Diego Oyarzun
Professor
Leads the Biomolecular Control Group, combining machine learning and mathematical modelling for synthetic and systems biology.
Diego Oyarzun's lab at Edinburgh works on proteins, drugs & materials and genomics & computational biology. 23 papers since 2023, 2 at top venues, mostly Nature Communications and npj Systems Biology and Applications. 4 PhD students. 9 alumni.
At a glance
- papers, 2023–2026
- 6/ year
- at T1 venues
- 9%
- PhD students
- 4
- MS students
- 0
- alumni listed
- 9
Papers by yearfrom the lab homepage, 2023–2026
- 6 papers2026 · T1 0 · collecting
- 6 papers2025 · T1 1
- 6 papers2024 · T1 0
- 5 papers2023 · T1 1
T1 venuesOtherVenues: Nature Communications, npj Systems Biology and Applications, Nucleic Acids Research
Research focuspaper titles that use each topic's words
- Proteins, drugs & materials6 papers
- Genomics & computational biology1 paper
- systems biology0 papers
- combining machine0 papers
- biomolecular0 papers
Paper list
From the lab homepage.
20266 papers
- iScienceA comprehensive pharmacological survey across heterogeneous patient-derived GBM stem cell models
- ACS Synthetic BiologyEffective sequence-to-expression prediction for membrane proteins using machine learning and computational protein design
- Digital DiscoveryIdentification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models
- Computational and Structural Biotechnology JournalMultiobjective design of growth media with genome-scale metabolic models and Bayesian optimization
- Cell SystemsRisk-averse optimization of genetic circuits under uncertainty
- ACS CatalysisSignatures of Electron–Hole Hopping in Myoglobin Peroxidase Activity Revealed by Deep Mutational Learning
20256 papers
- Nature Communications (T1)Accurate prediction of gene deletion phenotypes with Flux Cone Learning
- Trends in BiotechnologyCan protein expression be ‘solved’?
- Nucleic Acids ResearchImproving the generalization of protein expression models with mechanistic sequence information
- Metabolic EngineeringModelling host-pathway dynamics at the genome scale with machine learning
- Computational and Structural Biotechnology JournalOptimization of regulatory DNA with active learning
- Protein Engineering, Design and SelectionTuning ProteinMPNN to reduce protein visibility via MHC Class I through direct preference optimization
20246 papers
- Journal of Clinical EpidemiologyA Call for Caution When Using Network Methods to Study Multimorbidity: An Illustration Using Data from the Canadian Longitudinal Study on Aging (CLSA)
- Synthetic Biology - Methods and Protocols: Methods in Molecular BiologyA machine learning approach for predicting essentiality of metabolic genes
- ImmunoInformaticsGuiding a language-model based protein design method towards MHC Class-I immune-visibility targets in vaccines and therapeutics
- npj Systems Biology and ApplicationsIntegration of graph neural networks and genome-scale metabolic models for predicting gene essentiality
- Microbial Cell FactoriesMultiscale modelling of bioprocess dynamics and cellular growth
- Computational and Structural Biotechnology JournalOptimisation of surfactin yield in Bacillus using active learning and high-throughput mass spectrometry
20235 papers
- Biochemical Society TransactionsApplications of artificial intelligence and machine learning in dynamic pathway engineering
- ACS Synthetic BiologyBayesian optimization for design of multiscale biological circuits
- Current Opinion in BiotechnologyDeep learning for optimization of protein expression
- Nature Communications (T1)Discovery of senolytics using machine learning
- ACS Synthetic BiologyP1 bacteriophage-enabled delivery of CRISPR-Cas9 antimicrobial activity against Shigella flexneri
Where alumni went9 listed
Apply to EdinburghFall 2027
Not announced yet
Informatics PhD
- Round
- Rolling (funding consideration)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Not yet confirmed for this intake.
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Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.