Updates

There is a tendency to use bioinformatics and computational biology as interchangeable terms. This is understandable: both use computation, algorithms, and data to study biological problems, and their boundaries overlap. But they are not quite the same.

We do computational biology.

Our starting point is not necessarily a biological dataset that we want to analyze. Our starting point is a molecular system that we want to understand: a protein, a molecular interaction, a membrane, a conformational transition, or a molecule whose behavior we want to explain and predict, or that we want to modify or design to address a biological problem.

To do so, we develop and use theoretical models—physical, mathematical, and computational—that represent different aspects of molecular reality.

Bioinformatics has historically developed around the analysis of biological information: sequences, genomes, gene expression, and, today, large datasets of many different kinds. The distinction, however, is not absolute: bioinformatics also uses models, and computational biology also uses data. There is a vast shared territory. The fundamental difference lies in what the model represents and what role it plays in the research.

Our systems have geometry, energy, interactions, and degrees of freedom. Molecules obey physical and chemical principles. By translating the physical principles that govern molecular systems into mathematical and computational models, we aim to reproduce their thermodynamics, kinetics, and dynamics in order to understand and predict their behavior in biological processes.

For us, therefore, programming is necessary but not sufficient. Our work lies at the intersection of:

physics + chemistry + biochemistry + mathematics + computation + data science.

Distinguishing this approach from bioinformatics does not imply a hierarchy. They are closely related, complementary, and increasingly interconnected disciplines. But using computation to study biology does not automatically make every such endeavor bioinformatics.

Within the broad landscape of computational biology, our work is much closer to computational molecular science and computational biophysics than to bioinformatics, and naturally intersects with molecular modeling and molecular simulation.

If we had to summarize our way of doing science:

We build and use physical, mathematical, and computational models of molecular systems to understand and address biological problems. Doing so requires combining modeling with a solid understanding of chemistry and biochemistry.

That is what we mean by computational biology.

We do not merely study information about molecules. We study molecules through models.

We are happy to announce pytest-receptor 1.0, now available on both PyPI and the uibcdf conda channel.

When a coding agent runs a test suite, plain pytest output costs it hundreds or thousands of tokens — and, worse, it can lead the agent to “guess” results that never happened. pytest-receptor is a pytest reporter built for exactly that setting:

• Compact output that dramatically reduces token usage.
• It never fabricates a verdict: if something is unknown, it says so.
• It groups failures by root cause, not by symptom.
• Zero configuration: it is auto-discovered on install and changes nothing about normal pytest unless you opt in.

It supports Python 3.11–3.13 and pytest 8 or later, and every combination is exercised in CI.

Install it with:

conda install -c uibcdf pytest-receptor

or:

pip install pytest-receptor

Documentation: https://www.uibcdf.org/pytest-receptor/ · Source (MIT): https://github.com/uibcdf/pytest-receptor

Welcome to the new UIBCDF website.

We are very happy to open this new space and share it with our community. Our goal with this website is simple: make it easier to discover who we are, what we work on, and how our research evolves over time.

Here you can explore our research lines, meet the people behind the projects, and browse selected publications and open software developed in the unit. You will also find a unified updates stream where we will post seminars, scientific news, opportunities, releases, and announcements.

We hope this platform helps students, collaborators, and colleagues connect with our work more easily. Thank you for visiting, and please check back regularly as we continue growing this site and sharing new content.