We're now accumulating data at an incredible rate. I mentioned electron microscopy to study the ribosome—each experiment generates several terabytes of data, which is then massaged, analyzed, and reduced, and finally you get a structure. At least in this data analysis, we believe we know what's happening. We know what the programs are doing, we know what the algorithms are, we know how they come up with the result, and so we feel that intellectually we understand the result. What is now happening in a lot of fields is that you have machine learning, where computers are essentially taught to recognize patterns with deep neural networks. They're formulating rules based on patterns. There are are statistical algorithms that allow them to give weights to various things, and eventually they come up with conclusions.

When they come up with these conclusions, we have no idea how; we just know the general process. If there's a relationship, we don't understand that relationship in the same way that we would if we came up with it ourselves or came up with it based on an intellectual algorithm. So we're in a situation where we're asking, how do we understand results that come from this analysis? This is going to happen more and more as datasets get bigger, as we have genome-wide studies, population studies, and all sorts of things.

We're at the threshold of a new age of structural biology, where these things that everybody thought were too difficult and would take decades and decades, are all cracking. Now we're coming to pieces of the cell. The real advance is that you're going to be able to look at all these machines and large molecular complexes inside the cell. It will tell you detailed molecular organization of the cell. That's going to be a big leap, to go from molecules to cells and how cells work.

In almost every disease, there's a fundamental process that's causing the disease, either a breakdown of a process, or a hijacking of a process, or a deregulation of a process. Understanding these processes in the cell in molecular terms will give us all kinds of ways to treat disease. They'll give us new targets for drugs. They'll give us genetic understanding. The impact on medicine is going to be quite profound over the long-term.

VENKATRAMAN "VENKI" RAMAKRISHNAN is a Nobel Prize-winning biologist whose many scientific contributions include his work on the atomic structure of the ribosome. He is Group Leader and Former Deputy Director of the MRC Laboratory of Molecular Biology in Cambridge, and the current President of the Royal Society. Venki Ramakrishnan's Edge Bio Page

What we're trying to do in behavioral genetics and medical genetics is explain differences. It's important to know that we all share approximately 99 percent of our DNA sequence. If we sequence, as we can now readily do, all of our 3 billion base pairs of DNA, we will be the same at over 99 percent of all those bases. That's what makes us similar to each other. It makes us similar to chimps and most mammals. We're over 90 percent similar to all mammals. There's a lot of genetic similarity that's important from an evolutionary perspective, but it can't explain why we're different. That's what we're up to, trying to explain why some children are reading disabled, or some people become schizophrenic, or why some people suffer from alcoholism, et cetera. We're always talking about differences. The only genetics that makes a difference is that 1 percent of the 3 billion base pairs. But that is over 10 million base pairs of DNA. We're looking at these differences and asking to what extent they cause the differences that we observe. 

ROBERT PLOMIN is a professor of behavioral genetics at King's College London and deputy director of the Social, Genetic and Developmental Psychiatry Centre at the Institute of Psychiatry, Psychology and Neuroscience. Robert Plomin's Edge Bio Page

CONTENTS: Evolvability  Richard Dawkins  Genomic Imprinting  David Haig  A Full-Force Storm with Gale Winds Blowing  Robert Trivers  What Evolution Is  Ernst Mayr  Genetics Plus Time  Steve Jones  A United Biology  E. O. Wilson  Is Life Analog or Digital?  Freeman Dyson  Life: What a Concept!  Freeman Dyson, J. Craig Venter, George Church, Dimitar Sasselov, Seth Lloyd, Robert Shapiro  The Gene-Centric View: A Conversation  Richard Dawkins, J. Craig Venter  The Nature of Normal Human Variety Armand Marie Leroi  Brains Plus Brawn  Daniel Lieberman  Mapping the Neanderthal Genome  Svante Pääbo  On Biocomputation  J. Craig Venter, Ray Kurzweil, Rodney Brooks  Engineering Biology  Drew Endy  Eat Me Before I Eat You: A New Foe for Bad Bugs  Kary Mullis  Duck Sex and Aesthetic Evolution  Richard Prum  Toxo Robert Sapolsky  The Adjacent Possible Stuart Kauffman (with an introduction by John Brockman)   

The big story of the 20th and the 21st century is that we’re learning to control the world better. With the development of quantum mechanics, we understand the fundamental principles of what matter is and how it behaves that’s adequate for all engineering purposes.                                 

The limitation is just our imagination and our ability to calculate the consequences of the laws. We’re getting better at both of those as we gain experience. We have more imagination. As computing develops, we learn how to calculate the consequences of the laws better and better. There’s also a feedback cycle: when you understand matter better, you can design better computers, which will enable you to calculate better. It's kind of an ascending helix.

FRANK WILCZEK, currently the Herman Feshbach Professor of Physics at MIT, has received many prizes for his work in physics, including the Nobel Prize (2004) for work he did as a graduate student at Princeton University. Frank Wilczek's Edge Bio Page