacked a critical and thorough overview. This book fills that gap. It is the first to provide the necessary critical comparison of concepts and approaches, with an emphasis on their possible applications. It presents key concepts and their theoretical background, including the concepts of robustness and modularity and their exploitation to study biological systems; the best-known modeling approaches, and their advantages and disadvantages; lessons from the application of mathematical models to the study of cellular biology; and available modeling tools and datasets, along with their computational limitations.