Models from experiments: combinatorial drug perturbations of cancer cells

Research output: Contribution to journalArticle


We present a novel method for deriving network models from molecular profiles of perturbed cellular systems. The network models aim to predict quantitative outcomes of combinatorial perturbations, such as drug pair treatments or multiple genetic alterations. Mathematically, we represent the system by a set of nodes, representing molecular concentrations or cellular processes, a perturbation vector and an interaction matrix. After perturbation, the system evolves in time according to differential equations with built-in nonlinearity, similar to Hopfield networks, capable of representing epistasis and saturation effects. For a particular set of experiments, we derive the interaction matrix by minimizing a composite error function, aiming at accuracy of prediction and simplicity of network structure. To evaluate the predictive potential of the method, we performed 21 drug pair treatment experiments in a human breast cancer cell line (MCF7) with observation of phospho-proteins and cell cycle markers. The best derived network model rediscovered known interactions and contained interesting predictions. Possible applications include the discovery of regulatory interactions, the design of targeted combination therapies and the engineering of molecular biological networks.


  • Sven Nelander
  • Weiqing Wang
  • Björn Nilsson
  • Qing-Bai She
  • Christine Pratilas
  • Neal Rosen
  • Peter Gennemark
  • Chris Sander
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Medical Genetics


  • network pharmacology, combination therapy, network dynamics, synthetic, biology
Original languageEnglish
JournalMolecular Systems Biology
StatePublished - 2008
Publication categoryResearch