A First Course in Systems Biology

Eberhard Voit

• Publisher: Garland Science
• Year: 2017
• ISBN:
  9781351332934
• Edition: 2
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

A First Course in Systems Biology is an introduction for advanced undergraduate and graduate students to the growing field of systems biology. Its main focus is the development of computational models and their applications to diverse biological systems. The book begins with the fundamentals of modeling, then reviews features of the molecular inventories that bring biological systems to life and discusses case studies that represent some of the frontiers in systems biology and synthetic biology. In this way, it provides the reader with a comprehensive background and access to methods for executing standard systems biology tasks, understanding the modern literature, and launching into specialized courses or projects that address biological questions using theoretical and computational means.

New topics in this edition include: default modules for model design, limit cycles and chaos, parameter estimation in Excel, model representations of gene regulation through transcription factors, derivation of the Michaelis-Menten rate law from the original conceptual model, different types of inhibition, hysteresis, a model of differentiation, system adaptation to persistent signals, nonlinear nullclines, PBPK models, and elementary modes.

The format is a combination of instructional text and references to primary literature, complemented by sets of small-scale exercises that enable hands-on experience, and large-scale, often open-ended questions for further reflection.

Biological Systems

1

When you have read this chapter, you should be able to:

• Describe the generic features of biological systems

• Explain the goals of systems biology

• Identify the complementary roles of reductionism and systems biology

• List those challenges of systems biology that cannot be solved with intuition alone

• Assemble a “to-do” list for the field of systems biology

When we think of biological systems, our minds may immediately wander to the Amazon rainforest, brimming with thousands of plants and animals that live with each other, compete with each other, and depend on each other. We might think of the incredible expanse of the world’s oceans, of colorful fish swimming through coral reefs, nibbling on algae. Two-meter-high African termite mounds may come to mind, with their huge colonies of individuals that have their specific roles and whose lives are controlled by an intricate social structure (Figure 1.1). We may think of an algae-covered pond with tadpoles and minnows that are about to restart yet another life cycle.

These examples are indeed beautiful manifestations of some of the fascinating systems nature has evolved. However, we don’t have to look that far to find biological systems. Much, much smaller systems are in our own bodies and even within our cells. Kidneys are waste-disposal systems. Mitochondria are energy-production systems. Ribosomes are intracellular machines that make proteins from amino acids. Bacteria are amazingly complicated biological systems. Viruses interact with cells in a well-controlled, systemic way. Even seemingly modest tasks often involve an amazingly large number of processes that form complicated control systems (Figure 1.2). The more we learn about the most basic processes of life, such as cell division or the production of a metabolite, the more we have to marvel the incredible complexity of the systems that facilitate these processes. In our daily lives, we usually take these systems for granted and assume that they function adequately, and it is only when, for example, disease strikes or algal blooms kill fish that we realize how complex biology really is and how damaging the failure of just a single component can be.

We and our ancestors have been aware of biological systems since the beginning of human existence. Human birth, development, health, disease, and death have long been recognized as interwoven with those of plants and animals, and with the environment. For our forebears, securing food required an understanding of seasonal changes in the ecological systems of their surroundings. Even the earliest forays into agriculture depended on detailed concepts and ideas of when and what to plant, how and where to plant it, how many seeds to eat or to save for sowing, and when to expect returns on the investment. Several thousand years ago, the Egyptians managed to ferment sugars to alcohol and used the mash to bake bread. Early pharmaceutical treatments of diseases certainly contained a good dose of superstition, and we are no longer convinced that rubbing on the spit of a toad during full moon will cure warts, but the beginnings of pharmaceutical science in antiquity and the Middle Ages also demonstrate a growing recognition that particular plant products can have significant and specific effects on the well-being or malfunctioning of the systems within the human body.

Figure 1.1 Biological systems abound at all size scales. Here, a termite mound in Namibia is visible evidence of a complex social system. This system is part of a larger ecological system, and it is at once the host to many systems at smaller scales. (Courtesy of Lothar Herzog under the Creative Commons Attribution 2.0 Generic license.)

In spite of our long history of dealing with biological systems, our mastery of engineered systems far outstrips our capability to manipulate biological systems. We send spaceships successfully to faraway places and predict correctly when they will arrive and where they will land. We build skyscrapers exceeding by hundreds of times the sizes of the biggest animals and plants. Our airplanes are faster, bigger, and more robust against turbulence than the most skillful birds. Yet, we cannot create new human cells or tissues from basic building blocks and we are seldom able to cure diseases except with rather primitive methods like cutting into the body or killing a lot of healthy tissue in the process, hoping that the body will heal itself afterwards. We can anticipate that our grandchildren will only shake their heads at such medieval-sounding, draconian measures. We have learned to create improved microorganisms, for instance for the bulk production of industrial alcohol or the generation of pure amino acids, but the methods for doing so rely on bacterial machinery that we do not fully understand and on artificially induced random mutations rather than targeted design strategies.

Figure 1.2 Diagram of a complicated system of molecules that coordinate the response of plants to drought. While the details are not important here, we can see that a key hormone, called abscisic acid (ABA), triggers a cascade of reactions that ultimately promote the closure of stomata and thereby reduce water evaporation [1]. Even a narrowly defined response like this closure process involves a complicated control system that contains a multitude of molecules and their interactions. In turn, this system is just one component within a much larger, physiological stress response system (cf. Figure 1.7). (From Saadatpour A, Albert I & Albert A. J. Theor. Biol. 266 [2010] 641–656. With permission from Elsevier.)

Before we discuss the roots of the many challenges associated with understanding and manipulating biological systems in a targeted fashion, and our problems predicting what biological systems will do under yet-untested conditions, we should ask whether the goal of a deeper understanding of biological systems is even worth the effort. The answer is a resounding “Yes!” In fact, it is impossible even to imagine the potential and scope of advances that might develop from biological systems analyses. Just as nobody during the eighteenth century could foresee the ramifications of the Industrial Revolution or of electricity, the Biological Revolution will usher in an entirely new world with incredible possibilities. Applications that are already emerging on the horizon are personalized medical treatments with minimal side effects, pills that will let the body regain control over a tumor that has run amok, prevention and treatment of neurodegenerative diseases, and the creation of spare organs from reprogrammed stem cells. A better understanding of ecological systems will yield pest- and drought-resistant food sources, as well as means for restoring polluted soil and water. It will help us understand why certain species are threatened and what could be done effectively to counteract their decline. Deeper insights into aquatic systems will lead to cleaner water and sustainable fisheries. Reprogrammed microbes or nonliving systems composed of biological components will dominate the production of chemical compounds from prescription drugs to large-scale industrial organics, and might create energy sources without equal. Modified viruses will become standard means for supplying cells with healthy proteins or replacement genes. The rewards of discovering and characterizing the general principles and the specifics of biological systems will truly be unlimited.

If it is possible to engineer very sophisticated machines and to predict exactly what they will do, why are biological systems so different and difficult? One crucial difference is that we have full control over engineered systems, but not over biological systems. As a society, we collectively know all details of all parts of engineered machines, because we made them. We know their properties and functions, and we can explain how and why some engineer put a machine together in a particular fashion. Furthermore, most engineered systems are modular, with each module being designed for a unique, specific task. While these modules interact with each other, they seldom have multiple roles in different parts of the system, in contrast to biology and medicine, where, for instance, the same lipids can be components of membranes and have complicated signaling functions, and where diseases are often not restricted to a single organ or tissue, but may affect the immune system and lead to changes in blood pressure and blood chemistry that secondarily cause kidney and heart problems. A chemical refinery looks overwhelmingly complicated to a layperson, but for an industrial engineer, every piece has a specific, well-defined role within the refinery, and every piece or module has properties that were optimized for this role. Moreover, should something go wrong, the machines and factories will have been equipped with sensors and warning signals pinpointing problems as soon as they arise and allowing corrective action.

In contrast to dealing with sophisticated, well-characterized engineered systems, the analysis of biological systems requires investigations in the opposite direction. This type of investigation resembles the task of looking at an unknown machine and predicting what it does (Figure 1.3). Adding to this challenge, all scientists collectively know only a fraction of the components of biological systems, and the specific roles and interactions between these components are often obscure and change over time. Even more than engineered systems, biological systems are full of sensors and signals that indicate smooth running or ensuing problems, but in most cases our experiments cannot directly perceive and measure these signals and we can only indirectly deduce their existence and function. We observe organisms, cells, or intracellular structures as if from a large distance and must deduce from rather coarse observations how they might function or why they fail.

Figure 1.3 Analyzing a biological system resembles the task of determining the function of a complicated machine that we have never seen before. Shown here as an example is the cesium fountain laser table of the United States Naval Observatory, which is used to measure time with extreme accuracy. This atomic clock is based on transitions in cesium, which have a frequency of 9,192,631,770 Hz and are used to define the second. See also [2].

What exactly is it that makes biological systems so difficult to grasp? It is certainly not just size. Figure 1.4 shows two networks. One shows the vast highway system of the continental United States, which covers several million miles of major highways. It is a very large system, but it is not difficult to understand its function or malfunction: if a highway is blocked, it does not take much ingenuity to figure out how to circumvent the obstacle. The other network is a comparably tiny system: the web of a diadem spider. While we can observe the process and pattern with which Ms. Spider spins her web, we do not know which neurons in her brain are responsible for different phases of the complicated web production process and how she is able to produce the right chemicals for the spider silk, which in itself is a marvel of material science, let alone how she manages to survive, multiply, and maybe even devour her husband.

Figure 1.4 The size of a network or system is not necessarily correlated with its complexity. (A) The network of major highways in the continental United States covers over 3 million square miles. Nonetheless, its functionality is easy to grasp, and problems with a particular road are readily ameliorated with detours. (B) The web of the European diadem spider (Araneus diadematus) (C) is comparatively small, but the functional details of this little network are complex. Some lines are made of silk proteins that have the tensile strength of steel but can also be eaten and recycled by the spider; other lines are adhesive due to a multipurpose glue that may be sticky or rubbery depending on the situation; yet others are guide and signa...