The problem of genome-scale metabolic model (GEM) reconstruction is a non-trivial bioinformatics task with many computer scientific challenges. The goal of the thesis is to get familiar with the state-of-the art methods and prepare an efficient pipeline chaining several existing tools into a unified workflow. With respect to the inherent complexity of GEM reconstruction, the student is primarily expected to identify particular problems suitable for concrete use-case scenarios and develop a preliminary solution at a prototype level.  

In the theoretical part, the student will make a brief overview of existing tools for GEM reconstruction and analysis (i.e., pathway tools, Merlin). The main aim is to describe the problems related with the model reconstruction procedure. Next, the methods allowing to assess the predicted reaction network as a graph will be discussed. Finally, tools for computing flux-balance analysis will be described as well at the general level.

In the practical part, the student will design and develop a workflow to employ a selected model reconstruction framework to obtain a draft of the reaction network. The main goal will be to design and implement algorithms assisting the creation, iterative improvement, and analysis of the network and the model. The student will combine graph-based techniques as well as analyses based on stoichiometric matrix (e.g., flux balance analysis).  The proposed methods will be evaluated on real-world metabolic networks (e.g., Escherichia Coli, Pseudomonas Putida or potentially other biotechnologically relevant microorganisms).  

The workflow will be implemented primarily in Python as an easily extensible and well-documented framework.