Current Research Projects

P. falciparum ookinete development and maturation

Plasmodium falciparum is a deadly parasite responsible for most of the malaria cases worldwide. This parasite requires a mosquito vector in order to infect humans. Inside the mosquito, the parasite goes through several life stages that are subject to intense investigations. The ookinete stage is the focus of this project. Here I developed a mathematical model that recapitulates the known experimental kinetic steps of the parasite in this stage. The model produces predictions that are currently subject of experimental investigation.  This project is an experimental and theoretical collaboration with the group of Elena A. Levashina at the Max Planck Institute for Infection Biology in Berlin. The experimental work is performed by Giulia Costa and Pablo Suárez Cortés in the Levashina group. The results of this project may help uncovering differences in the development of the parasite under different conditions that could be better exploited in the fight against the disease.

Artificial neural networks to aid molecular dynamics simulations

Molecular dynamics (MD) simulations are quite time expensive. The correct choice of the parameters of the molecular model (also known as force field parameters) is essential in order to reproduce the experimental results. The knowledge of the correct values of these parameters can be useful both for the information that they deliver about the molecular interactions and for the use of the MD tools in order to simulate similar but yet unexplored configurations and molecules. The search for the correct force field parameters is usually also a laborious endeavor. In collaboration with Markus Miettinen (Chemistry department, University of Bergen), we are looking for strategies to speed up the search of the correct parameters based on applicatins of neural networks.

An artificial neural network to classify AFM single molecule force spectroscopy

Coiled-coil interacting molecules are a simple single molecule interaction system whose mechanical properties can be characterized by means of single molecule force spectroscopy. When the AFM cantilever is attached to one of the two molecules and is moved away, the molecules stretch and eventually their bond is ruptured. The force required to reach rupture can be fine-tuned, at least in principle, by modifying the chemical composition and the pulling geometry during the experiment. Since coiled-coil molecular interaction can be quite useful in anchoring cells in artificial scaffolds, an understanding of their mechanical properties as function of their chemical composition is fundamental for their application in living systems. The analysis of the rupture force curves is subject to intensive studies and is still a challenging matter. Several features like the appearance of a plateau in the force extension curve just before the rupture event are difficult to detect by naked eyes in hundreds of curves but very important for the characterization of the molecular system. Zeynep Atris, a PhD student in the group of Kerstin Blank(University of Linz), is developing an artificial neural network that once trained would be able to recognize when a force extension curve presents a plateau or not. This development of the neural network is supervised by Kerstin Blank and myself. The experimental part of the work is performed by Zeynep Atris under the supervision of Kerstin Blank.

Modeling tumor progression in bones

Contrary to common wisdom, human bone is not just a piece of dull material that we should take care of only when it breaks. In fact, bone is a living tissue that even in adults gets continuously remodeled, often in response to mechanical stimuli. The cells embedded into the bone material and other cells surrounding the bone interact with each other and exchange information as to where the bone needs to be remodeled. When cancer cells invade the tumor tissue, they engage in a dynamical interaction with the native bone remodeling cells. This interaction may disrupt the balance between the native bone remodeling cells thus seriously compromising the bone structure. To uncover the details of the interaction between cancer cells and bone remodeling cells, and the group of Amaia Cipitria (Biodonostia Health Research Insitute) is leading an intensive experimental work to shed light into this matter with her student Sarah Young. To understand the phenomenon of the interaction between bone remodeling cells and cancer cells, Anna Dorothea Heller, a PhD student co-supervised by Amaia Cipitria and myself, is developing a mathematical model that exploits the information provided by the experimental work. The model - a hybrid between a cellular automaton and a spatial evolutionary game- aims at determining the interaction between the cells involved. The interaction parameters that will be obtained when validating the model prediction with the experimental data will hopefully reveal biological phenomena to investigate with further experiments.

Ribosome drop-off in yeast cells

Ribosomes are the machines that are responsible for the synthesis of proteins in the cells. Thanks to ribosomes, the protein content of the cells can increase thus eventually having enough material for a successful cell division. Nevertheless, as molecular machines also ribosomes are error prone. Together with Davide Chiarugi, (currently at the Max Planck Institute for Human Cognitive and Brain Sciences), we are looking at a special type of error that we call drop-off: this error occurs when the ribosome stops protein synthesis prematurely without any specific signal that it should do so. The rate of this unspecific termination of synthesis is a key parameter in understanding what is called the processivity of the molecular machine. So far, in a collaboration project between Davide Chiarugi and myself only the drop-off rate of the bacterium Escherichia coli has been determined. Sherine Awad, a postdoctoral fellow in the group of Davide Chiarugi, has taken the mammoth challenge to look at the drop-off rate of the yeast S. cerevisiae, another well-known model organism, by analyzing riso-seq data available in the literature. Under the supervision of Davide Chiarugi and myself, Sherine Awad’s results will finally shed light into the different level of processivity in eukaryotic cells compared to prokaryotic and hopefully help us understand why eukaryotic organisms tend to have longer genes than bacteria. Sherine Awad’s data analysis tool will also help analyzing ribo-seq data of other organisms.

Uncovering slow and fast translated regions in E. coli transcriptome

When ribosome translate a messenger RNA they don’t do it at a uniform speed. This fact is well-known in molecular biology. Many studies looking into this phenomenon have tried to uncover both the mechanisms responsible for local slow-downs and its possible consequences for the expression of the genes.  Together with Davide Chiarugi, we developed a method to look for the subsequences in the transcriptome of Escherichia coli where ribosomes are slow (or fast) in a reproducible manner when comparing many independent experimental studies under the same growth conditions. One big challenge in this analysis is the low signal-to-noise ratio, which requires ad-hoc tools to harvest a few sequences. After a first publication in collaboration with the group of Monica Bianchini of the University of Siena, we are looking to generalize our findings in order to make predictions beyond the capacity of the experimental technique.

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