Biology subjects
Publications and source records attributed to McCubbin, T..
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SummaryWe present a novel "pseudo batch" transformation algorithm that maps analytical data obtained for fed-batch bioreactor cultivations onto a constant volume batch process, significantly decreasing the complexity of characterizing the fed-batch process. Availability and implementationOur method is implemented in both Excel and Python and is available with tutorials and example data from https://github.com/biosustain/pseudobatch. The Python package is also available on PYPI under the name "pseudobatch". ContactLars Keld Nielsen, e-mail: lars.nielsen@uq.edu.au Supplementary informationA comprehensive explanation of the simulated fed-batch, parameter estimation procedures, and the Bayesian model can be found in supplementary information (S1-S5).
Methanogenesis allows methanogenic archaea (methanogens) to generate cellular energy for their growth while producing methane. Hydrogenotrophic methanogens thrive on carbon dioxide and molecular hydrogen as sole carbon and energy sources. Thermophilic and hydrogenotrophic Methanothermobacter spp. have been recognized as robust biocatalysts for a circular carbon economy and are now applied in power-to-gas technology. Here, we generated the first manually curated genome-scale metabolic reconstruction for three Methanothermobacter spp.. We investigated differences in the growth performance of three wild-type strains and one genetically engineered strain in two independent chemostat bioreactor experiments. In the first experiment, with molecular hydrogen and carbon dioxide, we found the highest methane production rate for Methanothermobacter thermautotrophicus {Delta}H, while Methanothermobacter marburgensis Marburg reached the highest biomass growth rate. Systems biology investigations, including implementing a pan-model that contains combined reactions from all three microbes, allowed us to perform an interspecies comparison. This comparison enabled us to identify crucial differences in formate anabolism. In the second experiment, with sodium formate, we found stable growth with an M. thermautotrophicus {Delta}H plasmid-carrying strain with similar performance parameters compared to wild-type Methanothermobacter thermautotrophicus Z-245. Our findings reveal that formate anabolism influences the diversion of carbon to biomass and methane with implications for biotechnological applications of Methanothermobacter spp. in power-to-gas technology and for chemical production. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/522236v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1d468aeorg.highwire.dtl.DTLVardef@24a595org.highwire.dtl.DTLVardef@1f2dd60org.highwire.dtl.DTLVardef@1a3d8fc_HPS_FORMAT_FIGEXP M_FIG C_FIG Broader contextRenewable energy sources (e.g., wind and solar) provide carbon-free electric power. However, their intermittency and offset between peak production and demand generate the need to store this electric power. Furthermore, these technologies alone do not satisfy the demand for carbon-based commodities. Power-to-gas technology provides a means to store intermittent renewable electric power with concomitant carbon dioxide recycling into a chemical energy carrier, such as methane, on a centralized and decentralized scale. This is particularly important to establish equitable energy strategies for all countries, as is highlighted by the United Nations Sustainable Development Goals. With this work, we provide an integrated systems-biology platform for Methanothermobacter spp. to optimize biological power-to-gas technology and formulate strategies to produce other value-added products besides methane.
SummaryWe achieve a significant improvement in thermodynamic-based flux analysis (TFA) by introducing multivariate treatment of thermodynamic variables and leveraging component contribution, the state-of-the-art implementation of the group contribution methodology. Overall, the method greatly reduces the uncertainty of thermodynamic variables. ResultsWe present multiTFA, a Python implementation of our framework. We evaluated our application using the core E. coli model and achieved a median reduction of 6.8 kJ/mol in reaction Gibbs free energy ranges, while three out of 12 reactions in glycolysis changed from reversible to irreversible. Availability and implementationOur framework along with documentation is available on https://github.com/biosustain/multitfa.