Making living cells blink fluorescently like party lights may sound frivolous. But the demonstration that it's possible could be a step toward someday programming our body's immune cells to attack cancers more effectively and safely. That's the promise of the field called synthetic biology. While molecular biologists strip cells down to their component genes and molecules to see how they work, synthetic biologists tinker with cells to get them to perform new feats -- discovering new secrets about how life works in the process. Listen on Apple Podcasts, Spotify, Google Podcasts, Stitcher, TuneIn or your favorite podcasting app, or you can stream it from Quanta. Steve Strogatz (00:03): I'm Steve Strogatz, and this is The Joy of Why, a podcast from Quanta Magazine that takes you into some of the biggest unanswered questions in science and math today. In this episode, we're going to be talking about synthetic biology. Simply put, we could say that synthetic biology is a fusion of biology, especially molecular biology, and engineering. The distinctive thing about it is that it treats cells as programmable devices. It's a kind of tinker toy approach that builds circuits, but not out of wires and switches like we're used to, but rather out of biological components, like proteins and genes. But also, the approach holds promise for illuminating how life works at the deepest level. It's one thing to strip cells apart to see how they work. But it's another thing to tinker with cells to try to get them to perform new tricks, which is something that my guest, Michael Elowitz, does. For example, a while back, he engineered cells to blink on and off like Christmas lights. Michael Elowitz is a professor of biology and biological engineering at Caltech and Howard Hughes Medical Institute. It's great to be here. Strogatz (01:53): So let's talk about the foundational idea of synthetic biology. I mentioned it in the intro, that's -- that living cells, we could think of as programmable devices. The field, synthetic biology, it seems like you guys have this philosophy that you can learn about cells by building functionality into cells yourself.

His personal style may lean toward the conventional, but the Rice University mechanical engineer is here to tell me about his creative new fashion design. His team has made a shiny black jacket that performs logic--without electronics. Specifically, the jacket can raise and lower its own hood at the push of a button, and it contains a simple 1-bit memory that stores the state of the hood. Or, as Preston says, it's "a non-electronic durable logic in a textile-based device." Here's where we need to emphasize the wildness of this design.

You may not be able to teach an old dog new tricks, but Cornell researchers have found a way to train physical systems, ranging from computer speakers and lasers to simple electronic circuits, to perform machine-learning computations, such as identifying handwritten numbers and spoken vowel sounds. The experiment is no mere stunt or parlor trick. By turning these physical systems into the same kind of neural networks that drive services like Google Translate and online searches, the researchers have demonstrated an early but viable alternative to conventional electronic processors--one with the potential to be orders of magnitude faster and more energy efficient than the power-gobbling chips in data centers and server farms that support many artificial-intelligence applications. "Many different physical systems have enough complexity in them that they can perform a large range of computations," said Peter McMahon, assistant professor of applied and engineering physics in the College of Engineering, who led the project. "The systems we performed our demonstrations with look nothing like each other, and they seem to [be] having nothing to do with handwritten-digit recognition or vowel classification, and yet you can train them to do it."

You may not be able to teach an old dog new tricks, but Cornell researchers have found a way to train physical systems, ranging from computer speakers and lasers to simple electronic circuits, to perform machine-learning computations, such as identifying handwritten numbers and spoken vowel sounds. Cornell researchers have successfully trained (from left to right) a computer speaker, a simple electronic circuit and a laser to perform machine-learning computations. The experiment is no mere stunt or parlor trick. By turning these physical systems into the same kind of neural networks that drive services like Google Translate and online searches, the researchers have demonstrated an early but viable alternative to conventional electronic processors – one with the potential to be orders of magnitude faster and more energy efficient than the power-gobbling chips in data centers and server farms that support many artificial-intelligence applications. "Many different physical systems have enough complexity in them that they can perform a large range of computations," said Peter McMahon, assistant professor of applied and engineering physics in the College of Engineering, who led the project.

A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamical devices interconnected by links of varying strength. Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, analog electronic circuits, and control systems. Analog networks are typically complex systems which include nonlinear feedback loops and possess temporal dynamics at different time scales. Both the synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, for which few systematic techniques exist. In this paper we will discuss the general relevance of the analog network concept and describe an evolutionary approach to the automatic synthesis and the reverse engineering of analog networks.

If I were to picture futuristic bots that could revolutionize both microrobotics and medicine, a Pop-Tart with four squiggly legs would not be on top of my list. Last week, Drs. Marc Miskin*, Itai Cohen, and Paul McEuen at Cornell University spearheaded a collaboration that tackled one of the most pressing problems in microrobotics--getting those robots to move in a controllable manner. They graced us with an army of Pop-Tart-shaped microbots with seriously tricked-out actuators, or motors that allow a robot to move. In this case, the actuators make up the robot's legs. Each smaller than the width of a human hair, the bots have a blocky body equipped with solar cells and two pairs of platinum legs, which can be independently triggered to flex using precise laser zaps.

We are an amalgam of genes, knowledge, culture and religion, which have influenced our lineage for thousands, tens of thousands of years. One thing is linked to the other, and the history of humanity could not be explained without physical evolution, religion, politics, or science. We are different from our ancestors who lived in the 19th century, or in Prehistory, because we have evolved in different ways. But, What is causing the great changes in humanity? A conquering king, a pandemic, a new religion can change the destiny of hundreds of millions of people. But, objectively, the most important evolutionary changes have come from the hand of technology.

We all remember the painful arithmetic exercises at school. It takes at least a minute to multiply numbers like 3,752 and 6,901 with pencil and paper. Of course, today, when we have phones at hand, we can quickly check that the result of our exercise should be 25 892 552. Processors of modern phones can perform more than 100 billion of such operations per second. Moreover, these chips consume only a few watts, which makes them much more efficient than our slower brains, which consume 20 watts and require much more time to achieve the same result. Of course, the brain has not evolved to do arithmetic.

Small robots aren't anything new, from DARPA's insect-sized disaster relief bots to diminutive inchworms powered by humidity. Now, though, researchers at MIT have likely created the smallest robots, yet: Microscopic, cell-sized electronic circuits made of two-dimensional materials that catch a ride on colloids, insoluble particles that stay suspended in liquid or even air. Since these minuscule devices can sense their environment, store data and carry out computational tasks, they could eventually be found in oil and gas pipelines, checking for leaks. They could be deployed into the air at a chemical refinery to sense harmful byproducts, or even into the human digestive tract for early detection of illness. "We wanted to figure out methods to graft complete, intact electronic circuits onto colloidal particles," MIT's Michael Strano said in a blog post.

Solar-powered self-driving cars, reusable space ships, Hyperloop transportation, a mission to colonize Mars: Elon Musk is hell-bent on turning these once-far-fetched fantasies into reality. But none of these technologies has made him as leery as artificial intelligence. At Code Conference 2016, Musk stated publicly that given the current rate of A.I. advancement, humans could ultimately expect to be left behind--cognitively, intellectually--"by a lot." His solution to this unappealing fate is a novel brain-computer interface similar to the implantable "neural lace" described by the Scottish novelist Iain M. Banks in Look to Windward, part of his "Culture series" books. Along with serving as a rite of passage, it upgrades the human brain to be more competitive against A.I.'s with human-level or higher intelligence.