Wednesday, 11 January 2017

A simple biomolecular machine for exploiting information

Biological systems at many scales exploit information to extract energy from their environment. In chemotaxis, single-celled organisms use the location of food molecules to navigate their way to more food; humans use the fact that food is typically found in the cafeteria. Although the general idea is clear, the fundamental physical connection between information and energy is not yet well-understood. In particular, whilst energy is inherently physical, information appears to be an abstract concept, and relating the two consistently is challenging. To overcome this problem, we have designed two microscopic machines that can be assembled out of naturally-occurring biological molecules and exploit information in the environment to charge a chemical battery. The work has just been published as an Editor's selection in Physical Review Letters:

The basic idea behind the machines is simple, and makes use of pre-existing biology. We employ an enzyme that can take a small phosphate group (one phosphorus and several oxygen atoms bound together) from one molecule and attach it to another – a process known as phosphorylation. Phosphorylation is the principal signaling mechanism within a cell, as enzymes called kinases use phosphyrlation to activate other proteins. In addition to signalling, phosphates are one of the cell’s main stores of energy; chains of phosphate bonds in ATP (the cell’s fuel molecule) act as batteries. By ‘recharging’ ATP through phosphorylation, we store energy in a useful format; this is effectively what mitochondria do via a long series of biochemical reactions.

Fig 1.: The ATP molecule (top) and ADP molecule (bottom). Adenosine (the "A") is the group of atoms on the right of the pictures; the phosphates (the P) are the basic units that form the chains on the left. In ADP (Adenosinediphosphate) there are two phosphates in the chain; in ATP((Adenosinetriphosphate) there are three. 

The machines we consider have three main components: the enzyme, the ‘food’ molecule that acts as a source of phosphates to charge ATP, and an activator for the enzyme, all of which are sitting in a solution of ATP and its dephosphorylated form ADP. Food molecules can either be charged (i.e. have a phosphate attached) or uncharged (without phosphate). When the enzyme is bound to an activator, it allows transfer of a phosphate from a charged food molecule to an ADP, resulting in an uncharged food molecule and ATP. The reverse reaction is also possible.

In order to systematically store energy in ATP, we want to activate the enzyme when a charged food molecule is nearby. This is possible if we have an excess of charged food molecules, or if charged food molecules are usually located near activators. In the second case, we're making use of information: the presence of an activator is informative about the possible presence of a charged food molecule. This is a very simple analogue of the way that cells and humans use information as outlined above. Indeed, mathematically, the 'mutual information' between the food and activator molecules is simply how well the presence of an activator indicates the presence of a charged food molecule. This mutual information  acts as an additional power supply that we can use to charge our ATP-batteries. We analyse the behaviour of our machines in environments containing information, and find that they can indeed exploit this information, or expend chemical energy in order to generate more information. By using well-known and simple components in our device, we are able to demystify much of the confusion over the connection between abstract information and physical energy.

A nice feature of our designs is that they are completely free-running, or autonomous. Like living systems, they can operate without any external manipulation, happily converting between chemical energy and information on its own. There’s still a lot to do on this subject; we have only analysed the simplest kind of information structure possible and have yet to look at more complex spatial or temporal correlations. In addition, our system doesn’t learn, but relies on ‘hard-coded’ knowledge about the relation between food and activators. It would be very interesting to see how machines that can learn and harness more complex correlation structures would behave.

Authored by Tom McGrath