System Biology is an inter-disciplinary field that studies systems of biological components at different scales, which may be molecules, cells or entire species. Our Research Group is part of EMBL-EBI European Bioinformatic Institute; it uses System Biology to understand functional deregulation within human cells (e.g. cancers), in particular the deregulation of signalling pathways (i.e., extra cellular signal that propagates to gene expression via protein interactions).
In this talk, we will first present BioServices that allows developers to easily access to online biological resources. Indeed, Bioinformatics make use of a plethora of online resources (e.g., databases of gene identifiers), which are highly specialised. Most of these resources can be accessed to programmatically via web services (based either on SOAP or REST technologies). In order to ease the development of pipelines that relies on web services (in life science), we developed BioServices that provides a Python library to programmaticaly access to about 25 of those web services (e.g. UniProt, Kegg). We will show some examples and demonstrate how it can be useful for the scientific community involved in life sciences.
In the second part of the talk, we will present CellNOpt. This software provides tools to perform logic modelisation at the protein level starting from complex network of protein interactions. The core of the software consist in (1) transforming protein network into logical networks (2) simulate the flow of signalling within the graph using for instance a boolean formalism (3) compare real biological data with the simulated data. The software is essentially an optimisation problem, which can be solved by various algorithms (e.g., Genetic Algorithm). The final goal consists in providing a protein network that is a faithful representation of the actual interactions between protein within our cells.
Although CellNOpt is originally written with the R language, which may seem off-topic, we will present it from a Python user perspective. We will therefore describe the Python wrappers that have been written using the RPy2 package and the advantages and drawbacks of such methods (cellnopt.wrapper). This is going to be an opportunity to compare some features available in R/Python and a discussion about the present predominance of the R language in the life science, how Python is catching up, and how R/Python can be combined. We will finally show how building blocks such as NetworkX and Pandas can ease the manipulation of the biological data and protein network used in CellNOpt.
References: C Terfve, T Cokelaer, A MacNamara, D Henriques, E Goncalves, MK Morris, M van Iersel, DA Lauffenburger, J Saez-Rodriguez. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Systems Biology, 2012, 6:133 PDF T Cokelaer, D Pultz, L.M. Harder, J. Serra-Musach and J. Saez-Rodriguez BioServices: a common Python package to access biological Web Services programmatically Journal reference: Bioinformatics, 29 (24) 3241-3242 (2013)