Erik Hartman

I’m a PhD student in computational biology at Lund University, working with Johan Malmström. I’m currently a visiting research scholar in César de la Fuente’s Machine Biology Group at the University of Pennsylvania. During my PhD, I also spent time at A*STAR in Singapore, working with Peter J. Bond.

I’m interested in how mathematics and machine learning can help us understand biology, from interpreting molecular data to designing proteins and peptides. I am particularly interested in fragments of proteins that have biological functions of their own, so-called encrypted peptides. I study how these fragments are generated and what roles they may play across the tree of life.

Erik Hartman

Research

I study how proteins give rise to bioactive peptides, build computational methods to discover and design them, and model molecular changes during infection.

Encrypted peptides

I investigate bioactive peptides released through protein degradation, particularly antimicrobial peptides and their role in host defense.

A folded protein made of blue-gray ribbons contains a coral peptide segment, shown separately after release.
Discovering bioactive fragments hidden within proteins.

Protein and peptide design

I develop methods for protein and peptide design using machine learning, Bayesian optimization, and biological information.

Four structure-informed protein and peptide folds lead through a sequence-refinement cycle to a coral peptide helix in a blue-gray protein cleft.
Learning from candidate sequences to guide molecular design.

Infectious disease modelling

I use proteomics and machine learning to study molecular changes during infection and model how disease progresses over time.

Host molecular measurements feeding into branching disease trajectories, with one path highlighted in coral.
Connecting molecular measurements with disease trajectories.
Interactive structure · exampleExample structure · PDB 1YCR
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MDM2 proteinp53 peptide

Drag to rotate · scroll or pinch to zoom. Keyboard: arrows, +/−, R.

Selected publications

All publications

Software

GitHub

BoPep

Bayesian optimization for exploring protein and peptide sequence space.

Sequence design

Degradation graphs

Model and analyze sequential protein degradation from peptide measurements.

Peptidomics

BINN

Build and interpret neural networks informed by biological pathways.

Interpretable ML

pepnets

Cluster related peptides to reveal patterns in large peptidomics datasets.

Peptidomics

FK-RFdiffusion

Guide protein generation toward structural and biochemical objectives.

Protein design

ProteoCFM

Reconstruct proteomic disease trajectories with conditional flow matching.

Proteomics

Writing

All writing

Frequently asked questions

Answers to questions from family and friends about AI and my research.

The role of protein degradation during infection

A background to my PhD projects

The broken game of academic research: how journals are shifting the incentives of researchers and ruining science

the flawed academic system through the lens of game theory

Media