Discusses multi-objective optimization from an engineer’s perspective Contains the theoretic design methods of multi-objective optimization schemes Includes a wide spectrum of recent research topics in control design, especially for stochastic mean field stochastic diffusion problems Covers practical applications in each chapter, like missile guidance design, economic and financial system, power control tracking, and minimization design in communication Explores practical multi-objective optimization design examples in control, signal processing, communication, and cyber-financial systems

Systems Evolutionary Biology: Biological Network Evolution Theory, Stochastic Evolutionary Game Strategies, and Applications to Systems Synthetic Biology discusses the evolutionary game theory and strategies of nonlinear stochastic biological networks under random genetic variations and environmental disturbances and their application to systematic synthetic biology design. The book provides more realistic stochastic biological system models to mimic the real biological systems in evolutionary process and then introduces network evolvability, stochastic evolutionary game theory and strategy based on nonlinear stochastic networks in evolution. Readers will find remarkable, revolutionary information on genetic evolutionary biology that be applied to economics, engineering and bioscience.

Considers both time-domain and frequency domain robust design techniques of partial differential systems Illustrates both theoretical robust design techniques and practical applications Discusses partial differential systems with both Dirichlet and Neuman boundary conditions in robust design procedure Addresses deterministic and stochastic partial differential systems Explores theoretical mathematical background, robust signal processing design, robust control system design and robust biological system design with application

Game theory involves multi-person decision making and differential dynamic game theory has been widely applied to n-person decision making problems, which are stimulated by a vast number of applications. This book addresses the gap to discuss general stochastic n-person noncooperative and cooperative game theory with wide applications to control systems, signal processing systems, communication systems, managements, financial systems, and biological systems. H8 game strategy, n-person cooperative and noncooperative game strategy are discussed for linear and nonlinear stochastic systems along with some computational algorithms developed to efficiently solve these game strategies.

The H control has been one of the important robust control approaches since the 1980s. This book extends the area to nonlinear stochastic H2/H control, and studies more complex and practically useful mixed H2/H controller synthesis rather than the pure H control. Different from the commonly used convex optimization method, this book applies the Nash game approach to give necessary and sufficient conditions for the existence and uniqueness of the mixed H2/H control. Researchers will benefit from our detailed exposition of the stochastic mixed H2/H control theory, while practitioners can apply our efficient algorithms to address their practical problems.

Game theory involves multi-person decision making and differential dynamic game theory has been widely applied to n-person decision making problems, which are stimulated by a vast number of applications. This book addresses the gap to discuss general stochastic n-person noncooperative and cooperative game theory with wide applications to control systems, signal processing systems, communication systems, managements, financial systems, and biological systems. H game strategy, n-person cooperative and noncooperative game strategy are discussed for linear and nonlinear stochastic systems along with some computational algorithms developed to efficiently solve these game strategies.

The H control has been one of the important robust control approaches since the 1980s. This book extends the area to nonlinear stochastic H2/H control, and studies more complex and practically useful mixed H2/H controller synthesis rather than the pure H control. Different from the commonly used convex optimization method, this book applies the Nash game approach to give necessary and sufficient conditions for the existence and uniqueness of the mixed H2/H control. Researchers will benefit from our detailed exposition of the stochastic mixed H2/H control theory, while practitioners can apply our efficient algorithms to address their practical problems.

Systems Immunology and Infection Microbiology provides a large amount of biological system models, diagrams and flowcharts to illustrate development procedures and help users understand the results of systems immunology and infection microbiology. Chapters discuss systems immunology, systems infection microbiology, systematic inflammation and immune responses in restoration and regeneration process, systems' innate and adaptive immunity in infection process, systematic genetic and epigenetic pathogenic/defensive mechanism during bacterial infection on human cells is introduced, and the systematic genetic and epigenetic pathogenic/defensive mechanisms during viral infection on human cells. This book provides new big data-driven and systems-driven systems immunology and infection microbiology to researchers applying systems biology and bioinformatics in their work. It is also invaluable to several members of biomedical field who are interested in learning more about those approaches.

H Robust design is an advancing technology which aims to achieve the system design purpose under intrinsic random fluctuation and external disturbance. This book introduces several robust design methods, some of which include linear to nonlinear systems and frequency to time domain. This book provides not only a complete theoretical development and application of H robust design over the last three decades, but also an integrated platform for control, signal processing, communication, systems and synthetic biology. Based on the theoretical H robust design results, the authors also give some practical design examples to illustrate the procedure and validate the performance of the proposed H method with computational simulations and tables.

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.

Systems biology sits at the heart of new integrative paradigm in the 21st century, the book could gain an insight into (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signalling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviours, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This book not only describes the current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers but also discusses challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

This book develops a rational design and systematic approach to construct a gene network with desired behaviors. In order to achieve this goal, the registry of standard biological parts and experimental techniques are introduced at first. Then these biological components are characterized by a standard modeling method and collected in the component libraries, which can be efficiently reused in engineering synthetic gene networks. Based on the system theory, some design specifications are provided to engineer the synthetic gene networks to robustly track the desired trajectory by employing the component libraries.