Augmented sparse reconstruction of protein signaling networks

D. Napoletani*, T. Sauer, D. C. Struppa, E. Petricoin, L. Liotta

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The problem of reconstructing and identifying intracellular protein signaling and biochemical networks is of critical importance in biology. We propose a mathematical approach called augmented sparse reconstruction for the identification of links among nodes of ordinary differential equation (ODE) networks, given a small set of observed trajectories with various initial conditions. As a test case, the method is applied to the epidermal growth factor receptor (EGFR) driven signaling cascade, a well-studied and clinically important signaling network. Our method builds a system of representation from a collection of trajectory integrals, selectively attenuating blocks of terms in the representation. The system of representation is then augmented with random vectors, and l1 minimization is used to find sparse representations for the dynamical interactions of each node. After showing the performance of our method on a model of the EGFR protein network, we sketch briefly the potential future therapeutic applications of this approach.

Original languageEnglish
Pages (from-to)40-52
Number of pages13
JournalJournal of Theoretical Biology
Volume255
Issue number1
DOIs
StatePublished - 7 Nov 2008

Keywords

  • Biochemical pathways
  • Protein interaction models
  • Sparse representations

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