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Big Mechanisms in Systems Biology: Big Data Mining, Network Modeling, and Genome-Wide Data Identification explains big mechanisms of systems biology by system identification and big data mining methods using models of biological systems. Systems biology is currently undergoing revolutionary changes in response to the integration of powerful technologies. Faced with a large volume of available literature, complicated mechanisms, small prior knowledge, few classes on the topics, and causal and mechanistic language, this is an ideal resource. This book addresses system immunity, regulation, infection, aging, evolution, and carcinogenesis, which are complicated biological systems with inconsistent findings in existing resources. These inconsistencies may reflect the underlying biology time-varying systems and signal transduction events that are often context-dependent, which raises a significant problem for mechanistic modeling since it is not clear which genes/proteins to include in models or experimental measurements. The book is a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in an in-depth understanding on how to process and apply great amounts of biological data to improve research. Written in a didactic manner in order to explain how to investigate Big Mechanisms by big data mining and system identification Provides more than 140 diagrams to illustrate Big Mechanism in systems biology Presents worked examples in each chapter
This comprehensively revised second edition of Computational Systems Biology discusses the experimental and theoretical foundations of the function of biological systems at the molecular, cellular or organismal level over temporal and spatial scales, as systems biology advances to provide clinical solutions to complex medical problems. In particular the work focuses on the engineering of biological systems and network modeling. - Logical information flow aids understanding of basic building blocks of life through disease phenotypes - Evolved principles gives insight into underlying organizational principles of biological organizations, and systems processes, governing functions such as adaptation or response patterns - Coverage of technical tools and systems helps researchers to understand and resolve specific systems biology problems using advanced computation - Multi-scale modeling on disparate scales aids researchers understanding of dependencies and constraints of spatio-temporal relationships fundamental to biological organization and function.
This book presents a novel molecular description for understanding the regulatory mechanisms behind the autonomy and self-organization in biological systems. Chapters focus on defining and explaining the regulatory molecular mechanisms behind different aspects of autonomy and self-organization in the sense of autonomous coding, data processing, structure (mass) formation and energy production in a biological system. Subsequent chapters discuss the cross-talk among mechanisms of energy, and mass and information, transformation in biological systems. Other chapters focus on applications regarding therapeutic approaches in regenerative medicine. Molecular Mechanisms of Autonomy in Biological Systems is an indispensable resource for scientists and researchers in regenerative medicine, stem cell biology, molecular biology, tissue engineering, developmental biology, biochemistry, biophysics, bioinformatics, as well as big data sciences, complexity and soft computing.
Since the discovery of the Warburg effect in the 1920s cancer has been tightly associated with the genetic and metabolic state of the cell. One of the hallmarks of cancer is the alteration of the cellular metabolism in order to promote proliferation and undermine cellular defense mechanisms such as apoptosis or detection by the immune system. However, the strategies by which this is achieved in different cancers and sometimes even in different patients of the same cancer is very heterogeneous, which hinders the design of general treatment options. Recently, there has been an ongoing effort to study this phenomenon on a genomic scale in order to understand the causality underlying the disease. Hence, current “omics” technologies have contributed to identify and monitor different biological pieces at different biological levels, such as genes, proteins or metabolites. These technological capacities have provided us with vast amounts of clinical data where a single patient may often give rise to various tissue samples, each of them being characterized in detail by genomescale data on the sequence, expression, proteome and metabolome level. Data with such detail poses the imminent problem of extracting meaningful interpretations and translating them into specific treatment options. To this purpose, Systems Biology provides a set of promising computational tools in order to decipher the mechanisms driving a healthy cell’s metabolism into a cancerous one. However, this enterprise requires bridging the gap between large data resources, mathematical analysis and modeling specifically designed to work with the available data. This is by no means trivial and requires high levels of communication and adaptation between the experimental and theoretical side of research.
Systems biology is the inevitable outcome of long years of knowledge acquisition and data accumulation. The aim of systems biology is to integrate in a seamless way all existing knowledge in interconnected disciplines, stretching from modern biomedical research to physics, chemistry, and mathematics. The main integration tool of such complex biomedical systems is via computational and mathematical modeling. In this direction, a series of state-of-the-art computer science techniques are used, namely, data mining and fusion, machine learning, and deep learning all under the prism of big data. All in all, systems biology is at the arrowhead of modern and state-of-the-art biomedical research by attempting to address key biological questions describing holistically complex biological systems.
The first comprehensive single-authored textbook on genome-scale models and the bottom-up approach to systems biology.
This new book takes an in-depth look at the emerging and prospective field of computational biology and bioinformatics, which possesses the ability to analyze large accumulated biological data collected from sequence analysis of proteins and genes and cell population with an aim to make new predictions pertaining to drug discovery and new biology. The book explains the basic methodology associated with a bioinformatics and computational approach in drug designing. It then goes on to cover the implementation of computational programming, bioinformatics, pharmacophore modeling, biotechnological techniques, and pharmaceutical chemistry in designing drugs. The major advantage of intervention of computer language or programming is to cut down the number of steps and costs in the field of drug designing, reducing the repeating steps and saving time in screening the potent component for drug or vaccine designing. The book describes algorithms used for drug designing and the use of machine learning and AI in drug delivery and disease diagnosis, which are valuable in clinical decision-making. The implementation of robotics in different diseases like stroke, cancer, COVID-19, etc. is also addressed. Topics include machine learning, AI, databases in drug design, molecular docking, bioinformatics tools, target-based drug design, and immunoinformatics, chemoinformatics, and nanoinformatics in drug design. Drug repurposing in drug design in general as well as for specific diseases, including cancer, Alzheimer’s disease, tuberculosis, COVID-19, etc., is also addressed in depth.
A brilliant young scientist introduces us to the fascinating field that is changing our understanding of how the body works and the way we can approach healing. SYSTEMATIC is the first book to introduce general readers to systems biology, which is improving medical treatments and our understanding of living things. In traditional bottom-up biology, a biologist might spend years studying how a single protein works, but systems biology studies how networks of those proteins work together--how they promote health and how to remedy the situation when the system isn't functioning properly. Breakthroughs in systems biology became possible only when powerful computer technology enabled researchers to process massive amounts of data to study complete systems, and has led to progress in the study of gene regulation and inheritance, cancer drugs personalized to an individual's genetically unique tumor, insights into how the brain works, and the discovery that the bacteria and other microbes that live in the gut may drive malnutrition and obesity. Systems biology is allowing us to understand more complex phenomena than ever before. In accessible prose, SYSTEMATIC sheds light not only on how systems within the body work, but also on how research is yielding new kinds of remedies that enhance and harness the body's own defenses.
A detailed understanding of biomolecular mechanisms enables predictive modeling in biological systems. In the late 1990's, whole-genome sequencing and the development of various high-throughput technologies led to the emergence of systems biology, primarily in simple model organisms such as bacteria and yeast. Mechanisms between biological components and processes were cataloged and placed in mathematical frameworks to explain the role of genotype and environmental factors on phenotypes. Some modeling formalisms, such as constraints-based modeling, have been shown to accurately recapitulate biological findings, and provided new insights for applications ranging from metabolic engineering to evolutionary landscapes. Recently, systems biology of human cells, with the same aim of characterizing mechanisms, has been employed to study drug off-target effects, host-pathogen interactions, cancer metabolism, and multicellular interactions between brain cell types. However, mechanism-based systems biology of human cells is still in its infancy and has not achieved the level of adoption as systems biology in unicellular organisms. Therefore, a broad, mechanism-centric approach to human systems biology is expounded in this dissertation, and was used to address open problems concerning blood platelets and cancer cells to make inroads in the study of disease, longevity, and phenotypic diversity with regard to human cells. Mechanisms were cataloged into a computable database for blood platelet metabolism. This systems-level assessment of the platelet was used to study the effects of aspirin resistance and delineate pathway utilization during platelet storage. Computational methods were developed to handle the scale of information in these systems biology applications with the motivation of reporting digestible results. To this end, BioNetView was developed as a clustering and visualization tool to utilize structural information to build interpretable, data-influenced pathway maps. Discovery of new mechanisms for future systems biology applications was also explored. Representing an initial foray towards large-scale mechanistic discovery in human cells, a novel bioinformatics pipeline was developed and deployed for processing and scoring genetic interactions in cancer cell lines via gene knockout screens utilizing the unprecedented precision of CRISPR/Cas9 genome editing. Therefore, in an effort to contextualize and understand mechanisms, several aspects are presented, geared towards a comprehensive, interpretable systems-level perspective of human biology.
Systems Biology is an interdisciplinary approach to the study of life made possible through the explosion of molecular data made available through the genome revolution and the simultaneous development of computational technologies that allow us to interpret these large data sets. Systems Biology has changed the way biological science views and studies life and has been implemented in research efforts across the biological sciences. Systems Biology and Livestock Science will be the first book to review the latest advances using this research methodology in efforts to improve the efficiency, health, and quality of livestock production. Systems Biology and Livestock Science opens with useful introductory chapters explaining key systems biology principles. The chapters then progress to look at specific advances in fields across livestock science. Coverage includes, but is not limited to, chapters on systems biology approaches to animal nutrition, reproduction, health and disease, and animal physiology. Written by leading researchers in the field, Systems Biology and Livestock Science, will be an invaluable resource to researchers, professionals, and advance students working in this rapidly developing discipline.