## Abstract

Cellular networks are inherently complex due to their large number of genes and proteins interacting through non-linear feedback loops. The identification of cellular networks from large-scale parallel measurements from the activity of genes and proteins is a tremendous challenge in the postgenomic era i. e. after the sequencing of the genome. Traditionally, this system identification problem has been viewed as a huge parameter estimation problem of an unknown non-linear system. Here we develop an approach where we approximate the complex system equations with a piecewise linear system, using a computational model of the cell cycle as a proof of principle. The modular and sparse structure of the cellular network makes it possible, to divide the model into subsystems, each having only a small number of inputs and outputs. As a rule, the subsystems operate as switches with or without delay. Since the cell cycle, as most biological systems, is robust against perturbations, the subsystems can be replaced by step functions and the main dynamical behaviour of the full complex cellular network can therefore be captured by the piecewise linear system. If other cellular networks are robust, sparse and modular, our approach of targeting a reduced system description instead of the full complex system, set the stage for not only a thorough characterization of the system dynamics but most importantly, it reduces the complexity of the parameter estimation problem.

Original language | English (US) |
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Article number | TuA04.2 |

Pages (from-to) | 117-123 |

Number of pages | 7 |

Journal | Proceedings of the IEEE Conference on Decision and Control |

Volume | 1 |

State | Published - Dec 1 2004 |

Event | 2004 43rd IEEE Conference on Decision and Control (CDC) - Nassau, Bahamas Duration: Dec 14 2004 → Dec 17 2004 |

## ASJC Scopus subject areas

- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization