© 2025. All rights reserved.
© 2025. All rights reserved.
Here are some of the projects in which I participate(d). For papers that resulted from some of them, see the tab PUBLICATIONS.
Rather than in a particular subject or model organism, I specialise in a set of research methods: agent-based modelling and computer analysis of vast volumes of data.
The COVID-19 pandemic was the first global infectious disease outbreak of the information era. The multitude of data gathered during the pandemic allows for a better understanding of the issue and the identification of the population’s vulnerabilities to future outbreaks of infectious respiratory disease. The project aims to identify the critical differences in mortality patterns between the regions of Poland and what factors stood behind them to help minimise the impact of future outbreaks.
We gathered data from Statistics Poland (GUS) and its 2021 National Census, Ministry of Health, and academic literature in a handy SQL database and are crunching the numbers using GLMs in R.
Homophily is a sociological concept suggesting that individuals who share similar traits—such as age, gender, race, religion, occupation, worldview, or political beliefs—are more likely to be drawn to and form bonds with one another. We are investigating how potential divisions between compliance and non-compliance with measures designed to limit the spread of infectious diseases can influence the dynamics of an epidemic. Specifically, we aim to explore how the extent of such divisions affects the two groups, particularly in terms of the number of infected individuals within each group.
I am a member of an interdisciplinary team of historians, epidemiologists, and physicists trying to utilise data found in historical records and reconstruct the Late Antiquity geography to uncover the possible trajectory and spread of the Justinian Plague.
The influenza virus, a small RNA virus, causes seasonal flu epidemics, such as those in France during winter. Vaccines exist, but due to the virus’s rapid evolution, their effectiveness is limited to specific strains, which change annually. This project, conceived by Chiara Poletto, investigates how France’s population structure and travel patterns influence flu epidemics and virus evolution.
We adapted an agent-based model based on INSEE census data, originally developed by Livio Bioglio, to track infection chains and analyse epidemic dynamics. The model is validated using Réseau Sentinelles surveillance data.
My role includes modifying the C++11 model, preparing data, calibrating parameters, and automating simulations with Python scripting. Synthetic population generated by the model was used in COVID-19 studies.
The major histocompatibility complex (MHC) proteins are responsible for self-non-self recognition of molecules, thus play a crucial role in the defence against pathogens in all vertebrates. That triggers a co-evolution race between hosts and pathogens (the ‘Red-Queen race’) what, as many believe, leads to the observed massive variation of the MHC genes, making them the most polymorphic gene family in vertebrates. Among humans, there are ~17,000 alleles known, but the human genome has only 6-7 loci to harbour MHC genes - it’s a surprisingly low number! Some species, e.g. rodents like voles, passerine birds, fish called sticklebacks have their number of MHC loci varying between individuals. The model I coded investigates how the number of pathogens in the environment and sexual selection impact the individual copy number variation in species.
We were using prof Jacek Radwan many years of experience researching the MHC genes to design an agent-based model simulating MHC evolution in host-pathogen Red-Queen race. The model implements a simplified mechanism of binding of the pathogenic antigens by the MHC protein-binding region. We simulate each host and pathogen individual with its unique genome. MHC population diversity and individual copy number variations emerge as a result of hosts being selected for pathogen recognition abilities, thus avoiding infection. Also, we used this framework to test how different mating rules may impact MHC diversity if we add sexual selection among hosts.
My job was to participate in the model design, implemented it, run all the required simulations, analyse and interpret the results. The model is written in C++11 and it’s designed to run on cluster computers. The analysis is done using Python scripts.
The size of the genomes of known free-living prokaryotes varies from ~1.3 mega base-pairs (Mbp) to ~13.0 Mbp, an order of magnitude. I proposed a possible explanation of this variation due to the variability of the physical conditions of the environment. In a stable environment, competition for the resource becomes the main force of selection and smaller (thus energetically cheaper) genomes are favoured. In more variable conditions, larger genomes will dominate, as they have a wider range of response to a less predictable environment.
I devolpped an agent-based model of genome evolution in a free-living prokaryotic population. Using the classic Hutchinson niche space model, I defined a gene as a Gaussian function over a corresponding niche dimension. The single cell can have more than one gene along a given dimension, and the envelope of all the corresponding responses is considered a full description of a cell’s phenotype over that dimension. Gene deletion, gene duplication, and modifying mutations are permitted during reproduction, so the number of genes and their phenotypic effect (height and position of the Gaussian envelope) are free to evolve. The surface under the curve of a gene is fixed to prevent ‘supergenes’ from occurring. Change of the environmental conditions is simulated as a bounded random walk with a varying length of the step (a parameter representing the variability of the environment). By employing this approach I can reproduce the phenomenon of genome streamlining in more stable environments (analogical to, e.g. oligotrophic gyre regions of the ocean) and genome complexification in variable environments.
The model is written in C++ and Python is used to analyse the results. This was my Ph.D. thesis completed in prof Thomas Mock’s laboratory of marine microbial genomics.
The drugs used by humans also have a biological impact on other organism sharing similar biochemical pathways (due to shared evolutionary history). And we’re releasing then into the environment. We tested how hormone melatonin (my M.Sc. project) impacts the diurnal migration and life-history traits of a freshwater crustacean Daphnia magna (water flea). I also took part in investigating how antidepressant drugs impact freshwater fish anti-predatory behaviour.
I took part in the design of the experiments and later set them up. I developed an experimental setup that allowed for night time surveillance of animals using IR light invisible to them. Part of my studies under prof Joanna Pijanowska.