Improving accuracy by leaps and unbounds

Abstract

In the same way that arrangements of digital logic gates form the building blocks of electronic computers, networks of biochemical reactions constitute the computing hardware of cells. These networks carry out the processes of life, from cell growth and death to responses to environmental cues such as nutrients and toxins. Biological receptors—the cell’s “particle detectors”—measure the concentrations of external and internal signaling molecules, performing a generic and essential task of the cell’s computing machinery. Readouts from the receptors are used to regulate decisionmaking circuits within the cell that control the expression of genes and proteins, and cellular motion, growth and division. The energy scale for biomolecular interactions is typically a few times kBT, so it is natural to ask: How reliably can these measurements be carried out in the face of inherent fluctuations? Do biological sensors reach the detection limits set by the laws of physics? Thierry Mora and Ned Wingreen at Princeton University in the US report theoretical results in Physical Review Letters[1] that add to recent efforts exploring these questions. Specifically, they suggest cells may be able to measure changes in time in the concentration of signaling molecules twice as accurately as previous theoretical bounds. The ability to perform increasingly quantitative and noninvasive experiments on biological systems places us in an exciting era in which to look at cellular phenomena from the standpoint of statistical physics. We know of many examples where signal processing occurs with exceptional accuracy, such as the ability of rod photoreceptor cells to count single photons [2], the reproducibility of concentration profiles of morphogens (signaling molecules governing the pattern of tissue development in the embryo) and the accuracy with which they can be read out by cells [3], and the sensitivity of the bacterial flagellar motor to changes in concentration of an internal signal [4, 5]. In these examples, the reliability of the output is limited by the inherent random nature of the input signal—photon shot noise or diffusive counting noise—thereby approaching a physical lower limit. The requirement of nearly perfect detection and processing of the input to generate the macroscopic output poses constraints on the underlying strategies, resulting in the adoption of common signal processing approaches in these biological systems [3, 6]. Roughly thirty years ago, Berg and Purcell derived statistical limits of cell sensing in their classic article on the physics of chemoreception [7]. Their work focused on chemotaxis, the behavioral response of singlecelled organisms that helps them move toward favorable chemicals and away from harmful ones. (Chemotaxis is involved in the motile response of immune cells hunting down bacterial predators, wound healing, the spread of cancerous cells, sperm navigation, and the preferential growth of neurons.) This canonical sensory response is best studied in the well-controlled and experimentally accessible setting offered by two model organisms. The eukaryotic social slime mold Dictystelium discoidium (or Dicty) and the prokaryotic bacterium Escherichia coli employ very different methods of chemotactic sensing and response and have emerged as “hydrogen atoms” in the study of cellular sensing. A central result from Berg and Purcell’s work was that the accuracy with which cells can detect the concentration of signaling molecules is fundamentally limited due to the random arrival of diffusing signaling molecules at their targets. A hypothetical “perfect” device, able to count molecules exactly, is limited by this diffusive noise only. In actuality, biological sensors “count” the number of diffusing signaling molecules by registering chemical binding and unbinding interactions (Fig.1). Thermal fluctuations in the free energy levels of the bound and unbound receptor-signal system lead to effective binding/unbinding reaction rate fluctuations and result in additional counting error. This means that in addition