It has almost become a cliché. One African-American, one Asian, one Caucasian, one Hispanic, one Mid-Eastern and one Native American child are all playing cheerfully together, while advertising a new gadget or the best detergent of all time. And, of course, the group consists of three girls and three boys. The picture displays an idealized world we all want to see: peaceful, harmonic diversity. We are not just all equal before the law; if we set our mind to it, we can all get along just fine, in spite of our differences. In fact, we believe that diversity expands and enriches what we as a community can accomplish.

In nature, diversity takes on a dimension of an entirely different magnitude, and a few little Homo sapiens of different hues playing together are just that: child's play. A long-term investigation of a lake in Wisconsin identified roughly 10,000 different species of bacteria, all living together in what modern lingo calls a metapopulation or a microbiome. According to some estimates, a single liter of ocean water can contain 10 billion viruses; that is more than one virus per person in the entire world. These populations of populations can be found in essentially any place where life can thrive. They live in oceans, lakes, and rivers, below and above ground, as well as in and on plants and animals. A single gram of soil can contain enough microbial DNA to stretch almost 1,000 miles. Some microbial metapopulations form biofilms, which are thin layers on moist surfaces and in all kinds of pipes, sewage systems, and water tubing that one might find in less than sanitary dentists’ offices, and even on our teeth. All in all, it has been estimated that 5x1030 prokaryotic cells inhabit the earth – that is a 5 with 30 zeroes – and very few live in a monoculture.

Whether we are looking at the macro- or micro-world, no organism thrives in isolation, and the coexistence of many species indicates that complex communities have evolved together in a dynamic and adaptive combination of genomic and ecological processes. The human body is no exception. It serves as the host to an estimated 100 trillion microbes, a number that corresponds to roughly 10 times the total number of human cells in the body. Particularly fertile is the gut, where the density of microorganisms can reach 1 trillion cells per milliliter.

The agorá was the common gathering place and market of cities in ancient Greece. It was the center of activities, and nothing exhibited the pulse of the city better than the hustle and bustle in the agorá. If a philosopher felt compelled to share his wisdom with the world, if a vote were to be taken, or if one just needed to buy goods, the agorá was the place to be.

Combined with the all-important ancient concept of the division of labor, a common center for trading goods and thoughts is arguably one of the oldest and persistent creations of humankind. If I grow grain and my neighbor has sheep, it is quite natural to meet and barter. Another neighbor may trade wood or bricks, fine lace or expensive jewelry, and it soon makes a lot of sense to centralize the trading activities in a common market. While bartering is the most natural means of exchanging goods, the daily haggling soon becomes repetitive and tiring, and frequent traders develop guidelines and conversion factors, and eventually create a widely accepted currency.

Systems biology is the modern agorá in the intersection of many disciplines, most notably biology, chemistry, physics, math, computer science, and engineering. Scientists from different backgrounds meet because they see great value in solving “grand challenge” problems whose solutions are out of their own reach. It takes many bright minds and just the right confluence of knowledge and methods to address questions of cancer or the growing threat of microorganisms that are resistant to our best drugs. No single person or even discipline is capable of developing new agricultural products that can feed expanding populations, yet grow in impoverished soils under both drought and flood conditions. So the experts meet to discuss. Biologists pose complex problems and devise and execute experiments that shed light on the problem, engineers offer to create machines for collecting data more efficiently, mathematicians and physicists provide rigorous ways to formalize and address the problems with models that are anchored in a solid theoretical foundation, and computer scientists offer fast techniques for analyzing data and models. The practitioners of each discipline know that they alone cannot manage the entire spectrum of activities that are needed to solve the complex problems that arise time and again when we deal with biomedical and environmental systems.

On July 15, 2013, the last telegram was sent by the only remaining telegraph office in the world, the Central Telegraph Office in Janpath, India. The technology had lasted for almost two centuries, since the American artist and inventor Samuel Morse in 1837 first experimented successfully with an electrical telegraph. In 1844, he demonstrated the feasibility of his invention to the world, by sending the message “What hath God wrought” from Washington, DC to his colleague Alfred Vail in Baltimore. By doing so, he ushered in the era of the electrical and electronic information age.

The appeal of sending information across long distances is obvious and, of course, was not new even in Morse's time. The Estonian diplomat Pavel Schilling had demonstrated the feasibility of an electrical telegraph a few years earlier, and French engineers had operated light signaling systems since the late eighteenth century. Long before these inventions, Native Americans had been using smoke signals to warn their peers of impending danger.

Considering the enormous importance of knowing what's going on at a distance, it is not surprising that the transmission of information is an integral part of essentially all biological systems. The specific mechanisms of signal transduction within and between cells vary greatly in their molecular basis, transmission distance, time scale, and a number of other features. For instance, the speed of signal transduction ranges from electrical signals at the order of milliseconds to protein-based signaling cascades responding at the order of minutes and signals involving genomic and physiological responses that occur within tens of minutes, hours or even days.

In many cases, multiple signals are transmitted in parallel. For instance, the demand for a metabolite often triggers feedback signals directly at the metabolic level. These signals modify the activity of enzymes that are responsible for the synthesis of the desired metabolite. They accomplish this typically by slightly altering the physical shape of the enzyme molecules, which occurs within seconds. If this mechanism is insufficient, the availability or activity of other proteins may be changed, which takes minutes or hours. Persistent demands are signaled to the genome, where the expression of appropriate genes is up-regulated and secondarily leads to higher enzyme activities over longer time horizons that may span hours, days or weeks. Intriguingly, metabolic, protein-based and genomic alterations may all be initiated simultaneously.

“Mommy, why is the sky blue? Why do zebras have stripes? Why has Aunt Maud got hair growing out of her nose? Why are bananas crooked?” All good parents try hard to answer such questions, or at least most of them, but it is hard-core scientists who most encourage their kids to ask a lot of questions. After all, questions are a sign of curiosity, and curiosity is the ultimate, indispensable prerequisite for becoming a good scientist. Yet, as soon as the same scientists return to their labs, “why” becomes a taboo word. Why? What happens on the way to work?

Many scientists subscribe to the tenet that “why?” is not a scientific question. “How?” and “what?” are scientific questions, but “why?” is not. The famous evolutionary biologist Richard Dawkins even derided it as a “silly” question. One reason for the discrepancy is probably that children are usually satisfied with a single answer. “Bananas grow faster on one side than on the other” might just do. No need for a lecture on phototropism or auxins or other phytohormones that play a role in coordinating growth processes in plants. Not so with scientists. Every answer leads to new questions, and while many in the chain may have answers, there comes the inevitable point at which one has to admit “I don't know” or “nobody knows,” or a supernatural deity is to be evoked quasi as a deus ex machina. None of these three options is particularly desirable to scientists, and “why” is therefore a priori to be excluded from scientific discussions as a preemptive measure. Answering “why” implies that biological processes are driven by intent toward a goal; it reeks of teleology, the many-centuries-old philosophical concept that searches for the ultimate reasons of being and the purpose of human existence. It also comes uncomfortably close to questions of creationism and intelligent design, which are counter-scientific because they do not admit testable hypotheses.

If we take a detour around the semantics of the “silly why,” it does not take long to discover that scientists really do look for explanations. Thousands of studies have searched for causes of cancer or other diseases, and the investigation of just about every mechanism in biology tries to answer a question of causality. In fact, chains of causes and effects are the most prominent means of explaining biology.

Biological research has had a long and esteemed history. So it is not surprising that its concepts, approaches, and methods have been subjected to dramatic changes time and again. Early trial and error in agriculture and animal domestication matured into simple plant manipulations and animal husbandry. Observations of birth and death, growth and decay, led to methods for preserving food for times of dearth. Exploratory dissections of corpses turned into primitive forms of surgery. The worldview of biology exploded with the invention of the microscope, which opened a window into an entirely new world of cells and microorganisms and pathogens. The exploration of medicinal herbs and poisons, as well as the procedures of alchemy and chemistry, motivated the invention of ever-more accurate methods and refined measurement tools.

The search for scientific truth reached a high point in the seventeenth century with the acceptance of the so-called scientific method, which is still considered fundamental today. According to this method, scientific inquiry advances through well-structured, iterative cycles of posing a hypothesis, testing it with experiments, analyzing results, making predictions, testing them, and formulating new hypotheses. In all fairness, one should mention that the roots of this structured type of scientific thinking and experimentation can actually be traced back two millennia to the third century bc Greek physician and anatomist Herophilus, who cofounded the most famous medical school of the time in the Egyptian city of Alexandria. Herophilus performed systematic dissections, which he documented in great detail, and maintained that trustworthy scientific knowledge can only be found on an empirical basis. Nevertheless, the scientific method became the gold standard only in the seventeenth century.

Then the twentieth century rolled along and modern biomedical research exploded. Powerful experimental tools and custom-tailored machines rendered it possible to characterize biological phenomena with a resolution never seen before, down to the level of individual molecules. A prominent highlight was the identification of the structure of DNA, but many other classes of molecule were identified and characterized, and uncounted small and large discoveries occurred during the second half of the century. Most of these breakthroughs resulted directly from the application of the scientific method, which brought forth incredible amounts of precise data and unprecedented insights into the inner workings of life.

Whether it is acting, race car driving, or exploring the moon, it is usually the actors, drivers or astronauts who make the covers of magazines and receive celebrity accolades. And most of them certainly deserve praise for their hard work and the perfect execution of the task at hand. But we all know that their rise to stardom happened on the shoulders of uncounted others whose names flash by in very small font at the end of a movie or are not even mentioned. If we consider car racing, for example, a huge number of individuals contributed to the creation of the infrastructure for the complex system within which an Indy 500 victory is possible. Engineers not only designed and built the cars, but also thought up and actually realized the idea of tough yet light helmets, fire-resistant suits, pits custom-made for speed, the race track itself, and the entire support system allowing drivers to race, spectators to cheer, and venders to sell their wares. And so it happens in many situations that the thinkers and tinkerers and organizers, the makers and builders behind the scenes, remain in the shadows and out of the public eye, even though it is they who make miracles possible. To some degree this is not surprising, as engineers, mathematicians, computer scientists, and ingenious nerds of various types are not notorious for their interest in social hobnobbing or braggadocio. The situation in systems biology is not all that different. Here, it is experimental biologists or clinicians, founders of biotech start-up companies, or producers of novel medicines, who may receive at least a bit of attention from the public, and whose success rests firmly on the shoulders of uncounted, unsung heroes from the field of engineering.

The most dramatic example is arguably the paradigm of experimental systems biology itself, the –omics revolution, where widely noticed insights into the inner workings of genomes, micro-RNAs or protein interactions have only been possible due to ingenious engineering that led to miniaturization and permitted the high-throughput execution of many parallel experiments with robots. Unluckily for their image in the public eye, these robots are nothing like Star Wars’ R2-D2; they don't smile, they don't look sad when scolded, and they do not shake hands.

More than 5,000 performances and translations into 17 languages leave little doubt that the off-Broadway musical I Love You, You're Perfect, Now Change hit a nerve with the theater community who vicariously enjoyed the trials and tribulations of their fellow humans in the ever-changing dating and mating turmoil of the late twentieth century. Life may be good, but there is this nagging feeling that something better could be just around the corner. Constancy is comfortable and calms the soul; change is exciting. Indeed, we know that nothing really stands still and that all is in flux, as Heraclitus of Ephesus proclaimed 2,500 years ago.

Except for the “Love You” part, it seems that nature has the same attitude toward its subjects and forces them to change without end. All species are in some way optimized and perfect, because they are clearly more successful than their competitors and survived for generation after generation where others did not. But this “optimal” is merely a current “optimal.” It resulted from unceasing change since life began and will most certainly transform into a new “optimal” in the future. Nature never stops exploring new options, and the only constant throughout the eons seems to have been change.

We all grow and age, and we really do not notice significant changes from one generation to the next, except, of course, that we are much more knowledgeable and sophisticated than all generations before us and most certainly incomparably wiser than our younger peers. But in spite of all evolutionary events, most of us have two eyes, two kidneys, and all those features that make us humans, and these just don't change, at least not fundamentally. Yes, we know that our roots go back to Neanderthals, Denisovans, Homo habilis, and early African hominids like Lucy, but those distant relatives disappeared a very long time ago. Evolution is a topic to be pondered among paleontologists and does not seem to have an urgent role in daily life.

It is actually not all that difficult to observe evolution at work. One only needs to start with a single bacterium and let it divide and multiply. All future cells should be the same, as there is no new source of genes. However, if we check merely a few weeks later, it is very likely that the genetic makeup of the population has become diverse.

It is an interesting exercise to surf the blogosphere and other, similarly reliable sources on the Internet in search of the origin of this saying. It's a Spanish quote. It's Russian, Arabic, Mexican. One blogger gives credit to his mother. Several websites assure us that the famous Greek bard Euripides proclaimed it in his tragedy Phoenissae (c. 410 bc) about a group of Phoenician women caught up in a fight among Oedipus’ sons for dominion over Thebes. As Euripides said, “every man is like the company he is wont to keep.” Some bloggers attribute the quote to Confucius’ Words of Wisdom. Others go even further back, crediting the saying to the Assyrians. It seems that we may never know the true origin. But whatever its long history, the widespread usage of this morsel of wisdom points to the universality of the deep human appreciation of companionship and the unique character that bonds a group of friends or associates. Whether it is membership of a club, guilt by association, Benjamin Franklin's pithy comment during the signing of the Declaration of Independence that “we must all hang together, or assuredly we shall all hang separately,” or the astounding popularity of social media: the company one keeps defines one's being and identity.

The observation that connectivity contains genuine information has not escaped the attention of systems biologists, who are utilizing this relationship within the realm of biological networks. We are well aware that nothing in biology occurs in a vacuum and that every component is connected to many others. The investigative challenge derives from the fact that biological networks and their connections are often only vaguely known. Because it is the task of scientists to discover the unknown, the rationale is the following: if the connectivity within a human group contains information about its members, the same could be true for biological networks. If so, it should be possible to extract novel information regarding unknown biological components from the analysis of better known components with which they associate.

The best-characterized associations and networks among biomolecules are formed by metabolites, due to two facts. First, biochemists had been working on metabolites long before DNA was identified and proteins could be manipulated with any efficiency. In fact, one of the important roots of systems biology is the work of nineteenth- and twentieth-century biochemists who formulated mathematical models for chemical reactions in metabolism.

Surprises are great for children's birthday parties, but we surely don't like to receive them from our machines. We would find it rather annoying if the remote control changed TV channels on its own, and we would certainly not want to fly in an airplane that surprised us by deciding to start its descent while high over the ocean. Surprises are events that we do not expect to happen. In the more somber language of science, a surprise is quite similar to the notion of an emergence or emerging behavior. Many definitions of emergence have been proposed, but the core concept is that we cannot explain an emergent property of a system by only studying its parts. One does not have to look far to find emergence. Wave patterns on a lake are difficult to explain in terms of individual water molecules. Table salt enhances the taste of many foods in a pleasing manner, but we would not want to taste its constituents, sodium and chlorine. The parts of a clock by themselves do not tell time. Aristotle already wrote in his book Sophistical Refutations about this “fallacy of division” and the corresponding “fallacy of composition,” which debunks the faulty inference that everything that is true for a part is also true for the whole, and vice versa.

Emergence has had a prominent role for a long time, both in biology and in philosophy. This prominence is not surprising, because no emergence is more stunning than life itself. About 99 percent of the mass of our bodies consists of hydrogen, oxygen, nitrogen, carbon, sulfur, and phosphorus, and roughly 99 percent of all molecules in a typical living cell are water. Yet, if we would buy water and other chemicals, we would still be eons away from a human body. What exactly constitutes the difference between a functioning cell and its components? What is different just before and after the death of a cell or organism? Is emergence the secret of life? The difference between life and its nonliving parts poses a fundamental, unanswered question that has been keeping philosophers and biologists wondering and pondering for a very long time. Similar persistent puzzles can be found throughout nature, from insect colonies to the relationships between thought, memory, and consciousness and their biological foundation.

Unsurprisingly, health and disease have always been on the minds of humans. After all, this most fundamental yin and yang of life is without doubt one of the strongest drivers of our happiness, success, and general outlook on life. The more we learn the more we realize that our physical and mental well-being are ultimately a reflection of the myriad of biochemical, electrical, and mechanical processes chugging along in our bodies every minute, day and night. Slight derailments of some processes we might not even notice, but others, just think toothache, are annoyingly distractive, even if 99.99 percent of our physiological machinery is working just fine. No wonder that the desire to understand the connections between molecules and a happy or not so happy life have fascinated biologists for many years.

When systems biology began to gain popularity at the beginning of this century, health and disease were portrayed as prominent targets, but with a new twist: the novel promise of personalized medicine and predictive health. Building upon this promise, the director of the National Institutes of Health, and the commissioner of the US Food and Drug Administration, laid out a road map for investments in this new field of “4P medicine,” which was to become Personalized, Predictive, Preventive, Participatory. They also proposed efforts to streamline the path to new and improved treatments, which were to be custom-tailored to the individual. These treatments should dispel the old cliché of the doctor's advice to “take two pills and call me in the morning.” Instead, they portrayed a vision of science-based efforts to adjust and individualize important disease treatments according to each patient's age, metabolism, and many other personal characteristics. This vision implied that government, universities and drug companies would team up to develop new, effective drugs and diagnostics so doctors would be enabled to prescribe “the right drug at the right dose at the right time.”

The vision of predictive health goes one step further. The basic idea is quite simple, but its implementation will be time-consuming and require vast resources. In the first phase of this implementation, dedicated health centers will perform thousands of measurements on large cohorts of healthy individuals of various ages and repeat these measurements regularly over a time period of several decades. As one might expect, some of these individuals will eventually develop a serious chronic disease.

No, it is not a misprint. And no, that command is certainly not cited from religious scripture or a guide to ethical living. It is a top imperative that the human body, like every other organism, must obey while dealing with the rough and tumble world, where rolling out the welcome mat to microbial or other visitors and invaders is a risky proposition. Not that all visitors are bad. There is strong suspicion that our mitochondria, which manage the energy needs of every single cell, were once free-living microbes. Also, it is not quite clear when and how it happened, but over 40 percent of our very own DNA has its origin in so-called retrotransposons, which most probably were parts of viruses that became integrated into our genome and gave us genes that we did not possess before but that are apparently useful. Finally, the body has learned not only to tolerate but to benefit greatly from complex, diverse, and distinct microbial communities that live on the skin, in the gut, and in all cavities that are connected to the outside world: the mouth, nose, ears, anus, urinary tract, and vagina.

Notwithstanding these beneficial exceptions, most encounters with foreign invaders are potentially a matter of life and death, and every higher organism must therefore take this threat very seriously. And because of what's at stake, the immune system defending our body could hardly be more complicated in its structure and function. It is truly a multi-faceted system of systems that covers every level of biological functionality, has lots of inbuilt redundancy, and is very robust, amazingly adaptive, and able to respond to threats never before encountered. It is a paradigm of what systems biology must learn to address.

In fact, while anthropocentric comparisons often fall flat, it does not take much imagination to identify a number of parallels between our body's immune system and a typical military defense apparatus or police force.

We all use thermostats, shock absorbers, and various kinds of insurance to buffer our life against strong fluctuations. Yet, if two kids are sick, the dog just ran away, the car has a flat tire, and we are late for work anyway, life is just chaotic. Is it even possible that there is normal life without chaos? If we could ask the Greek theologian and philosopher Hesiod, who lived in about 700 bc, he would have insisted without doubt on the utmost importance of chaos. After all, Chaos was a primeval deity that existed before all the others and gave birth to Love, Silence, and the Night. Allegedly, Chaos even preceded the gods responsible for the underworld and for Earth itself. Chaos was imagined as the gap of total emptiness between heaven and Earth, from which the cosmos was later created. This vision was actually not all that different from the Judeo-Christian version of the beginnings of the earth, which initially was tohu wa bohu: without form and void.

Today, the notion of chaos is rather different. In the vernacular, it refers to disorder or confusion, mayhem and unpredictability. In math, and by extension in the theory of dynamical systems and in systems biology, chaos has been given a much more specific and narrow definition. Namely, it describes the behavior of a system that follows deterministic rules but nonetheless seems to be unpredictable and random. Is chaos possible in biological systems and does it play a significant, positive or negative, role?

To appreciate the definition of modern-day chaos in dynamical systems, one needs to start a little earlier and look at random, stochastic effects, and deterministic processes. A first question, which sounds innocuous but really isn't, is whether there is true randomness. We often associate randomness with a lottery or with betting games, such as flipping a coin, rolling the dice or playing roulette. We call the outcomes of these games random, because we simply cannot predict them, no matter how long we play. At the same time, some aspects are not entirely unpredictable. For instance, if we flip a fair coin 1,000 times, we expect that we should see about 500 heads and 500 tails. If we roll a single die 6,000 times, we expect to find each of the 6 faces roughly 1,000 times.

The rapidly emerging high-throughput data generation of the new world of –omics has radically changed the way we approach many questions in biology. The traditional approach had called for a painstaking, step-by-step process of formulating an hypothesis, testing it with well-designed and well-controlled experiments, analyzing the results for new insights, interpreting these, and posing the next round of hypotheses. In stark contrast, a single –omics experiment generates thousands of data points, and the researcher knows full well that most of them will not advance our current state of knowledge. Yet, there is justifiable hope in finding the proverbial diamond in the rough or maybe at least the needle in the haystack.

Because it has become relatively easy to perform –omics experiments, the amount of biomedical data generated every year has become overwhelmingly huge, leading to the new buzzword of “BigData” which vaguely refers to datasets that are so large and complicated that traditional tools of database management, analysis, and interpretation are no longer workable, at least not with any degree of efficiency. In the case of –omics, the big data experiments often come from interdependent collections of different types of experiment – such as genomics and proteomics, performed on the same system, maybe on different days, and maybe under different conditions or stimuli. The rationale for these combined approaches is the expectation that parallel datasets, elucidating different aspects of the same phenomenon or disease, should be much more informative than datasets of the same type, because they complement each other. For instance, one would expect to see some changes in gene expression reflected in the amounts of various proteins. As a consequence, the combined datasets are huge in size and heterogeneous in nature. For the traditionally trained biologist, this means that “double, double toil and trouble” are brewing in the world of modern biology, leading to ever-louder SOS cries begging computer nerds for help. In many of these situations, the computer whizzes are asked for apparitions: please make patterns appear in the vast expanse of our data!

Computer whizzes actually like this type of challenge, because it allows them to play with bigger and faster machines that can handle numbers of bits and bytes whose prefixes only hard-core scientists have even heard of, such as peta–, exa–, and yotta–.

Yes, I called this first section Appetizer because Preface or Foreword is just too boring. Many a preface consists of a laundry list of topics, which the author felt obliged to include, followed by acknowledgments in often cumbersome prose. And even if a preface actually contains true morsels of wisdom, readers don't expect as much and don't do what readers are supposed to do: read it. An appetizer, by contrast, is an artfully created tidbit to whet your appetite. Here it goes loosely with the theme of vignettes, that is, wine, vine leaves and stories that are short enough to fit on them. It is also more in line with the somewhat provocative chapter titles such as I'd rather be fishin’, but I will not launch into a laundry list. Mind you, esteemed reader, you are still with me, and I hope you will keep on reading until, as your mother might have said, you have earned the privilege to enjoy the Dessert. In the process, I hope to ease you into the fascinating field of systems biology as it attempts to decipher and understand the inner workings of life. As you will see, systems biology uses a lot of sophisticated techniques, all with their own jargon, so this is not exactly an easy topic to discuss. But there are so many exciting things going on in this young field, and in biology in general, that I am convinced you would be at a loss if you remained in the dark. You did not have the opportunity to experience the industrial revolution firsthand, and you may have missed the electronics revolution, so don't miss out on the biological revolution!

Allow me to back up a little and provide some context for the rise of systems biology. To some degree, we all have been systems biologists since our early days. We have marveled at ecological systems in our backyards and across the world, in deserts, on mountains, and in oceans, either with our own eyes and ears, or maybe through magazines and documentaries. We have watched with amazement how tiny ants carry heavy loads into their nests, apparently knowing that this is their assigned task in life and that other ants have their own roles and responsibilities for the greater good of the colony.

Excuse me? That has to be a misprint! Even with super-hyperinflation, how is it possible that a thought that used to cost a penny just a few years ago now is supposed to cost a billion dollars?

Admittedly, the comparison is not quite fair. The random thought to be divulged at an unexpected point in time probably was not worth all that much. Maybe more than a penny, but certainly not hundreds of millions of dollars or more! By contrast, the billion offered today is really awarded for answers to a fundamental question of being human: how intelligent thoughts are formed, stored, and recalled. Many governmental funding agencies around the world are offering substantial amounts of research funds to figure out how our brain works. In the US alone, the National Institutes of Health, the National Science Foundation, the Department of Education, and various other public and private foundations have been supporting a wide spectrum of research topics in neuroscience for a long time. In addition to these ongoing programs, the US government announced in 2013 the launch of a brand new $100 million initiative with the goal of mapping the connections among the neurons in human and animal brains. The project is fittingly called BRAIN, or Brain Research through Advancing Innovative Neurotechnologies, and has the ambitious goal of reconstructing the activities of individual neurons and their firing patterns in brain circuits. Specifically, the BRAIN initiative is being challenged to create new technologies that can simultaneously record the activity patterns of millions of neurons with a time resolution corresponding to actual mental processes. The expectation – or at least the hope – is that an understanding of these patterns will reveal how we create and manage thoughts and memories, what cognition is, how we learn and make inferences, and how molecular perturbations can lead to changes in behavior. A better understanding might also suggest how to intervene if the brain goes awry due to accidents or degenerative disease. In similar strategic efforts, the European Union plans to spend over a billion euros on simulations that explain brain circuits on the basis of neurotransmitter molecules and their dynamics, and several institutes for research in neuroscience have sprung up in Asia during the past two decades. So, the $1 billion in the heading of this chapter is really a vast underestimate.

INTRODUCTION

Communication is a central component of representation (Mansbridge, 2003; Disch, 2012). Legislators invest time and resources in crafting speeches in Congress, composing press releases to send to newspapers, and distributing messages directly to their constituents (Yiannakis, 1982; Quinn et al., 2010; Lipinski, 2004; Grimmer, 2013). Indeed, the primary problem in studying the role of communication in representation is that legislators communicate so much that analysts are quickly overwhelmed. Traditional hand-coding is simply unable to keep pace with the staggering amount of text that members of Congress produce each year.

In this chapter I use a text as data method and a collection of press releases to measure how legislators present their work to constituents (Grimmer and Stewart, 2013). Specifically, I measure legislators’ expressed priorities: the attention they allocate to topics and issues when communicating with constituents (Grimmer, 2010). Using the measures of legislators’ expressed priorities, I characterize how Republicans respond to the drastic change in institutional and electoral context after the 2008 election. Not only did the Republican party lose the White House but also the Tea Party movement mobilized and articulated conservative objections to particularistic spending. Replicating a finding from Grimmer, Westwood, and Messing (2014) with alternative measures, I show that Republicans abandon credit claiming. Instead, Republicans articulate criticisms of the Democratic party, the Obama administration, and Democratic policy proposals. In contrast, Democrats embrace credit claiming and defend Democratic policies – though less vocally than Republicans criticize those same proposals. In spite of the shifts in rhetoric, though, I demonstrate that there is a strong year-to-year relationship in legislators’ presentational styles. So, although legislators are responsive at the margin to changing conditions, the basic strategy remains the same.

This chapter contributes to a growing literature that examines legislative speech using automated methods for text analysis (Hillard, Purpura, and Wilkerson, 2008; Monroe, Colaresi, and Quinn, 2008; Quinn et al., 2010; Grimmer, 2013; Cormack, 2014). This literature has demonstrated how computational tools can be successfully used to examine the content of legislation and how the types of bills passed over time have changed (Adler and Wilkerson, 2012). Other studies have demonstrated how text analysis can be used to provide nuanced measures of legislators’ ideal points (Gerrish and Blei, 2012). Still other studies have demonstrated how legislators use communication to create an impression of influence over expenditures (Grimmer, Westwood, and Messing, 2014).