Towards improved bioprocess operation: monitoring, modeling and control

Transcription

1 Towards improved bioprocess operation: monitoring, modeling and control Krist Gernaey, Anna Eliasson Lantz, Mads Albæk and Ulrich Krühne, DTU OUTLINE The use of advanced monitoring techniques to guide development and optimization of fermentation processes, Anna E Lantz Overview of modeling approaches and recent developments, Krist V. Gernaey Application of computational fluid dynamics across reactor scales, Ulrich Krühne Case story Modeling enzyme production with Aspergillus oryzae, Mads Orla Albæk

2 The use of advanced monitoring techniques to guide development and optimization of fermentation processes Anna Eliasson Lantz Associate professor Center for Microbial Biotechnology Department of Systems Biology Technical University of Denmark

3 entire mass range to ponents Cellmass and metabolite monitoring - MS via Process mass spectrometer - setup TU in iology Sparge Gas Analyzed components by Prima PRO: O 2 CO 2 Ar, N 2 Thermo Scientific Prima PRO On-line measurement of CO 2 evolution and O 2 consumption monitors state of fermentation On-line measurement of all BIOFUELS in vent gas CER CO2 evolution rate OUR O2 consumption rate RQ Respiratory Quotient Vent Gas Analyzed components by Prima PRO: O 2 CO 2 H 2 CH 4 Methanol Ethanol Propanol 5x Pentanol H 2 S H 2 O Furfural Isoprene Farnesan Ar, N 2 3 DTU Biosys, Technical University of Denmark Scanning magnetic sector MS, for process monitoring

4 Cellmass monitoring dielectric permittivity FOGALE Nanotech probe 4 DTU Biosys, Technical University of Denmark

5 Monitoring in perfusion reactor 80,00 70, CO 2 (Thermo MS) Viable cell density (FOGDALE) 60, ,00 40,00 30, Biomass Temperatur IgG CO2 20, ,00 1 0, ,00 0,00 100,00 200,00 300,00 400,00 500,00 600,00 5 DTU Biosys, Technical University of Denmark

6 VCD pf/cm OD AU Cellmass monitoring in yeast cultivations 8,00 2,5 7,00 6,00 2 5,00 1,5 4,00 3,00 1 Biomass 5 g/l Biomass 300 g/l OD 5 g/l OD 300 g/l 2,00 0,5 1,00 0,00 0 0,00 20,00 40,00 60,00 80,00 100,00 120,00 140,00 160,00 180,00 200,00 Hours 6 DTU Biosys, Technical University of Denmark

7 Cellmass heterogeneity- Project aim To obtain an improved understanding of the mechanisms that are influential on the population dynamics and heterogeneity of a microorganism production culture General approach Determine level of heterogeneity caused by dynamic environmental conditions What is the nature of cell heterogeneity with respect to cell physiology? Are some cells less affected by a dynamic environment? Why? Can we envision improved strains? 7 DTU Biosys, Technical University of Denmark

8 Cellmass heterogeneity-background Enfors et al J Biotechnol decrease in biomass when scaling up -due to metabolic and stress responses resulting from glucose and oxygen gradients -highly dynamic conditions -heterogenic population However, higher cell viability in large scale Heterogeneity leads to lower yields and productivities Heterogeneity necessary to cope with dynamics in the process 8 DTU Biosys, Technical University of Denmark

9 Construction of S. cerevisiae growth reporter strains Choice of reporter promoter: RPL22A: Protein component of the large (60S) ribosomal subunit, expression is directly related with growth Fused to GFP; Both as multicopy plasmid strains and integrated Construction of S. cerevisiae production level reporter strains Model protein: Human carbonyl reductase 1 (CBR1) Reporter construct: Fused to RFP Modified PGK1 and TEF1 constitutive promoters Multicopy and integrated P PGK1 hcbr1 RFP T CYC1 9 DTU Biosys, Technical University of Denmark

10 Characterisation of growth reporter strains Standard physiology analyses sample Flow cytometry analyses: Biosensor signal FISH (targeting 16S rrna region) Metabolic activity staining Pertubations glucose pulse shift in aeration 10 DTU Biosys, Technical University of Denmark

11 Batch cultivation of S. cerevisiae growth reporter 7 11 DTU Biosys, Technical University of Denmark

12 FSC and GFP Distributions: Percentile Analysis FSC Histogram Regions defined by FSC percentiles e.g. 0-10%, 10-20%, 70-80% for each time point (sample) during a batch Mean FSC and Mean GFP is calculated for the cells in each FSC percentile interval 12 DTU Biosys, Technical University of Denmark

13 Freeze stress to test cell wall robustness -S. cerevisiae growth reporter Cells growing on glucose were more sensitive Tolerance inversively correlated to cell growth rate 13 DTU Biosys, Technical University of Denmark

14 P RPL22a T CYC1 EGFP Freeze-thaw stress Cell g r o w t h a n d r o b u s t n e s s d u a l r e p o r t e r s y s t e m Use to D e c i d e h a r v e s t t i m e G u i d e p r o c e s s o p t i m i s a t i o n, e.g. F e r m d e s i g n a n d f e e d i n g p r o f i l e 14 DTU Biosys, Technical University of Denmark

15 Data for PBM: Bivariate distributions of TPC and DNA Bivariate distribution of total protein content and DNA during a batch cultivation of S. cerevisiae: exponential growth on glucose (t=12.9, 16.8), diauxic shift (t=18.9, 20.4 h), exponential growth on ethanol (t=21.9, 23.9, 27.4 h), and stationary state (t=31.9, 38.7 h). The color code corresponds to the number density of the cells. The vertical lines corresponds to the critical budding (to the left) and division (to the right) threshold identified based on the DNA distribution. 15 DTU Biosys, Technical University of Denmark

16 Standardized procedure for estimation of critical budding and division sizes (TPC) DNA Histogram Critical Division Band Critical Budding Band Mean Total Protein Content = µ D Mean Total Protein Content = µ B 16 DTU Biosys, Technical University of Denmark

17 Acknowledgements Biomass monitoring Bent Svanholm Tina Johansen Bioneer Heterogeneity Magnus Carlqvist, DTU, LTH Anna-Lena Heins, DTU Rita Lencastre, DTU Krist Gernaey, DTU Søren Helmark, Fermenco Søren Sørensen, KU Luisa Lundin, KU ERA-IB (Danish council for independent research).. Danish council for strategic research 17 DTU Biosys, Technical University of Denmark