CN-121999854-A - Metabolic flux detection method and system based on genome scale metabolic network model

CN121999854ACN 121999854 ACN121999854 ACN 121999854ACN-121999854-A

Abstract

The embodiment of the invention provides a metabolic flux detection method and system based on a genome scale metabolic network model, and belongs to the technical field of detection of biological metabolic flux. The method comprises the steps of obtaining a DCW sequence, an air inlet change rate, an exhaust change rate and a glucose concentration sequence of a fermentation liquid to be measured, determining the apparent specific growth rate of the fermentation liquid based on a multidimensional dynamics model, determining the overall metabolism flux distribution of the fermentation liquid according to the DCW sequence, the air inlet concentration sequence, the exhaust concentration sequence, the glucose concentration sequence and the apparent specific growth rate by adopting a QP-pFBA algorithm, and detecting the metabolism flux according to the overall metabolism flux distribution. The method and the system can predict the metabolic flux of the fermentation liquor.

Inventors

JIANG JINGYAN
Gan Zhiren
ZHOU MENGXUAN
XU ZHIGUO
DING JIAN
XIE ZHENGGANG
LI XUELIANG

Assignees

迪必尔生物工程(上海)有限公司

Dates

Publication Date: 20260508
Application Date: 20260119

Claims (10)

1. A metabolic flux detection method based on a genome-scale metabolic network model, comprising: acquiring a DCW sequence, an air inlet change rate, an exhaust change rate and a glucose concentration sequence of fermentation liquor to be measured; determining the apparent specific growth rate of the fermentation broth based on the multidimensional kinetic model; Determining the overall metabolic flux distribution of the fermentation broth according to the DCW sequence, the air inlet concentration sequence, the tail gas concentration sequence, the glucose concentration sequence and the apparent specific growth rate by adopting a QP-pFBA algorithm; And performing metabolic flux detection according to the global metabolic flux distribution.
2. The method of claim 1, wherein obtaining a DCW sequence, a feed gas change rate, a tail gas change rate, and a glucose concentration sequence of the fermentation broth to be measured comprises: obtaining the fermentation broth A sequence; The Logistic growth model is adopted for the alignment Fitting the sequences to obtain A curve; The extended Kalman filtering algorithm is adopted for the method Smoothing the curve; based on preset And a linear standard curve of DCW according to the following The curve determines the corresponding DCW curve.
3. The method of claim 2, wherein the fermentation broth is obtained A sequence comprising: detection of the fermentation broth Using an OD sensor A value; recording the said in time series Values to obtain the Sequence.
4. The method of claim 1, wherein obtaining a DCW sequence, a feed gas change rate, a tail gas change rate, and a glucose concentration sequence of the fermentation broth to be measured comprises: Detecting the concentration of O 2 and the concentration of CO 2 in the inlet gas and the tail gas of the fermentation liquid; And determining the corresponding O 2 consumption rate and the corresponding CO 2 consumption rate according to the O 2 concentration and the CO 2 concentration.
5. The method of claim 4, wherein obtaining a DCW sequence, a feed gas change rate, a tail gas change rate, and a glucose concentration sequence of the fermentation broth to be measured comprises: And determining the glucose concentration of the fermentation liquor by adopting a biosensing analyzer every other preset time period.
6. The method of claim 4, wherein detecting the O 2 and CO 2 concentrations in the inlet and outlet gases of the fermentation broth comprises: detecting the O 2 concentration and the CO 2 concentration in the inlet and outlet gases of the fermentation broth, comprising: The O 2 and CO 2 concentrations of the feed gas and the O 2 and CO 2 concentrations of the tail gas of the fermentation broth were monitored on-line by a tail gas analyzer.
7. The method of claim 1, wherein determining the apparent specific growth rate of the fermentation broth based on the multidimensional kinetic model comprises: determining the apparent specific growth rate according to the following formulas (1) to (10): ,(1) Wherein, the To rely on the instantaneous specific growth rate of the substrate of the fermentation broth, At the maximum specific growth rate of the wafer, For the concentration of the substrate to be described, Is a substrate affinity constant; ,(2) Wherein, the In order to grow the activity of the activation, As a constant of the rate of activation for the growth, A growth time; ,(3) Wherein, the In order to activate the degree of death, In order to activate the rate constant for death, As the threshold sensitivity coefficient of the sample, For the total cell concentration of the fermentation broth, For the cell concentration bearing capacity, the cell concentration is high, Activating a threshold for death; ,(4) Wherein, the For the rate of glucose flow to be high, For the volume of the fermentation broth, For the glucose concentration in the fed-batch medium, In order to maintain the coefficient of the coefficient, In order to achieve a viable cell biological concentration, Is the yield coefficient of the cells to glucose; ,(5) Wherein, the As a basis for the mortality rate constant, For the congestion stress factor, Is stress non-linear index; ,(6) Wherein, the For the concentration of dead cells, Is the constant of the lysis rate of dead cells; ,(7) Wherein, the Is the rate of evaporation of water; ,(8) Wherein, the Is that The total biomass concentration at the moment in time, Is that Total living cell biological concentration at the moment in time, Is that Total dead cell biomass concentration at time; ,(9) Wherein, the In order to be the number of g groups, Group g of the first column The predicted value of the time point is calculated, Group g of the first column The actual observations at the time points, At the maximum value of the corresponding quantity, Is that The number of groups is set to be equal to the number of groups, Is that Under group (b) The predicted value of the time point is calculated, Is that Under group (b) The actual observed values at the time points are compared, Is the maximum of the corresponding amount; ,(10) Wherein, the For the apparent specific growth rate in question, Is the total biomass concentration.
8. The method of claim 1, wherein determining the global metabolic flux profile of the fermentation broth from the DCW sequence, the feed gas concentration sequence, the tail gas concentration sequence, and the glucose concentration sequence, apparent specific growth rate using QP-pFBA algorithm comprises: as the objective function, equations (11) to (17) are adopted: ,(11) Wherein, the 、、、 For the weight coefficient of the corresponding reaction flux error in the objective function, For other flux reaction rates obtained by on-line measurements, For the specific growth rate of cells in GSMM, Is in GSMM The specific rate of generation is set to be, Is in GSMM A specific generation rate; ,(12) ,(13) ,(14) ,(15) ,(16) ,(17) Wherein, the For on-line measurement of the specific growth rate of cells, Obtained for on-line measurement The ratio of the emissions is such that, Obtained for on-line measurement The ratio of the emissions is such that, As a matrix of stoichiometric coefficients for the metabolic network, For the flux of each reaction, For the lower limit of all metabolic fluxes, Is the upper limit for all metabolic fluxes.
9. A metabolic flux detection system based on a genome-scale metabolic network model, the metabolic flux detection system comprising: the DCW measuring unit is used for acquiring a DCW sequence of the fermentation broth to be detected; the gas measuring unit is used for measuring the gas inlet change rate and the tail gas change rate of the fermentation liquid; a glucose measurement unit for measuring a glucose concentration sequence of the fermentation broth; a processor for performing the method of any one of claims 1 to 8.
10. A computer readable storage medium storing instructions for reading by a machine to cause the machine to perform the method of any one of claims 1 to 8.

Description

Metabolic flux detection method and system based on genome scale metabolic network model Technical Field The invention relates to the technical field of detection of biological metabolic flux, in particular to a metabolic flux detection method and system based on a genome scale metabolic network model. Background Genome-scale metabolic network model (GSMM) is a core guiding tool for microbial metabolic engineering, and can assist in screening key genes and optimizing fermentation process by simulating metabolic process. In the prior art, the prediction accuracy of GSMM is highly dependent on the accuracy of the known metabolic reaction rate, which is obtained through cell concentration conversion, so that the accurate detection of living cell parameters is a key premise. However, the current living cell parameter detection has the core contradiction of technical suitability and cost, namely, on one hand, the method capable of distinguishing living cells from dead cells by plate counting, fluorescent staining and the like is complex and time-consuming to operate and depends on expensive special equipment, so that the on-line real-time monitoring requirement of industrial fermentation cannot be met, and on the other hand, an on-line Optical Density (OD) sensor widely applied to industrial scenes can only detect the total cell concentration and cannot distinguish the cell survival state. Under the middle and later period of fermentation or environmental stress, the cell death and autolysis can lead to the significant reduction of the number of living cells, but the OD value is still stable, and the constrained parameters of GSMM are directly distorted, so that the estimation deviation of key physiological parameters is triggered, the actual metabolic state of the cells can not be reflected by the metabolism flux distribution prediction, and the large-scale application of GSMM in fermentation optimization is limited. Disclosure of Invention The embodiment of the invention aims to provide a metabolic flux detection method and a metabolic flux detection system based on a genome scale metabolic network model, wherein the method and the system can predict the metabolic flux of fermentation liquor. In order to achieve the above object, an embodiment of the present invention provides a metabolic flux detection method based on a genome-scale metabolic network model, including: acquiring a DCW sequence, an air inlet change rate, an exhaust change rate and a glucose concentration sequence of fermentation liquor to be measured; determining the apparent specific growth rate of the fermentation broth based on the multidimensional kinetic model; Determining the overall metabolic flux distribution of the fermentation broth according to the DCW sequence, the air inlet concentration sequence, the tail gas concentration sequence, the glucose concentration sequence and the apparent specific growth rate by adopting a QP-pFBA algorithm; And performing metabolic flux detection according to the global metabolic flux distribution. Optionally, obtaining a DCW sequence, an intake change rate, an exhaust change rate, and a glucose concentration sequence of the fermentation broth to be measured, including: obtaining the fermentation broth A sequence; The Logistic growth model is adopted for the alignment Fitting the sequences to obtainA curve; The extended Kalman filtering algorithm is adopted for the method Smoothing the curve; based on preset And a linear standard curve of DCW according to the followingThe curve determines the corresponding DCW curve. Optionally, obtaining the fermentation brothA sequence comprising: detection of the fermentation broth Using an OD sensor A value; recording the said in time series Values to obtain theSequence. Optionally, obtaining a DCW sequence, an intake change rate, an exhaust change rate, and a glucose concentration sequence of the fermentation broth to be measured, including: Detecting the concentration of O 2 and the concentration of CO 2 in the inlet gas and the tail gas of the fermentation liquid; And determining the corresponding O 2 consumption rate and the corresponding CO 2 consumption rate according to the O 2 concentration and the CO 2 concentration. Optionally, obtaining a DCW sequence, an intake change rate, an exhaust change rate, and a glucose concentration sequence of the fermentation broth to be measured, including: And determining the glucose concentration of the fermentation liquor by adopting a biosensing analyzer every other preset time period. Optionally, detecting the O 2 concentration and the CO 2 concentration in the inlet and outlet gases of the fermentation broth includes: detecting the O 2 concentration and the CO 2 concentration in the inlet and outlet gases of the fermentation broth, comprising: The O 2 and CO 2 concentrations of the feed gas and the O 2 and CO 2 concentrations of the tail gas of the fermentation broth were monitored on-line by a tail gas analyzer. Opti