Monomorphic

(This post is an attempt at elaborating the ideas I outlined in my talk at Bio-pitch in February.)

The academic and investigative relationship to biology – our discourse about biology – is becoming increasingly technological. In fields such as bioinformatics and computational biology, the technological/instrumental relationship to nature is always at work, constructing deterministic models of phenomena. By using these models, we may repeatedly extract predictable results from nature. An example would be a cause-effect relationship like: exposing a cell to heat causes “heat shock proteins” to be transcribed and translated.

The implicit understanding in all of these cases is that nature can be turned into engineering. Total success, in this understanding, would amount to one or both of the following:

1. Replacement/imitation as success. If we can replace the phenomena under study by its model (concretely, a machine or a simulation), we have achieved success.
2. Control as success. If we can consistently place the phenomena under study in verifiable, fully defined states, we have achieved success. (Note that this ideal implies that we also possess perfect powers of observation, down to a hypothetical “lowest level”).

These implicitly held ideals are not problematic as long as we acknowledge that they are mere ideals. They are very well suited as horizons for these fields to work under, since they stimulate the further development of scientific results. But if we forget that they are ideals and begin to think that they really can become realities, or if we prematurely think that biology really must be like engineering, we might be in trouble. Such a belief conflates the object of study with our relatedness to that object. It misunderstands the role of the equipment-based relationship. The model – and associated machines, software, formulae. et cetera – is equipment that constitutes our relatedness to the phenomena. It cannot be the phenomena themselves.

Closely related to the ideals of replacement and control is the widespread application of abstraction and equality in engineering-like fields (and their application to new fields that are presently being clad in the trappings of engineering, such as biology). Abstraction and equality – – the notion that two entities, instances, moments, etc., are in some way the same – allow us to introduce an algebra, to reason in the general and not in specifics. And this is of course what computers do. It also means that two sequences of actions (laboratory protocols for example), although they are different sequences, or the same sequence but at different instances in time, can lead to the same result. Just as 3+1 and 2+2 both “equal” 4. In other words, history becomes irrelevant, the specific path taken no longer means very much. But it is not clear that this can ever truly be the case outside of an algebra, and that is what risks being forgotten.

We might call all this the emergence of technological biology, or technological nature, the conquest of biology by λόγος, et cetera. The principal danger seems to be the conflation of method with object, of abstraction with the specific. And here we see clearly how something apparently simple – studying RNA expression levels in the software package R, for example – opens up the deepest metaphysical abysses. One of the most important tasks right now, then, would be the development of a scientific and technological culture that keeps the benefits of the technological attitude without losing sight of a more basic non-technological relatedness. The path lies open…