Summary:
13C-based metabolic flux analysis (13C-MFA) is the state-of-the-art
method to quantitatively determine in vivo metabolic reaction rates in
microorganisms. 13CFLUX2 contains all tools for composing flexible computational
13C-MFA workflows to design and evaluate carbon labeling experiments. A
specially developed XML language, FluxML, highly efficient data structures and simulation
algorithms achieve a maximum of performance and effectiveness. Support of multicore CPUs,
as well as compute clusters, enables scalable investigations. 13CFLUX2 outperforms
existing tools in terms of universality, flexibility and built-in features. Therewith,
13CFLUX2 paves the way for next-generation high-resolution 13C-MFA applications
on the large scale.Availability and implementation: 13CFLUX2 is implemented in C++
(ISO/IEC 14882 standard) with Java and Python add-ons to run under Linux/Unix. A demo
version and binaries are available at www.13cflux.net.Contact:
info@13cflux.net or k.noeh@fz-juelich.deSupplementary information:
Supplementary data are available at Bioinformatics
online.
Cell-to-cell heterogeneity typically evolves due to a manifold of biological and environmental factors and special phenotypes are often relevant for the fate of the whole population but challenging to detect during conventional analysis. We demonstrate a microfluidic single-cell cultivation platform that incorporates several hundred growth chambers, in which isogenic bacteria microcolonies growing in cell monolayers are tracked by automated time-lapse microscopy with spatiotemporal resolution. The device was not explicitly developed for a specific organism, but has a very generic configuration suitable for various different microbial organisms. In the present study, we analyzed Corynebacterium glutamicum microcolonies, thereby generating complete lineage trees and detailed single-cell data on division behavior and morphology in order to demonstrate the platform's overall capabilities. Furthermore, the occurrence of spontaneously induced stress in individual C. glutamicum cells was investigated by analyzing strains with genetically encoded reporter systems and optically visualizing SOS response. The experiments revealed spontaneous SOS induction in the absence of any external trigger comparable to results obtained by flow cytometry (FC) analyzing cell samples from conventional shake flask cultivation. Our microfluidic setup delivers detailed single-cell data with spatial and temporal resolution; complementary information to conventional FC results. V C 2015 International Society for Advancement of Cytometry
A microfluidic device for microbial single-cell cultivation of bacteria was modeled and simulated using COMSOL Multiphysics. The liquid velocity field and the mass transfer within the supply channels and cultivation chambers were calculated to gain insight in the distribution of supplied nutrients and metabolic products secreted by the cultivated bacteria. The goal was to identify potential substrate limitations or product accumulations within the cultivation device. The metabolic uptake and production rates, colony size, and growth medium composition were varied covering a wide range of operating conditions. Simulations with glucose as substrate did not show limitations within the typically used concentration range, but for alternative substrates limitations could not be ruled out. This lays the foundation for further studies and the optimization of existing picoliter bioreactor systems.
The biotechnological production of fine chemicals, proteins and pharmaceuticals is usually hampered by loss of microbial performance during scale-up. This challenge is mainly caused by discrepancies between homogeneous environmental conditions at laboratory scale, where bioprocesses are optimized, and inhomogeneous conditions in large-scale bioreactors, where production takes place. Therefore, to improve strain selection and process development, it is of great interest to characterize these fluctuating conditions at large-scale and to study their effects on microbial cells. In this paper, we demonstrate the potential of computational fluid dynamics (CFD) simulation of large-scale bioreactors combined with dynamic microfluidic single-cell cultivation (dMSCC). Environmental conditions in a 200 L bioreactor were characterized with CFD simulations. Computational lifelines were determined by combining simulated turbulent multiphase flow, mass transport and particle tracing. Glucose availability for Corynebacterium glutamicum cells was determined. The reactor was simulated with average glucose concentrations of 6 g m−3, 10 g m−3 and 16 g m−3. The resulting computational lifelines, discretized into starvation and abundance regimes, were used as feed profiles for the dMSCC to investigate how varying glucose concentration affects cell physiology and growth rate. In this study, each colony in the dMSCC device represents a single cell as it travels through the reactor. Under oscillating conditions reproduced in the dMSCC device, a decrease in growth rate of about 40% was observed compared to continuous supply with the same average glucose availability. The presented approach provides insights into environmental conditions observed by microorganisms in large-scale bioreactors. It also paves the way for an improved understanding of how inhomogeneous environmental conditions influence cellular physiology, growth and production.
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