WO2023077683A1 - Procédé d'estimation en ligne d'état de culture cellulaire et de régulation et de commande d'optimisation de réapprovisionnement - Google Patents

Procédé d'estimation en ligne d'état de culture cellulaire et de régulation et de commande d'optimisation de réapprovisionnement Download PDF

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WO2023077683A1
WO2023077683A1 PCT/CN2022/073489 CN2022073489W WO2023077683A1 WO 2023077683 A1 WO2023077683 A1 WO 2023077683A1 CN 2022073489 W CN2022073489 W CN 2022073489W WO 2023077683 A1 WO2023077683 A1 WO 2023077683A1
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cell
state
cell culture
estimation
optimal
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刘飞
吴杰
栾小丽
赵顺毅
陈珺
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江南大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/25—Design optimisation, verification or simulation using particle-based methods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65—Raman scattering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]

Definitions

  • the invention relates to the technical field of biomanufacturing control, in particular to a method for online estimation of cell culture status and optimization of feeding regulation.
  • the formation of products is closely related to the growth and metabolism of bacterial cells, and the supply of nutrient substrates directly affects cell growth and metabolism.
  • the feeding of substrates can avoid substrate inhibition, cell starvation and metabolism. Repression, which optimizes the feed rate of substrates according to the different conditions of bacterial cell growth and product synthesis, provides an optimal growth environment for microorganisms, and is an effective means of regulation to improve the efficiency of biomanufacturing processes.
  • the feeding method of the cell culture process initially relied on manual experience, and gradually studied according to the mathematical model of cell culture and production goals to solve the optimal feeding trajectory in advance, and then use the feeding trajectory as the process control curve in the actual production process.
  • actual cell growth involves complex physical, chemical and biological reactions, and its metabolic flow and kinetic evolution are complex.
  • the optimal process curve obtained based on mathematical models may not be able to achieve optimal results. Even seriously affect cell metabolism and product synthesis.
  • the optimization control method of cell culture and product production process based on mathematical model is essentially an open-loop control, which fails to feed back the optimization effect and adjust the future feeding strategy.
  • the purpose of the present invention is to provide a method for online estimation of cell culture status and optimization of feeding regulation, which integrates estimation of cell growth status, optimization of production benefits, online process control and real-time feedback of results, and can sense the growth status of bacterial cells in a timely manner.
  • Rolling optimized feeding at different stages provides an optimal culture environment and maximizes economic benefits.
  • the present invention provides a method for online estimation of cell culture status and optimization of feed regulation, comprising the following steps:
  • establishing a cell culture state model specifically includes the following steps:
  • x(k), u(k) are the cell state and feeding rate at time k, respectively, and f[x(k), u(k)] is the linear or nonlinear relationship between x(k), u(k) function, considering the disturbance noise of the cultivation process as w(k+1).
  • step S2 specifically includes the following steps:
  • x(T f ), u(T f ) are the cell state and feeding rate at the final moment, respectively, and m[x(T f ),u(T f )] is about x(T f ), u(T f ) is a linear or nonlinear function corresponding to the volume of the culture solution, n[x(k), u(k)] is a linear or nonlinear function about x(k), x(k) corresponding to the substrate concentration, u max and u min denote the upper and lower limits of the cell feeding rate u(k) at time k, respectively.
  • the economic benefit optimization target includes:
  • the optimization objective J is defined as the maximum output P(T f ) at the end point.
  • the cell culture state x(k) in is related to the feeding rate u(k), that is, the benefit optimization objective is expressed as follows:
  • L[x(k),u(k)] is the functional relationship between the terminal output, cell culture state and feeding rate
  • the cost factor is introduced, and the economic benefit optimization goal selects the ratio of the net profit of a batch production to the production time, that is, the process benefit.
  • the specific benefit optimization goal is expressed as follows:
  • r is the sales price of the unit product
  • c is the cost price of the unit feed
  • T p is the time interval between two adjacent production batches of the same bioreactor
  • the conversion rate from substrate to product is pursued, and the economic benefit optimization goal selects the ratio of the product amount of a batch to the total amount of feed, that is, the product yield.
  • the specific benefit optimization goal is expressed as follows :
  • an indirect measurement method is used to estimate the cell state, or a direct measurement method is used to obtain an estimated value of the cell state at the current moment.
  • the indirect measurement method when the indirect measurement method is selected to estimate the cell state, it includes the following steps: based on biochemical mechanism or experiment, analyze and synthesize the relationship between the basic variable y(k) in the bacterial cell and the cell state x(k), Construct the measurement equation:
  • g[x(k)] is the constructed measurement function, assuming that v(k) is the measurement noise, first measure y(k) by glucosamine method, ergosterol method or nucleic acid method, and then obtain indirectly by estimation method Estimated cell state at the current moment
  • the estimation method selects Kalman filter method, extended Karl One of the methods of Mann filter, rolling time domain estimation, unscented Kalman filter, Bayesian estimation, particle filter or finite impulse response filter is used to estimate the cell state. Predict the cell state, update and correct the prediction to obtain the estimated value of the cell state at the current moment according to the current measured value of the basic variable y(k)
  • the extended Kalman filter algorithm is used to estimate the cell culture state, including the following steps:
  • P(k) is the estimated value of covariance at time k
  • Q is the covariance matrix of process noise
  • F(k) is the state transition matrix, if the cell culture model is nonlinear, then
  • solving the optimal feed rate trajectory satisfying the constraint conditions under the optimized economic benefits specifically includes the following steps:
  • the nonlinear programming optimization algorithm is used to solve the economic benefit J optimization problem satisfying the constraints, that is, to solve Get the optimal feeding trajectory U k , where the optimization algorithm includes interior point method, exterior point method, sequential quadratic programming method, genetic algorithm and particle swarm algorithm;
  • the present invention takes the economic benefit of the biomanufacturing process as the optimization target, and is different from the commonly used optimal feeding method, and directly integrates production optimization, online control and feedback mechanism into one framework for implementation, at each sampling time , estimate the state of cell culture through the current production basic variables, use the mathematical model to predict the future state and economic benefits, use the feeding rate in the future production time domain as the decision variable to optimize the benefits, and implement the optimization results in time.
  • the present invention is based on the measurable state of cell culture. Since most states of the cell growth process (such as intracellular metabolites, cell concentration, substrate concentration, etc.) The measurement of variables, and then use the state estimation method to estimate the cell state that is difficult to obtain online. On the other hand, advanced measuring instruments such as Raman spectroscopy, near-infrared spectroscopy, and emission spectroscopy can be used to directly measure the state of some cell cultures. , these estimation and measurement tools provide the support for rolling optimized feeding.
  • Fig. 1 is a schematic diagram of the implementation process of the present invention
  • Fig. 2 is to utilize fructose to produce polyhydroxybutyric acid process to optimize feeding rate locus figure;
  • Fig. 3 is to utilize urea to produce polyhydroxybutyric acid process to optimize feeding rate locus figure
  • Fig. 4 is the graph of the change of bacterial cell specific growth rate
  • Fig. 5 is the state change diagram of substrate concentration in the production process of polyhydroxybutyric acid
  • Fig. 6 is a diagram showing the state change of fructose concentration during the production of polyhydroxybutyric acid
  • Fig. 7 is a diagram of the state change of urea concentration in the production process of polyhydroxybutyric acid
  • Fig. 8 is a graph showing the state change of fermentation broth volume during the production of polyhydroxybutyric acid.
  • the present invention provides a method for online estimation of cell culture status and optimization of feed regulation, comprising the following steps:
  • the present invention establishes the cell culture state model (1), analyzes the production process requirements, and determines the constraint conditions (2)-(4) that the cell culture manufacturing process needs to meet; selects the economic benefit optimization target , if the feeding cost is low, use the maximum yield (5) as the economic benefit index; if the pursuit of high yield and take into account the substrate cost, use the maximum process benefit (6) as the index; if the conversion rate from substrate to product is emphasized, then Select the maximum product yield (7) as the benefit index. Benefit optimization depends on the state of cell culture.
  • the first measurement method is to construct the measurement equation (8), by measuring the basic variable y(k) related to the cell state, and indirectly obtain the estimated value of the cell state by the estimation method
  • the second measurement method is to use advanced spectral technology to directly measure the estimated value of the state
  • the extended Kalman filter and other estimation methods are used to predict the current state from the previous cell state according to (9) (10), and then according to (11)-(13) from the current measurement of the basic variables value, update the revised predicted value to obtain the estimated value Estimated by cell state and the proposed feeding rate U k as input, use the computer numerical solution method to solve the cell state model, and obtain the cell state vector in the future; then use the nonlinear programming optimization method to solve A new optimal feeding trajectory U k is obtained.
  • Implement u(k) at the current moment in the feeding trajectory U k in the cell culture production process, and repeat the above process until the production process ends.
  • the whole process involves the feed regulation technology of the nutrient substrate in the biomanufacturing process.
  • Optimizing the feed rate of the substrate according to the different conditions of bacterial cell growth and product synthesis is an effective regulation method to improve the efficiency of the biomanufacturing process.
  • the present invention takes the economic benefits of the biomanufacturing process as the optimization goal, integrates the cell culture process requirements, growth state estimation, online rolling optimization, real-time implementation and other links in a framework, and at each sampling time , estimate the state of cell culture through the current production variables, use the mathematical model to predict the future state and economic benefits, use the feeding rate in the future production time domain as the decision variable to optimize the benefits, and implement the optimization results in time, so rolling operation, this method is suitable for Cell culture models and processes require known biomanufacturing processes.
  • Step 1 Establish a mathematical model of the cell culture state:
  • x(k) and u(k) are the cell state and feeding rate at time k, respectively, f[ ] is a linear or nonlinear function, and w(k+1) is considered to be the disturbance noise in the culture process.
  • the Euler method is used to discretize it with T f sampling intervals.
  • Step 2 Determine the constraints of the cultivation process:
  • m[ ] and n[ ] are linear or nonlinear functions, u max and u min represent the upper limit and lower limit of the feeding rate respectively;
  • formula (2) represents the terminal constraints of the culture process, such as the volume of culture medium in production Do not overflow the bioreactor at the end;
  • formula (3) represents the restrictive conditions that need to be met throughout the manufacturing process, such as in order to avoid the metabolic inhibition of bacterial cells caused by excessive substrate concentration, the substrate concentration in the production process must be is lower than a certain upper limit;
  • formula (4) is the feed rate constraint imposed by considering the operating capacity of the actual equipment and the impact on the growth of bacterial cells;
  • Step 3 Select the economic benefit optimization target:
  • the economic benefit objectives of the biomanufacturing process are diverse and should be set according to the actual needs of production. If the substrate cost is low, the economic benefit optimization objective is to maximize the amount of optional products, and the optimization objective J is defined as the maximum output P(T f ) at the end point, and the end point output is generally the product of the product concentration at the end time and the volume of the fermentation broth, and is related to The cell culture state x(k) in the production process is related to the feeding rate u(k), that is, the benefit index is expressed as follows:
  • L[ ⁇ ] is to describe the functional relationship between the end point production, cell culture state and feed rate.
  • the cost factor is introduced, and the economic benefit optimization goal selects the ratio of the net profit of a batch production to the production time, that is, the process benefit.
  • the specific benefit indicators are expressed as follows:
  • r is the sales price of the unit product
  • c is the cost price of the unit feed
  • Tp is the time interval between two adjacent production batches of the same bioreactor, that is, the time required for tank release, cleaning, sterilization, inoculation, etc. operating time.
  • the conversion rate of substrate to product is pursued, and the economic benefit optimization goal is to choose the ratio of the product amount of a batch to the total amount of fed materials, that is, the product yield.
  • the specific benefit indicators are expressed as follows:
  • Cost-effective optimization goals also include best cell growth, lowest by-products, and lowest energy consumption.
  • Step 4 Select the cell culture state measurement method:
  • the end-point yield P(T f ), process efficiency, conversion rate and other indicators of manufacturing directly depend on the state of cell culture, and the detection of cell culture state is difficult.
  • g[ ⁇ ] is the constructed measurement function, assuming that v(k) is the measurement noise.
  • Step 1 Spectral preprocessing, the methods include smoothing, wavelet transform, multivariate scattering correction (MSC), standard normal variable transformation (SNV), orthogonal signal correction (OSC) , derivative algorithm (Der), etc.; step 2: selection of characteristic bands, the methods include continuous projection method (SPA), partial least squares method (PLS), uninformative variable elimination method (UVE), etc.; step 3: spectral data and
  • the establishment of the cell culture state mapping model includes principal component regression (PCR), partial least squares regression (PLSR), support vector machine regression (SVMR), deep learning methods, etc.
  • Step 5 Estimation of cell state based on state model and measurement equation:
  • the cell state can be obtained indirectly by using the measurable basic variables and selecting estimation or filtering methods.
  • estimation or filtering methods According to different situations such as the form of the state model, the process and the statistical distribution of the measurement noise, Kalman filter, extended Kalman filter, rolling time domain estimation, unscented Kalman filter, Bayesian estimation, particle filter, finite impulse response can be used State estimation methods such as filtering and corresponding extended forms.
  • f[ ] and g[ ] are nonlinear in the biological manufacturing process.
  • the extended Kalman filter algorithm is used to estimate the cell culture state as follows:
  • Step 1 Prediction of cell state and its covariance:
  • P(k) is the estimated value of covariance at time k
  • Q is the covariance matrix of process noise
  • F(k) is the state transition matrix, if the cell culture model f[ ⁇ ] is nonlinear, then
  • Step 2 Update of cell state and its covariance:
  • Step 6 Online solution and rolling implementation of optimal feeding rate trajectory:
  • the benefit index J is related to the future state and the feeding rate trajectory, based on the estimated value of the state at the current moment and the required rate trajectory U k , use the production process state model to iteratively calculate the cell state in the future time domain, and then calculate the benefit index.
  • Step 3 According to the future X k+1 , use nonlinear programming optimization method to solve Get the new feeding trajectory U k , the optimization algorithm includes interior point method, exterior point method, sequential quadratic programming method, genetic algorithm, particle swarm algorithm, etc.;
  • Step 4 Implement u(k) at the current moment in the feeding trajectory U k in the cell culture production process
  • Step 6 If you use the fourth step scheme 2 to directly measure and obtain the cell state value Then go to step 9 of the sixth step; otherwise, apply the solution 1 of the fourth step and go to the following step 7;
  • Step 7 From step 1 of the fifth step, predict the state of the current moment from the state of the cell at the previous moment;
  • Step 8 Use step 2 of the fifth step to update and correct the predicted value from the newly collected measured value y(k) to obtain the estimated value of the cell state
  • Step 9 If the production process is not over, go to Step 2 of Step 6.
  • the online estimation and optimization control method of cell culture state in biomanufacturing proposed by the present invention is applied to the process of producing polyhydroxybutyric acid (PHB) by fed-batch fermentation with fructose and urea, and its continuous kinetic state model is:
  • x 1 is the concentration of non-PHB substances in the cell
  • x 2 is the concentration of product PHB
  • x 3 is the concentration of fructose
  • x 4 is the concentration of urea
  • x 5 is the volume of fermentation broth
  • u 1 is the feed rate of fructose
  • u 2 is the feed rate of urea.
  • the input constraints are:
  • the covariance matrix is set to
  • the method of the present invention is used to realize the optimization and regulation of cell culture in the PHB manufacturing process, and its optimal feeding trajectory, specific growth rate and state changes are shown in Figure 2- Figure 8 respectively. It can be seen from Figures 2-8 that the entire manufacturing process can be roughly divided into two stages: cell growth and product synthesis.
  • the first stage is the cell growth stage (0-25h).
  • fructose and urea are supplemented at the same time as a carbon source and a nitrogen source, respectively, to maintain the growth of the cell at the maximum specific growth rate, wherein between 0-10h, Since the initial substrate concentration in the culture medium was sufficient for the growth of the bacteria, the feeding amount of fructose and urea was very small, and then the feeding was gradually increased with the consumption of nutrients.
  • the second stage is the product synthesis stage (25-49h). Due to the reduction of the nitrogen source utilization capacity of the bacteria, the number of bacteria could not be obtained in the early stage.
  • the online rolling optimization feeding method proposed by the present invention can timely regulate the flow acceleration rate of the nutrient substrate according to the actual situation of bacterial cell culture, thereby effectively improving the yield of biomanufacturing and increasing economic benefits.

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Abstract

Un procédé d'estimation en ligne d'état de culture cellulaire et de régulation et de commande d'optimisation de réapprovisionnement est divulgué. Le procédé comprend les étapes suivantes consistant : à établir un modèle d'état de culture cellulaire ; à déterminer une condition de contrainte qui doit être respectée pendant la culture cellulaire ; à sélectionner un objectif d'optimisation de bénéfices économiques ; à effectuer une estimation d'état cellulaire, et à obtenir une valeur d'estimation d'état cellulaire à un moment présent ; à résoudre le modèle d'état de culture cellulaire, et à obtenir un vecteur d'état cellulaire à un moment ultérieur ; à l'aide d'une programmation non linéaire, à obtenir une trajectoire de taux de réapprovisionnement optimale respectant la condition de contrainte ; et pendant la production de culture cellulaire, à mettre en œuvre le taux de réapprovisionnement au moment présent dans la trajectoire de taux de réapprovisionnement, et à optimiser et à résoudre de manière répétée la trajectoire de taux de réapprovisionnement jusqu'à ce que la production soit terminée. Dans la présente invention, une estimation d'état de croissance cellulaire, une optimisation de bénéfices de production, une commande en ligne de processus, et une rétroaction de résultat en temps réel sont intégrées dans un ensemble. Les conditions de croissance de cellules bactériennes peuvent être détectées rapidement, une optimisation de réapprovisionnement par laminage est effectuée pour différents stades, et l'environnement de culture optimal est fourni, maximisant ainsi les bénéfices économiques.
PCT/CN2022/073489 2021-11-04 2022-01-24 Procédé d'estimation en ligne d'état de culture cellulaire et de régulation et de commande d'optimisation de réapprovisionnement WO2023077683A1 (fr)

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