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Biology subjects

Herrgard, M.

Publications and source records attributed to Herrgard, M..

2 recordsLinked to original sources

A genome-scale metabolic model for Methylococcus capsulatus predicts reduced efficiency uphill electron transfer to pMMO.

BackgroundGenome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modifications in silico, without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts like E. coli and S. cerevisiae, few such models exist for one-carbon (C1) metabolizers.\n\nResultsHere we present a genome-scale metabolic model for Methylococcus capsulatus, a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 877 metabolites connected via 898 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base for M. capsulatus. With it, we determined that oxidation of methane by the particulate methane monooxygenase is most likely driven through uphill electron transfer operating at reduced efficiency as this scenario matches best with experimental data from literature.\n\nConclusionsThe metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies towards improved SCP production processes in M. capsulatus.

systems biology

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories

Computational systems biology methods enable rational design of cell factories on a genomescale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, for the majority of these methods implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knock-out, knock-in, over-expression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated website including documentation, examples, and installation instructions can be found at http://cameo.bio. Users can also give cameo a try at http://try.cameo.bio.

bioengineering