Evangelos-Marios Nikolados
Head of AI · Myria Biosciences
Hi, I’m Evangelos, Head of AI at Myria Biosciences. I lead the development of a full digital twin for the company’s R&D and operational pipeline— integrating every layer from wet-lab automation and mass-spectrometry analytics to machine-learning-ready datasets.
My work bridges synthetic biology, computational modelling, and AI-driven discovery to accelerate biotechnology innovation and improve the predictive power of biological models.
Previously, I completed my PhD at the University of Edinburgh, focusing on mechanistic and data-driven models of protein expression and cellular growth. I also hold degrees from Imperial College London and Harvard University.
If you want to get in touch, send me an email.
Data Science & Biology Skills
Languages
Python · Julia · C · MATLAB
Analysis
Machine Learning · Systems Biology · Dynamical Modelling
Tools
Git · SQL · Bash · PyTorch · TensorFlow
GitHub Activity
Education
Ph.D., Quantitative Biology, Biochemistry, and Biotechnology
University of Edinburgh · 2019–2024
“Next-generation computational optimization of protein expression systems.”
M.Res., Systems & Synthetic Biology (Distinction)
Imperial College London · 2017–2018
Project: Growth defects and loss-of-function in synthetic gene circuits.
B.L.A., Biological Sciences (GPA: 3.87)
Harvard University, Extension School · 2015–2017
Thesis: Intrusion Prevention & Detection Models for DNA Data Storage Systems.
B.Sc., Molecular Biology & Genetics
Democritus University of Thrace · 2011–2013
Transferred to Harvard after four semesters.
Experience
Head of AI
Myria Biosciences · Nov 2024 – Present
Building the company’s AI and data infrastructure, creating a digital twin of wet-lab and
analytical processes to streamline design-build-test-learn loops.
Research Assistant
Weiße Group, University of Edinburgh · Jun 2023 – Nov 2023
Implemented mechanistic bacterial growth models in Julia and validated simulation codebases.
Project Supervisor
University of Edinburgh · 2020 – 2023
Guided undergraduate and PhD students in data preparation, dimensionality reduction, and
ML pipelines for biological imaging.
Tutor
Quickhelp, Inc. (Boston, MA) · 2016 – 2017
Provided academic tutoring and mentoring in biology and quantitative sciences.
Teaching Assistant
Democritus Univ. of Thrace · 2012 – 2013
Assisted in Computational & Structural Biology; supervised lab work.
Selected Projects
Digital Twin for Myria Biosciences
Developing an integrated data-modeling pipeline linking wet-lab automation, mass-spectrometry, and machine learning to enable end-to-end operational insight.
Learning the Koopman Operator with Attention-Free Transformers
Introduced an attention-free latent memory and adaptive re-encoding to stabilize long-horizon Koopman forecasts across nonlinear dynamical systems.
Deep Learning for Protein Expression
Built neural models predicting protein expression levels from DNA sequences, guiding construct optimization in synthetic biology.
Growth Defects in Synthetic Circuits
Quantified cellular burden using host-circuit models; explored non-linear feedback in E. coli expression systems.
Mechanistic Models of Bacterial Growth
Implemented Julia-based dynamical models linking ribosome allocation and nutrient uptake.
Selected Publications
Bonfá G., Martino G., Sellitto A., Rinaldi A., Tedeschi F., Caliendo F., Melchiorri L., Perna D., Starkey F., Nikolados E. et al. (2025). Design of novel synthetic promoters to tune gene expression in T cells. bioRxiv.
Baranowski C., Martin H. G., Oyarzún D. A., Spinner A., Desai B., Petzold C. J., Nikolados E.-M., Jaaks-Kraatz S., Gaber A., Chalkley R. J. et al. (2025). Can protein expression be ‘solved’? Trends in Biotechnology.
Nikolados E.-M., Oyarzún D. A. (2023). Deep learning for optimization of protein expression. Current Opinion in Biotechnology 81: 102941.
Nikolados E.-M., Wongprommoon A., Mac Aodha Ó., Cambray G., Oyarzún D. A. (2022). Accuracy and data efficiency in deep learning models of protein expression. Nature Communications 13(1): 7755.
Nikolados E.-M., Weiße A. Y., Oyarzún D. A. (2021). Prediction of cellular burden with host–circuit models. In: Synthetic Gene Circuits: Methods and Protocols. Springer, pp. 267–291.
Nikolados E.-M., Weiße A. Y., Ceroni F., Oyarzún D. A. (2019). Growth defects and loss-of-function in synthetic gene circuits. ACS Synthetic Biology 8(6): 1231–1240.