A tunable metabolic valve for precise growth control and increased product formation in Pseudomonas putida.

Metabolic engineering of microorganisms aims to design strains capable of producing valuable compounds under relevant industrial conditions and in an economically competitive manner. From this perspective, and beyond the need for a catalyst, biomass is essentially a cost-intensive, abundant by-product of a microbial conversion. Yet, few broadly applicable strategies focus on the optimal balance between product and biomass formation. Here, we present a genetic control module that can be used to precisely modulate growth of the industrial bacterial chassis Pseudomonas putida KT2440. The strategy is based on the controllable expression of the key metabolic enzyme complex pyruvate dehydrogenase (PDH) which functions as a metabolic valve. By tuning the PDH activity, we accurately controlled biomass formation, resulting in six distinct growth rates with parallel overproduction of excess pyruvate. We deployed this strategy to identify optimal growth patterns that improved the production yield of 2-ketoisovalerate and lycopene by 2.5- and 1.38-fold, respectively. This ability to dynamically steer fluxes to balance growth and production substantially enhances the potential of this remarkable microbial chassis for a wide range of industrial applications.

Live cell imaging of meiosis in Arabidopsis thaliana.

To follow the dynamics of meiosis in the model plant Arabidopsis, we have established a live cell imaging setup to observe male meiocytes. Our method is based on the concomitant visualization of microtubules (MTs) and a meiotic cohesin subunit that allows following five cellular parameters: cell shape, MT array, nucleus position, nucleolus position, and chromatin condensation. We find that the states of these parameters are not randomly associated and identify 11 cellular states, referred to as landmarks, which occur much more frequently than closely related ones, indicating that they are convergence points during meiotic progression. As a first application of our system, we revisited a previously identified mutant in the meiotic A-type cyclin TARDY ASYNCHRONOUS MEIOSIS (TAM). Our imaging system enabled us to reveal both qualitatively and quantitatively altered landmarks in tam, foremost the formation of previously not recognized ectopic spindle- or phragmoplast-like structures that arise without attachment to chromosomes.

DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems.

BACKGROUND: Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed. RESULTS: In this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics. CONCLUSION: The presented pipeline automates the modelling process for large metabolic networks. From this, users can simulate their pathway of interest and obtain a better understanding of how altering conditions influences cellular dynamics. By testing the effects of different parameterisations we are also able to provide suggestions to help construct more accurate models of complete metabolic systems in the future.