Phenotypic Variability in Synthetic Biology Applications: Dealing with Noise in Microbial Gene Expression

Abstract

The stochasticity due to the infrequent collisions among low copy-number molecules within the crowded cellular compartment is a feature of living systems. Single cell variability in gene expression within an isogenic population (i.e., biological noise) is usually described as the sum of two independent components: intrinsic and extrinsic stochasticity. Intrinsic stochasticity arises from the random occurrence of events inherent to the gene expression process (e.g., the burst-like synthesis of mRNA and protein molecules). Extrinsic fluctuations reflect the state of the biological system and its interaction with the intra and extracellular environments (e.g., concentration of available polymerases, ribosomes, metabolites, and micro-environmental conditions). A better understanding of cellular noise would help synthetic biologists design gene circuits with well-defined functional properties. In silico modeling has already revealed several aspects of the network topology's impact on noise properties; this information could drive the selection of biological parts and the design of reliably engineered pathways. Importantly, while optimizing artificial gene circuitry for industrial applications, synthetic biology could also elucidate the natural mechanisms underlying natural phenotypic variability. In this review, we briefly summarize the functional roles of noise in unicellular organisms and address their relevance to synthetic network design. We will also consider how noise might influence the selection of network topologies supporting reliable functions, and how the variability of cellular events might be exploited when designing innovative biotechnology applications.

Keywords: biological noise; intrinsic/extrinsic stochasticity; network topology; standard biological parts; synthetic biology.

FIGURE 1

Intrinsic and extrinsic components of noise. Extrinsic noise results from changes in the environmental conditions and cellular state. This contribution equally affects two fluorescence reporter proteins transcribed from the same promoter (a). Their expression is modulated in an uncorrelated way by intrinsic noise, due to the stochasticity of biochemical events (b).

FIGURE 2

Noise as a positive regulator of cellular behavior. Variability in protein expression levels among isogenic individuals might improve the overall performance of a cell population. Boxes of different colors are used to represent cells where a protein (or a set of proteins), critical for the cell survival, is differently expressed among the individuals of an isogenic population. When the population is featured by limited phenotypic variability (left hand side of the lower diagram), different cells have comparable expression levels. In a noisy population (right hand side of the lower diagram), the same average expression level, as accessible with bulk measurements, result from a more dispersed distribution of protein concentrations at the single-cell level. If the cell survival depends on the expression level of a given protein (i.e., only cells with expression levels corresponding to light-blue, blue, and purple boxes survive) a noisier population might be more robust to changes in environmental conditions causing selective stress, thereby ensuring the species survival. Similar strategies could be adopted to improve sensing properties in synthetic applications. dead; alive. The reader is referred to (Smits et al., 2006) for additional examples.