Reconstruction of cellular signal transduction networks using perturbation assays and linear programming

PLoS One. 2013 Jul 30;8(7):e69220. doi: 10.1371/journal.pone.0069220. Print 2013.

Abstract

Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4(+) T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.

MeSH terms

  • Algorithms
  • CD4-Positive T-Lymphocytes / metabolism
  • Cluster Analysis
  • Computational Biology / methods
  • Computer Simulation
  • ErbB Receptors / metabolism
  • Humans
  • Models, Biological*
  • Programming Languages
  • Programming, Linear*
  • Protein Interaction Maps*
  • Signal Transduction*

Substances

  • ErbB Receptors

Grant support

We acknowledge funding from the German Ministry of Education and Research (BMBF) via ForSys/ViroQuant (grant 0313923) and GerontoSys/AgeNet (grant 0315898), the European Union seventh framework program via SysPatho (grant number 260429), and the German Research Foundation and the Open Access Publication Funds of the TU Dresden. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.