Functional Modularity Methods and Applications for Human Diseases

Community detection in complex networks (graphs) has been the subject of investigation in numerous domains. In biological networks, communities are functionally contextual and often provide insights into mechanisms. Detecting communities and analyzing their biological functions is an important aspect of studying biological networks. Communities (aka modules) can yield useful insights into the structure of networks and serve as a basis for analyzing them at topological and functional levels. The work presented in this dissertation is aimed at community detection in human disease (specifically colorectal cancer) networks using different approaches, with the focus on analyzing their biological functions.The study begins with the exploration of existing community detection algorithms and evaluation of their findings on two important Protein-Protein Interaction (PPI) networks, namely, Saccharomyces cerevisiae (Yeast) and Homo sapiens (Human) at both topological and functional levels. The main criteria to assess the performance of each method are 1) appropriate community size (neither too small nor too large), 2) representation of only one or two broad biological functions within a community, 3) most genes from the network belonging to a pathway should also belong to only one or two communities, and 4) performance speed. These criteria enable us to select one of the best methods for detecting communities in biological networks. Next, a gene expression microarray dataset of colorectal cancer (CRC) is analyzed to detect stage-specific biomarkers as well as modular mechanisms, potentially causal for the progression of CRC from normal to stages I to IV. Constructing unweighted and weighted correlation networks for each stage, communities are identified and compared topologically and functionally across stages. Short Time-series Expression Miner (STEM) algorithm is also used to detect potential biomarkers having a role in CRC. Constructing a drug-target-PPI network provides insight, in the light of analyzed data, into understanding the functional mechanisms for some of the current drugs used in CRC treatment. Lastly, gene modules across stages of CRC are analyzed from a single cell transcriptomic dataset to decipher mechanistic changes likely contributing to tumor growth and cancer progression. Cell down-sampling process is firstly performed at stages pT2 to pT4 (as well as right colon) to make cell count equal across different stages and tissue sites. Functional modules at early stage (pT1) and also right colon are identified utilizing Weighted Gene Co- expression Network Analysis (WGCNA). In particular, WGCNA’s preservation statistics are used to detect gene modules that exhibit weak/strong preservation of network topology in late stages (pT234) vs. early stage (pT1) as well as left colon vs. right colon. Functional enrichment analysis of the non-preserved modules reveals mechanisms related to the initiation, progression and metastasis of CRC

A Swedish Presence ”A case study of Swedish companies in Japan”

As one of the largest economies in the world, Japan obtains great potential for foreign investors. Nonetheless, the Japanese market has been known to be difficult to penetrate for foreign companies. Research areas of adjustment regarding Swedish companies’ market presence in Japan were identified as; forming the organisation characteristics, culture and leadership approach, acquiring skills, managing networks and relationships and adapting to the market demand. There exists limited literature involving Swedish companies’ market presence in Japan. Thus, the purpose of this study was to identify patterns of, and describe, how Swedish companies in Japan have chosen to establish themselves, how they have managed to sustain their market presence and if the presence in return has contributed to the Swedish company. This was accomplished by conducting six case studies of Swedish companies active in Japan. The study found that the Swedish companies have market-seeking motives when entering the Japanese market. However, these evolve to non-marketable asset seeking motives during the market presence. The majority of these companies currently manage wholly owned subsidiaries, which were established through transitional phases with distributors or by risk-averse actions. It was also found that Swedish companies do experience difficulties when being active on the Japanese market, in particularly regarding managing the acquirement of new skills and adapting to Japanese market demand. Other areas of market presence such as organisation characteristics, leadership and culture were managed to a limited extent as they where also seen to be affected by local factors. Networks and partnerships were managed according to the business model or overall industry standards, rather than adjusted to local conditions. Furthermore, the study also found that the Japanese market offers valuable insights for multinational organisations, applicable to several markets internationally

Topological and functional comparison of community detection algorithms in biological networks

Abstract Background Community detection algorithms are fundamental tools to uncover important features in networks. There are several studies focused on social networks but only a few deal with biological networks. Directly or indirectly, most of the methods maximize modularity, a measure of the density of links within communities as compared to links between communities. Results Here we analyze six different community detection algorithms, namely, Combo, Conclude, Fast Greedy, Leading Eigen, Louvain and Spinglass, on two important biological networks to find their communities and evaluate the results in terms of topological and functional features through Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology term enrichment analysis. At a high level, the main assessment criteria are 1) appropriate community size (neither too small nor too large), 2) representation within the community of only one or two broad biological functions, 3) most genes from the network belonging to a pathway should also belong to only one or two communities, and 4) performance speed. The first network in this study is a network of Protein-Protein Interactions (PPI) in Saccharomyces cerevisiae (Yeast) with 6532 nodes and 229,696 edges and the second is a network of PPI in Homo sapiens (Human) with 20,644 nodes and 241,008 edges. All six methods perform well, i.e., find reasonably sized and biologically interpretable communities, for the Yeast PPI network but the Conclude method does not find reasonably sized communities for the Human PPI network. Louvain method maximizes modularity by using an agglomerative approach, and is the fastest method for community detection. For the Yeast PPI network, the results of Spinglass method are most similar to the results of Louvain method with regard to the size of communities and core pathways they identify, whereas for the Human PPI network, Combo and Spinglass methods yield the most similar results, with Louvain being the next closest. Conclusions For Yeast and Human PPI networks, Louvain method is likely the best method to find communities in terms of detecting known core pathways in a reasonable time

Modular and mechanistic changes across stages of colorectal cancer.

BackgroundWhile mechanisms contributing to the progression and metastasis of colorectal cancer (CRC) are well studied, cancer stage-specific mechanisms have been less comprehensively explored. This is the focus of this manuscript.MethodsUsing previously published data for CRC (Gene Expression Omnibus ID GSE21510), we identified differentially expressed genes (DEGs) across four stages of the disease. We then generated unweighted and weighted correlation networks for each of the stages. Communities within these networks were detected using the Louvain algorithm and topologically and functionally compared across stages using the normalized mutual information (NMI) metric and pathway enrichment analysis, respectively. We also used Short Time-series Expression Miner (STEM) algorithm to detect potential biomarkers having a role in CRC.ResultsSixteen Thousand Sixty Two DEGs were identified between various stages (p-value ≤ 0.05). Comparing communities of different stages revealed that neighboring stages were more similar to each other than non-neighboring stages, at both topological and functional levels. A functional analysis of 24 cancer-related pathways indicated that several signaling pathways were enriched across all stages. However, the stage-unique networks were distinctly enriched only for a subset of these 24 pathways (e.g., MAPK signaling pathway in stages I-III and Notch signaling pathway in stages III and IV). We identified potential biomarkers, including HOXB8 and WNT2 with increasing, and MTUS1 and SFRP2 with decreasing trends from stages I to IV. Extracting subnetworks of 10 cancer-relevant genes and their interacting first neighbors (162 genes in total) revealed that the connectivity patterns for these genes were different across stages. For example, BRAF and CDK4, members of the Ser/Thr kinase, up-regulated in cancer, displayed changing connectivity patterns from stages I to IV.ConclusionsHere, we report molecular and modular networks for various stages of CRC, providing a pseudo-temporal view of the mechanistic changes associated with the disease. Our analysis highlighted similarities at both functional and topological levels, across stages. We further identified stage-specific mechanisms and biomarkers potentially contributing to the progression of CRC