Systems Biology: A Textbook (2nd Revised edition)

Book Preface

Systems biology is the scientific discipline that studies thesystemicpropertiesand dynamicinteractionsina biological object, be it a cell, an organism, a virus, or an infected host, in a qualitative and quantitative manner and by combining experimental studies with mathemati­cal modeling. Scientists can describe the inner processes of stars a thousand light years away with great accuracy. But how a tiny cell under our microscope grows and divides remains puzzling in many ways. We see kids growing, people aging, plants blooming, and microbes degrading their remains. We use yeast for brewery and bakery, and doctors prescribe drugs to cure diseases. But do we understand how processes of life work?

Starting in the nineteenth century, such processes have no longer been explained by referring to special “life forces,” but by laws of physics and chemistry. By studying the structure and dynamics of living systems in finer and finer details, researchers from different disci­plines have revealed how life processes arise from the structure and functional organization of cells, how tens of thousands of biochemical components interact in orchestrated ways, and how these systems are regulated by genetic information and continuously adapted through mutations and selection. With this conceptual shift, new questions became central in biology: How does an organism’s phenotype emerge from the geno­type, as encoded in the organism’s DNA, and how is it shaped by environmental factors? Initially, such ques­tions were approached by statistics, for example, by studying what mutations are associated with specific inheritable diseases. But the task, now, is to understand the mechanistic details.

We can easily understand the effects of gene disrup­tions when gene products have simple, specificfunc­tions. However, most gene mutations have only weak or quantitative effects on physiology, and many genetic dis­eases are multifactorial. Tracing the effects of multiple mutations, of mutations affecting gene regulation, or of drugs requires a deep, quantitative, and dynamical understanding of cell physiology. In recent years, high-throughput experiments, time series experiments, and imaging techniques with high resolution have provided us with a detailed picture of the cellular machinery. We can observe how physical structures are built, main­tained, and reproduced, how the metabolic state is changing, and how signaling and regulation systems allow cells to adapt to their environment. However, to understand how all these systems act together – and how cells can work as complex, robust systems – cata­loging and understanding single-cell components is not enough. Instead, we need to capture the global dynamics between these components. This is where mathematical models come into play.

Mathematical modeling has a long, though relatively marginal, tradition in biology, and has influenced the field in many ways. Models can be used to test hypothe­ses and to yield quantitative predictions or reveal gaps or inconsistencies in previous arguments, thus helping us to improve our understanding of biochemical pro­cesses. Inspired by the ideas of cybernetics in the sixties and seventies, dynamical systems theory and control the­ory have been increasingly applied to biochemical path­ways. Thanks to powerful experimental techniques in genomics and proteomics, a wealth of biological data has accumulated and computational models of cells are now within reach. Systems biology, the discipline devoted to developing such models, uses biochemical networks as a main concept. It studies biological systems by investigating the network components and their interactions with the help of experimental high-through­put techniques and dedicated small-scale investigations and by integrating these data into networks and dynami­cal simulation models.