Bachelor Thesis Student In Systems And Computational Biology

Integration of Transcriptomics Data into a Transcript Allocation Model of Xanthobacter sp. SoF1:Genome-scale metabolic models (GEMs) provide a mathematical representation of an organism’s metabolic network and can be used to predict growth, substrate uptake, and intracellular flux distributions under different environmental conditions. However, conventional GEMs mainly describe metabolic stoichiometry and do not explicitly account for limitations in gene expression or cellular resource allocation.To achieve a more physiologically realistic representation of cellular metabolism, the genome-scale model of Xanthobacter sp. SoF1 has been extended into a Transcript Allocation Model (TAM). In addition to metabolic reactions and enzyme constraints, the TAM introduces transcript variables that connect gene expression to enzyme availability and metabolic reaction capacity.The aim of this Bachelor thesis is to integrate experimental transcriptomics data into the TAM and evaluate its ability to reproduce the physiology of Xanthobacter sp. SoF1 under autotrophic growth conditions. Preliminary work has shown that directly constraining the model with the complete transcriptomics dataset can lead to infeasibility. The project will therefore focus on developing a biologically meaningful transcriptomics subset containing key metabolic pathways, including central carbon metabolism, the electron transport chain (ETC), the Calvin–Benson–Bassham cycle, and other essential metabolic functions.The student will work on the following main tasks:-Become familiar with genome-scale metabolic modeling, flux balance analysis, and the structure of GEM, protein allocation, and transcript allocation models.-Process and curate RNA-seq/transcriptomics data for integration into the TAM.-Identify and select genes associated with central and essential metabolic pathways.-Integrate condition-specific transcript concentrations as model constraints and systematically investigate model feasibility.-Compare TAM predictions with the corresponding GEM and protein allocation model.-Validate model predictions against experimental growth rates and gas uptake measurements, including H₂, O₂, and CO₂.-Investigate how transcript-level constraints influence predicted metabolic fluxes and identify potential transcriptional or metabolic bottlenecks.The project combines systems biology, computational biology, transcriptomics, and metabolic modeling and provides practical experience with Python-based scientific computing and constraint-based modeling. Basic knowledge of Python is beneficial, while previous experience with genome-scale metabolic models is not required.If you are interested send me an email to aziz.ben.ammar@rwth-aachen.de including your CV . This bachelor can be entirely remote and can be begin as soon as possible.