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.

The implementation of model-based designs in metabolic engineering and synthetic biology may fail. One of the reasons for this failure is that only a part of the real-world complexity is included in models. Still, some knowledge can be simplified and taken into account in the form of optimization constraints to improve the feasibility of model-based designs of metabolic pathways in organisms. Some constraints (mass balance, energy balance, and steady-state assumption) serve as a basis for many modelling approaches. There are others (total enzyme activity constraint and homeostatic constraint) proposed decades ago, but which are frequently ignored in design development. Several new approaches of cellular analysis have made possible the application of constraints like cell size, surface, and resource balance. Constraints for kinetic and stoichiometric models are grouped according to their applicability preconditions in (1) general constraints, (2) organism-level constraints, and (3) experiment-level constraints. General constraints are universal and are applicable for any system. Organism-level constraints are applicable for biological systems and usually are organism-specific, but these constraints can be applied without information about experimental conditions. To apply experimental-level constraints, peculiarities of the organism and the experimental set-up have to be taken into account to calculate the values of constraints. The limitations of applicability of particular constraints for kinetic and stoichiometric models are addressed.

Acetaldehyde is a valuable product of microbial biosynthesis, which can be used by the chemical industry as the entry point for production of various commodity chemicals. In ethanologenic microorganisms, like yeast or the bacterium Zymomonas mobilis, this compound is the immediate metabolic precursor of ethanol. In aerobic cultures of Z. mobilis, it accumulates as a volatile, inhibitory byproduct, due to the withdrawal of reducing equivalents from the alcohol dehydrogenase reaction by respiration. The active respiratory chain of Z. mobilis with its low energy-coupling efficiency is well-suited for regeneration of NAD⁺ under conditions when acetaldehyde, but not ethanol, is the desired catabolic product. In the present work, we sought to improve the capacity Z. mobilis to synthesize acetaldehyde, based on predictions of a stoichiometric model of its central metabolism developed herein. According to the model analysis, the main objectives in the course of engineering acetaldehyde producer strains were determined to be: (i) reducing ethanol synthesis via reducing the activity of alcohol dehydrogenase (ADH), and (ii) enhancing the respiratory capacity, either by overexpression of the respiratory NADH dehydrogenase (NDH), or by mutation of other components of respiratory metabolism. Several mutants with elevated respiration rate, decreased alcohol dehydrogenase activity, or a combination of both, were obtained. They were extensively characterized by determining their growth rates, product yields, oxygen consumption rates, ADH, and NDH activities, transcription levels of key catabolic genes, as well as concentrations of central metabolites under aerobic culture conditions. Two mutant strains were selected, with acetaldehyde yield close to 70% of the theoretical maximum value, almost twice the previously published yield for Z. mobilis. These strains can serve as a basis for further development of industrial acetaldehyde producers.

Mathematical modeling of metabolism is essential for rational metabolic engineering. The present work focuses on several types of modeling approach to quantitative understanding of central metabolic network and energetics in the bioethanol-producing bacterium Zymomonas mobilis. Combined use of Flux Balance, Elementary Flux Mode and thermodynamic analysis of its central metabolism, together with dynamic modeling of the core catabolic pathways, can help to design novel substrate and product pathways by systematically analysing the solution space for metabolic engineering, and yields insights into the function of metabolic network, hardly achievable without applying modeling tools.

Motivation: Due to their universal applicability, global stochastic optimization methods are popular for designing improvements of biochemical networks. The drawbacks of global stochastic optimization methods are: (i) no guarantee of finding global optima, (ii) no clear optimization run termination criteria and (iii) no criteria to detect stagnation of an optimization run. The impact of these drawbacks can be partly compensated by manual work that becomes inefficient when the solution space is large due to combinatorial explosion of adjustable parameters or for other reasons. Results: SpaceScanner uses parallel optimization runs for automatic termination of optimization tasks in case of consensus and consecutively applies a pre-defined set of global stochastic optimization methods in case of stagnation in the currently used method. Automatic scan of adjustable parameter combination subsets for best objective function values is possible with a summary file of ranked solutions. Availability and implementation:https://github.com/atiselsts/spacescanner. Contact:egils.stalidzans@lu.lv Supplementary information: Supplementary data are available at Bioinformatics online.