The topology of metabolic isotope labeling networks

<p>Abstract</p> <p>Background</p> <p>Metabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures.</p> <p>Results</p> <p>With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of <it>E. coli </it>comprising 10390 isotopomer pools.</p> <p>Conclusion</p> <p>Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the <it>E. coli </it>example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively describe and understand the general patterns of label flow in complex networks. This is an invaluable tool for the structural design of new experiments and the interpretation of measured data.</p

Visualizing regulatory interactions in metabolic networks

<p>Abstract</p> <p>Background</p> <p>Direct visualization of data sets in the context of biochemical network drawings is one of the most appealing approaches in the field of data evaluation within systems biology. One important type of information that is very helpful in interpreting and understanding metabolic networks has been overlooked so far. Here we focus on the representation of this type of information given by the strength of regulatory interactions between metabolite pools and reaction steps.</p> <p>Results</p> <p>The visualization of such interactions in a given metabolic network is based on a novel concept defining the regulatory strength (RS) of effectors regulating certain reaction steps. It is applicable to any mechanistic reaction kinetic formula. The RS values are measures for the strength of an up- or down-regulation of a reaction step compared with the completely non-inhibited or non-activated state, respectively. One numerical RS value is associated to any effector edge contained in the network. The RS is approximately interpretable on a percentage scale where 100% means the maximal possible inhibition or activation, respectively, and 0% means the absence of a regulatory interaction. If many effectors influence a certain reaction step, the respective percentages indicate the proportion in which the different effectors contribute to the total regulation of the reaction step. The benefits of the proposed method are demonstrated with a complex example system of a dynamic <it>E. coli </it>network.</p> <p>Conclusion</p> <p>The presented visualization approach is suitable for an intuitive interpretation of simulation data of metabolic networks under dynamic as well as steady-state conditions. Huge amounts of simulation data can be analyzed in a quick and comprehensive way. An extended time-resolved graphical network presentation provides a series of information about regulatory interaction within the biological system under investigation.</p

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other “-omics sciences,” in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an “all-around carefree package” for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA

Mitochondrial Respiratory Chain Alternative Components Activity During Different Growth Phases in Yarrowia Lipolytica

Additional file 4. Spreadsheet file containing BioLector raw data set with backscatter and DO readings for twenty-four cultivations of C. glutamicum strains, used for calculation of growth rates in the application example

Automated Characterization of Catalytically Active Inclusion Body Production in Biotechnological Screening Systems

We here propose an automated pipeline for the microscopy image-based characterization of catalytically active inclusion bodies (CatIBs), which includes a fully automatic experimental high-throughput workflow combined with a hybrid approach for multi-object microbial cell segmentation. For automated microscopy, a CatIB producer strain was cultivated in a microbioreactor from which samples were injected into a flow chamber. The flow chamber was fixed under a microscope and an integrated camera took a series of images per sample. To explore heterogeneity of CatIB development during the cultivation and track the size and quantity of CatIBs over time, a hybrid image processing pipeline approach was developed, which combines an ML-based detection of in-focus cells with model-based segmentation. The experimental setup in combination with an automated image analysis unlocks high-throughput screening of CatIB production, saving time and resources. Biotechnological relevance - CatIBs have wide application in synthetic chemistry and biocatalysis, but also could have future biomedical applications such as therapeutics. The proposed hybrid automatic image processing pipeline can be adjusted to treat comparable biological microorganisms, where fully data-driven ML-based segmentation approaches are not feasible due to the lack of training data. Our work is the first step towards image-based bioprocess control

Machine learning in bioprocess development: From promise to practice

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges and point out domains that can potentially benefit from technology transfer and further progress in the field of ML

Rapid inoculation of single bacteria into parallel picoliter fermentation chambers

Probst C, Grünberger A, Braun N, et al. Rapid inoculation of single bacteria into parallel picoliter fermentation chambers. Analytical methods. 2015;7(1):91-98.Microfluidic single-cell cultivation devices have been successfully utilized in a variety of biological research fields. One major obstacle to the successful implementation of high throughput single-cell cultivation technology is the requirement for a simple, fast and reliable cell inoculation procedure. In the present report, an air-bubble-based cell loading methodology is described and validated for inoculating single bacteria into multiple picoliter sized growth chambers arranged in a highly parallel manner. It is shown that the application of the injected air bubble can serve as a reproducible mechanism to modify laminar flow conditions. In this way, convective flow was temporarily induced in more than 1000 cultivation chambers simultaneously, which under normal conditions operate exclusively under diffusive mass transport. Within an inoculation time of 100 s, Corynebacterium glutamicum cells were inoculated by convection at minimal stress level and single bacteria remain successfully trapped by cell-wall interactions. The procedure is easy, fast, gentle and requires only minimal fluidic control and equipment. The technique is well suited for microbial cell loading into commonly used microfluidic growth sites arranged in parallel intended for high throughput single-cell analysis

Polydimethylsiloxane (PDMS) Sub-Micron Traps for Single-Cell Analysis of Bacteria

Probst C, Grünberger A, Wiechert W, Kohlheyer D. Polydimethylsiloxane (PDMS) Sub-Micron Traps for Single-Cell Analysis of Bacteria. Micromachines. 2013;4(4):357-369.Microfluidics has become an essential tool in single-cell analysis assays for gaining more accurate insights into cell behavior. Various microfluidics methods have been introduced facilitating single-cell analysis of a broad range of cell types. However, the study of prokaryotic cells such as Escherichia coli and others still faces the challenge of achieving proper single-cell immobilization simply due to their small size and often fast growth rates. Recently, new approaches were presented to investigate bacteria growing in monolayers and single-cell tracks under environmental control. This allows for high-resolution time-lapse observation of cell proliferation, cell morphology and fluorescence-coupled bioreporters. Inside microcolonies, interactions between nearby cells are likely and may cause interference during perturbation studies. In this paper, we present a microfluidic device containing hundred sub-micron sized trapping barrier structures for single E. coli cells. Descendant cells are rapidly washed away as well as components secreted by growing cells. Experiments show excellent growth rates, indicating high cell viability. Analyses of elongation and growth rates as well as morphology were successfully performed. This device will find application in prokaryotic single-cell studies under constant environment where by-product interference is undesired

Germination and Growth Analysis of Streptomyces lividans at the Single-Cell Level Under Varying Medium Compositions

Quantitative single-cell cultivation has provided fundamental contributions to our understanding of heterogeneity among industrially used microorganisms. Filamentous growing Streptomyces species are emerging platform organisms for industrial production processes, but their exploitation is still limited due to often reported high batch-to-batch variations and unexpected growth and production differences. Population heterogeneity is suspected to be one responsible factor, which is so far not systematically investigated at the single-cell level. Novel microfluidic single-cell cultivation devices offer promising solutions to investigate these phenomena. In this study, we investigated the germination and growth behavior of Streptomyces lividans TK24 under varying medium compositions on different complexity levels (i.e., mycelial growth, hyphal growth and tip elongation) on single-cell level. Our analysis reveals a remarkable stability within growth and germination of spores and early mycelium development when exposed to constant and defined environments. We show that spores undergo long metabolic adaptation processes of up to &gt; 30 h to adjust to new medium conditions, rather than using a “persister” strategy as a possibility to cope with rapidly changing environments. Due to this uniform behavior, we conclude that S. lividans can be cultivated quite robustly under constant environmental conditions as provided by microfluidic cultivation approaches. Failure and non-reproducible cultivations are thus most likely to be found in less controllable larger-scale cultivation workflows and as a result of environmental gradients within large-scale cultivations

Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation

Grünberger A, Probst C, Heyer A, Wiechert W, Frunzke J, Kohlheyer D. Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation. Journal of visualized experiments. Bioengineering. 2013;82(82): e50560.In this protocol the fabrication, experimental setup and basic operation of the recently introduced microfluidic picoliter bioreactor (PLBR) is described in detail. The PLBR can be utilized for the analysis of single bacteria and microcolonies to investigate biotechnological and microbiological related questions concerning, e.g. cell growth, morphology, stress response, and metabolite or protein production on single-cell level. The device features continuous media flow enabling constant environmental conditions for perturbation studies, but in addition allows fast medium changes as well as oscillating conditions to mimic any desired environmental situation. To fabricate the single use devices, a silicon wafer containing sub micrometer sized SU-8 structures served as the replication mold for rapid polydimethylsiloxane casting. Chips were cut, assembled, connected, and set up onto a high resolution and fully automated microscope suited for time-lapse imaging, a powerful tool for spatio-temporal cell analysis. Here, the biotechnological platform organism Corynebacterium glutamicum was seeded into the PLBR and cell growth and intracellular fluorescence were followed over several hours unraveling time dependent population heterogeneity on single-cell level, not possible with conventional analysis methods such as flow cytometry. Besides insights into device fabrication, furthermore, the preparation of the preculture, loading, trapping of bacteria, and the PLBR cultivation of single cells and colonies is demonstrated. These devices will add a new dimension in microbiological research to analyze time dependent phenomena of single bacteria under tight environmental control. Due to the simple and relatively short fabrication process the technology can be easily adapted at any microfluidics lab and simply tailored towards specific needs