Efficiently counting all orbits of graphlets of any order in a graph using autogenerated equations

Motivation: Graphlets are a useful tool to determine a graph's small-scale structure. Finding them is exponentially hard with respect to the number of nodes in each graphlet. Therefore, equations can be used to reduce the size of graphlets that need to be enumerated to calculate the number of each graphlet touching each node. Hocevar and Demsar first introduced such equations, which were derived manually, and an algorithm that uses them, but only graphlets with four or five nodes can be counted this way. Results: We present a new algorithm for orbit counting, which is applicable to graphlets of any order. This algorithm uses a tree structure to simplify finding orbits, and stabilizers and symmetry-breaking constraints to ensure correctness. This method gives a significant speedup compared to a brute force counting method and can count orbits beyond the capacity of other available tools

A Cytoscape app for motif enumeration with ISMAGS

We present a Cytoscape app for the ISMAGS algorithm, which can enumerate all instances of a motif in a graph, making optimal use of the motif's symmetries to make the search more efficient. The Cytoscape app provides a handy interface for this algorithm, which allows more efficient network analysis

Investigating pathogen-host interactions and adaptation with network biology approaches

Serovars of the genus Salmonella are widespread enteric pathogens, causing acute inflammatory gut infections. However, a subgroup of Salmonella adapted to a systemic lifestyle instead of a mucosal one. A systems-level understanding of how molecular level changes accompanying this adaptive process potentially modify the behaviour of these invasive strains is crucial for future intervention processes, and possible treatments. In this thesis, I generated and analysed multi-layered interaction networks for 20 strains in the genus Salmonella. I collated protein-protein, transcriptional regulatory, and metabolic interaction data from low and high-throughput experiments and performed predictive measures to add further connections to the systems. The resulting networks culminated in the update to SalmoNet, the first integrated network database for Salmonella serovars. Through comparative network approaches, users can highlight elements under selection in these invasive serovars, increasing our understanding of the host adaptation process leading to their systemic lifestyle. During the last year of my PhD, I redeployed for 6 months to work on COVID-19 related research. This effort led to a systematic literature curation highlighting different cytokine responses in patients caused by SARS-CoV-2 compared to other similar viruses. I also led the effort to establish a new network resource, CytokineLink, aimed at highlighting avenues of cell-to-cell communication mediated by cytokines, to better understand inflammatory and infectious diseases. Overall, the work presented in this thesis has increased our understanding of the Salmonella host adaptation process, by highlighting specific elements under selection, while also exhibiting how network information can be created, and used for understanding such evolutionary processes