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Microorganisms produce extracellular compounds that affect the final product quality in fermentation processes. Selection of overproducing mutants requires coupling of the extracellular product to the producer genotype, which can be achieved by single-cell compartmentalization. Emulsions contain up to billions of microdroplets/mL which significantly increases the screening throughput compared to microtiter-plate-based selections. Factors affecting the success of screening in microdroplets include the nature of the producing organism (robust, no invasive growth), the product (not soluble in oil) and the product assay (preferably fluorescence based). Together these factors determine the required microdroplet production technique and sorting set-up. Because microdroplets allow relatively inexpensive ultrahigh-throughput screening, they are likely to become a standard tool in the strain selection toolbox of the fermentation industry.

Microbial starter cultures are essential for consistent product quality and functional properties such as flavor, texture, pH or the alcohol content of various fermented foods. Strain improvement programs to achieve desired properties in starter cultures are diverse, but developments in next-generation sequencing lead to an increased interest in evolutionary engineering of desired phenotypes. We here discuss recent developments of strain selection protocols and how computational approaches can assist such experimental design. Furthermore the analysis of evolved phenotypes and possibilities with complex consortia are highlighted. Studies carried out with mainly yeast and lactic acid bacteria demonstrate the power of evolutionary engineering to deliver strains with novel phenotypes as well as insight into underlying mechanisms.

We review the uses and limitations of modelling approaches that are in use in the field of Lactic Acid Bacteria (LAB). We describe recent developments in model construction and computational methods, starting from application of such models to monocultures. However, since most applications in food biotechnology involve complex nutrient environments and mixed cultures, we extend the scope to discuss developments in modelling such complex systems. With metagenomics and meta-functional genomics data becoming available, the developments in genome-scale community models are discussed. We conclude that exploratory tools are available and useful, but truly predictive mechanistic models will remain a major challenge in the field. © 2012 Elsevier Ltd.

The biotechnology industry continuously explores new ways to improve the performance of microbial strains in fermentation processes. Recent focus has been on new genome-wide modelling approaches in functional genomics, which aim to take full advantage of genome sequence data, transcription profiling, proteomics and metabolite profiling for strain improvement. The integration of global metabolic models with genetic and regulatory models will be essential for the practice of metabolic engineering for strain improvement to move forward, simply because we cannot rely on our intuition to grasp the complexity of the biological systems involved.

Contains fulltext : 60473teusink.pdf (Publisher’s version ) (Closed access) Lactic acid bacteria (LAB) have been widely used in food fermentations and, more recently, as probiotics in health-promoting food products. Genome sequencing and functional genomics studies of a variety of LAB are now rapidly providing insights into their diversity and evolution and revealing the molecular basis for important traits such as flavor formation, sugar metabolism, stress response, adaptation and interactions. Bioinformatics plays a key role in handling, integrating and analyzing the flood of 'omics' data being generated. Reconstruction of metabolic potential using bioinformatics tools and databases, followed by targeted experimental verification and exploration of the metabolic and regulatory network properties, are the present challenges that should lead to improved exploitation of these versatile food bacteria.