Model-based biotechnological potential analysis of Kluyveromyces marxianus central metabolism

The non-conventional yeast Kluyveromyces marxianus is an emerging industrial producer for many biotechnological processes. Here, we show the application of a biomass-linked stoichiometric model of central metabolism that is experimentally validated, and mass and charge balanced for assessing the carbon conversion efficiency of wild type and modified K. marxianus. Pairs of substrates (lactose, glucose, inulin, xylose) and products (ethanol, acetate, lactate, glycerol, ethyl acetate, succinate, glutamate, phenylethanol and phenylalanine) are examined by various modelling and optimisation methods. Our model reveals the organism's potential for industrial application and metabolic engineering. Modelling results imply that the aeration regime can be used as a tool to optimise product yield and flux distribution in K. marxianus. Also rebalancing NADH and NADPH utilisation can be used to improve the efficiency of substrate conversion. Xylose is identified as a biotechnologically promising substrate for K. marxianus.

ScrumPy: metabolic modelling with Python

ScrumPy is a software package used for the definition and analysis of metabolic models. It is written using the Python programming language that is also used as a user interface. ScrumPy has features for both kinetic and structural modelling, but the emphasis is on structural modelling and those features of most relevance to analysis of large (genome-scale) models. The aim is at describing ScrumPy's functionality to readers with some knowledge of metabolic modelling, but implementation, programming and other computational details are omitted. ScrumPy is released under the Gnu Public Licence, and available for download from http://mudshark.brookes.ac.uk/ ScrumPy.

Challenges to be faced in the reconstruction of metabolic networks from public databases

In the post-genomic era, the biochemical information for individual compounds, enzymes, reactions to be found within named organisms has become readily available. The well-known KEGG and BioCyc databases provide a comprehensive catalogue for this information and have thereby substantially aided the scientific community. Using these databases, the complement of enzymes present in a given organism can be determined and, in principle, used to reconstruct the metabolic network. However, such reconstructed networks contain numerous properties contradicting biological expectation. The metabolic networks for a number of organisms are reconstructed from KEGG and BioCyc databases, and features of these networks are related to properties of their originating database.

A method for the determination of flux in elementary modes, and its application to Lactobacillus rhamnosus

In this article we address the question of how, given information about the reaction fluxes of a system, flux values can be assigned to the elementary modes of that system. Having described a method by which this may be accomplished, we first illustrate its application to a hypothetical, in silico system, and then apply it to fermentation data from Lactobacillus rhamnosus. This reveals substantial changes in the flux values assigned to elementary modes, and thus to the internal metabolism, as the fermentation progresses. This is information that could not, to our knowledge, be obtained by existing methods. The relationship between our technique and the well-known method of Metabolic Flux Analysis is also discussed.

Applications of metabolic modelling to plant metabolism

In this paper some of the general concepts underpinning the computer modelling of metabolic systems are introduced. The difference between kinetic and structural modelling is emphasized, and the more important techniques from both, along with the physiological implications, are described. These approaches are then illustrated by descriptions of other work, in which they have been applied to models of the Calvin cycle, sucrose metabolism in sugar cane, and starch metabolism in potatoes.

Computer modelling and experimental evidence for two steady states in the photosynthetic Calvin cycle

We present observations of photosynthetic carbon dioxide assimilation, and leaf starch content from genetically modified tobacco (Nicotiana tabacum) plants in which the activity of the Calvin cycle enzyme, sedoheptulose-1,7-bisphosphatase, is reduced by an antisense construct. The measurements were made on leaves of varying ages and used to calculate the flux control coefficients of sedoheptulose-1,7-bisphosphatase over photosynthetic assimilation and starch synthesis. These calculations suggest that control coefficients for both are negative in young leaves, and positive in mature leaves. This behaviour is compared to control coefficients obtained from a detailed computer model of the Calvin cycle. The comparison demonstrates that the experimental observations are consistent with bistable behaviour exhibited by the model, and provides the first experimental evidence that such behaviour in the Calvin cycle occurs in vivo as well as in silico.

Modelling photosynthesis and its control

The dynamic and steady-state behaviour of a computer simulation of the Calvin cycle reactions of the chloroplast, including starch synthesis and degradation, and triose phosphate export have been investigated. A major difference compared with previous models is that none of the reversible reactions are assumed to be at equilibrium. The model can exhibit alternate steady states of low or high carbon assimilation flux, with hysteresis in the transitions between the steady states induced by environmental factors such as phosphate and light intensity. The enzymes which have the greatest influence on the flux have been investigated by calculation of their flux control coefficients. Different patterns of control are exhibited over the assimilation flux, the flux to starch and the flux to cytosolic triose phosphate. The assimilation flux is mostly sensitive to sedoheptulose bisphosphatase and Rubisco, with the exact distribution depending on their relative activities. Other enzymes, particularly the triose phosphate translocator, become more influential when other fluxes are considered. These results are shown to be broadly consistent with observations on transgenic plants.