Emily Singer - Evolution Is NOT so Random After All - We Evolve Toward Fitness

This is an interesting article on evolution experiments involving yeast and what they tell us about our own history and development. This article originally appeared at Quanta Magazine, under the title,
Evolution’s Random Paths Lead to One Place: A massive statistical study suggests that the final evolutionary outcome — fitness — is predictable.

If the World Started Over, Would Life Evolve the Same Way?

By Emily Singer, Quanta Magazine
10.03.14 | Permalink


Different strains of yeast grown under identical conditions develop different mutations but ultimately arrive at similar evolutionary endpoints.  Daniel Hertzberg for Quanta Magazine
In his fourth-floor lab at Harvard University, Michael Desai has created hundreds of identical worlds in order to watch evolution at work. Each of his meticulously controlled environments is home to a separate strain of baker’s yeast. Every 12 hours, Desai’s robot assistants pluck out the fastest-growing yeast in each world — selecting the fittest to live on — and discard the rest. Desai then monitors the strains as they evolve over the course of 500 generations. His experiment, which other scientists say is unprecedented in scale, seeks to gain insight into a question that has long bedeviled biologists: If we could start the world over again, would life evolve the same way?

Many biologists argue that it would not, that chance mutations early in the evolutionary journey of a species will profoundly influence its fate. “If you replay the tape of life, you might have one initial mutation that takes you in a totally different direction,” Desai said, paraphrasing an idea first put forth by the biologist Stephen Jay Gould in the 1980s.

Desai’s yeast cells call this belief into question. According to results published in Science in June, all of Desai’s yeast varieties arrived at roughly the same evolutionary endpoint (as measured by their ability to grow under specific lab conditions) regardless of which precise genetic path each strain took. It’s as if 100 New York City taxis agreed to take separate highways in a race to the Pacific Ocean, and 50 hours later they all converged at the Santa Monica pier.

The findings also suggest a disconnect between evolution at the genetic level and at the level of the whole organism. Genetic mutations occur mostly at random, yet the sum of these aimless changes somehow creates a predictable pattern. The distinction could prove valuable, as much genetics research has focused on the impact of mutations in individual genes. For example, researchers often ask how a single mutation might affect a microbe’s tolerance for toxins, or a human’s risk for a disease. But if Desai’s findings hold true in other organisms, they could suggest that it’s equally important to examine how large numbers of individual genetic changes work in concert over time.

Michael Desai, a biologist at Harvard University, uses statistical methods to study basic questions in evolution.  Sergey Kryazhimskiy

“There’s a kind of tension in evolutionary biology between thinking about individual genes and the potential for evolution to change the whole organism,” said Michael Travisano, a biologist at the University of Minnesota. “All of biology has been focused on the importance of individual genes for the last 30 years, but the big take-home message of this study is that’s not necessarily important.

The key strength in Desai’s experiment is its unprecedented size, which has been described by others in the field as “audacious.” The experiment’s design is rooted in its creator’s background; Desai trained as a physicist, and from the time he launched his lab four years ago, he applied a statistical perspective to biology. He devised ways to use robots to precisely manipulate hundreds of lines of yeast so that he could run large-scale evolutionary experiments in a quantitative way. Scientists have long studied the genetic evolution of microbes, but until recently, it was possible to examine only a few strains at a time. Desai’s team, in contrast, analyzed 640 lines of yeast that had all evolved from a single parent cell. The approach allowed the team to statistically analyze evolution.

To efficiently analyze many strains of yeast simultaneously, scientists grow them on plates like this one, which has 96 individual wells.  Sergey Kryazhimskiy

“This is the physicist’s approach to evolution, stripping down everything to the simplest possible conditions,” said Joshua Plotkin, an evolutionary biologist at the University of Pennsylvania who was not involved in the research but has worked with one of the authors. “They could partition how much of evolution is attributable to chance, how much to the starting point, and how much to measurement noise.”

Desai’s plan was to track the yeast strains as they grew under identical conditions and then compare their final fitness levels, which were determined by how quickly they grew in comparison to their original ancestral strain. The team employed specially designed robot arms to transfer yeast colonies to a new home every 12 hours. The colonies that had grown the most in that period advanced to the next round, and the process repeated for 500 generations. Sergey Kryazhimskiy, a postdoctoral researcher in Desai’s lab, sometimes spent the night in the lab, analyzing the fitness of each of the 640 strains at three different points in time. The researchers could then compare how much fitness varied among strains, and find out whether a strain’s initial capabilities affected its final standing. They also sequenced the genomes of 104 of the strains to figure out whether early mutations changed the ultimate performance.

Fluid-handling robots like this one make it possible to study hundreds of lines of yeast over many generations. Courtesy of Sergey Kryazhimskiy

Previous studies have indicated that small changes early in the evolutionary journey can lead to big differences later on, an idea known as historical contingency. Long-term evolution studies in E. coli bacteria, for example, found that the microbes can sometimes evolve to eat a new type of food, but that such substantial changes only happen when certain enabling mutations happen first. These early mutations don’t have a big effect on their own, but they lay the necessary groundwork for later mutations that do.

But because of the small scale of such studies, it wasn’t clear to Desai whether these cases were the exception or the rule. “Do you typically get big differences in evolutionary potential that arise in the natural course of evolution, or for the most part is evolution predictable?” he said. “To answer this we needed the large scale of our experiment.”

As in previous studies, Desai found that early mutations influence future evolution, shaping the path the yeast takes. But in Desai’s experiment, that path didn’t affect the final destination. “This particular kind of contingency actually makes fitness evolution more predictable, not less,” Desai said.

Sidebar: Diminishing Returns

Desai’s study isn’t the first to suggest that the law of diminishing returns applies to evolution. A famous decades-long experiment from Richard Lenski’s lab at Michigan State University, which has tracked E. coli for thousands of generations, found that fitness converged over time. But because of limitations in genomics technology in the 1990s, that study didn’t identify the mutations underlying those changes. “The 36 populations we had then would have been much more expensive to sequence than the hundred they did here,” said Michael Travisano of the University of Minnesota, who worked on the Michigan State study.

More recently, two papers published in Science in 2011 mixed and matched a handful of beneficial mutations in different types of bacteria. When the researchers engineered those mutations into different strains of bacteria, they found that the fitter strains enjoyed a smaller benefit. Desai’s study examined a much broader combination of possible mutations, showing that the rule is much more general.

Desai found that just as a single trip to the gym benefits a couch potato more than an athlete, microbes that started off growing slowly gained a lot more from beneficial mutations than their fitter counterparts that shot out of the gate. “If you lag behind at the beginning because of bad luck, you’ll tend to do better in the future,” Desai said. He compares this phenomenon to the economic principle of diminishing returns — after a certain point, each added unit of effort helps less and less.

Scientists don’t know why all genetic roads in yeast seem to arrive at the same endpoint, a question that Desai and others in the field find particularly intriguing. The yeast developed mutations in many different genes, and scientists found no obvious link among them, so it’s unclear how these genes interact in the cell, if they do at all. “Perhaps there is another layer of metabolism that no one has a handle on,” said Vaughn Cooper, a biologist at the University of New Hampshire who was not involved in the study.

It’s also not yet clear whether Desai’s carefully controlled results are applicable to more complex organisms or to the chaotic real world, where both the organism and its environment are constantly changing. “In the real world, organisms get good at different things, partitioning the environment,” Travisano said. He predicts that populations within those ecological niches would still be subject to diminishing returns, particularly as they undergo adaptation. But it remains an open question, he said.

Nevertheless, there are hints that complex organisms can also quickly evolve to become more alike. A study published in May analyzed groups of genetically distinct fruit flies as they adapted to a new environment. Despite traveling along different evolutionary trajectories, the groups developed similarities in attributes such as fecundity and body size after just 22 generations. “I think many people think about one gene for one trait, a deterministic way of evolution solving problems,” said David Reznick, a biologist at the University of California, Riverside. “This says that’s not true; you can evolve to be better suited to the environment in many ways.”

Kevin Kelly - Why You Should Embrace Surveillance, Not Fight It

In this opinion piece for Wired, Kevin Kelly argues that we should embrace surveillance because it is a necessary piece of the new technological world we are creating. But there are two kinds of surveillance, and only one of them is workable.

[O]ur central choice now is whether this surveillance is a secret, one-way panopticon — or a mutual, transparent kind of “coveillance” that involves watching the watchers. The first option is hell, the second redeemable.

The answer, he says, is coveillance, creating symmetry and transparency in how we are being watched, and in watching the watchers. Sounds about right.

Still, I cringe at the thought of how little privacy we now have in the technological present. Anybody and everybody can know your business - it's like living in a small town all over again.

Why You Should Embrace Surveillance, Not Fight It

By Kevin Kelly
03.10.14


Image: Twentieth Century Fox & Dreamworks

I once worked with Steven Spielberg on the development of Minority Report, derived from the short story by Philip K. Dick featuring a future society that uses surveillance to arrest criminals before they commit a crime. I have to admit I thought Dick’s idea of “pre-crime” to be unrealistic back then. I don’t anymore.

Most likely, 50 years from now ubiquitous monitoring and surveillance will be the norm. The internet is a tracking machine. It is engineered to track. We will ceaselessly self-track and be tracked by the greater network, corporations, and governments. Everything that can be measured is already tracked, and all that was previously unmeasureable is becoming quantified, digitized, and trackable.
If today’s social media has taught us anything about ourselves as a species it is that the human impulse to share trumps the human impulse for privacy.

We’re expanding the data sphere to sci-fi levels and there’s no stopping it. Too many of the benefits we covet derive from it. So our central choice now is whether this surveillance is a secret, one-way panopticon — or a mutual, transparent kind of “coveillance” that involves watching the watchers. The first option is hell, the second redeemable.

We can see both scenarios beginning today. We have the trade-secret algorithms of Google and Facebook on one hand and the secret-obsessed NSA on the other. Networks require an immune system to remain healthy, and intense monitoring and occasional secrets are part of that hygiene to minimize the bad stuff. But in larger doses secrecy becomes toxic; more secrecy requires more secrets to manage and it sets up a debilitating auto-immune disease. This pathology is extremely difficult to stop, since by its own internal logic it must be stopped in secret.

The remedy for over-secrecy is to think in terms of coveillance, so that we make tracking and monitoring as symmetrical — and transparent — as possible. That way the monitoring can be regulated, mistakes appealed and corrected, specific boundaries set and enforced. A massively surveilled world is not a world I would design (or even desire), but massive surveillance is coming either way because that is the bias of digital technology and we might as well surveil well and civilly.

In this version of surveillance — a transparent coveillance where everyone sees each other — a sense of entitlement can emerge: Every person has a human right to access, and benefit from, the data about themselves. The commercial giants running the networks have to spread the economic benefits of tracing people’s behavior to the people themselves, simply to keep going. They will pay you to track yourself. Citizens film the cops, while the cops film the citizens. The business of monitoring (including those who monitor other monitors) will be a big business. The flow of money, too, is made more visible even as it gets more complex.

Much of this scenario will be made possible by the algorithmic regulation of information as pioneered by open source projects. For instance, while a system like Bitcoin makes anonymous bank accounts possible, it does so by transparently logging every transaction in its economy, therefore making all financial transactions public. PGP encryption relies on code that anyone can inspect, and therefore trust and verify. It generates “public privacy”, so to speak.

Encoding visible systems open to all eyes makes gaming them for secret ends more difficult.

Every large system of governance — especially a digital society — is racked by an inherent tension between rigid fairness and flexible personalization. The cloud sees all: The cold justice of every tiny infraction by a citizen, whether knowingly or inadvertent, would be as inescapable as the logic of a software program. Yet we need the humanity of motive and context. One solution is to personalize justice to the context of that particular infraction. A symmetrically surveilled world needs a robust and flexible government — and transparency — to enforce adaptable fairness.

But if today’s social media has taught us anything about ourselves as a species it is that the human impulse to share trumps the human impulse for privacy. So far, at every juncture that offers a technological choice between privacy or sharing, we’ve tilted, on average, towards more sharing, more disclosure. We shouldn’t be surprised by this bias because transparency is truly ancient. For eons humans have lived in tribes and clans where every act was open and visible and there were no secrets. We evolved with constant co-monitoring. Contrary to our modern suspicions, there wouldn’t be a backlash against a circular world where we constantly spy on each other because we lived like this for a million years, and — if truly equitable and symmetrical — it can feel comfortable.
Bitcoin generates ‘public privacy’, so to speak.

Yet cities have “civilized” us with modern habits such as privacy. It is no coincidence that the glories of progress in the past 300 years parallel the emergence of the private self and challenges to the authority of society. Civilization is a mechanism to nudge us out of old habits. There would be no modernity without a triumphant self.

So while a world of total surveillance seems inevitable, we don’t know if such a mode will nurture a strong sense of self, which is the engine of innovation and creativity — and thus all future progress. How would an individual maintain the boundaries of self when their every thought, utterance, and action is captured, archived, analyzed, and eventually anticipated by others?

The self forged by previous centuries will no longer suffice. We are now remaking the self with technology. We’ve broadened our circle of empathy, from clan to race, race to species, and soon beyond that. We’ve extended our bodies and minds with tools and hardware. We are now expanding our self by inhabiting virtual spaces, linking up to billions of other minds, and trillions of other mechanical intelligences. We are wider than we were, and as we offload our memories to infinite machines, deeper in some ways.

Amplified coveillance will shift society to become even more social; more importantly it will change how we define ourselves as humans.



Kevin Kelly is Senior Maverick at WIRED. He co-founded Wired in 1993, and served as its Executive Editor from its inception until 1999. Kelly is the author of What Technology Wants (2010), Cool Tools: A Catalog of Possibilities (2013), and other books. He was involved with the launch of the pioneering online community The WELL (1985) and also co-founded the ongoing Hackers’ Conference.