TY - GEN

T1 - DETERMINISTIC GROWTH MODEL OF SACCHAROMYCES CEREVISIAE, PARAMETER IDENTIFICATION AND SIMULATION.

AU - Bellgardt, K. H.

AU - Kuhlmann, W.

AU - Meyer, H. D.

PY - 1983

Y1 - 1983

N2 - A mathematical model of the growth of SACCHAROMYCES CEREVISIAE H1022 was developed, which can describe oxidative and aerobic fermentative (Crabtreeeffect)growth on glucose, oxidative growth on ethanol, oxygen limited growth on glucose and/or ethanol and the anaerobic growth. A description of the complex growth behaviour with good model accuracy can be obtained with reaction kinetic models which take into consideration the more important metabolic processes in form of balance equations. In this paper a simplification of modelling is obtained by application of Blackman's master-reaction-concept and a quasi steady state approach for cellular metabolic reactions. Most of the parameters of the biological model are advantageously identified with stationary chemostat data by an iterative two-level optimization algorithm. In a following step the parameters which influence the simulated growth lag phases are identified with batch data. Simulations of batch and chemostat cultures show good correspondence of measurements and model predictions if the effectiveness of the respiratory chain (P/0) is not constant but increases with more fermentative growth and reduced oxygen uptake rates.

AB - A mathematical model of the growth of SACCHAROMYCES CEREVISIAE H1022 was developed, which can describe oxidative and aerobic fermentative (Crabtreeeffect)growth on glucose, oxidative growth on ethanol, oxygen limited growth on glucose and/or ethanol and the anaerobic growth. A description of the complex growth behaviour with good model accuracy can be obtained with reaction kinetic models which take into consideration the more important metabolic processes in form of balance equations. In this paper a simplification of modelling is obtained by application of Blackman's master-reaction-concept and a quasi steady state approach for cellular metabolic reactions. Most of the parameters of the biological model are advantageously identified with stationary chemostat data by an iterative two-level optimization algorithm. In a following step the parameters which influence the simulated growth lag phases are identified with batch data. Simulations of batch and chemostat cultures show good correspondence of measurements and model predictions if the effectiveness of the respiratory chain (P/0) is not constant but increases with more fermentative growth and reduced oxygen uptake rates.

UR - http://www.scopus.com/inward/record.url?scp=0020877525&partnerID=8YFLogxK

U2 - 10.1016/b978-0-08-029978-5.50013-6

DO - 10.1016/b978-0-08-029978-5.50013-6

M3 - Conference contribution

AN - SCOPUS:0020877525

SN - 0080299784

SN - 9780080299785

SP - 67

EP - 74

BT - Modelling and Control of Biotechnical Processes

ER -