Naama Barkai

Robustness in simple biochemical networks

Cells use complex networks of interacting molecular components to transfer and process information. These “computational devices of living cells” are responsible for many important cellular processes, including cell-cycle regulation and signal transduction. Here we address the issue of the sensitivity of the networks to variations in their biochemical parameters. We propose a mechanism for robust adaptation in simple signal transduction networks. We show that this mechanism applies in particular to bacterial chemotaxis. This is demonstrated within a quantitative model which explains, in a unified way, many aspects of chemotaxis, including proper responses to chemical gradients. The adaptation property is a consequence of the networks connectivity and does not require the ‘fine-tuning’ of parameters. We argue that the key properties of biochemical networks should be robust in order to ensure their proper functioning.

Robustness in bacterial chemotaxis

Networks of interacting proteins orchestrate the responses of living cells to a variety of external stimuli, but how sensitive is the functioning of these protein networks to variations in theirbiochemical parameters? One possibility is that to achieve appropriate function, the reaction rate constants and enzyme concentrations need to be adjusted in a precise manner, and any deviation from these ‘fine-tuned’ values ruins the networks performance. An alternative possibility is that key properties of biochemical networks are robust; that is, they are insensitive to the precise values of the biochemical parameters. Here we address this issue in experiments using chemotaxis of Escherichia coli, one of the best-characterized sensory systems,. We focus on how response and adaptation to attractant signals vary with systematic changes in the intracellular concentration of the components of the chemotaxis network. We find that some properties, such as steady-state behaviour and adaptation time, show strong variations in response to varying protein concentrations. In contrast, the precision of adaptation is robust and does not vary with the protein concentrations. This is consistent with a recently proposed molecular mechanism for exact adaptation, where robustness is a direct consequence of the networks architecture.

Mechanisms of noise-resistance in genetic oscillators

A wide range of organisms use circadian clocks to keep internal sense of daily time and regulate their behavior accordingly. Most of these clocks use intracellular genetic networks based on positive and negative regulatory elements. The integration of these “circuits” at the cellular level imposes strong constraints on their functioning and design. Here, we study a recently proposed model [Barkai, N. & Leibler, S. (2000) Nature (London), 403, 267–268] that incorporates just the essential elements found experimentally. We show that this type of oscillator is driven mainly by two elements: the concentration of a repressor protein and the dynamics of an activator protein forming an inactive complex with the repressor. Thus, the clock does not need to rely on mRNA dynamics to oscillate, which makes it especially resistant to fluctuations. Oscillations can be present even when the time average of the number of mRNA molecules goes below one. Under some conditions, this oscillator is not only resistant to but, paradoxically, also enhanced by the intrinsic biochemical noise.

Circadian clocks limited by noise.

Circadian rhythms, which provide internal daily periodicity, are used by a wide range of organisms to anticipate daily changes in the environment. It seems that these organisms generate circadian periodicity by similar biochemical networks within a single cell. A model based on the common features of these biochemical networks shows that a circadian network can oscillate reliably in the presence of stochastic biochemical noise and when cellular conditions are altered. We propose that the ability to resist such perturbations imposes strict constraints on the oscillation mechanisms underlying circadian periodicity in vivo.

Protease helps yeast find mating partners

The choice of mating partner by the yeast Saccharomyces cerevisiae involves the detection of mating pheromones produced by other yeast cells. A cell that is capable of mating deduces the position of its nearest mating partner from the spatial gradient of pheromone. While studying this process, we realized that, in the presence of many potential mating partners, the gradient might not point in the direction of the nearest partner. Here we show that degradation of some of the mating pheromone by protease enzymes helps to align the gradient in the direction of the nearest partner, which increases mating efficiency.

Robust amplification in adaptive signal transduction networks

Abstract Amplification of small changes in input signals is an essential feature of many biological signal transduction systems. An important problem is how sensitivity amplification can be reconciled with wide dynamic range of response. Here a general molecular mechanism is proposed, in which both high amplification and wide dynamic range of a sensory system is obtained, and this without fine-tuning of biochemical parameters. The amplification mechanism is based on inhibition of the enzymatic activity of the sensory complex. As an example, we show how this ‘inhibition-driven amplification’ mechanism might function in the bacterial chemotaxis network, where it could explain several intriguing experimental observations connected with the existence of high gain, wide dynamic range and robust adaptation.

Bacterial chemotaxis. United we sense...

Some biological systems, such as those involved in chemotaxis, combine high sensitivity with wide dynamic range. For instance the bacterium Escherichia coli can sense chemical attractants over a concentration range of several orders of magnitude, and respond even to small signals in which ligand binds to only a minute fraction of chemotaxis receptors. New theoretical work shows how this may be achieved & the authors put forward the idea that adaptive receptor clustering is the mechanism concerned.