Application of Bayesian optimization method in the production of 1,3-propanediol by Vibrio natriegens

doi:10.11949/0438-1157.20221102

Abstract

Abstract:

1,3-Propanediol is an important chemical raw material. The preparation of 1,3-propanediol by biological fermentation has the advantages of simple operation, mild reaction conditions and few by-products. The use of Vibrio natriegens as a new industrial chassis to produce 1,3-propanediol has important application prospects, but the construction of high-yielding strains has the characteristics of long single experiment period, high cost, and strong nonlinearity. In order to fast optimize the combination of key gene intensity of 1,3-propanediol synthesis pathway, we use a Bayesian optimization method with Gaussian process regression algorithm as surrogate model and expected improvement as acquisition function. In each iteration, the Gaussian process regression algorithm is used to fit the current data and predict the probability distribution of unknown points. The expected improvement function maps the probability distribution to the solution space, selects the point corresponding to the maximum value in the solution space as the experimental point in the next iteration, and enters the next iteration after experiment. The Bayesian optimization method was used to optimize the key gene intensity combination in the process of constructing a high-yielding 1,3-propanediol-producing Vibrio natriegens. After three iterations, the optimal gene strength combination was searched. The 1,3-propanediol yield reached (13.01±0.63) g/L, which was 8.32% higher than the highest value in the first group of test points.

Key words: 1,3-propanediol, Vibrio natriegens, Bayesian optimization, optimal design, bioprocess

摘要：

1，3-丙二醇是一种重要的化工原料，生物法发酵制备1，3-丙二醇具有操作简便、反应条件温和、副产物少等优点，使用需钠弧菌作为新的工业底盘细胞生产1，3-丙二醇有很好的应用前景。高产菌株的构建过程中优化基因强度组合时，具有单次实验周期长、成本高且体系复杂、非线性强的特点。为了实现对1，3-丙二醇合成途径关键基因强度组合的快速优化，使用了一种以高斯过程回归算法为代理模型，以增益期望为采集函数的贝叶斯优化方法。在每轮迭代中，高斯过程回归算法用于拟合当前数据并预测未知点的概率分布，增益期望函数将概率分布映射到解空间中，选择解空间中最大值对应的点作为下一轮的实验点，实验后进入下一轮迭代。构建高产1，3-丙二醇的需钠弧菌过程中使用贝叶斯优化方法优化关键基因强度组合，在三轮迭代后搜索到了最优的基因强度组合，1，3-丙二醇的产量达到（13.01±0.63）g/L，较第一组实验点中最高值提高了8.32%。

关键词: 1, 3-丙二醇, 需钠弧菌, 贝叶斯优化, 优化设计, 生物过程

CLC Number:

TQ 033

Xinye HUANG, Ye ZHANG, Shuyuan ZHANG, Zhen CHEN, Tong QIU. Application of Bayesian optimization method in the production of 1,3-propanediol by Vibrio natriegens[J]. CIESC Journal, 2022, 73(11): 5039-5046.

黄新烨, 张冶, 张书源, 陈振, 邱彤. 贝叶斯优化方法在需钠弧菌生产1，3-丙二醇中的应用[J]. 化工学报, 2022, 73(11): 5039-5046.

Figures/Tables 9

Fig.1 Synthesis of 1,3-PDO by Vibrio natriegens

Fig.2 Schematic diagram of gene promoter regulation

Table 1 Constitutive promoters used in this study［16］

No.	核苷酸序列（5'→3'）	相对强度
1	CTTATGGCTAGCTCAGTCCTAGGGACAGTGCTAGC	0.024
2	TTTACGGCTAGCTCAGTCCTAGGGATAGTGCTAGC	0.045
3	CTGACGGCTAGCTCAGTCCTAGGGATAGTGCTAGC	0.065
4	TTGATGGCTAGCTCAGTCCTAGGGATTATGCTAGC	0.078
5	TTGATGGCTAGCTCAGTCCTAGGTACAGTGCTAGC	0.116
6	TTGATGGCTAGCTCAGTCCTAGGTATTGTGCTAGC	0.131
7	TTGATGGCTAGCTCAGTCCTAGGTACTATGCTAGC	0.147
8	TTGACGGCTAGCTCAGTCCTAGGTACTGTGCTAGC	0.192
9	TTGATGGCTAGCTCAGTCCTAGGTACAATGCTAGC	0.235
10	TTGATGGCTAGCTCAGTCCTAGGTATAGTGCTAGC	0.265
11	TTGACGGCTAGCTCAGTCCTAGGTATTGTGCTAGC	0.297
12	TTGATGGCTAGCTCAGTCCTAGGTATAATGCTAGC	0.371
13	TTGACGGCTAGCTCAGTCCTAGGTACAGTGCTAGC	0.457
14	TTGACAGCTAGCTCAGTCCTAGGTATTGTGCTAGC	0.645
15	TTGACAGCTAGCTCAGTCCTAGGTATAATGCTAGC	1.000

Fig.3 Bayesian optimization algorithm for Vibrio natriegens

Table 2 Raw data of 1，3-propanediol synthesis by Vibrio natriegens

$d h a B C E$	$y q h D$	$l n (d h a B C E)$	$l n (y q h D)$	12 h 1,3-PDO/(g/L)
0.024	0.116	-3.730	-2.154	1.13±0.80
0.147	0.065	-1.917	-2.733	0.41±0.99
0.192	0.045	-1.650	-3.101	3.20±0.75
0.045	0.192	-3.101	-1.650	0.15±1.16
0.131	0.265	-2.033	-1.328	2.97±0.13
0.116	0.131	-2.154	-2.033	3.78±0.39
0.235	0.457	-1.448	-0.783	8.22±0.69
0.235	0.371	-1.448	-0.992	7.54±0.83
0.265	0.192	-1.328	-1.650	6.31±0.55
0.371	0.192	-0.992	-1.650	3.15±0.16
0.371	0.645	-0.992	-0.439	7.31±0.99
0.645	0.045	-0.439	-3.101	0.90±0.03
0.024	1.000	-3.730	0	0.08±0.01
0.371	0.457	-0.992	-0.783	3.05±0.56
0.291	1.000	-1.234	0	12.01±0.33

Table 2 Raw data of 1，3-propanediol synthesis by Vibrio natriegens

$d h a B C E$	$y q h D$	$l n (d h a B C E)$	$l n (y q h D)$	12 h 1,3-PDO/(g/L)
0.024	0.116	-3.730	-2.154	1.13±0.80
0.147	0.065	-1.917	-2.733	0.41±0.99
0.192	0.045	-1.650	-3.101	3.20±0.75
0.045	0.192	-3.101	-1.650	0.15±1.16
0.131	0.265	-2.033	-1.328	2.97±0.13
0.116	0.131	-2.154	-2.033	3.78±0.39
0.235	0.457	-1.448	-0.783	8.22±0.69
0.235	0.371	-1.448	-0.992	7.54±0.83
0.265	0.192	-1.328	-1.650	6.31±0.55
0.371	0.192	-0.992	-1.650	3.15±0.16
0.371	0.645	-0.992	-0.439	7.31±0.99
0.645	0.045	-0.439	-3.101	0.90±0.03
0.024	1.000	-3.730	0	0.08±0.01
0.371	0.457	-0.992	-0.783	3.05±0.56
0.291	1.000	-1.234	0	12.01±0.33

Fig.4 Parameter adjustment of Gaussian process regression

Fig.5 Detailed iterative process of Bayesian optimization

Fig.6 Optimization of 1,3-propanediol production by Vibrio natriegens

Table 3 Prediction point results of iterative process

预测点	预测均值	预测方差	实际值	训练集RMSE
1	12.38	0.262	12.49	1.84×10^-9
2	11.49	0.248	13.01	1.66×10^-9
3	14.56	0.230	9.51	2.83×10^-9

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