دانلود کتاب محاسبات بیولوژیکی

In 1996, I was deeply engaged in addressing complex graph theory challenges, such as the Traveling Salesman Problem, the Graph Coloring Problem, and the HamiltonianProblem,utilizingArtificialNeuralNetworks(ANNs).Theseproblems are categorized as NP-complete, and I aspired to develop efficient algorithms for them with the help of ANNs. I guided my doctoral students in publishing several academic papers focused on tackling NP-complete problems through the use of ANNs. One day, one of my doctoral students mentioned discovering an article in Science that presented a method for solving the Directed Hamiltonian Path Problem using DNA molecules. I was exhilarated by this finding, convinced it could offer a novel approach to addressing NP-complete problems, and I was eager to delve deeper into the subject. I rushed to the library to photocopy the article and began reading it. However, as I immersed myself in the text, I felt a sense of confusion— having never studied biology in high school, I struggled to understand the biological concepts and experimental designs described in the paper. From that moment on, I resolved to make a significant contribution to the field of biological computing by learning biology. I started from the ground up, initially focusing on high school-level biology before progressing to more advanced topics in molecular biology and biochemistry. After over a decade of dedicated effort, I became proficient in independently executing essential stages of utilizing DNA computing to address NP-complete problems, including mathematical model construction, experimental design, and solution verification. Over the past 30 years, DNA computing has progressed from enumeration-based models to non-enumerative and parallel models. A significant milestone occurred between 2002 and 2016 with the introduction of the probe machine, which represented a groundbreaking advancement in the field. The probe machine is a fully parallel mathematical computing model developed to establish a DNA model for graph coloring. Its advent has provided computer science with a new mathematical computing framework that distinguishes itself from the Turing machine, garnering considerable attention from scholars and industry professionals alike. This book provides a comprehensive overview of the research accomplishments of scholars worldwide in the realm of biological computing over several decades. The main topics encompassed include fundamental theories, key technologies, experimental methodologies, computational models, and application examples of DNA computing. In the section dedicated to foundational theory, we detail the structural characteristics of DNA molecules, the mechanisms of biological enzymes, and the fundamental principles underlying DNA computing. This content lays the groundwork for readers to comprehend the operation of DNA computing. The chapters focusing on key technologies and experimental methodologies examine essential techniques such as DNA encoding, PCR technology, and gel elec-trophoresis. These techniques are critical for DNA computing, enabling readers to understandthevitalaspectsofexperimentaldesignandexecution. Inourexploration of computational models, we delve into various DNA computing frameworks, including enumeration-based, non-enumerative, and parallel models. We discuss the construction concepts behind these models, along with their respective advantages and disadvantages, providing diverse options for addressing different types of computational challenges. Furthermore, we present several case studies showcasing the applications of DNA computing in fields such as cryptography, bioinformatics, and optimization problems, highlighting the extensive potential of this innovative computing approach. The strength of this book lies in its systematic, innovative, and practical approach. The systematic nature is evident in the comprehensive organization and integration of knowledge pertaining to DNA computing, further enhanced by an in-depth introduction to RNA computing and protein computing. By covering everything from fundamental concepts to applications and effectively bridging theory with practice, it establishes a robust knowledge framework. The innovative aspect is showcased through a timely exploration of the latest advancements in DNA computing research, including a thorough analysis of pioneering technologies such as probe machines and DNA self-assembly, ensuring that readers are well-informed about current developments in the field. The practical component is emphasized through detailed descriptions of experimental procedures and clear explanations of methods for constructing computational models, offering valuable guidance for those involved in related research. In our writing methodology, we have integrated both theoretical and practical approaches. Initially, we undertook comprehensive research and systematic sum-marization of the fundamental theories and essential technologies related to DNA computing by reviewing a substantial body of literature. Building on our practical experience gained from DNA computing experiments, we then provided a detailed elaboration of the experimental procedures and key precautions to consider.