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Robustness and Evolvability in Living Systems
All living things are remarkably complex, yet their DNA is unstable, undergoing countless random mutations over generations. Despite this instability, most animals do not grow two heads or die, plants continue to thrive, and bacteria continue to divide. Robustness and Evolvability in Living Systems tackles this perplexing paradox. The book explores why genetic changes do not cause organisms to fail catastrophically and how evolution shapes organisms' robustness. Andreas Wagner looks at this problem from the ground up, starting with the alphabet of DNA, the genetic code, RNA, and protein molecules, moving on to genetic networks and embryonic development, and working his way up to whole organisms. He then develops an evolutionary explanation for robustness. Wagner shows how evolution by natural selection preferentially finds and favors robust solutions to the problems organisms face in surviving and reproducing. Such robustness, he argues, also enhances the potential for future evolutionary innovation. Wagner also argues that robustness has less to do with organisms having plenty of spare parts (the redundancy theory that has been popular) and more to do with the reality that mutations can change organisms in ways that do not substantively affect their fitness. Unparalleled in its field, this book offers the most detailed analysis available of all facets of robustness within organisms. It will appeal not only to biologists but also to engineers interested in the design of robust systems and to social scientists concerned with robustness in human communities and populations.
eBook
Hypergraph models of biological networks to identify genes critical to pathogenic viral response
Background
Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets.
Results
We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality.
Conclusions
Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.
Journal Article
Biomolecular networks
Alternative techniques and tools for analyzing biomolecular networks. With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the import
eBook
Constructing and analysing dynamic models with modelbase v1.2.3: a software update
Background
Computational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies, and disease evolution and transmission. Unfortunately, despite community efforts leading to the development of SBML and the BioModels database, many published models have not been fully exploited, largely due to a lack of proper documentation or the dependence on proprietary software. To facilitate the reuse and further development of systems biology and systems medicine models, an open-source toolbox that makes the overall process of model construction more consistent, understandable, transparent, and reproducible is desired.
Results and discussion
We provide an update on the development of
modelbase
, a free, expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. It provides intuitive and unified methods to construct and solve these systems. Significantly expanded visualisation methods allow for convenient analysis of the structural and dynamic properties of models. After specifying reaction stoichiometries and rate equations modelbase can automatically assemble the associated system of differential equations. A newly provided library of common kinetic rate laws reduces the repetitiveness of the computer programming code.
modelbase
is also fully compatible with SBML. Previous versions provided functions for the automatic construction of networks for isotope labelling studies. Now, using user-provided label maps,
modelbase
v1.2.3 streamlines the expansion of classic models to their isotope-specific versions. Finally, the library of previously published models implemented in
modelbase
is growing continuously. Ranging from photosynthesis to tumour cell growth to viral infection evolution, all these models are now available in a transparent, reusable and unified format through
modelbase
.
Conclusion
With this new Python software package, which is written in currently one of the most popular programming languages, the user can develop new models and actively profit from the work of others.
modelbase
enables reproducing and replicating models in a consistent, tractable and expandable manner. Moreover, the expansion of models to their isotopic label-specific versions enables simulating label propagation, thus providing quantitative information regarding network topology and metabolic fluxes.
Journal Article
Terrestrial ecosystem ecology : principles and applications
\"Human activities impact the environment and modify the cycles of important elements such as carbon and nitrogen from local to global scales. In order to maintain long-term and sustainable use of the world's natural resources it is important that we understand how and why ecosystems respond to such changes. This book explains the structure and functioning of terrestrial ecosystems, using examples ranging from the Arctic to the tropics to demonstrate how they react under differing conditions. This knowledge is developed into a set of principles that can be used as starting points for analysing questions about ecosystem behaviour. Ecosystem dynamics are also considered, illustrating how ecosystems develop and change over a range of temporal and spatial scales and how they react to perturbations, whether natural or man-made. Throughout the book, descriptive studies are merged with simple mathematical models to reinforce the concepts discussed and aid the development of predictive tools\"-- Provided by publisher.
Book
The Conceptual Foundations of Systems Medicine
\"Medicine is facing several significant challenges as the twenty-first century unfolds, which represent barriers or limitations that threaten to cripple the advancement of medicine and its practice. One of the responses to these challenges is the emergence of systems medicine. And one of the more pertinent challenges is identifying and clarifying systems medicine's conceptual and theoretical foundations. The present book represents a sustained effort to examine this challenge and to map the terrain by which to engage it and to pursue possible solutions. This conceptual and theoretical challenge in particular needs to be addressed to ensure the future success of systems medicine. To that end the book explores the conceptual and theoretical foundations of systems medicine, including the major concepts of organicism, emergence, and robustness, and contrasts these concepts to those for biomedicine, including mechanism, resultant, and homeos
eBook