One of the most important recent conceptual advances in biology, in my opinion, is the realization that much of the business carried out by the nanoscale machinery of the cell is as much about processing information as processing matter. Dennis Bray pointed out, in an important review article (8.4 MB PDF) published in Nature in 1995, that mechanisms such as allostery, by which the catalytic activity of an enzyme can be switched on and off by the binding of another molecule, mean that proteins can form the components of logic gates, which themselves can be linked together to form biochemical circuits. These information processing networks can take information about the environment from sensors at the cell surface, compute an appropriate action, and modify the cell’s behaviour in response. My eye was recently caught by a paper from 2008 which illustrates rather nicely how it is that the information processing capacity of a single cell can be quite significant.
The paper – Emergent decision-making in biological signal transduction networks (abstract, subscription required for full article in PNAS), comes from Tomáš Helikar, John Konvalina, Jack Heidel, and Jim A. Rogers at the University of Nebraska. What these authors have done is construct a large scale, realistic model of a cell signalling network in a generic eukaryotic cell. To do this, they’ve mined the literature for data on 130 different network nodes. Each node represents a protein; in a crucial simplification they reduce the complexities of the biochemistry to simple Boolean logic – the node is either on or off, depending on whether the protein is active or not, and for each node there is a truth table expressing the interactions of that node with other proteins. For some more complicated cases, a single protein may be represented by more than one node, expressing the fact that there may be a number of different modified states.
This model of the cell takes in information from the outside world; sensors at the cell membrane measure the external concentration of growth factors, extracellular matrix proteins, and calcium levels. This is the input to the cell’s information processing system. The outputs of the systems are essentially decisions by the cell about what to do in response to its environment. The key result of the simulations is that the network can take a wide variety of input signals, often including random noise, and for each combination of inputs produce one of a small number of biologically appropriate responses – as the authors write, “this nonfuzzy partitioning of a space of random, noisy, chaotic inputs into a small number of equivalence classes is a hallmark of a pattern recognition machine and is strong evidence that signal transduction networks are decision-making systems that process information obtained at the membrane rather than simply passing unmodified signals downstream.”