Using the yeast Saccharomyces cerevisiae as a model organism, we study the genetic and phenotypic changes cells implement and experience during the processes of Cellular Aging and Gene Network Evolution.

We are interested in understanding how gene networks are rewired while cells evolve in controlled laboratory environments. Our work combines experimental, theoretical, and computational approaches to investigate general design principles that help gene networks robustly function in a variety of genetic backgrounds and environmental conditions.

We are also interested in uncovering the genetic mechanisms of cellular aging. Which genes and gene networks are responsible from controlling the aging process? Which decision-making sequences can be executed to maximize the life span of a living system? Despite the fundamental nature of these questions, we have very limited understanding on the cellular mechanisms governing aging. Our laboratory applies quantitative Systems Biology approaches, single-cell time-dynamic imaging techniques, and novel microfluidic platforms to the study of this complex phenotype, with the goal of gaining novel insights into the regulation of cellular aging.

Projects in our lab make extensive use of single-cell level experimental methods such as fluorescence microscopy and flow cytometry. For a mechanistic understanding of experimental results, we also build simple quantitative models of cellular activities.

Projects completed in the past include:

1. Synthetic Reprogramming of Cellular Memory

We have used the galactose utilization pathway (GAL pathway) of the budding yeast Saccharomyces cerevisiae as a model gene network to:

(i) reveal the effect of feedback regulation on protein expression levels
(ii) reprogram the stability of gene expression states

Figure 1: The galactose signaling pathway. Red arrows denote the four stage signaling cascade in which the external galactose signal controls the transcriptional activity of the GAL genes. The galactose bound state of Gal3p is denoted by Gal3p*. Pointed and blunt arrows reflect activation and inhibition, respectively. The blue arrows denote feedback loops established by Gal2p, Gal3p, and Gal80p.

The GAL network has a bistable expression profile. Bistability is a dynamical system property giving rise to two distinct gene expression states (OFF and ON) for isogenic cells grown in the same environment.

Our work (Acar et al., Nature, 2005) has shown how the pathway could store information about previous exposures to galactose for hundreds of generations in a programmable manner. By using synthetic biology techniques, we have systematically ‘opened’ the feedback loops in the pathway and uncovered their role on the establishment and preservation of such cellular memory.

By engineering a strain that randomly switched between two phenotypic states (ON and OFF) as a result of stochastic gene expression, we showed that the rates at which cells switch between these states could be experimentally tuned. In this way, we reprogrammed the pathway activity so that memory at the single-cell level could be tuned from hours to months. We then modeled the effect of stochastic fluctuations on cellular switching and memory by using energy landscapes to represent cellular states. The technique of systematically eliminating feedback loops provided a general strategy for the quantitative analysis of gene network regulatory dynamics.

2. Phenotypic Switching as a Survival Strategy

We have made the first direct experimental demonstration of how single cells can enhance survival in altering environmental conditions by utilizing stochasticity in their gene expression (Acar et al., Nature Genetics, 2008).

Despite the progress made in understanding the origins of noise in gene expression, experimental studies addressing its biological relevance had been missing. Noise has the potential to be detrimental to the coordination of cellular activities; however, through the population heterogeneity it creates, noise might also act as a mechanism to help cells cope with the uncertainty in environments with fluctuating nutrient and fitness conditions.

Our work provided evidence that noise in gene expression could be beneficial for cells in constantly fluctuating environments. We synthetically modified the bistable galactose network so that each phenotype (ON or OFF) would confer a growth advantage over the other phenotype in a certain environment.

Figure 2: Two states (phenotypes) exist for each cell, ON (orange) and OFF (green). Cells randomly switch between the two states with frequencies rON and rOFF. The first environment (E1) has no uracil, whereas the second (E2) has both 5-FOA and uracil. By having the GAL1 promoter drive URA3 gene expression, a cell was made either fit or unfit to its environment depending on its specific phenotype. For example, in E1, on-state cells are fit and have a growth rate of gamma(ON), but the unfit off-state cells proliferate with a smaller growth rate, gamma(OFF).

Comparing the growth of two bistable populations with two different phenotypic switching rates, we found that fast-switching populations outgrew slow switchers when the environment fluctuated rapidly, whereas slow-switching populations outgrew fast switchers when the environment changed rarely. These results suggest that cells tune inter-phenotype switching rates to the frequency of environmental changes. The results from this work give a quantitative basis for understanding the effect of gene expression noise on the growth of cell populations.

3. Network-Dosage Compensation in Gene Circuits

We have investigated how varying the dosage of a gene network affects its activity and what roles the interaction topology of the network play in this process (Acar et al., Science, 2010).

The number of copies of a gene network in a cell, or network dosage, has a direct effect on cellular phenotypes. Network dosage is altered in situations such as the switching of some organisms between haploid and diploid life forms, doubling of chromosomes during cell cycle, genome-wide duplication or loss of genetic content, and global variation in gene expression. Different phenotypes have different levels of sensitivity to such variations and the need for effective compensation mechanisms arises when cells cannot tolerate these alterations.

We explored how network structure facilitates network-level dosage compensation. By using the yeast galactose network as a model, we combinatorially deleted one of the two copies of its four regulatory genes and found that network activity was robust to the change in network dosage. A mathematical analysis revealed that a two-component genetic circuit with elements of opposite regulatory activity (activator and inhibitor) constitutes a minimal requirement for network-dosage compensation. Specific interaction topologies and a one-to-one interaction stoichiometry between the activating and inhibiting agents were additional essential elements facilitating dosage compensation. Robust features such as network-dosage compensation could represent a general design principle for gene network architecture in cells.

Figure 3: Systematic dosage variations and network-dosage compensation. The color of each circle represents the network inducibility level. The rectangular, color-coded bars reflect the predictions of a quantitative model. The genetic background of each strain is specified by a big square at its immediate left. The small squares represent the four regulatory genes of the GAL network. Gray color marks the presence of two copies of a specific gene, and white marks one copy of a specific gene. A line between two strains indicates that the two genetic backgrounds differ by a single copy of a specific gene, and the color of the line codifies that gene (blue for GAL3, red for GAL80, green for GAL2, and orange for GAL4).

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