What we don't know can, and will, hurt us

Abstract

There is an old saying (at least in English) that what you don't know can't hurt you. In science it is the reverse. So how much are we thinking about the deep unknowns in the life sciences, compared with gathering data? The European Commission set the tone for research with its launch of the “Information Society,” and proclamation (made in 2000 and known as the “Lisbon Agenda”) to make Europe the world-leading knowledge-based economy by 2010. With the rapid technological advances in molecular biology (particularly genome sequencing), some of this knowledge was practically in the bag, so it seemed. It was a “known” entity with predictable growth prospects. Thinking about what we already know, and know how to do, enables us to build on knowledge in an evolutionary, incremental, sense. Thinking about what we don't know, and don't know how to do, on the other hand, raises the truly fascinating questions in biology that are likely to culminate in revolutions in understanding. But at a more practical level, carefully thinking about what we don't know can help us make the right decision as to which data to gather, how to interpret them, and which other advances we need to support this endeavor. It is not necessarily easy to get the concept across these days, because large sectors of the life sciences are concerned with massive data collecting and interpretation projects. It might very well have been more implicitly understood 50 years ago, for instance, when we had but a fraction of the biological data to ponder that we do today. How to sequence (this has been known, in principle, for a very long time) How to decode the sequence (this has also been known for a very long time) But the ambitious aim of understanding the data needed to be matched by ambitious thinking about the unknowns. What we did not know was how to embed the knowledge of the sequence into the model of gene interactions as complex networks with phenotypic outputs that tend to be unpredictable. However, network biology as a discipline had been around for quite some time already. Now we tend to call it systems biology, and systems biology has been a defined discipline for probably more than 40 years: Mihajlo Mesarovic defined it as a research field in 1966 with an international symposium “Systems Theory and Biology” (though Ludwig von Bertalanffy's book “General Systems Theory in Physics and Biology” was published in 1950). Concerted funding for systems biology only really started in the late 1990s. If discovery of information advances faster than the rate at which we are developing the means to understand that information, it is inevitable that we will start misunderstanding it, and – even worse – misapplying it. A hackneyed example is the concept that the human genome sequence would give us the “genes for X, Y, Z”. Unfortunately, this worked against a positive trend in perception that individual genes were not determinants of phenotype.