Systems biology of monovalent cation homeostasis in yeast: the translucent contribution.

Maintenance of monovalent cation homeostasis (mainly K(+) and Na(+)) is vital for cell survival, and cation toxicity is at the basis of a myriad of relevant phenomena, such as salt stress in crops and diverse human diseases. Full understanding of the importance of monovalent cations in the biology of the cell can only be achieved from a systemic perspective. Translucent is a multinational project developed within the context of the SysMO (System Biology of Microorganisms) initiative and focussed in the study of cation homeostasis using the well-known yeast Saccharomyces cerevisiae as a model. The present review summarize how the combination of biochemical, genetic, genomic and computational approaches has boosted our knowledge in this field, providing the basis for a more comprehensive and coherent vision of the role of monovalent cations in the biology of the cell.

TLM-Quant: an open-source pipeline for visualization and quantification of gene expression heterogeneity in growing microbial cells.

Gene expression heterogeneity is a key driver for microbial adaptation to fluctuating environmental conditions, cell differentiation and the evolution of species. This phenomenon has therefore enormous implications, not only for life in general, but also for biotechnological applications where unwanted subpopulations of non-producing cells can emerge in large-scale fermentations. Only time-lapse fluorescence microscopy allows real-time measurements of gene expression heterogeneity. A major limitation in the analysis of time-lapse microscopy data is the lack of fast, cost-effective, open, simple and adaptable protocols. Here we describe TLM-Quant, a semi-automatic pipeline for the analysis of time-lapse fluorescence microscopy data that enables the user to visualize and quantify gene expression heterogeneity. Importantly, our pipeline builds on the open-source packages ImageJ and R. To validate TLM-Quant, we selected three possible scenarios, namely homogeneous expression, highly 'noisy' heterogeneous expression, and bistable heterogeneous expression in the Gram-positive bacterium Bacillus subtilis. This bacterium is both a paradigm for systems-level studies on gene expression and a highly appreciated biotechnological 'cell factory'. We conclude that the temporal resolution of such analyses with TLM-Quant is only limited by the numbers of recorded images.

Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism.

Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and model-based data analyses of dynamic transcript, protein, and metabolite abundances and promoter activities. Adaptation to malate was rapid and primarily controlled posttranscriptionally compared with the slow, mainly transcriptionally controlled adaptation to glucose that entailed nearly half of the known transcription regulation network. Interactions across multiple levels of regulation were involved in adaptive changes that could also be achieved by controlling single genes. Our analysis suggests that global trade-offs and evolutionary constraints provide incentives to favor complex control programs.

Structural identification of piecewise-linear models of genetic regulatory networks.

We present a method for the structural identification of genetic regulatory networks (GRNs), based on the use of a class of Piecewise-Linear (PL) models. These models consist of a set of decoupled linear models describing the different modes of operation of the GRN and discrete switches between the modes accounting for the nonlinear character of gene regulation. They thus form a compromise between the mathematical simplicity of linear models and the biological expressiveness of nonlinear models. The input of the PL identification method consists of time-series measurements of concentrations of gene products. As output it produces estimates of the modes of operation of the GRN, as well as all possible minimal combinations of threshold concentrations of the gene products accounting for switches between the modes of operation. The applicability of the PL identification method has been evaluated using simulated data obtained from a model of the carbon starvation response in the bacterium Escherichia coli. This has allowed us to systematically test the performance of the method under different data characteristics, notably variations in the noise level and the sampling density.