External control of the GAL network in S. cerevisiae: a view from control theory

Positive Feedback Genetic Circuit Incorporating a Constitutively Active Mutant Gal3 into Yeast GAL Induction System

The GAL expression system is the most frequently used induction technique in the yeast Saccharomyces cerevisiae. Here we report a simple but powerful genetic circuit for use with the GAL induction system. Briefly, an artificial positive feedback circuit was incorporated into the GAL regulatory network. We selected green fluorescent protein (GFP) as a reporter of GAL1 induction, and designed a strain that expressed a constitutively active Gal3 mutant protein (Gal3^c) under control of the GAL10 promoter. In the resulting strain, GAL1 and GAL10 promoters regulate the expression of GFP and GAL3^c, respectively. Because Gal3^c sequesters the Gal80 repressor away from the Gal4 transcriptional activator in the same manner as the galactose-bound Gal3, the expressed Gal3^c protein provokes further expression of GFP and Gal3^c, yielding further enhancement of GAL induction. Thus, this GAL3^c-mediated positive feedback circuit permits substantially enriched induction of a target gene at extremely low concentrations, or even in the absence, of galactose, while maintaining the strict glucose-mediated repression of the target.

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.

Validation of a model of the GAL regulatory system via robustness analysis of its bistability characteristics

Background

In Saccharomyces cerevisiæ, structural bistability generates a bimodal expression of the galactose uptake genes (GAL) when exposed to low and high glucose concentrations. This indicates that yeast cells can decide between using either the limited amount of glucose or growing on galactose under changing environmental conditions. A crucial requirement for any plausible mechanistic model of this system is that it reproduces the robustness of the bistable response observed in vivo against inter-individual parametric variability and fluctuating environmental conditions.

Results

We show how a control-theoretic analysis of the robustness of a model of the GAL regulatory network may be used to establish the model’s plausibility in characterizing the persistent memory of different carbon sources, without the need for extensive simulations. Chemical Reaction Network Theory is used to establish that the proposed network model is compatible with structural bistability. The robustness of each of the two operative conditions against fluctuations of the species concentrations is demonstrated by studying the Domains of Attraction of the corresponding equilibrium points. Finally, we use a global robustness analysis method based on Semi-Definite Programming to evaluate the modification of the bistable steady states induced by multiple parametric variations throughout bounded regions of the parameter space.

Conclusions

Our analysis provides convincing evidence for the robustness, and hence plausibility, of the GAL regulatory network model. The proposed workflow also demonstrates the power of analytical methods from control theory to provide a direct quantitative characterization of the dynamics of multistable biomolecular regulatory systems without recourse to extensive computer simulations.

How do control-based approaches enter into biology?

Control is intrinsic to biological organisms, whose cells are in a constant state of sensing and response to numerous external and self-generated stimuli. Diverse means are used to study the complexity through control-based approaches in these cellular systems, including through chemical and genetic manipulations, input-output methodologies, feedback approaches, and feed-forward approaches. We first discuss what happens in control-based approaches when we are not actively examining or manipulating cells. We then present potential methods to determine what the cell is doing during these times and to reverse-engineer the cellular system. Finally, we discuss how we can control the cell's extracellular and intracellular environments, both to probe the response of the cells using defined experimental engineering-based technologies and to anticipate what might be achieved by applying control-based approaches to affect cellular processes. Much work remains to apply simplified control models and develop new technologies to aid researchers in studying and utilizing cellular and molecular processes.