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This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then reviewed, in particular Hill functions and Schwarzian derivatives. One chapter is devoted to the analysis of GRNs under negative feedback with time delays and a special case of a homogenous GRN is considered. Asymptotic stability analysis of GRNs under positive feedback is then considered in a separate chapter, in which conditions leading to bi-stability are derived. Graduate and advanced undergraduate students and researchers in control engineering, applied mathematics, systems biology and synthetic biology will find this brief to be a clear and concise introduction to the modeling and analysis of GRNs.
Evolutionary algorithms constitute a class of well-known algorithms, which are designed based on the Darwinian theory of evolution and Mendelian theory of heritage. They are partly based on random and partly based on deterministic principles. Due to this nature, it is challenging to predict and control its performance in solving complex nonlinear problems. Recently, the study of evolutionary dynamics is focused not only on the traditional investigations but also on the understanding and analyzing new principles, with the intention of controlling and utilizing their properties and performances toward more effective real-world applications. In this book, based on many years of intensive research of the authors, is proposing novel ideas about advancing evolutionary dynamics towards new phenomena including many new topics, even the dynamics of equivalent social networks. In fact, it includes more advanced complex networks and incorporates them with the CMLs (coupled map lattices), which are usually used for spatiotemporal complex systems simulation and analysis, based on the observation that chaos in CML can be controlled, so does evolution dynamics. All the chapter authors are, to the best of our knowledge, originators of the ideas mentioned above and researchers on evolutionary algorithms and chaotic dynamics as well as complex networks, who will provide benefits to the readers regarding modern scientific research on related subjects.
With the invitation to edit this volume, I wanted to take the opportunity to assemble reviews on different aspects of circadian clocks and rhythms. Although most c- tributions in this volume focus on mammalian circadian clocks, the historical int- duction and comparative clocks section illustrate the importance of various other organisms in deciphering the mechanisms and principles of circadian biology. Circadian rhythms have been studied for centuries, but only recently, a mole- lar understanding of this process has emerged. This has taken research on circadian clocks from mystic phenomenology to a mechanistic level; chains of molecular events can describe phenomena with remarkable accuracy. Nevertheless, current models of the functioning of circadian clocks are still rudimentary. This is not due to the faultiness of discovered mechanisms, but due to the lack of undiscovered processes involved in contributing to circadian rhythmicity. We know for example, that the general circadian mechanism is not regulated equally in all tissues of m- mals. Hence, a lot still needs to be discovered to get a full understanding of cir- dian rhythms at the systems level. In this respect, technology has advanced at high speed in the last years and provided us with data illustrating the sheer complexity of regulation of physiological processes in organisms. To handle this information, computer aided integration of the results is of utmost importance in order to d- cover novel concepts that ultimately need to be tested experimentally.
An introduction to the mathematical, computational, and analytical techniques used for modeling biological rhythms, presenting tools from many disciplines and example applications. All areas of biology and medicine contain rhythms, and these behaviors are best understood through mathematical tools and techniques. This book offers a survey of mathematical, computational, and analytical techniques used for modeling biological rhythms, gathering these methods for the first time in one volume. Drawing on material from such disciplines as mathematical biology, nonlinear dynamics, physics, statistics, and engineering, it presents practical advice and techniques for studying biological rhythms, with a common language. The chapters proceed with increasing mathematical abstraction. Part I, on models, highlights the implicit assumptions and common pitfalls of modeling, and is accessible to readers with basic knowledge of differential equations and linear algebra. Part II, on behaviors, focuses on simpler models, describing common properties of biological rhythms that range from the firing properties of squid giant axon to human circadian rhythms. Part III, on mathematical techniques, guides readers who have specific models or goals in mind. Sections on “frontiers” present the latest research; “theory” sections present interesting mathematical results using more accessible approaches than can be found elsewhere. Each chapter offers exercises. Commented MATLAB code is provided to help readers get practical experience. The book, by an expert in the field, can be used as a textbook for undergraduate courses in mathematical biology or graduate courses in modeling biological rhythms and as a reference for researchers.
The nonequilibrium behavior of nanoscopic and biological systems, which are typically strongly fluctuating, is a major focus of current research. Lately, much progress has been made in understanding such systems from a thermodynamic perspective. However, new theoretical challenges emerge when the fluctuating system is additionally subject to time delay, e.g. due to the presence of feedback loops. This thesis advances this young and vibrant research field in several directions. The first main contribution concerns the probabilistic description of time-delayed systems; e.g. by introducing a versatile approximation scheme for nonlinear delay systems. Second, it reveals that delay can induce intriguing thermodynamic properties such as anomalous (reversed) heat flow. More generally, the thesis shows how to treat the thermodynamics of non-Markovian systems by introducing auxiliary variables. It turns out that delayed feedback is inextricably linked to nonreciprocal coupling, information flow, and to net energy input on the fluctuating level.
"This book highlights the use of systems approaches including genomic, cellular, proteomic, metabolomic, bioinformatics, molecular, and biochemical, to address fundamental questions in complex diseases like cancer diabetes but also in ageing"--Provided by publisher.
Computational cell biology courses are increasingly obligatory for biology students around the world but of course also a must for mathematics and informatics students specializing in bioinformatics. This book, now in its second edition is geared towards both audiences. The author, Volkhard Helms, has, in addition to extensive teaching experience, a strong background in biology and informatics and knows exactly what the key points are in making the book accessible for students while still conveying in depth knowledge of the subject.About 50% of new content has been added for the new edition. Much more room is now given to statistical methods, and several new chapters address protein-DNA interactions, epigenetic modifications, and microRNAs.
An understanding of the nervous system at virtually any level of analysis requires an understanding of its basic building block, the neuron. From Molecules to Networks provides the solid foundation of the morphologic, biochemical, and biophysical properties of nerve cells. All chapters have been thoroughly revised for this second edition to reflect the significant advances of the past 5 years. The new edition expands on the network aspects of cellular neurobiology by adding a new chapter, Information Processing in Neural Networks, and on the relation of cell biological processes to various neurological diseases. The new concluding chapter illustrates how the great strides in understanding the biochemical and biophysical properties of nerve cells have led to fundamental insights into important aspects of neurodegenerative disease. - Written and edited by leading experts in the field, the second edition completely and comprehensively updates all chapters of this unique textbook - Discusses emerging new understanding of non-classical molecules that affect neuronal signaling - Full colour, professional graphics throughout - Includes two new chapters: Information Processing in Neural Networks - describes the principles of operation of neural networks and the key circuit motifs that are common to many networks in the nervous system. Molecular and Cellular Mechanisms of Neurodegenerative Disease - introduces the progress made in the last 20 years in elucidating the cellular and molecular mechanisms underlying brain disorders, including Amyotrophic Lateral Sclerosis (ALS), Parkinson disease, and Alzheimer's disease
Modeling Biomolecular Networks in Cells shows how the interaction between the molecular components of basic living organisms can be modelled mathematically and the models used to create artificial biological entities within cells. Such forward engineering is a difficult task but the nonlinear dynamical methods espoused in this book simplify the biology so that it can be successfully understood and the synthesis of simple biological oscillators and rhythm-generators made feasible. Such simple units can then be co-ordinated using intercellular signal biomolecules. The formation of such man-made multicellular networks with a view to the production of biosensors, logic gates, new forms of integrated circuitry based on "gene-chips" and even biological computers is an important step in the design of faster and more flexible "electronics". The book also provides theoretical frameworks and tools with which to analyze the nonlinear dynamical phenomena which arise from the connection of building units in a biomolecular network.