Metabolism and Cell Cycle Modelling by Means of Neural Network Based Structures

Abstract

Due to their importance to the pharmaceutical industry and implicitly to the health, the animal cell cultures and the research carried out over their behaviour has gained recently an increasing attention. The paper presents a case study concerning the application of the neural networks in the modelling of the main parameters of an animal cell culture: the metabolic components and the cell cycle phases. For the metabolism a hybrid (neural-classical) structure is described for the building of a continuous simulator capable to reconstruct the trajectory of the main components from the initial conditions. A special attention is paid to the choice of the cost function and to the "amount" of the classical contribution to the hybrid model. For the cell cycle the outputs of the above continuous simulator are used and a neural network black box approach is employed in order to obtain the evolution of the three phases. The structures were tested on two batch animal cell cultures for which few and asynchronous measurements are available, namely one culture growing on microcarriers, the other in suspension.