Development of a two-step segregated model for the optimization of plant cell growth

Sirois, Joël; Perrier, Michel; Archambault, Jean

doi:10.1016/s0967-0661(00)00033-2

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…It is evident that biomass is lost due to cell death and biomass degradation once a plant cell suspension culture passes through its stationary phase (Bailey and Nicholson, 1989; Sirois et al, 2000). As the PBE (1) is formulated to conserve volume, an additional sink term must be added to account for the biomass loss which is observed experimentally.…”

Section: Methodsmentioning

confidence: 99%

“…These models however are unsegregated and treat biomass as a culture‐averaged parameter, and do not account for the heterogeneity of biomass induced by aggregation. The segregated models which have been developed typically divide biomass into general classifications (i.e., active and non‐active) and use kinetic descriptions to account for growth dynamics (Bailey and Nicholson, 1989; Schlatmann et al, 1999; Sirois et al, 2000; Vangulik et al, 1993). While providing some detail regarding the heterogeneity of biomass and indirectly accounting for aggregates of different sizes via the biomass classifications, these models do not explicate the phenomena underlying the transitions amongst classifications.…”

Section: Introductionmentioning

confidence: 99%

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A population balance equation model of aggregation dynamics in Taxus suspension cell cultures

Kolewe

Roberts

Henson

2011

Biotech & Bioengineering

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The nature of plant cells to grow as multicellular aggregates in suspension culture has profound effects on bioprocess performance. Recent advances in the measurement of plant cell aggregate size allow for routine process monitoring of this property. We have exploited this capability to develop a conceptual model to describe changes in the aggregate size distribution that are observed over the course of a Taxus cell suspension batch culture. We utilized the population balance equation framework to describe plant cell aggregates as a particulate system, accounting for the relevant phenomenological processes underlying aggregation, such as growth and breakage. We compared model predictions to experimental data to select appropriate kernel functions, and found that larger aggregates had a higher breakage rate, biomass was partitioned asymmetrically following a breakage event, and aggregates grew exponentially. Our model was then validated against several data sets with different initial aggregate size distributions and was able to quantitatively predict changes in total biomass and mean aggregate size, as well as actual size distributions. We proposed a breakage mechanism where a fraction of biomass was lost upon each breakage event, and demonstrated that even though smaller aggregates have been shown to produce more paclitaxel, an optimum breakage rate was predicted for maximum paclitaxel accumulation. We believe this is the first model to use a segregated, corpuscular approach to describe changes in the size distribution of plant cell aggregates, and represents an important first step in the design of rational strategies to control aggregation and optimize process performance.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A population balance equation model of aggregation dynamics in Taxus suspension cell cultures

Kolewe

Roberts

Henson

2011

Biotech & Bioengineering

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“…Afterwards, Sirois et al () constructed a segregated model for an Eschscholzia californica (California poppy) batch culture. The model construction was done in two phases, starting with a not complex model, expanding to a segregated one by way of reduced overall error and improved lysis phase.…”

Section: Models Concerning Plant Suspension Cultures (Su)mentioning

confidence: 99%

Modeling of plant in vitro cultures: Overview and estimation of biotechnological processes

Maschke

Geipel

Bley

2014

Biotech & Bioengineering

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Plant cell and tissue cultivations are of growing interest for the production of structurally complex and expensive plant-derived products, especially in pharmaceutical production. Problems with up-scaling, low yields, and high-priced process conditions result in an increased demand for models to provide comprehension, simulation, and optimization of production processes. In the last 25 years, many models have evolved in plant biotechnology; the majority of them are specialized models for a few selected products or nutritional conditions. In this article we review, delineate, and discuss the concepts and characteristics of the most commonly used models. Therefore, the authors focus on models for plant suspension and submerged hairy root cultures. The article includes a short overview of modeling and mathematics and integrated parameters, as well as the application scope for each model. The review is meant to help researchers better understand and utilize the numerous models published for plant cultures, and to select the most suitable model for their purposes.

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“…When modelling a system, first principles models are valuable since they provide a direct insight into the process. Although parameter estimation for first principles models is challenging, parameter estimation methods for first principles models exist in the literature, [14][15][16][17][18][19] and they have been applied to bioreactors. [5] More recently, Sartorius Inc. has developed a high-fidelity simulator for the monoclonal antibody process, and it is used in the present manuscript to illustrate the control approach.…”

Section: Introductionmentioning

confidence: 99%

Process‐aware data‐driven modelling and model predictive control of bioreactor for the production of monoclonal antibodies

Sarna

Patel

Corbett³

et al. 2022

Can J Chem Eng

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This manuscript addresses the problem of controlling a bioreactor to maximize the production of a desired product while respecting the constraints imposed by the nature of the bio-process. The approach is demonstrated by first building a data-driven model and then formulating a model predictive controller (MPC) with the results illustrated by implementing a detailed monoclonal antibody production model (the test bed) created by Sartorius Inc. In particular, a recently developed data-driven modelling approach using an adaptation of subspace identification techniques is utilized that enables the incorporation of known physical relationships in the data-driven model development (constrained subspace model identification), making the data-driven model process aware. The resultant controller implementation demonstrates a significant improvement in production compared to the existing proportional integral (PI) controller strategy used in the monoclonal antibody production. Simulation results also demonstrate the superiority of the process-aware or constrained subspace MPC compared to traditional subspace MPC. Finally, the robustness of the controller design is illustrated via the implementation of a model developed using data from a test bed with a different set of parameters, thus showing the ability of the controller design to maintain good performance in the event of changes such as a different cell line or feed characteristics.

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Development of a two-step segregated model for the optimization of plant cell growth

Cited by 8 publications

References 14 publications

A population balance equation model of aggregation dynamics in Taxus suspension cell cultures

A population balance equation model of aggregation dynamics in Taxus suspension cell cultures

Modeling of plant in vitro cultures: Overview and estimation of biotechnological processes

Process‐aware data‐driven modelling and model predictive control of bioreactor for the production of monoclonal antibodies

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