An understanding of biological systems at cellular and molecular levels helps researchers to model cellular behavior in different experimental conditions. This, in turn, can lead to insights about the influence of cell culture environment and the effect of knockout gene research when studying mutations that affect specific metabolic pathways. A systems biology approach, therefore, allows researchers to simulate experimental observations in order to predict outcomes at the cellular level. Fundamentals of Systems Analysis and Modeling of Biosystems and Metabolism presents the basic concepts required for a systems biology approach towards cellular modeling. The book is intended as a primer for systems biology and biomedical engineering graduates and researchers. The text introduces readers to concepts related to cellular metabolism and its regulation, (enzymatic regulation and transcriptional regulation) which are also incorporated into a main metabolic model of a cell. The book also has chapters dedicated to identifying and incorporating steady-state and dynamic characteristics when considering a biological model for a computer simulation. Readers will be able to (1) understand the basis of systems analysis towards creating appropriate biological models and simulations, (2) develop useful kinetic models based on cellular transport phenomena and metabolic regulation, (3) understand how to simulate a cell growth phenotype, and analyze it with experimental data.

The global warming problem is becoming critical year by year, causing climate disaster all over the world, where it has been believed that the CO2 gas emitted from the factories and the burning of fossil fuels may be one of the reasons of global warming. Moreover, the global stock of fossil fuels is limited, and may run out soon within several tens of years. Although wind, geo-thermal, and tide energies have been considered as clean energy sources, those depend on the land or sea locations and subject to the climate change. Biofuel and biochemical production from renewable bio-resources has thus been paid recent attention from environmental protection and energy production points of view, where the current chemical and energy producing plants can be also utilized with slight modification. The so-called 1st generation biofuels have been produced from corn starch and sugarcane in particular in USA and Brazil. However, this causes the problem of the so-called "food and energy issues" as the production scale increases. The 2nd generation biofuel production from lingo-cellulosic biomass or wastes has thus been paid recent attention. However, it requires energy intensive pretreatment for the degradation of lingo-cellulosic biomass, and the fermentation is slow due to low growth rate, and thus the productivity of biofuels and bio-chemicals is low. The 3rd generation biofuel production from photosynthetic organisms such as cyanobacteria and algae has been also paid attention, because such organisms can grow with only sun light and CO2 in the air, but the cell growth rate and thus the productivity of the fuels is significantly low. The main part or core of such production processes is the fermentation by micro-organisms. In particular, it is critical to properly understand the cell metabolism followed by the efficient metabolic engineering. The book gives comprehensive explanation of the cell metabolism and the metabolic regulation mechanisms of a variety of micro-organisms. Then the efficient metabolic engineering approaches are explained to properly design the microbial cell factories for the efficient cell growth and biofuel and biochemical production.

A comprehensive overview of the topic, highlighting recent developments, ongoing research trends and future directions. Experts from Europe, Asia and the US cover five core areas of imminent importance to the food, feed, pharmaceutical and water treatment industries in terms of sustainable and innovative processing and production. In the field of enzyme engineering, they summarize historic developments and provide an overview of molecular enzyme engineering, while also discussing key principles of microbial process engineering, including chapters on process development and control. Further sections deal with animal and plant cell culture engineering. The final section of the book deals with environmental topics and highlights the application of bioengineering principles in waste treatment and the recovery of valuable resources. With its cutting-edge visions, extensive discussions and unique perspectives, this is a ready reference for biotechnologists, bioengineers, bioengineers, biotechnological institutes, and environmental chemists.

Drawing on the latest research in the field, Systems Biology: Mathematical Modeling and Model Analysis presents many methods for modeling and analyzing biological systems, in particular cellular systems. It shows how to use predictive mathematical models to acquire and analyze knowledge about cellular systems. It also explores how the models are systematically applied in biotechnology. The first part of the book introduces biological basics, such as metabolism, signaling, gene expression, and control as well as mathematical modeling fundamentals, including deterministic models and thermodynamics. The text also discusses linear regression methods, explains the differences between linear and nonlinear regression, and illustrates how to determine input variables to improve estimation accuracy during experimental design. The second part covers intracellular processes, including enzymatic reactions, polymerization processes, and signal transduction. The author highlights the process–function–behavior sequence in cells and shows how modeling and analysis of signal transduction units play a mediating role between process and function. The third part presents theoretical methods that address the dynamics of subsystems and the behavior near a steady state. It covers techniques for determining different time scales, sensitivity analysis, structural kinetic modeling, and theoretical control engineering aspects, including a method for robust control. It also explores frequent patterns (motifs) in biochemical networks, such as the feed-forward loop in the transcriptional network of E. coli. Moving on to models that describe a large number of individual reactions, the last part looks at how these cellular models are used in biotechnology. The book also explains how graphs can illustrate the link between two components in large networks with several interactions.

Modeling is fast becoming fundamental to understanding the processes that define biological systems. High-throughput technologies are producing increasing quantities of data that require an ever-expanding toolset for their effective analysis and interpretation. Analysis of high-throughput data in the context of a molecular interaction network is particularly informative as it has the potential to reveal the most relevant network modules with respect to a phenotype or biological process of interest.Analysis of Biological Systems collects classical material on analysis, modeling and simulation, thereby acting as a unique point of reference. The joint application of statistical techniques to extract knowledge from big data and map it into mechanistic models is a current challenge of the field, and the reader will learn how to build and use models even if they have no computing or math background. An in-depth analysis of the currently available technologies, and a comparison between them, is also included. Unlike other reference books, this in-depth analysis is extended even to the field of language-based modeling. The overall result is an indispensable, self-contained and systematic approach to a rapidly expanding field of science.

Systems biology is defined as the mathematical and computational modeling of biological systems. It is also integral to the field of bioinformatics. The concepts of systems biology are used across several fields of study such as genomics, phonemics, proteomics, etc. The field also involves the study of metabolic and cell signaling networks to understand the properties and functions of cells, tissues, etc. in living organisms. This book elucidates the concepts and innovative models around prospective developments with respect to systems biology. The topics included in this book are of utmost significance and bound to provide incredible insights to the readers. With its detailed analyses and data, this book will prove immensely beneficial to professionals and students involved in this area at various levels.

I Principles 1 1 Models of Systems 3 1. 1 Systems. Models. and Modeling . . . . . . . . . . . . . . . . . . . . 3 1. 2 Uses of Scientific Models . . . . . . . . . . . . . . . . . . . . . . . . 4 1. 3 Example: Island Biogeography . . . . . . . . . . . . . . . . . . . . . 6 1. 4 Classifications of Models . . . . . . . . . . . . . . . . . . . . . . . . 10 1. 5 Constraints on Model Structure . . . . . . . . . . . . . . . . . . . . . 12 1. 6 Some Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1. 7 Misuses of Models: The Dark Side . . . . . . . . . . . . . . . . . . . 13 1. 8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2 The Modeling Process 17 2. 1 Models Are Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2. 2 Two Alternative Approaches . . . . . . . . . . . . . . . . . . . . . . 18 2. 3 An Example: Population Doubling Time . . . . . . . . . . . . . . . . 24 2. 4 Model Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2. 5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3 Qualitative Model Formulation 32 3. 1 How to Eat an Elephant . . . . . . . . . . . . . . . . . . . . . . . . . 32 3. 2 Forrester Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3. 3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3. 4 Errors in Forrester Diagrams . . . . . . . . . . . . . . . . . . . . . . 44 3. 5 Advantages and Disadvantages of Forrester Diagrams . . . . . . . . . 44 3. 6 Principles of Qualitative Formulation . . . . . . . . . . . . . . . . . . 45 3. 7 Model Simplification . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3. 8 Other Modeling Problems . . . . . . . . . . . . . . . . . . . . . . . . 49 viii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. 9 Exercises 53 4 Quantitative Model Formulation: I 4. 1 From Qualitative to Quantitative . . . . . . . . . . . . . . . . . Finite Difference Equations and Differential Equations 4. 2 . . . . . . . . . . . . . . . . 4. 3 Biological Feedback in Quantitative Models . . . . . . . . . . . . . . . . . . . . . . . . . . 4. 4 Example Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. 5 Exercises 5 Quantitative Model Formulation: I1 81 . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 1 Physical Processes 81 . . . . . . . . . . . . . . . 5. 2 Using the Toolbox of Biological Processes 89 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3 Useful Functions 96 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 4 Examples 102 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 5 Exercises 104 6 Numerical Techniques 107 . . . . . . . . . . . . . . . . . . . . . . . 6. 1 Mistakes Computers Make 107 . . . . . . . . . . . . . . . . . . . . . . . . . . 6. 2 Numerical Integration 110 . . . . . . . . . . . . . . . . 6. 3 Numerical Instability and Stiff Equations 115 . . . . . . . . . . . . . .