The focus of our research is mainly on biological network modeling, perturbation and analysis and classification of phenotypes by coding and non-coding expression. Our research goes from Genome to Network analysis in order to understand biological systems under specific conditions. In particular, we develop algorithms for searching in labeled directed and undirected graphs or multigraphs. Up to date, we have designed ones of the fastest algorithms for subgraph database search, subgraph matching with or without redundancy on the results. We have addressed the problem of finding significant unusually subneworks, named motifs, overcoming to the consuming step of network simulation by providing analytical models. Our algorithms are the basis to understand the topological proprieties of biological systems. In order to analyze their dynamics, we model signaling and metabolic pathways by using languages, techniques, and tools well established in the context of electronic design automation. Our intuition here is that several characteristics and issues to model biological systems are common to the electronics system modelling, such as concurrency, reactivity, abstraction levels, as well as state space explosion during validation. We study static and dynamic network characteristics by associating to nodes profiles information from genomic and trascriptomic data. We work both with bulk and single cell data. Here we customize and develop pipelines for genomic variants detections and annotation, for coding and non coding deferential analysis. We also design predict models that from data expression construct network of interactions in particular for small, circular, circulate RNA interferences. We study new theoretical methods aimed at investigating biological sequences by using information theory, algorithmic approaches and advanced data structure engineering, from DNA to RNA level. The goal is reached by defining mathematical representations that characterize biological sequences and that allow us to investigate single molecule properties as well as to examine the composition of set of sequences. Our algorithms are developed sequential and in several cases, also in parallel (multicore or GPU). Our studies produce software and data available for the open research development and distributed in the appropriate platforms (web based, Cytoscape and R).