دانلود کتاب زیست‌شناسی سیستم‌های سرطان -انکولوژی ریاضی ترجمه‌ای

Civilization over centuries has seen considerable advances in healthcare. Cancer is a challenging issue, and a number of discoveries have led to better care of some patients. Despite all the progress and the promise regarding early detection and precision medicine, we are still faced with the nettlesome problem— cancer is a moving target. Even within an individual tumour, deep sequencing analyses now indicate multiple, phenotypically distinct subpopulations whose representation seems to vary dramatically from one stage to the next as the tumour progresses. The discovery of driver mutations and ‘actionable’ genes added to the genetic underpinning and fuelled the era of – omic approaches starting from whole tissue to the single- cell level. The reductionist perspective, together with these new developments, initially established cancer as a complex and heterogeneous disease driven by genetic mutations. According to this view, cancer per se, its progression, and the acquisition of drug resistance, whether innate or acquired, are highly deterministic. However, recent research has promoted the idea that phenotypic plasticity and non- genetic mechanisms are important in these events. Increasingly, it is acknowledged that cancer is a complex adaptive and dynamic system and that, in addition to genetic mutations, intratumoral heterogeneity can also arise through non- genetic mechanisms. Furthermore, it is also becoming evident that the malignant phenotype results from complex interactions between genetic and non- genetic mechanisms. These interactions contribute to functional changes across multiple spatiotemporal scales, from the molecular to cellular and tissue level, creating a heterogeneous cancer cell population. Technological advances to study single cells and single molecules, the availability of powerful computing platforms, and the application of evolutionary game theory to discern group behaviour have galvanized multiscale modelling enabling a quantitative view of the key processes in tumorigenesis. These efforts have served as a powerful means to investigate biological phenomena more comprehensively in experimentally relevant ways. Therefore, a deeper understanding of the disease can not only provide novel insight needed to personalize treatments, but this knowledge may also be used to address therapeutic resistance by administering ‘adaptive’ (intermittent) therapy rather than the standard (continuous) therapy that typically, albeit inadvertently, encourages the emergence of drug- resistant tumours. However, performing pre- clinical studies and clinical trials for multiple specific targets in varied dosing sequence and timing schedules is often too resource- and time- consuming, and hence challenging. Therefore, calibrated and validated mathematical models offer an attractive approach to evaluate untested protocols in silico to narrow the set of promising treatment schemas to be evaluated, to identify new treatment targets, and to reduce the risk of adverse clinical outcomes due to complex feedback mechanisms. The main intent of this book, in addition to providing stateof- the- art reviews and thought- provoking ideas in a concise and succinct manner, is to encourage cross- pollination of ideas from multiple disciplines between clinicians and scientists interested in integrating both theoretical and experimental approaches to study cancer. The chapters provide new ideas and concepts outlining how a quantitative picture of cancer can provide a deeper understanding of the disease and how a systems- level perspective may hold the key to fully comprehend how cancer arises and progresses. The book embodies 41 chapters that are organized in 9 sections. The first two introductory chapters provide an overview of the systems approach using multiple paradigms. The following section on single- cell – omics provides in- depth perspective on the analyses of big data derived from single cells at the systems level. The cardinal features of such single- cell multi- omic analysis include technologies for single- cell isolation, barcoding, and sequencing to measure multiple types of molecules from individual cells as well as the integrative analysis of molecules to characterize cell types and their functions regarding pathophysiological processes based on molecular signatures. Furthermore, new hypotheses about functionality and consequences of expression heterogeneity from timeresolved measurements of gene expression and how the principles from information theory can guide models of transcriptional regulation and gene network connectivity are presented. Next, computational approaches to drug discovery are presented. Computational approaches form an important part of the tools employed in drug discovery and development. Their applications span almost all stages in the discovery and development pipeline, from target identification to lead discovery and from lead optimization to preclinical and even clinical trials. Thus, the chapters devoted to this section cover concepts of structure- and ligand- based drug designing, protein modelling and visualization, molecular docking, virtual screening, molecular dynamics simulation, pharmacophore modelling, and quantitative structure– activity relationship approaches that are typically used in conjunction with conventional biophysical techniques. Some also address the broad area of data analysis, including data mining algorithms, statistical approaches, and practical applications. Topics in this section include problems involving massive and complex datasets, solutions utilizing innovative data mining algorithms and/ or novel statistical approaches, and the objective evaluation of analyses and solutions.