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  • Review Article
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Modelling strategies for the industrial exploitation of lactic acid bacteria

Nature Reviews Microbiology volume 4, pages 46–56 (2006)Cite this article

Key Points

  • Lactic acid bacteria (LAB) have a long tradition of use in the food industry, and the number and diversity of their applications have increased considerably over the years. Industrial applications of LAB can be divided into three types on the basis of their specific optimization criteria: applications involving biomass production (starter cultures, probiotics), those involving bulk chemicals production (lactic acid, polyols) and those producing fine chemicals and functional ingredients (flavour compounds, exopolysaccharides, vitamins).

  • Recent technological advances in the field of functional genomics have moved the focus from more traditional modelling techniques used in biotechnology to global modelling techniques. Genome-scale models and their analysis by constraint-based modelling specifically have attracted a lot of attention.

  • Genome-scale models are a condensed inventory of the metabolic capacity of an organism, in which the metabolic reactions are coupled to the genes and their gene products. As such, these models can be used to integrate high-throughput data sets, such as transcriptomics and metabolomics data.

  • Mapping of the global modelling techniques onto the industrial applications of LAB results in many possibilities for the use of these models in scanning process conditions and exploring the metabolic capabilities. However, it also becomes clear that this is only the first step towards strain improvement: for truly rational metabolic engineering, understanding of the control structure of the network will be essential.

  • A major challenge for future work therefore lies in the incorporation of kinetic and regulatory information into the global metabolic models, so that the control structure can be derived and understood in terms of the interactions between all components in the cell.

Abstract

Lactic acid bacteria (LAB) have a long tradition of use in the food industry, and the number and diversity of their applications has increased considerably over the years. Traditionally, process optimization for these applications involved both strain selection and trial and error. More recently, metabolic engineering has emerged as a discipline that focuses on the rational improvement of industrially useful strains. In the post-genomic era, metabolic engineering increasingly benefits from systems biology, an approach that combines mathematical modelling techniques with functional-genomics data to build models for biological interpretation and — ultimately — prediction. In this review, the industrial applications of LAB are mapped onto available global, genome-scale metabolic modelling techniques to evaluate the extent to which functional genomics and systems biology can live up to their industrial promise.

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Figure 1: Schematic diagram of part of primary metabolism in certain lactic acid bacteria.
Figure 2: The basics of common modelling strategies.
Figure 3: The structure of genome-scale metabolic models.
Figure 4: Inductive and hypothesis-driven science in the post-genomic era.

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Acknowledgements

We thank F. Verhagen, D. Molenaar, J. Hugenholtz and many other colleagues at NIZO food research BV and the Wageningen Centre for Food Sciences for fruitful discussions, and W. de Vos for critically reading the manuscript. This work took place at the Kluyver Centre for Genomics of Industrial Fermentations.

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Authors and Affiliations

  1. Wageningen Centre for Food Sciences, P.O. Box 557, Wageningen, 6700AN, The Netherlands

    Bas Teusink & Eddy J. Smid

  2. NIZO food research BV, P.O. Box 20, 6710BA Ede, The Netherlands

    Bas Teusink & Eddy J. Smid

  3. Centre for Molecular and Biomolecular Informatics, Radboud University, P.O. Box 9010, Nijmegen, 6500GL, The Netherlands

    Bas Teusink

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  1. Bas Teusink
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Correspondence to Bas Teusink.

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DATABASES

Entrez

Escherichia coli

Lactobacillus rhamnosus

Lactococcus lactis

Leuconostoc mesenteroides

Lactobacillus plantarum

Saccharomyces cerevisiae

Streptococcus thermophilus

FURTHER INFORMATION

NIZO Food Research

Radboud University Nijmegen

Wageningen Centre for Food Sciences

Glossary

Starter culture

A culture containing yeast or bacteria that is used to start the process of fermentation or souring in making fermented food products.

Probiotics

Living microorganisms that, when ingested by humans or animals, can beneficially influence health by changing the intestinal flora.

Cell factory

A microbial cell culture that is used specifically for the production of food ingredients, pharmaceutical ingredients and specialty chemicals.

Polymer

A generic term used to describe a substantially long molecule that consists of structural units and repeating units strung together through chemical bonds. The process of converting these units to a polymer is called polymerization. These units consist of monomers, which are typically small molecules of low molecular weight.

Heterofermentative

Microorganisms that convert glucose to a mixture of lactic acid, acetic acid, formic acid, ethanol and CO2.

Polyol

A hydrogenated form of carbohydrate, of which the carbonyl group (aldehyde or ketone, reducing sugar) has been reduced to a primary or secondary hydroxyl group. They are commonly used as replacement of sucrose. Some common polyols (also called sugar alcohols) are mannitol, sorbitol, xylitol, maltitol and lactitol.

Homofermentative

Microorganisms that convert glucose almost quantitatively to lactic acid.

Exopolysaccharide

High-molecular-weight polymer that is composed of sugar residues and is secreted by a microorganism into the surrounding environment. The structures of the different exopolysaccharides produced by LAB are quite diverse and, even within one species, different strains can produce polysaccharides with different repeating units.

Black box model

A model of which the inputs, outputs and functional performance are known, but the internal implementation is unknown or irrelevant

Artificial neural network

Computational model that involves a network of relatively simple processing elements, in which the global behaviour is determined by the connections between the processing elements and element parameters.

White box model

A model in which all necessary mechanistic information is implemented to compute functional performance on the basis of system parameters that represent properties of real objects or processes.

Metabolic flux analysis

A technique that estimates flux distributions through a metabolic network on the basis of the network structure and measured fluxes or flux ratios.

Stoichiometric model

A model that can be summarized in a stoichiometry matrix that represents the participation of the metabolites in each reaction. Metabolites are represented as rows and reactions as columns, with the stoichiometry coefficients as entries.

Elementary flux mode

An elementary flux mode is defined as a minimal set of enzymes that could operate at steady state. It can be seen as a mathematical definition of metabolic pathway. Any steady-state flux distribution can be expressed as a non-negative linear combination of elementary flux modes.

Energy coefficients

Parameters capturing the cell's energy needs for growth and maintenance that are not explicitly accounted for in the model reactions. When oxidative phosphorylation is also modelled, the unknown stoichiometric relationship between oxygen consumption and ATP production (the P/O ratio) is an additional parameter. In general, these parameters have a large impact on model predictions, in particular on biomass yields.

Metabolic control analysis

Mathematical framework, closely related to sensitivity analysis in engineering, in which the sensitivity of systemic behaviour, such as the steady-state flux or metabolite level, to changes in the system's parameters is analysed. These parameter sensitivities, better known as control coefficients, can be understood from the structure of the system (the network stoichiometry) and the interactions between the components of the system (the enzyme kinetics).

Multivariate analysis

A branch of statistics concerned with the analysis of multiple measurements, made on one or several samples of individuals. In functional genomics, typically many hundreds to thousands of variables are measured of a single sample.

Machine learning

The study of computer algorithms that improve automatically through experience. In the context of this review, data-mining programs that discover general rules in large data sets are most widely used. Artificial neural network modelling is an example of a machine-learning technique.

Reversibility of reactions

Depending on the equilibrium constant of a reaction, it can be claimed that a reaction is reversible or can only proceed in one direction. Although in principle any reaction should be reversible, there are physiological boundaries on metabolite concentrations that effectively allow some reactions only to proceed into one direction.

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Teusink, B., Smid, E. Modelling strategies for the industrial exploitation of lactic acid bacteria. Nat Rev Microbiol 4, 46–56 (2006). https://doi.org/10.1038/nrmicro1319

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