Systems biology based on computer-based datasets | “To Know, but Not Understand” | David Weinberger | The Atlantic

Systems biology, for properties that show up in the organism but not the parts, arose from molecular biology, based on computer-based datasets.  The 2002 Science article by Kitano is cited by David Weinberger.

As science has gotten too big to know, we’ve adopted different ideas about what it means to know at all.

[….] A new science called systems biology studies the ways in which external stimuli send signals across the cell membrane. Some stimuli provoke relatively simple responses, but others cause cascades of reactions. These signals cannot be understood in isolation from one another. The overall picture of interactions even of a single cell is more than a human being made out of those cells can understand. In 2002, when Hiroaki Kitano wrote a cover story on systems biology for Science magazine — a formal recognition of the growing importance of this young field — he said: “The major reason it is gaining renewed interest today is that progress in molecular biology … enables us to collect comprehensive datasets on system performance and gain information on the underlying molecules.” Of course, the only reason we’re able to collect comprehensive datasets is that computers have gotten so big and powerful. Systems biology simply was not possible in the Age of Books.

The result of having access to all this data is a new science that is able to study not just “the characteristics of isolated parts of a cell or organism” (to quote Kitano) but properties that don’t show up at the parts level. For example, one of the most remarkable characteristics of living organisms is that we’re robust — our bodies bounce back time and time again, until, of course, they don’t. Robustness is a property of a system, not of its individual elements, some of which may be nonrobust and, like ants protecting their queen, may “sacrifice themselves” so that the system overall can survive. In fact, life itself is a property of a system.

The problem — or at least the change — is that we humans cannot understand systems even as complex as that of a simple cell. It’s not that were awaiting some elegant theory that will snap all the details into place. The theory is well established already: Cellular systems consist of a set of detailed interactions that can be thought of as signals and responses. But those interactions surpass in quantity and complexity the human brains ability to comprehend them. The science of such systems requires computers to store all the details and to see how they interact. Systems biologists build computer models that replicate in software what happens when the millions of pieces interact. It’s a bit like predicting the weather, but with far more dependency on particular events and fewer general principles.

Models this complex — whether of cellular biology, the weather, the economy, even highway traffic — often fail us, because the world is more complex than our models can capture. But sometimes they can predict accurately how the system will behave. At their most complex these are sciences of emergence and complexity, studying properties of systems that cannot be seen by looking only at the parts, and cannot be well predicted except by looking at what happens.

Edited excerpt as “To Know, but Not Understand” | David Weinberger | Jan. 3, 2012 | The Atlantic at

To Know, but Not Understand: David Weinberger on Science and Big Data - David Weinberger - Technology - The Atlantic