Thesis topics

Are you a Bachelor or Master’s student looking for a thesis topic in Bioinformatics and Machine Learning on clinical data?

Here you can find topics that may interest you. SysBioBig will be glad to help you explore the topics that most stimulate your interest.

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In-silico models of the interaction between immune system and tumor cells informed by sequencing data

Recently, several computational modeling approaches have been applied to study and simulate the interaction dynamics between immune and tumor cells in human cancer. However, each tumor is characterized by a specific and unique tumor microenvironment (TME), emphasizing the need for specialized and personalized studies of each cancer scenario.
Theses on this topic will focus on developing computational models to simulate cancer progression using multi-agent based models to simulate the interactions between immune system cells and tumor cells, as well as the effects of treatments, and their implementation in a Python package. Data mining techniques and bioinformatics tools will be used to analyze patient specific sequencing data and thus simulate patient specific tumor progression and treatments.

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Development of bioinformatics methods for the analysis of cell-cell communication from single cell RNA-sequencing data

In complex organisms, the interaction between different cell types has a major role in controlling and coordinating cellular activities, such as tissue and organ development and function. For the first time ever, the advent of single cell RNA sequencing has enabled the possibility to study cellular communication in an high-throughput way. However, the bioinformatics analysis of cell communication from scRNA-seq data is a quite young and fast evolving research area, and much work has still to be done to improve the quality of current bioinformatics analyses.
Theses on this topic will focus on developing computational models to infer cell-cell communication from sequencing count data.

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Robust identification and simulation of biomarkers in RNA-sequencing and metagenomic data

The development of increasingly efficient and cost-effective sequencing techniques has enhanced the possibility of studying complex microbial systems. Mining microbiome data, however, requires specific computational methods to extract the information useful for analysing the micro-world of interest.
Another recent sequencing technology, Single-cell RNA-sequencing (scRNA-seq) has emerged in the last decade. scRNA-seq is a powerful technique for profiling the transcriptomes at the single-cell resolution, i.e. the amount of mRNA (expression level) of each gene transcribed in each individual cell.
Microbiome and scRNA-seq data show some characteristics in common. Consequently, bioinformatics analysis methods coming from the RNA-seq field can be used to perform microbiome analysis. However, although many methods have led to important conclusions in different fields, the lack of a known biological truth makes it impossible to validate the results obtained in both contexts.
Theses on this topic will focus on evaluation of bioinformatics methods for biomarkers discovery in sequencing data.

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Implementation of a Python simulator for microbial communities’ evolution via agent-based modeling

Modeling microbial communities’ evolution has gained immense importance in many scientific fields, from agronomy and food science to human medicine and even material engineering. However, such an interconnected system composed of several molecules and a plethora of bacterial species requires advanced modeling techniques to catch the intrinsic complexity.
Agent-based modeling can be exploited to model bacterial communities: by decomposing the complexity of the system in a simpler description of each species with its own decoupled behavior, we aim to simulate a system in which peculiar ecological mechanisms such as commensality or amensalism rises from a model-free interaction.
While the overall model structure and a preliminary GUI has already been drafted, many improvements still need to be implemented.

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