van Oudenaarden Lab - Research

Gene Networks and Noise

Gene Networks - The ability of a living cell to grow and divide, and to sense and respond to its environment is determined by a complex web of intracellular, and sometimes intercellular, protein and gene networks. During the last decade new technologies such as high-throughput genome sequencing and gene arrays have enabled a large-scale identification of these interaction networks. Although many of these networks have already been mapped, surprisingly little is known about the function of specific network architectures. Rather than taking a genome-wide approach, our lab focuses on the smaller, recurring network motifs buried in the larger networks. These motifs are built from a handful of genes and proteins and display a network structure that appears over and over again in different networks and different organisms. The underlying idea is that these motifs define autonomous functional modules that are the building blocks for the entire cellular network. In our lab we explore several network motifs that the cell uses to perform elementary operations such as comparing and choosing between two values, randomly selecting a value, memorizing a value, and generating a periodic or stochastic signal.

An example of network motifs that we explore in our lab are feedback loops. Complex gene and protein networks store cellular memory by creating two or many discrete, stable states of network activity. The generation of bistability by simple feedback loops in synthetic circuits is well understood. However, naturally occurring networks, in particular in eukaryotic organisms, possess a complex organization of multiple nested feedback loops, making an analysis of system dynamics disproportionately more complicated. These networks are exemplified by the galactose signalling pathway in the yeast Saccharomyces cerevisiae. Despite extensive data on its molecular interactions, a prediction of its dynamical system behavior a priori is challenging. The galactose signal propagates through a four-stage signalling cascade. At the uppermost stage is Gal2p, which imports extracellular galactose into the cell. Subsequently, intracellular galactose binds to and activates Gal3p. At the third stage of this cascade, the activated Gal3p binds to and sequesters Gal80p in the cytoplasm, depleting Gal80p from the nucleus. The transcriptional activator Gal4p, which is constitutively bound to promoters of the GAL genes, is then released from the inhibitory action of Gal80p and activates expression of genes at the output of the cascade, including GAL1, GAL2, GAL3 and GAL80 . Because an increase in Gal2p and Gal3p concentration results in enhanced transcriptional activity, these proteins close two positive feedback loops. The opposite holds for Gal80p, which is part of a negative feedback loop. To read out the Gal4p activity in single yeast cells we monitored the expression of yellow fluorescent protein (YFP) driven by the GAL1 promoter. (For more details see Acar et al.)

 

Stochastic Gene Expression - Biochemical reactions that involve small numbers of molecules are intrinsically noisy, being dominated by large concentration fluctuations. Although ignored in most models of gene networks, the reality is that the level of gene expression of the same gene can vary enormously from one cell to another within a genetically-identical population. Surprisingly, the functioning of a living organism is not significantly hindered by these random fluctuations. Biological cells can even exploit noise by deliberately introducing diversity into a population. In these cases noise is not a nuisance, but essential for survival. Advances in modern biochemistry and genetics have led to a detailed understanding of the molecular machinery involved in gene expression, and the constant flow of data from the Genome Project has enabled the identification of more and more genes. A millennial challenge is to quantitatively understand how different genes and their regulating proteins are grouped together in genetic circuits, and how stochastic fluctuations influence gene expression in these complex systems. In our group we focus on the importance of noise in the expression of genes by using both experimental and theoretical approaches.

The genetic program of a living cell is determined by a complex web of gene networks. The proper execution of this program relies on faithful signal propagation from one gene to the next. This process may be hindered by stochastic fluctuations arising from gene expression, because some of the components in these circuits are present at low numbers, which makes fluctuations in concentrations unavoidable. Additionally, reaction rates can fluctuate because of stochastic variation in the global pool of housekeeping genes or because of fluctuations in environmental conditions that affect all genes. For example, fluctuations in the number of available polymerases or in any factor that alters the cell growth rate will change the reaction rates for all genes. Recently we designed a gene network in which the interactions between adjacent genes could be externally controlled and quantified at the single-cell level.This synthetic network consisted of four genes, of which three were monitored in single Escherichia coli cells by cyan, yellow, and red fluorescent proteins (CFP, YFP, and RFP). The first gene, lacI, is constitutively transcribed and codes for the lactose repressor, which down-regulates the transcription of the second gene, tetR, that is bicistronically transcribed with cfp. The gene product of tetR, the tetracycline repressor, in turn down-regulates the transcription of the third gene, reported by YFP. The fourth gene, rfp, is under the control of the lambda repressor promoter PL, which is a strong constitutive promoter. Because this gene is not part of the cascade, this reporter was used to evaluate the effect of global fluctuations. This cascade was used to measure how fluctuations in an upstream gene (tetR, reported by CFP) transmit down-stream (and are reported by YFP). The inducers isopropyl-b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (ATC) bind to and inhibit the repression of the lactose and tetracycline repressors, respectively, and were used to tune, respectively, the expression of the upstream gene and the coupling between the two genes. For more details on this project see Pedraza et al.