Microbial communities

Microbial communities

Microbial communities drive most of the biogeochemical and biomedical processes that sustain life, yet their functional repertoire remains poorly characterised: metagenomic samples are dominated by proteins with no close homolog in curated databases, and most prediction tools have been trained on eukaryotic sequences. Our work in this area lifts single-protein function prediction up to the level of whole microbial communities by combining ontology-aware deep learning with multi-scale systems analysis. The distinctive angle is to treat metagenomes not as bags of genes but as functional systems

Data integration and ontologies for microbial cell factories

Industrial biotechnology depends on integrating data from sources that were never designed to be combined: enzyme and reaction databases, protein-family resources, pathway maps, strain collections, phenotypic screens, and the experimental literature itself. Each uses different identifiers, different vocabularies, and different levels of granularity, and naive integration produces brittle pipelines that break whenever a source is updated. The Computational Bioscience Research Center's Microbial Cell Factories initiative, led by Vladimir Bajic with Hoehndorf as CoI heading the data-integration

Sequencing and computational analysis of MRSA samples

Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of hospital-acquired infection worldwide, and Saudi Arabia presents a singular epidemiological setting: the Kingdom hosts more than two million Hajj pilgrims and over twenty million Umrah pilgrims annually, drawn from every region of the Muslim world. Mass gatherings of this scale offer a natural experiment in how human mobility shapes bacterial population structure and the dissemination of antimicrobial resistance, but until recently nationally representative genomic data have been lacking. The project was carried out as a

Computational methods for functional metagenomics: from protein functions to multi-scale interactions

Metagenomic sequencing has made it routine to read the DNA of an entire microbial community, but most analysis pipelines stop at taxonomic composition or at the level of individual protein families. The really biologically informative questions, which proteins do what, which proteins interact, which metabolic pathways are reconstructible, and how the community as a whole interacts with its environment or host, remain largely out of reach computationally. Even associations that are very robust empirically, for example between gut microbiome composition and colorectal cancer or inflammatory

Metagenomics-based surface prospecting

Hydrocarbon-bearing geological formations carry a characteristic microbial signature. Methanotrophs that oxidize seep methane, methanogens that produce it, and sulfate-reducing bacteria that thrive at the oxic-anoxic boundaries created by hydrocarbon migration leave a detectable imprint in surface and near-surface soils. If that imprint can be read reliably, it offers an independent, low-cost screening modality to complement seismic surveys in exploration. The project, carried out for Saudi Aramco's Upstream Research Center under a 24-month scope of work (PI Hoehndorf, co-PIs Takashi Gojobori

Enabling mangrove restoration by AI-tailored microbiome fortification

This short collaboration (2023, funded under the KAUST Center for Living Innovation programme) developed AI-tailored microbiome work in support of coastal mangrove restoration in Saudi Arabia. The project ran as a sister activity to the BORG group's larger desert-microbiome programme with the NTGC, applying analogous metagenomic and functional-annotation pipelines to mangrove sediment and root-associated communities rather than to arid-soil systems. Heribert Hirt (DARWIN21, KAUST) was the Principal Investigator, with Hoehndorf as Co-Investigator providing the computational microbiome analysis

Enabling desert revegetation by AI-tailored soil microbiome fortification

Roughly twelve million hectares of dryland are lost every year worldwide, and the megaprojects launched to reverse desertification — the Sahel's Great Green Wall is the canonical example — typically fail because most planted trees die when irrigation stops. The biological reason is that desert soils are low in nutrients and high in salinity, and the microbial communities that mediate nitrogen, phosphate, and mineral acquisition for plants are missing or unbalanced. This project, led by Heribert Hirt's plant-microbiome group with our group and the Modelling and Simulation group of Gabriel