Robustness and Evolvability in Living Systems: 15 (Princeton Studies in Complexity) - Softcover

Wagner, Andreas

Softcover

ISBN 10: 0691134049 ISBN 13: 9780691134048

Verlag: Princeton University Press, 2007

Inhaltsangabe

All living things are remarkably complex, yet their DNA is unstable, undergoing countless random mutations over generations. Despite this instability, most animals do not grow two heads or die, plants continue to thrive, and bacteria continue to divide. Robustness and Evolvability in Living Systems tackles this perplexing paradox. The book explores why genetic changes do not cause organisms to fail catastrophically and how evolution shapes organisms' robustness. Andreas Wagner looks at this problem from the ground up, starting with the alphabet of DNA, the genetic code, RNA, and protein molecules, moving on to genetic networks and embryonic development, and working his way up to whole organisms. He then develops an evolutionary explanation for robustness.


Wagner shows how evolution by natural selection preferentially finds and favors robust solutions to the problems organisms face in surviving and reproducing. Such robustness, he argues, also enhances the potential for future evolutionary innovation. Wagner also argues that robustness has less to do with organisms having plenty of spare parts (the redundancy theory that has been popular) and more to do with the reality that mutations can change organisms in ways that do not substantively affect their fitness.


Unparalleled in its field, this book offers the most detailed analysis available of all facets of robustness within organisms. It will appeal not only to biologists but also to engineers interested in the design of robust systems and to social scientists concerned with robustness in human communities and populations.

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Andreas Wagner is professor of biochemistry at the University of Zurich. He studies the evolution of biological systems on all levels of organismal organization, from genes and genomes to gene networks and embryonic development

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"This is a timely book that should appeal to biologists, engineers, and applied mathematicians."--David C. Krakauer, Santa Fe Institute

"This is a major contribution, addressing what are perhaps the central questions in the subject of complex adaptive systems: What makes systems robust and how does selection at different levels of organization act to shape robustness? It is a well-written, well-organized, provocative piece of scholarly work that will be widely read and debated."--Simon Levin, Princeton University

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Robustness and Evolvability in Living Systems

By Andreas Wagner

Princeton University Press

Copyright © 2005 Princeton University Press
All right reserved.
ISBN: 978-0-691-13404-8

Chapter One

INTRODUCTION

Living things are unimaginably complex, yet they have withstood a withering assault of harmful influences over several billion years. These influences include cataclysmic changes in the environment, as well as a constant barrage of internal mutations. And not only has life survived, it has thrived and radiated into millions of diverse species. Such resilience may be surprising, because complexity suggests fragility. If you have ever built a house of cards, you will know what I mean: The house eventually comes tumbling down. Why is an organism not a molecular house of cards? Why do not slight disturbances (especially genetic disturbances in the form of mutations) cause key organismal functions to fail catastrophically? And is the robustness of organisms to change itself a consequence of past evolution? How does it affect evolvability, the potential for future evolution? These are some of the key questions I will address here.

A biological system is robust if it continues to function in the face of perturbations. This is the working definition of robustness I use in this book. The perturbations can be genetic, that is, mutations, or nongenetic, for example, environmental change. A variety of other names-buffering, canalization, developmental stability, efficiency, homeorhesis, tolerance, etc. (171, 183, 186, 368, 472, 499, 578)-have been used for the same phenomenon, and my choice of one among them is arbitrary. The above definition implies that one can sensibly discuss robustness only if one has clarity about two cardinal questions: What feature of a living thing is robust? And what kind of change is this feature robust to?

With respect to the first of these cardinal questions, it is clear that ultimately robustness of only one organismal feature matters: fitness-the ability to survive and reproduce. However, fitness is hard to define rigorously and even more difficult to measure. In addition, a change in fitness can have many different causes. For instance, a mutation that blocks a chemical reaction in metabolism affects fitness for different reasons than a mutation blocking embryonic development. An examination of fitness and its robustness alone would thus not yield much insight into the opening questions. Instead, it is necessary to analyze, on all levels of organization, the systems that constitute an organism, and that sustain its life. I define such systems loosely as assemblies of parts that carry out well-defined biological functions. Examples include DNA with its nucleotide parts, proteins with their amino acids, metabolic pathways and their enzymes, genetic networks and their genes, and developing organs or embryos with their interacting cells. A good part of this book surveys what we know about the robustness of biological systems on multiple levels of biological organization.

With respect to the second cardinal question, what are organisms robust to, this book has a restricted scope: It focuses on robustness to genetic change. I will call this kind of robustness genetic robustness or mutational robustness. This focus has three motivations. First, genetic change has more serious consequences than nongenetic change. A genetic change is a permanent alteration in the "wiring" of a biological system, and its effects, or lack thereof, thus deserve special attention. Second, by and large, mostly genetic change is heritable, and thus has much more serious long-term consequences on organismal lineage than nongenetic change. Thirdly, a comprehensive account of robustness against nongenetic change would be daunting. For instance, an exhaustive treatment of robustness to environmental change would have to include just about all homeostatic phenomena in biology. These phenomena include the regulation of osmotic balance, metabolite concentrations, and gene expression, thermo-regulation in endotherm organisms, flight stabilization in birds, and on and on. The literature on many of these phenomena is already large and needs no further addition. Robustness to mutations, on the other hand, has not been as comprehensively studied. In addition, it is a well-defined phenomenon where a search for general principles that unify observations on different levels of organization is easier. I will propose some such principles here. All this is, of course, not to say that robustness to nongenetic change is unimportant. In fact, it is associated with mutational robustness and may be very important for the evolution of such robustness, as I argue in chapter 17.

Why Study Robustness?

The first and most important reason to study robustness is already stated in the opening paragraph: Why can unimaginably complex systems withstand so much change? As we shall see, biological systems are indeed robust on all levels of organization. Proteins can tolerate thousands of amino acid changes, metabolic networks continue to sustain life even after removal of important chemical reactions, gene regulation networks continue to function after alteration of key gene interactions, and radical transformations in embryonic development can lead to an essentially unchanged adult organism.

A second reason to study robustness (an evolutionary biologist's reason) derives from the fact that evolution by natural selection requires variation among organisms that reflects genetic variation. Genetic variation is abundant in most species, yet how it translates into phenotypic variation is still unknown. In the second part of the 20th century, a debate about precisely this question dominated evolutionary biology. This debate focused on the role and abundance of neutral mutations, mutations that do not affect the function of a biological system. The more neutral mutations a biological system allows, the greater is its mutational robustness, and mutational robustness thus has an important role to play in this debate. Mutational robustness influences the extent to which genetic variation, the result of past mutations, is translated into phenotypic variation. Even more importantly, if mutational robustness itself is subject to evolutionary change, then the ability to evolve by natural selection evolves, and thus evolvability evolves. For this and other reasons, neutral mutations will play a central role in this book. I will argue that they may play a very important role in promoting evolutionary innovation.

The third reason to study robustness regards engineering principles of robust systems. Is robustness in the living fundamentally similar and different from robustness in engineered systems? Can human engineers learn from robustness in the living? Only an engineer could be the judge, but the many examples scattered throughout the book may help in making this judgment. Although the book is primarily directed toward biological systems, I devote one short chapter to robustness in engineered systems.

How to Study Robustness

Empirical evidence for robustness comes in two different forms. First, one can perturb a part of an organism (a protein), a trait (wing shape), or a capability (amino acid biosynthesis) through mutations. The less the feature's properties change in the face of perturbation, the more robust it is. The second type of evidence relies on naturally occurring perturbations, mutations that occurred in evolutionary history. That is, one can compare closely related species that have the same trait or capability, and examine whether they achieve it by different means. If so, this indicates robustness, because not only can the same feature be designed in different ways, these different ways originated in a recent common ancestor and are thus reachable from each other by mutation or recombination. As with most applications of the comparative method, the results of this second approach are more tentative than the results of systematic perturbations.

Neither kind of evidence is easy to produce. Many biological systems, from macromolecules to genetic networks, have large numbers of parts that can occur in many configurations. To assess their robustness systematically requires many perturbations and subsequent measurements of system properties. For instance, to explore only a few variants at each amino acid positions of a protein, one needs to generate thousands of mutant proteins and measure their activity. The evolutionary approach to study robustness suffers from a related problem. First, to compare different organisms is to analyze only a few end products of many possible paths evolution could have taken. Second, sometimes even that is infeasible. There are preciously few well-studied organisms for which any one biological process above the gene level is well characterized, because such characterization is time-consuming. For instance, it took thousands of man-years to elucidate the structure of the genetic network responsible for segmenting a fruit fly's body. It would be prohibitive to analyze the same network in many related species to determine how much its structure has changed while leaving its function intact.

In sum, the experimental evidence to assess robustness in biological systems is hard to come by. The problem is partly alleviated by modeling of such systems, using both analytical and computational methods. Quantitative models that are based on experimental information can provide accurate predictions about a system's robustness, even when systematic perturbations or evolutionary comparisons are difficult. Many of the case studies below involve a tight integration between experimental evidence and quantitative modeling. Some of the most intriguing questions, such as whether robustness itself can evolve, have been mostly addressed with computational models. The heavy reliance on modeling to understand biological robustness may change as more experimental data accumulates. However, because of the many difficulties of providing such data, quantitative modeling will always play an important role in understanding the robustness of biological systems.

An Emphasis on Mechanism

One can analyze biological systems, their robustness, and its evolution from two very different perspectives. The first of them, exemplified by biochemistry and molecular biology, emphasizes mechanistic understanding, dissection of systems and their parts. Most of this book emphasizes this mechanistic perspective. A second approach is represented by population genetics and, even more so, by quantitative genetics. These disciplines emphasize the statistical effects of genes on fitness rather than the roles of genes in a molecular machinery. Both disciplines provide important perspectives complementary to those of molecular biology (55, 104, 141, 185, 186, 228, 244, 274, 305, 404, 415, 448, 462, 472, 499, 519, 520, 562, 579, 582, 585, 591, 592, 610). Population genetics, for example, identifies the conditions-selection pressures, mutation rates, population sizes, etc.-under which robustness can evolve, which is completely outside the scope of molecular biology. I have included general population genetic insights into the evolution of robustness. Nonetheless, the book contains comparatively little material from population genetics and next to none from quantitative genetics. The main reason is the following.

Population genetics and quantitative genetics have been very successful partly because they have eliminated the mechanistic details of biological systems from their thinking. However, the elimination of such detail and the resulting phenomenological perspective on organisms come at a price: Evolutionary explanations built on a statistical understanding of gene effects may be difficult to interpret. Take a recent example from a growing literature on how to measure robustness with quantitative genetic methods (244). Suppose you had found that during the evolution of an organismal lineage B from some ancestral lineage A, the mutational robustness of some trait, say the length of a fly's wing, has apparently increased. That is, the trait shows less change in response to the same amount of "mutation pressure" in lineage B than in lineage A. Houle pointed out that such apparent differences in robustness among lineages and traits could be caused by differences in the genome target size of these traits (244). The genome target size is the number of genes contributing to a trait. In other words, a trait's robustness may appear increased merely because the number of genes contributing to it decreased. To estimate this genome target size with the methods of quantitative genetics is difficult, partly because many genes with very subtle statistical effects contribute to most traits. Because quantitative genetics has not yet resolved such fundamental problems, I chose to focus here on systems whose inner workings are understood to some extent.

Principles of Robustness

This book could not have been written 15 years ago, because much of the mechanistic information I emphasize here has accumulated only recently. One consequence of this fact is that this field of research is not mature. It is rife with open questions, yes, dominated by open questions, questions that define entire research programs in systems biology. (I summarize some of these questions in the short epilogue.) This observation points to two motivations to write this book now. First, a survey of our knowledge brings our ignorance into sharp relief. Second, the available pieces of the puzzle enable us to see the outline of the whole, and allow us to make some general statements about it. Beyond the presentation of the evidence, you will thus find many informed guesses at the shape of the whole here. Whether or not there will be a unified theory of robustness in biological systems, some unifying principles will emerge once this field has reached maturity. Here is a brief summary of a few such principles (my credo, if you will), principles that later chapters elaborate in much greater detail and with concrete examples.

Most problems the living have solved have an astronomical number of equivalent solutions, which can be thought of as existing in a vast neutral space (chapter 13). A neutral space is a collection of equivalent solutions to the same biological problem. Such solutions are embodied in biological systems that ensure an organism's survival and reproduction. Both direct perturbation studies and indirect comparative studies support the notion that problems with many solutions are the rule rather than the exception. This holds on multiple levels of biological organization. We see it, for example, in the structure of important macromolecules such as proteins and RNA, where there are astronomically many different ways to build a molecule with a given structure and function. We see it also in the architecture of transcriptional regulatory regions, which can change drastically in evolution without any change in function. We see it in the structure of metabolic and genetic networks, where large changes in network structure can have negligible effects on network function in any one environment. We even see hints of it at the highest level of organismal organization, where radically different pathways of embryonic development may lead to essentially unchanged adult organisms.

Biological systems are mutationally robust for two reasons. First, robust systems are easier to find in the blindly groping search of biological evolution, simply because of the large neutral space associated with them (chapter 13). In other words, robust systems are systems with a large associated neutral space of equivalent solutions to a given problem. Such systems are easiest to discover in evolution, because they represent a large proportion of all possible solution. Their robustness results from the structure of neutral spaces itself, and may be independent of the particular circumstances under which an organism or the system evolved, such as population sizes or mutation rates. Second, natural selection can further increase robustness by incremental evolution of a system within a neutral space (chapters 13, 16, 17). Neutral spaces are not homogeneous. We know this from studies of the neutral spaces associated with the structure of biological macromolecules, and to a more limited extent from studies of genetic networks and the genetic code. This means that neutral spaces often have regions characterized by greater robustness, where mutations are less likely to change a system's structure or function, and regions of lesser robustness. Regions of lesser robustness are more sparsely populated with systems that perform a given function. Evolution by natural selection can drive an evolving population toward regions of a neutral space with high robustness.

(Continues...)

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Details zur Ausgabe

• Verlag: Princeton University Press
• Erscheinungsdatum: 2007
• Sprache: Englisch
• ISBN 10: 0691134049
• ISBN 13: 9780691134048
• Einband: Tapa blanda
• Anzahl der Seiten: 384
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: Nicht verfügbar