Open Positions

Reconstruction of a High-Quality Consensus Genome-Scale Metabolic Model for Methanococcus maripaludis
• Keywords: Python, Genome-scale metabolic modeling (GEM), Flux balance analaysis (FBA), Gene Knockout

Overview
Methanococcus maripaludis is a species distinct from bacteria and eukaryotes (1). It belongs to the group of archaea that inhabit anaerobic marine environments and are known for producing methane as a metabolic byproduct (2). Given its potential applications in clean energy and carbon recycling technologies, a genome-scale metabolic model (GEM) (3, 4, 5) is employed to elucidate the cellular behaviors of M. maripaludis and its role in methane production (2). There have been multiple models available since 2013, some automated and others not curated (6, 7, 8), achieving a maximum MEMOTE score (9) of 35% for quality checks. However, when using these models, because different tools are used in the reconstruction, the IDs are not consistent across models. Inconsistent IDs across models make it harder to compare their similarities. On the other hand, as none of the models were annotated and manually curated, matching information is difficult. To address these limitations, a high-quality consensus genome-scale metabolic model (GEM) (3, 4, 5, 6, 7, 8) will be reconstructed to integrate all available information and ensure validation of the compiled knowledge.

Objective
The objective of this project is to develop a high-quality consensus genome-scale metabolic model (GEM) (9, 10, 11, 12, 13). This Consensus GEM will integrate knowledge from multiple models and databases, merging them into a unified scaffold. The reconstruction process will emphasize accuracy, correctness, and validation through curation and manual reconstruction. The model will comply with the FAIR principles (14), incorporate standardized identifiers, and include comprehensive annotations for reactions, metabolites, and genes to facilitate database navigation. Additionally, the model will be validated using community test suites such as MEMOTE (9). The comprehensive and predictive nature of this model enables validation of growth on a defined medium, followed by analysis of methane production. Two nitrogen sources, N2 and ammonium, are considered for the conversion of CO2 into CH4 (8). Predicted fluxes through central metabolism and methanogenesis are compared to identify altered pathways and reactions that serve as bottlenecks. Furthermore, the model is used to predict essential genes for growth under each nitrogen source and to identify gene knockouts that could enhance methane yield. To enhance the project's industrial relevance, a dynamic flux balance analysis (FBA) (15) will be conducted to simulate optimal strategies under both conditions, aiming to maximize methane production over time. The impact of limiting biomass formation on methane yield will be assessed through a sensitivity analysis using shadow prices and reduced costs (16), and potential metabolic engineering interventions to optimize this trade-off will be identified.

Requirements
• A very good knowledge of Python is essential. 
• Familiarity with genome-scale metabolic models, COBRApy, and GitHub is recommended.
• Familiarity with R programming and with species important to industry is a plus.

References:

1- Jones, W.J., Paynter, M.J.B. & Gupta, R. Characterization of Methanococcus maripaludis sp. nov., a new methanogen isolated from salt marsh sediment. Arch. Microbiol. 135, 91–97 (1983). Doi:10.1007/BF00408015.

2- Kirschke, S., Bousquet, P., Ciais, P. et al. Three decades of global methane sources and sinks. Nature Geosci 6, 813–823 (2013). Doi: 10.1038/ngeo1955.

3- Oberhardt,M.A.,Puchałka,J.,MartinsdosSantos,V.A.,andPapin, J. A.(2011).Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis. PLoS ComputBiol 7:e1001116. Doi: 10.1371/journal.pcbi.1001116 .

4- Thiele, I., Palsson, B. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5, 93–121 (2010). Doi: 10.1038/nprot.2009.203.

5- Cuevas, Daniel A., et al. "From DNA to FBA: how to build your own genome-scale metabolic model." Frontiers in microbiology 7 (2016): 907. Doi: 10.3389/fmicb.2016.00907.

6- Goyal N, Widiastuti H, Karimi IA, Zhou Z. A genome-scale metabolic model of Methanococcus maripaludis S2 for CO2 capture and conversion to methane. Mol Biosyst. 2014 May;10(5):1043-54. Doi: 10.1039/c3mb70421a. 

7- Richards MA, Lie TJ, Zhang J, Ragsdale SW, Leigh JA, Price ND. Exploring Hydrogenotrophic Methanogenesis: a Genome Scale Metabolic Reconstruction of Methanococcus maripaludis. J Bacteriol. 2016 Nov 18;198(24):3379-3390. Doi: 10.1128/JB.00571-16.

8- Vo, Chi Hung, et al. "Carbon conversion by Methanococcus maripaludis S2 under diazotrophy and a revised genome-scale metabolic model." Chemical Engineering Science 278 (2023): 118910. Doi: 10.1016/j.ces.2023.118910.

9- Lieven, C., Beber, M.E., Olivier, B.G. et al. MEMOTE for standardized genome-scale metabolic model testing. Nat Biotechnol 38, 272–276 (2020). Doi:10.1038/s41587-020-0446-y.

10- Daniel Machado, Sergej Andrejev, Melanie Tramontano, Kiran Raosaheb Patil, Fast automated reconstruction of genome-scale metabolic models for microbial species and communities, Nucleic Acids Research, Volume 46, Issue 15, 6 September 2018, Pages 7542–7553, Doi: 10.1093/nar/gky537.

11- Mostolizadeh, Reihaneh, Finn Mier, and Andreas Dräger. "MCC: automated mass and charge curation at genome-scale applied to C. tuberculostearicum." bioRxiv (2024): 2024-11. Doi: 10.1101/2024.11.19.624331.

12- Leonidou, N.; Fritze, E.; Renz, A.; Dräger, A. SBOannotator: A Python Tool for the Automated Assignment of Systems Biology Ontology Terms. Bioinformatics 2023, Doi:10.1093/bioinformatics/btad437.

13- Famke Bäuerle, Gwendolyn O. Döbel, Laura Camus, Simon Heilbronner, and Andreas Dräger. Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum. Front. Bioinform., oct 2023. Doi:10.3389/fbinf.2023.1214074.

14- Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). Doi: 10.1038/sdata.2016.18.

15- Gilbert, David, et al. "Towards dynamic genome-scale models." Briefings in bioinformatics 20.4 (2019): 1167-1180. Doi: 10.1093/bib/bbx096.

16- Savinell, Joanne M., and Bernhard O. Palsson. "Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism." Journal of theoretical biology 154.4 (1992): 421-454. Doi: 10.1016/S0022-5193(05)80161-4.

Contact: Reihaneh Mostolizadeh

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