METABOLIC MODELLING FOR THE CONTROL OF INTRACELLULAR PROCESSES IN PLANT CELLS CULTURES

Transcription

1 2.5 ADP Pi + NADH + O ATP + NAD - 2 METABOLIC MODELLING FOR THE CONTROL OF INTRACELLULAR PROCESSES IN PLANT CELLS CULTURES Mathieu Cloutier Michel Perrier Mario Jolicoeur Montpellier, Mercredi 6 mai 2009

2 Outline Introduction and context Why plant cells? Challenges related to plant cells cultures Modelling of plant cells metabolism Application to bioreactor cultures in a context of secondary metabolites production 2

3 Sl Selection of genotype or phenotype Slow growth (2-5 years) Requires land space Subject to variability High specific production Strain development Callus Stabilization i Bioprocess development Cell suspension Screening, selection Cloning Optimization of culture parameters Scale-up Bioprocess optimization and validation Production Fast growth ( days) Controlled environment... loss of productivity 3

4 Plant cell nutrition, dynamics and productivity The literature t on plant cells nutrition vs. productivity is inconsistent Reports on Pi feeding states that Pi addition can have a positive, negative or no effect at all on productivity 1,2 Inconsistency are also reported for carbon and nitrogen sources 2 Interactions effects between nutrients are also observed 3 1-Lamboursain L, Jolicoeur M Biotechnology and Bioengineering 91(7): DiCosmo F, Towers GHN Recent advances in phytochemistry. New York: Plenum Press. p Weathers PJ, Hemmavanh DD, Walcerz DB, Cheetham RD, Smith TC Plant 33:

5 Elicitation and extraction in bioreactor cultures Elicitation (molecular or physical attack) metabolic response Alkaloidsl Extractive phase (polymeric resin) Cells Removing the inhibition by extracting the product has been studied extensively Limitations on productivity are still observed The intracellular dynamics during the production phase are important Inadequate nutrient feeding can be channelled for growth instead of production 5

6 Models for plant cells cultures Nutritional and metabolic models The kinetic modelling of intracellular processes is important t No model to describe the three major constitutive nutrients t (C, N, P) at the intracellular level Links between nutrients and metabolites production 6

7 The good worst-case scenario The nice case for metabolic studies Cells are in steadystate (constant growth, metabolites levels etc.) Constant enzymatic activity Pathways and regulation are well known Cells are not limited in nutrients E. coli, yeast

8 The good worst-case scenario The nice case for metabolic studies Cells are in steadystate (constant growth, metabolites levels etc.) Constant enzymatic activity Pathways and regulation are well known Cells are not limited in nutrients E. coli, yeast The worst-case scenario Cells are not in steadystate Enzyme activity can change Complex regulation, pathways for cell adaptation Cells are limited in inorganic phosphate (Pi) Plant cells!

9 Modelling of plant cells metabolism 1 1- Cloutier M, Chen J, Tagte F, McMurray-Beaulieu V, Perrier M, Jolicoeur M Kinetic metabolic modelling for the control of plant cells cytoplasmic phosphate. Journal of Theoretical Biology. In press. 9

10 Kinetic equations for metabolic reaction rates r41: NADH ADP Pi + O ATP + NAD + v r 41 = vmax 41 Km NADH + NADH NADH Km ADP + ADP ADP Pi Km 2 Pi 2 + Pi 2 O2 Km + O O ATP ADP + ATP 1+ e Multiplicative Michaelis- Menten kinetics with the substrates involved in the biochemical i reaction Metabolic flux regulation with switching functions 10

11 Application of the kinetic metabolic model to bioreactor cultures Use the model to... describe experimental results from bioreactor cultures batch cultures with extractive phase... get a better insight on the relationships between cell nutrition and productivity... implement a culture strategy to increase productivity 11

12 Model calibration with batch bioreactor cultures The model produces a good description of the culture separation between growth (days 0-6) and production (days 6-9) Nutritional limitations are observed Pi (nucleotides) limiting growth after 6 days GLC exhausted after 9 days: limits alkaloids production 12

13 Feeding GLC and N to maintain productivity it

14 Perfusion strategy to stabilize GLC 1 dglc dt Mass balance on intracellular = μ glucose (GLC). v ( t ) v ( t ) GLC D( t) EGLC 31( t) X ( t) v = in Glucose uptake (v 31 (t)) is fast and thus we consider that the cells uptake all the glucose in the feed. dglc dt = D( t) EGLC X (t) t in v ( t) 1 3 Combining equations 1 and 2 and neglecting dilution due to growth 4 D( t) = X ( t ) v 1 ( t ) EGLC in Solving equation 3 for a steady-state on GLC. The feedrate is thus proportional to X and GLC consumption 14

15 Implementation of the perfusion strategy through simulation The feeding strategy stabilizes intracellular nutrients The stabilizing effect is observed for the rate of alkaloids production 15

16 Bioreactor cultures 3L bioreactor (2.7L working vol.) Controlled environment Oxygen, mixing... Elicitation (chitin) and resins addition after 4 days of culture to stimulate production Can be operated in batch or perfusion batch cultures to characterize the system perfusion cultures to implement a modulation strategy t De Dobbeleer C, Cloutier M, Fouilland M, Legros R, Jolicoeur M Biotechnology and Bioengineering 95(6):

17 Algorithm for perfusion culture Stage Culture Perfusion rate time (days) (d -1 ) Medium Adaptation v Xˆ 1 ( t ) EGLC in Growth 2 t D( t) = e B5 Elicitation t e t e P + chitin v ˆ 1 X ( t ) = EGLC in Production t e D( t) P + chitin 17

18 Perfusion strategy An estimation of X(t) and v1(t) is sufficient for a perfusion strategy aiming at a stabilization of intracellular GLC X is estimated from the mass balance on O 2 : Xˆ = ( ˆ ˆ ) ( * ) k a + k a O O L B L ˆ S q O

19 Implementation of the perfusion strategy A good prediction is achieved for biomass concentration This allowed a modulation of the perfusion feed rate 19

20 Experimental implementation The perfusion strategy allowed stabilizing intracellular GLC A significant increase in the length of production phase is achieved The model predictions are in accordance with experimental data

21 Experimental implementation The perfusion strategy allowed stabilizing intracellular GLC A significant increase in the length of production phase is achieved The model predictions are in accordance with experimental data

22 Dynamic metabolic modelling for the control of plant cytoplasmic Pi 1 Why Pi? Limiting nutrient in many plant cells media Involved in regulation, both at the biochemical i and genetic level Involved in the fast dynamics of plant cells cultures energetic metabolism A key factor in the transition between growth and production phases Production media are Pi-free A tool is available for online, quantitative measurement of intracellular Pi pools A prerequisite it for control applications 1-Article submitted to Metabolic Engineering 22

23 Development of the control loop The extracellular feeding concentration can be used as the manipulated variable to control the cytoplasmic Pi The system has a first order dynamic (+delay) behaviour A PI controller could be a first try for that t system Tuning the control loop experimentally is complicated 23

24 Experimental set-up for control application Small-scale bioreactor for in vivo NMR experiments 1 Cells EPi cpi vpi PME, ATP Gmati D., Chen J., Jolicoeur M Biotechnology and Bioengineering, 89 (2),

25 Model calibration, identification μm step on extracellular phosphate 25

26 Experimental implementation It is possible to control intracellular concentrations using extracellular variables The dynamic metabolic model facilitates the implementation of the control loop 26

27 Conclusions It is possible to stabilize intracellular processes by manipulating extracellular variables Closed-loop control is possible if on-line intracellular measurements are available 1 The stabilization of intracellular concentrations can significantly improve plant cell productivity A good understanding of the dynamics of the problem is required Dynamic modelling facilitates the development and implementation of culture strategy 1- Cloutier M, Chen J, Tagte F, McMurray-Beaulieu V, Perrier M, Jolicoeur M Kinetic metabolic modelling for the control of plant cells cytoplasmic phosphate. Journal of Theoretical Biology. In press. 27

28 Mathieu Cloutier, Ph.D. Research Fellow Hamilton Institute, Ireland Mario Jolicoeur, Ph.D. Canada Chair on Applied Metabolic Engineering Tools Staff and former students: Jingkui Chen, Research assistant Frithjof Tagte, Hamburg University of Applied Sciences (Internship) 28

29 MERCI DE VOTRE ATTENTION!