The subject of this issue of BioEssays is the modelling of complex biological systems. Since all biological systems are complex and since every attempt to come to grips with particular biological systems involves an element of modelling, it could be argued that theme of this special issue is nothing less than biological science itself. Indeed, if the term ''complexity'' is simply a synonym for ''complicated'' or ''intricate'' or just ''hard to understand,'' then indeed most biological research is about complexity. But the term, as it is employed in Science, tends to mean something more precise than merely difficult or complicated: complexity is a characteristic of a system whose over-all properties are not readily predictable from what is known about its components. It is the unexpected (''emergent'') interactive properties of a system that makes a biological system ''complex''. There is nothing mystical about such properties; their existence simply points to gaps in our knowledge. One might, therefore, say that the role of research on complex biological systems is to reduce them, conceptually, to the merely complicated. Complexity studies take off from the point at which classical reductionist-analytical strategies, focussing on component properties and actions, begin to fail in describing the dynamic behaviour of the system of which they are a part.
Yet, if this qualitative view of complexity is a starting point, it is no more than that. Ideally, one would like to compare different kinds and degrees of complexity. As John Casti remarked seven years ago in the second issue of the then new journal journal devoted to the subject (see Casti, J.L. (1995). Complexity and simplicity, in the eye of the beholder. Complexity, 1(2): 2-3), there has been little general agreement on how complexity can be defined in ''usable formal terms''. In the first article in this issue, Christoph Adami begins with an examination of this analytical problem. He describes the different attempts that have been made to deduce metrics or measures of complexity, specifically in terms of information content in DNA sequences, describes the relationships between information and entropy and then defines the property of ''physical complexity'' in both intuitive and mathematical terms. Physical complexity is a measure of the amount of information contained in a genome about the environment in which the organism lives. Adami goes on to show how the evolution of physical complexity in digital organisms can occur