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Mutations are the driving force of evolution, yet they underlie many diseases and, in particular, cancer. They are thought to arise from a combination of stochastic errors in DNA processing, naturally occurring DNA damage (e.g., the spontaneous deamination of methylated CpG sites), replication errors, carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These processes often lead to distinctive patterns of mutations, called "mutational signatures". Starting with the seminal work of Alexandrov at al. [Ludmil B Alexandrov et al., 2013] several computational approaches have been developed to uncover such mutational signatures. However connecting mutational signatures to mutational processes is not always easy [Kim et al., 2021]. To gain insights into the relationships between mutational processes and computationally derived somatic mutation patterns (mutational signatures), we developed several complementary approaches that leverage different algorithmic techniques allowing us to link such patterns to their potential causes. For example, to investigate the genetic aberrations associated with mutational signatures, we took a network-based approach considering mutational signatures as phenotypes. Specifically, our analysis aims to answer the following two complementary questions: (i) what are functional pathways whose gene expression activities correlate with the strengths of mutational signatures, and (ii) are there pathways whose genetic alterations might have led to specific mutational signatures? To identify mutated pathways, we adopted an optimization method based on integer linear programming. Analyzing a breast cancer dataset, we identified pathways associated with mutational signatures on both expression and mutation levels. Our analysis captured important differences in the etiology of the APOBEC related signatures and the two clock-like signatures. In particular, it revealed that clustered and dispersed APOBEC mutations may be caused by different mutagenic processes. In addition, our analysis elucidated differences between two age related signatures - one of the signatures is correlated with the expression of cell cycle genes while the other has no such correlation but shows patterns consistent with the exposure to environmental/external processes [Kim et al., 2020]. Complementing this approach, we also developed a network-based method, named GENESIGNET that constructs an influence/information flow network connecting genes and mutational signatures [Amgalan et al., 2023]. The approach leverages sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes. Applying GENESIGNET to cancer data sets, we uncovered important relations between mutational signatures and several cellular processes that can shed light on cancer-related processes. In particular, GENESIGNET exposed a link between the SBS8 signature of unknown etiology and the Nucleotide Excision Repair (NER) pathway. Linking mutational signatures to molecular features can help understand the etiology and develop personalized cancer therapy. However, due to the complex and dynamic nature of tumor evolution, untangling the cause and effect relationship can be challenging and requires further integrated and comprehensive analyses.