Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1

Research Article

MANIA: A Gene Network Reverse Algorithm for Compounds Mode-of-Action and Genes Interactions Inference

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  • @INPROCEEDINGS{10.1007/978-3-642-02466-5_37,
        author={Darong Lai and Hongtao Lu and Mario Lauria and Diego Bernardo and Christine Nardini},
        title={MANIA: A Gene Network Reverse Algorithm for Compounds Mode-of-Action and Genes Interactions Inference},
        proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1},
        proceedings_a={COMPLEX PART 1},
        year={2012},
        month={5},
        keywords={gene network gene expression reverse engineering Ordinary Differential Equations (ODE) compound mode-of-action},
        doi={10.1007/978-3-642-02466-5_37}
    }
    
  • Darong Lai
    Hongtao Lu
    Mario Lauria
    Diego Bernardo
    Christine Nardini
    Year: 2012
    MANIA: A Gene Network Reverse Algorithm for Compounds Mode-of-Action and Genes Interactions Inference
    COMPLEX PART 1
    Springer
    DOI: 10.1007/978-3-642-02466-5_37
Darong Lai,*, Hongtao Lu1,*, Mario Lauria2,*, Diego Bernardo2,*, Christine Nardini3,*
  • 1: Shanghai Jiao Tong University
  • 2: TIGEM
  • 3: CAS-MPG PICB
*Contact email: darong.lai@gmail.com, lu-ht@cs.sjtu.edu.cn, lauria@tigem.it, dibernardo@tigem.it, christine@picb.ac.cn

Abstract

Understanding the complexity of the cellular machinery represents a grand challenge in molecular biology. To contribute to the deconvolution of this complexity, a novel inference algorithm based on linear ordinary differential equations is proposed, based on high-throughput gene expression data. The algorithm can infer (i) gene-gene interactions from steady state expression profiles (ii) mode-of-action of the components that can trigger changes in the system. Results demonstrate that the proposed algorithm can identify information with high performances, thus overcoming the limitation of current algorithms that can infer reliably only one.