Exact Science

Earlier this month, I blogged about The Principles of Life by Tibor Ganti (and collaborators). I was able to get my hands on a physical copy of the book (thanks to Interlibrary Loan) and I’m now in Chapter 3, “The unitary theory of life”. It begins by introducing the term “exact science”. Here’s how Ganti introduces the idea.

 

Exact sciences, such as mathematics, mechanics, the theory of electricity, thermodynamics, chemistry, etc., are characterized by the common fact that they all have specific model systems, i.e., systems which represent phenomena of the real world without disturbing factors… By using model systems the exact sciences can describe the phenomena under investigation in qualitative and quantitative respects, can formulate them mathematically.

 

I like how Ganti emphasizes the necessity of models in the physical sciences. As a chemist, we constantly use structure-based models – to represent too-small-to-see atoms and molecules. He also talks about model systems. We’re not just modeling individual structures. We’re also modeling systems and the relationships of different parts within the system. Ganti then emphasizes two things.

 

First… any one of the exact sciences models only one part of the real world and even this one only from a definite point of view, independently of the other phenomena. Second, it must be understood that it is not the real world which the exact sciences are capable of treating with an arbitrary(?) exactness, but their own model systems. Real-world phenomena are only approximated by them.

 

I’ve been thinking a lot about the modeling relation thanks to Robert Rosen’s work. I don’t quite grasp how it all works, but what I do know: When we formulate a model system (yes, with formulae!), we do so via reductionism. This is inherent in making a model. Ideally it captures the key characteristics of the system’s behavior or phenomena we’re trying to capture. But inevitably it will leave out some things. If the system is complex, and not merely complicated, probing some aspect of the system by setting some controlled test will inevitably result in surprises, somewhere down the road – they might not reveal themselves immediately depending on the underlying dynamics that we cannot capture.

 

Ganti goes on to playfully describe the absurdness of geometry (points, lines, planes) in the way it defines idealized ‘units’. Mechanics then looks ridiculous in how it uses ‘point’ masses. But the wonder is how well it works. We’ve been using Newton’s laws for over three centuries very, very fruitfully. Electricity begins with ‘point’ charges. Chemistry begins with ‘atoms’, but we have other strange hard-to-pin down elementary definitions such as the word ‘element’. But since we’re discussing Life, what is the ‘unit’ of entities that are alive? This question is tricky and Ganti spends quite a few pages discussing life, death, and the in-between – not dead, but not living – realm associated with cryptobiosis. The simple answer, the ‘cell’ isn’t quite sufficient and one has to account for different levels without privileging any one in particular – a biological relativity point of view.

 

This leads to an interesting discussion of stability. Ganti exhorts us to be careful because we have to describe narrow scientific ‘model’ terms using everyday language, much like how Bohr argues about the nature of reality using his ‘complementarity’ view given the strangeness of quantum mechanics. Ganti distinguishes equilibrium from stability. He then considers the stationary (or steady) state, which he will differentiate from homeostasis.

 

The stationary state is, by definition, a state of open systems with an equal rate of inward and outward movement of matter. However, living systems are fundamentally growing (accumulating) systems, in which more matter enters than leaves… [it] cannot be in a stationary state, and hence attempts to reduce the stability of living systems to the irreversible thermodynamics of open systems in the steady state are… doomed to failure.

 

The nub of my research studying proto-metabolic systems is how to move from thermodynamic (and kinetic) systems where equilibrium reigns starting, into the arena of non-equilibrium systems that exhibit some measure of stability (such as stationary states), and somehow layered hierarchies of control on top of all this. I’ve barely begun to learn how to deal with non-equilibrium thermodynamics, and already the looming field of control theory (of which I am mostly ignorant) already looms. I have a long, long way to go. Ganti does provide some direction – his idea of “constrained paths” embodied in the wetworks of cyclic chemistry. Autocatalytic networked cyclic chemistry, to be more specific.

 

This measure of control allows the living system to maintain a ‘stable’ internal environment (the idea of homeostasis) against what is going on external to it. This requires sampling some parameters of the external environment, and then responding to it in some way. An element of prediction or anticipation must be involved. The system needs to formulate a model that is sufficiently reduced to guess what might happen next – akin to running a quick simulation in a short timeframe before deciding how to respond. I find myself forced to use such anthropomorphic expressions: guessing and deciding seem to tread on consciousness and free-will. Indeed, there is a long way to go. But the way forward seems to be starting with ‘exact science’, recognizing its limitations, and continuing to refine better and better models.