10th EAI International Conference on Performance Evaluation Methodologies and Tools

Research Article

Dealing with indetermination in biochemical networks

  • @INPROCEEDINGS{10.4108/eai.25-10-2016.2266988,
        author={niccol\'{o} totis and Marco Beccuti and Francesca Cordero and Laura Follia and Chiara Riganti and Francesco Novelli},
        title={Dealing with indetermination in biochemical networks},
        proceedings={10th EAI International Conference on Performance Evaluation Methodologies and Tools},
        keywords={indetermination ordinary differential equation optimiza- tion problems petri net},
  • niccolò totis
    Marco Beccuti
    Francesca Cordero
    Laura Follia
    Chiara Riganti
    Francesco Novelli
    Year: 2017
    Dealing with indetermination in biochemical networks
    DOI: 10.4108/eai.25-10-2016.2266988
niccolò totis,*, Marco Beccuti1, Francesca Cordero1, Laura Follia2, Chiara Riganti3, Francesco Novelli2
  • 1: University of Turin, Computer Science Department
  • 2: University of Turin, Molecular Biotechnology and Health Sciences Department
  • 3: University of Turin, Oncology Department
*Contact email: niccolo.totis@gmail.com


A main aspect in computational modelling of biological sys- tems is the determination of model structure and model pa- rameters. Due to economical and technical reasons, only part of these details are well characterized, while the rest are unknown. To deal with this difficulty, many reverse en- gineering and parameter estimation methods have been pro- posed in the literature, however these methods often need an amount of experimental data not always available. In this paper we propose an alternative approach, which overcomes model indetermination solving an Optimization Problem (OP) with an objective function that, similarly to Flux Balance Analysis, is derived from an empirical bio- logical knowledge and does not require large amounts of data. The system behaviour is described by a set of Or- dinary Differential Equations (ODE). Model indetermina- tion is resolved selecting time-varying coefficients that max- imize/minimize the objective function at each ODE inte- gration step. Moreover, to facilitate the modelling phase we provide a graphical formalism, based on Petri Nets, which can be used to derive the corresponding ODEs and OP. Fi- nally, the approach is illustrated on a case study focused on cancer metabolism.