Method and object. Horizons for technological biology

(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…

Is bioinformatics possible?

I recently gave a talk at the Bio-Pitch event at the French-Japanese institute. I was fortunate to be able to speak about some of the ideas I’ve been developing here among so many interesting projects (MetaPhorest, HTGAA, Yoko Shimizu, Tupac Bio, Bento Lab etc).

The topic of my talk was “Is bioinformatics possible”? A deliberate provocation, since of course many people including myself work with this every day. I simply mean to suggest that there are intrinsic problems in the field that are not usually discussed or thought about, and that it might be valuable to confront those problems.

The slides are available, if anyone is interested.

The bigger topic that is hinted at, but not discussed, might be the instrumental relationship of humans to nature. I hope to return to this problem soon.

Reactive software and the outer world

At Scala Matsuri a few weeks ago (incidentally, an excellent conference), I was fortunate to be able to attend Jonas Bonér’s impassioned talk about resilience and reactive software. His theme: “without resilience, nothing else matters”.

At the core of it is a certain way of thinking about the ways that complex systems fail. Importantly, complex systems are not the same as complicated systems, although in everyday speech we tend to confuse the two. Perhaps a related or even identical question is: how do composite systems fail?

Using a terminology that originates with the Erlang language, Bonér talked about the “error kernel”, which is the part of a software system that must never fail, no matter what. As long as this innermost part stays alive, other parts are allowed to fail. There are mechanisms to replace, restart or route around failures in the outer parts.

This style of design leads to a well-structured failure and supervision hierarchy. Maybe this style of thinking itself is the most important contribution. In most software systems being designed today, the possibility of errors or failures is often a second class citizen, swept under the carpet, and certainly not part of a carefully considered structure of possibilities of failure. What if this structure becomes a primary concern?

Once errors are well structured and organised in a hierarchy, it also becomes easy to decide what to do when errors occur. The hierarchy structure clearly indicates which parts of a system have become defunct and need to be replaced or bypassed. Recoverability – being able to crash safely – at every level takes the software system a little bit closer, it seems, to biological systems.

Biological systems, Bonér pointed out, usually operate with some degree of inherent failure, be it disease, weakness, mutations or environmental stress. Perfect functioning is not typical, and it seems to me that for most organisms such a state may not even exist.

Recoverability at every level, resilience, and error hierarchies – “let it fail” – is truly a significant and very humble way of thinking about software. It means that as the developer, I acknowledge that the software I am writing does not control the universe (although as a developer I often fall prey to that illusion). The active principle, the “prime mover”, is somewhere outside the scope that I control. When it produces some unforeseen circumstance, we must respond properly. Reactive software to me seems to quietly acknowledge this order of things.

I have only had a very brief opportunity to try out Akka, Typesafe’s actor framework, in my projects so far, but I felt inspired by Boner’s talk and hope to use it more extensively in the future.

The inexhaustible wealth of appearance, information and specificity


When perceiving an object, for example a chair, the statement “this is X” (this is a chair) is almost entirely uninteresting. The concept by which we identify the object is a mere word, and in a sense entirely devoid of meaning.

That concept does help us align this object with other entities in space and time. It sets expectations about what has been done and what can be done to and with it, and it links the object to social practices. But none of these things are very interesting. After all, we understand quite well what society expects from chairs.

What is more interesting is all the other statements we could make about a particular chair, that is, all the qualities, information, phenomena and experiences that do not fit the general concept of a chair. Call this the chair’s particularity. It may be unusually sturdy or rickety. It may evoke a sense of sorrow or longing for a person who used to sit on it. It may make us think about economics. Its shape may even have something spiritual about it. It may, if it is a chair in an abandoned house, be decomposing. And even this is just scratching the surface.

In all likelihood, we are able to produce an unbounded number of interesting statements about this locus that is the chair. (Recall the famous school assignment about writing a story several hundred words long about the face of a coin.) And this would hold true both when we speak freely, metaphorically and poetically, and when we restrict ourselves to testable, scientific (in the modern sense) statements. New metaphors can always be invented, new scientific equipment may always be constructed. These additional modes of relatedness to the locus provide, perhaps, the basis for new statements.

How are we to understand this fundamental overflowing, this exuberant blossoming, the profound potential wealth that we draw upon and realise when we articulate statements about an entity such as this chair? It is not part of the concept “chair”. This concept is overlaid as an afterthought in order to make the surplus of impressions manageable and graspable. We are used to economising the use of our consciousness, dispensing it only sparingly, through the shielding, buffering and deflection that concepts afford us.

For Heidegger, being is the basis of intelligibility, a carrier of meaning. Language and intelligibility exists only on the basis of primordial being. He makes it his task to inquire as to what this being is.

For Georges Bataille, all activity that involves redistribution of energy, human and otherwise, accumulates a surplus that necessarily must be released in some way.

Myths and archetypes repeat themselves throughout history and society, in constantly renewed forms which are both always the same and always made from different specific constitutent parts. They can always be repeated in a different way. The hero myth exists in every culture (see for example Jung or Campbell). Conversely, this myth in all its specific detail is always different each time it appears.

In difference and repetition, Deleuze argues that conceptual machinery is constantly at work, extracting difference from whatever the underlying basis is.

Genetic material successfully reproduces and preserves itself, and perhaps prospers, only through the continual introduction of difference and variation at an appropriate rate.

The digital world, on the other hand, denies the possibility of generating an unbounded number of statements from some entity (such as a record in a database). In fact, its essence is the possibility of perfect copying, which happens only when the information being carried is strictly circumscribed and limited.

All these concepts, it seems, have something in common – the interaction between a specific form and the possibility of an infinite number of variations of and departures from that form.

Mysteries of the scientific method

mitScientific method can be understood as the following steps: formulating a hypothesis, designing an experiment, carrying out experiments, and drawing conclusions. Conclusions can feed into hypothesis formulation again, in order for a different (related or unrelated) hypothesis to be tested, and we have a cycle. This feedback can also take place via a general theory that conclusions contribute to and hypotheses draw from. The theory gets to represent everything we have learned about the domain so far. Some of the steps may be expanded into sub-steps, but in principle this cycle is how we generally think of science.

This looks quite simple, but is it really? Let’s think about hypothesis formulation and drawing conclusions. In both of these steps, the results are bounded by our imagination and intuition. Thus, something that doesn’t ever enter anybody’s imagination will not be established as scientific fact. In view of this, we should hope that scientists do have vivid imaginations. It is easy to imagine that there might be very powerful findings out there, on the other side of our current scientific horizon, that nobody has yet been creative enough to speculate about. It is not at all obvious that we can see the low hanging fruit or even survey this mountainous landscape well – particularly in an age of hyper-specialisation.

But scientists’ imaginations are probably quite vivid in many cases – thankfully. Ideas come to scientists from somewhere, and some ideas persist more strongly than others. Some ideas seduce scientists to years of hard labour, even when the results are meagre at first. Clearly this intuition and sense that something is worth investigating is absolutely crucial to high quality results.

A hypothesis might be: there is a force that make bodies with mass attract to each other, in a way that is inversely proportional to the squared distance between them. To formulate this hypothesis we need concepts such as force, bodies, mass, distance, attraction. Even though the hypothesis might be formulated in mere words, these words all depend on experience and practices – and thus equipment (even if the equipment used in some cases is simply our own bodies). If this hypothesis is successfully proven, then a new concept becomes available: the law of gravity. This concept in turn may be incorporated into new hypotheses and experiments, paving the way for ever higher and more complex levels of science and scientific phenomena.

Our ability to form hypotheses, to construct equipment and to draw conclusions, seem to be human capacities that are not easy to automate.

Entities such as matter, energy, atoms and electrons become accessible – I submit – primarily through the concepts and equipment that give access to them. In a world with an alternate history different from ours, it is conceivable that entirely different concepts and ideas would explain the same phenomena that are explained by our physics. For science to advance, new equipment and new concepts need to be constructed continually. This process is almost itself an organic growth.

Can we have automated science? Do we no longer need scientific theory? (!?) Can computers one day carry out our science for us? Only if either: a) science is not an essentially human activity, or b) computers become able to take on this human essence, including the responsibility for growing the conceptual-equipmental boundary. Data mining in the age of “big data” is not enough, since this (as far as I know) operates with a fixed equipmental boundary. As such, it would only be a scientific aid and not a substitute for the whole process. Can findings that do not result in concepts and theories ever be called scientific?

If computer systems ever start designing and building new I/O-devices for themselves, maybe something in the way of “artificial science” could be achieved. But it is not clear that the intuition guiding such a system could be equivalent to the human intuition that guides science. It might proceed on a different path altogether.