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This edited volume provides insights into and tools for the modeling, analysis, optimization, and control of large-scale networks in the life sciences and in engineering. Large-scale systems are often the result of networked interactions between a large number of subsystems, and their analysis and control are becoming increasingly important. The chapters of this book present the basic concepts and theoretical foundations of network theory and discuss its applications in different scientific areas such as biochemical reactions, chemical production processes, systems biology, electrical circuits, and mobile agents. The aim is to identify common concepts, to understand the underlying mathematical ideas, and to inspire discussions across the borders of the various disciplines.  The book originates from the interdisciplinary summer school “Large Scale Networks in Engineering and Life Sciences” hosted by the International Max Planck Research School Magdeburg, September 26-30, 2011, and will therefore be of interest to mathematicians, engineers, physicists, biologists, chemists, and anyone involved in the network sciences. In particular, due to their introductory nature the chapters can serve individually or as a whole as the basis of graduate courses and seminars, future summer schools, or as reference material for practitioners in the network sciences.  .

Large scale systems --- Differential equations --- Mathematical models. --- 517.91 Differential equations --- Systems, Large scale --- Engineering systems --- System analysis --- Mathematics. --- Differential Equations. --- Computer science. --- Applications of Mathematics. --- Ordinary Differential Equations. --- Computational Science and Engineering. --- Informatics --- Science --- Math --- Applied mathematics. --- Engineering mathematics. --- Differential equations. --- Computer mathematics. --- Computer mathematics --- Electronic data processing --- Mathematics --- Engineering --- Engineering analysis --- Mathematical analysis --- Data processing.

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Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. Over these 30 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today's applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-optimal performance. Consequently, the application of linear control methods does not always lead to satisfactory performance, and here nonlinear methods must be employed. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years,with a number of recent advances on both the theoretical and application frontier. Additionally, the widespread availability and steadily increasing power of today's computers, as well as the development of specially tailored numerical solution methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.

Applied physical engineering --- Engineering sciences. Technology --- procesautomatisering --- systeemtheorie --- systeembeheer --- regeltechniek

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Determination of bioreactor operating policies for optimal performance of a bioprocess is a challenging task due to the highly variable nature of biological systems and our limited process knowledge. To address this challenge, model-based optimization and control approaches can be implemented for conducting in silico experiments to derive optimal control strategies for improved performance of bioprocesses.For reliable performance of model-based optimization and control, it is crucial that the underlying model provides proper levels of detail to represent the real bioprocess and to address the full metabolic versatility. In this work, we consider bioprocess optimization and control by exploiting the capabilities of dynamic metabolic-genetic network models. In particular, we consider improving bioprocess productivity through temporal manipulations of metabolism using dynamic enzyme-cost FBA model (deFBA). The dynamic nature of this model and included details on gene level allow for direct temporal manipulation of gene expression, and through a proper formulation (a bilevel problem), one can identify optimal genetic and process level manipulation strategies according to the target performance criterion (productivity). Moreover, advanced bioprocess control and optimization requires flexible and robust control strategy which guarantees the performance of the model-based approach in the presence of disturbances and existing uncertainties. To this aim, on-line adaptation schemes are integrated within our modeling approach which are suitable to control highly uncertain biological processes with fast reactions to disturbances. The adaptive approach could allow for online adaptation of the underlying model (deFBA) by estimating uncertain and variable model parameters in different stages of the process. In this direction, the developed deFBA-based approach is implemented inside a model predictive control (MPC) routine, combined with a moving horizon estimation (MHE) algorithm in order to adjust the underlying model online for different metabolic modes.Considering the case study of ethanol formation in E. coli under different growthconditions, it is shown that the proposed approach is a suitable approach to optimizeand control time-varying bioprocesses. Desired engineering objectives can be addressed by the proposed approach through temporal manipulations of the metabolism while process uncertainties can be handled efficiently using the adaptive nature of the implemented control scheme.