Abstract
BackgroundNetwork controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combined multi-drug therapy in order to induce a desired response from a cell; this can lead to developments of novel therapeutic approaches for systemic diseases like cancer.ResultWe develop a novel bioinformatics data analysis pipeline called NetControl4BioMed based on the concept of target structural control of linear networks. Our pipeline generates novel molecular interaction networks by combining pathway data from various public databases starting from the user’s query. The pipeline then identifies a set of nodes that is enough to control a given, user-defined set of disease-specific essential proteins in the network, i.e., it is able to induce a change in their configuration from any initial state to any final state. We provide both the source code of the pipeline as well as an online web-service based on this pipeline http://combio.abo.fi/nc/net_control/remote_call.php.ConclusionThe pipeline can be used by researchers for controlling and better understanding of molecular interaction networks through combinatorial multi-drug therapies, for more efficient therapeutic approaches and personalised medicine.
Highlights
Network controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration
The pipeline can be used by researchers for controlling and better understanding of molecular interaction networks through combinatorial multi-drug therapies, for more efficient therapeutic approaches and personalised medicine
We chose MTOR and ERBB3 proteins as target proteins, as we found these as essential proteins in cancer cell lines MBD-MB-231
Summary
Network controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In order to study the structure, function and dynamics of directed PPI networks, multiple computational system biology approaches have been employed to reveal important links in various biological networks [7]. This includes, among others, finding physical interactions (e.g., between proteins in PPI networks) and functional interactions (e.g., between genes with similar or related functions, direct or indirect regulatory relationships between genes), identifying network modules (clusters of intensively interacting molecules) [7], interaction patterns and topological properties of disease networks (such as cancers, HIV infections, diabetes mellitus, Parkinson, Alzheimer, etc.) [8]
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