Pyquali//Bioquali is a python plugin dedicated to the validation and the prediction of qualitative data coupled with a biological network represented as an interaction graph.

The framework is the following :

• The user builds a network and download it, formalized as a set of interactions Factor -> Target [+]. Other signs are [-] or [?]. An interaction means that an increase of the Factor has an effect on the production rate of the Target. This action can be an increase ([+]), a decrease ([-]), a undetermined signed action ([?]) or a doubled signed action, depending on the environment of the cell ([?]).
• The user can also provide partial qualitative shift equilibrium data. Typically, this can be microarray data about a perturbed system starting from an equilibrium stata and reaching another equilibrium state. Data are formalized as Product1 [+] Product2 [-] ...

The following applications of pyquali are available.

• Checking the internal coherence of the network. We check wether there exists at least a set of variations (increase or decrease) for each product of the network, such that the variation of each product is explained by the influence of one of its predecessors in the graph.
• If the network is not coherent, identifying the smallest subnetwork that explains the incoherence. Then, the user can check interactions on this subnetwork and propose a corrected model.
• If the network is coherent, checking the coherence of the network with observation data. When observation data are not coherent with the network, Bioquali eventually identifies a subnetwork explaining inconsistency.
• When network and data are coherent, computing the variations of nonobserved products (prediction process).

The full python plugin can be downloaded here.

Underlying mathematical results are detailed in :

• Radulescu et al, Interface of the Royal Society (2006)
  Topology and linear response of interaction networks in molecular biology.
  O. Radulescu, S. Lagarrigue, A. Siegel, P. Veber, M. Le Borgne
  Journal of The Royal Society Interface 3(6):185 - 196
• Siegel et al, Biosystems (2006)
  Qualitative analysis of the relation between DNA microarray data and behavioral models of regulation networks
  A. Siegel, O. Radulescu, M. Le Borgne, P. Veber, J. Ouy and S. Lagarrigue
  Biosystems 84:153-74

Algorithms and implementation are detailed in :

• Borgne ML, et al, 2007
  Technical Report RR-6182, INRIA
  Decision Diagrams for Qualitative Biological Models
• Veber et al, Complesus (2004/2005)
  Complex Qualitative Models in Biology: A New Approach
  Complexus 2004/2005;2:140-151 (DOI: 10.1159/000093686)

Biological validation to E. Coli network is detailed in :

• Guziolowski et al, JBPC (2007)
  Checking Consistency Between Expression Data and Large Scale Regulatory Networks: A Case Study
  C Guziolowski, P Veber, M Le Borgne, O Radulescu, and A Siegel (2006). C
  Journal of Biological Physics and Chemistry 7 (2007) 37-43