Using Python to Dive into Signalling Data with CellNOpt and BioServices

TL;DR

This paper introduces Python packages that facilitate the use of CellNOptR for modeling signalling networks and discusses integrating web resources via BioServices for systems biology research.

Contribution

It presents new Python tools for accessing and manipulating CellNOptR functionalities and integrates web services to enhance systems biology data analysis capabilities.

Findings

Python packages enable full access to CellNOptR in Python

Tools simplify data manipulation and visualization

BioServices enhances programmatic access to life science web resources

Abstract

Systems biology is an inter-disciplinary field that studies systems of biological components at different scales, which may be molecules, cells or entire organism. In particular, systems biology methods are applied to understand functional deregulations within human cells (e.g., cancers). In this context, we present several python packages linked to CellNOptR (R package), which is used to build predictive logic models of signalling networks by training networks (derived from literature) to signalling (phospho-proteomic) data. The first package (cellnopt.wrapper) is a wrapper based on RPY2 that allows a full access to CellNOptR functionalities within Python. The second one (cellnopt.core) was designed to ease the manipulation and visualisation of data structures used in CellNOptR, which was achieved by using Pandas, NetworkX and matplotlib. Systems biology also makes extensive use of web resources and services. We will give an overview and status of BioServices, which allows one to access programmatically to web resources used in life science and how it can be combined with CellNOptR.