Joint Papers

2026

1. 

   [9] Dynamic model-based control for process variations in ion-exchange chromatography

   Ruo-Que Mao, Yu-Cheng Chen, Yu-Xin Liao, Yu-Xiang Yang, Shan-Jing Yao, Dong-Qiang Lin*

   Journal of Chromatography A, 2026, 1782: 467103

   2026/08/16 JCR Q1 IF=4.0 (2024) Abs KWs DOI Bib PDF Citations 0

   The capability of smart manufacturing to enable predictive and autonomous decision-making under uncertainty is highly valuable to ion-exchange chromatography (IEC), a critical purification step that is sensitive to variations in sample composition and loading density. However, conventional IEC operation relies on predefined elution conditions, including fixed elution gradient and collection window, which limits its adaptability to process variations. To address this limitation, a model-based control system was developed for the dynamic and autonomous optimization of IEC under process variations. The system incorporated the mechanistic models that combined equilibrium dispersive model and steric mass action model to quantitatively predict protein elution behavior. By integrating communication technologies, the model predictions were used to automatically determine and implement the optimal elution gradient and collection window, enabling model-based decision-making. The experimental validation demonstrated that the model-based control system consistently achieved purity above 96.0% and yield exceeding 88.0%. These results indicate that both product quality and process performance could be maintained despite process variability. Overall, the proposed model-based control system enabled the dynamic and simultaneous adjustment of elution gradient and collection window, transforming IEC operation from predefined conditions to a predictive and adaptive control approach, thereby enhancing process robustness and operational flexibility.

   Ion exchange chromatography
   Mechanistic model
   Model predictive control
   Process optimization
   Bioseparation

   @article{9,
     date = {2026/08/16},
     author = {Mao, Ruo-Que and Chen, Yu-Cheng and Liao, Yu-Xin and Yang, Yu-Xiang and Yao, Shan-Jing and Lin, Dong-Qiang},
     title = {Dynamic model-based control for process variations in ion-exchange chromatography},
     journal = {Journal of Chromatography A},
     volume = {1782},
     pages = {467103},
     keywords = {Ion exchange chromatography<br>
     Mechanistic model<br>
     Model predictive control<br>
     Process optimization<br>
     Bioseparation},
     doi = {10.1016/j.chroma.2026.467103},
     year = {2026},
   }

2. [8] 结合ASGARD的CRISPRa技术在索罗金小球藻FZU60中的应用

   马瑞娟, 林文槿, 陈煜成, 张春晓, 王玲, 鲁康乐, 宋凯, 李学山, 谢友坪*

   水生生物学报, 2026, 50(7): 173–183

   2026/04/29 Abs KWs DOI Bib

   为建立索罗金小球藻(Chlorella sorokiniana) FZU60的高效遗传转化体系用于分子调控机制研究并提高其高附加值代谢产物的产量, 研究建立了基于CRISPR/dCas9的转录激活系统(CRISPRa), 结合适应性单链向导RNA (sgRNA)辅助调控基因表达(ASGARD)技术, 对基因进行随机激活。抗性筛选结果显示, 600 μg/mL潮霉素B可以抑制藻细胞生长。将构建的连接有高鸟嘌呤含量sgRNA的质粒pLWR-dzCas9-VP64::sgHG进行电击转化, 在潮霉素B抗性平板上经过多轮筛选获得具有稳定抗性且生长较快的转化藻株, PCR和RT-qPCR验证结果显示质粒已经成功转入和表达。进一步分析发现, 异养条件下转化藻株的生物量浓度均高于野生型藻株, 且转化藻株的蛋白质、油脂和色素含量和产量显著提高。其中, 转化藻株V-6的蛋白质含量和产量分别达到了452.83 mg/g和2384.32 mg/L, 与野生型藻株相比分别增长了17.97%和32.82%; 油脂含量和产量高达169.38 mg/g和891.56 mg/L, 与野生型相比提高了23.20%和38.97%; 叶绿素含量与野生型相近, 产量与野生型相比提高了18.44%; 类胡萝卜素含量与野生型无显著性差异, 产量与野生型相比提高了13.57%。因此, 本研究获得的转化藻株具有优异的蛋白质、油脂和色素生产能力。研究结果为索罗金小球藻FZU60基因编辑技术的建立提供了技术支撑, 为提高其高附加值代谢产物的产量提供了重要的理论依据。

   CRISPRa
   sgRNA
   蛋白质
   油脂
   色素
   索罗金小球藻

   @article{8,
     date = {2026/04/29},
     author = {马瑞娟 and 林文槿 and 陈煜成 and 张春晓 and 王玲 and 鲁康乐 and 宋凯 and 李学山 and 谢友坪},
     title = {结合ASGARD的CRISPRa技术在索罗金小球藻FZU60中的应用},
     journal = {水生生物学报},
     volume = {50},
     number = {7},
     pages = {173–183},
     keywords = {CRISPRa<br>
     sgRNA<br>
     蛋白质<br>
     油脂<br>
     色素<br>
     索罗金小球藻},
     doi = {10.3724/1000-3207.2026.2026.0024},
     year = {2026},
   }

3. [7] The interaction between PII and NAGK regulates arginine biosynthesis in the green microalga Haematococcus pluvialis

   Ruijuan Ma, Ziyue Chen, Junjie Liu, Xing Meng, Xinyi Tao, Yucheng Chen, Chunxiao Zhang, Ling Wang, Kangle Lu, Xueshan Li, Kai Song, Jianfeng Chen, Youping Xie*

   Journal of Phycology, 2026, 50: 173–183

   2026/04/18 JCR Q1 IF=3.4 (2024) Abs DOI Bib Citations 0

   Abstract PII protein is widely acknowledged to regulate intracellular nitrogen and carbon metabolism by interacting with several crucial proteins. N-acetyl-L-glutamate kinase (NAGK), a rate-limiting enzyme for arginine biosynthesis, is regarded as a potential target of PII protein. Nevertheless, the regulatory function remains ambiguous in green algae and has not been investigated in Haematococcus pluvialis. In this study, the NAGK enzyme and PII protein of H. pluvialis (designated as HpNAGK and HpPII, respectively) and their interaction relationships were characterized. The results indicated that HpNAGK showed high similarity with the same enzyme in the green algae. A subcellular localization assay indicated that both HpPII and HpNAGK were located in the chloroplasts. Yeast two-hybrid, pull-down, and bimolecular fluorescence complementation assays distinctly verified the interaction between HpPII and HpNAGK, which occurs in the chloroplasts. The structure of the HpPII-HpNAGK complex was predicted through docking analysis. Moreover, the HpNAGK activity was significantly enhanced by HpPII in the presence of glutamine in vitro. Under nitrogen starvation, HpNAGK activity declined in vivo, concomitant with a reduction in arginine accumulation. The regulatory function of HpPII on HpNAGK activity aligned with that in Chlamydomonas reinhardtii but differed from that in Dunaliella salina, suggesting species specificity among green algae. These findings provide insights into the regulatory function of PII protein in green algae and help to unveil the response mechanisms of H. pluvialis to different nitrogen statuses.

   @article{7,
     date = {2026/04/18},
     author = {Ma, Ruijuan and Chen, Ziyue and Liu, Junjie and Meng, Xing and Tao, Xinyi and Chen, Yucheng and Zhang, Chunxiao and Wang, Ling and Lu, Kangle and Li, Xueshan and Song, Kai and Chen, Jianfeng and Xie, Youping},
     title = {The interaction between PII and NAGK regulates arginine biosynthesis in the green microalga Haematococcus pluvialis},
     journal = {Journal of Phycology},
     doi = {10.1111/jpy.70165},
     year = {2026},
   }

2025

1. 

   [6] Mechanistic modeling of retention time distribution under high breakthrough conditions for continuous Protein A affinity capture

   Wu-Wei Chen, Yan-Na Sun, Yu-Cheng Chen, Alois Jungbauer, Shan-Jing Yao, Hai-Bin Qu, Dong-Qiang Lin*

   Journal of Chromatography A, 2025, 1759: 466227

   2025/09/27 JCR Q1 IF=4.0 (2024) Abs KWs DOI Bib PDF Citations 2

   Regulatory authorities strongly recommend using residence time distribution (RTD) to achieve material traceability in continuous bioprocesses for non-adsorption units. For adsorption-based units, such as chromatography, retention time distribution (ReTD) is more suitable than RTD for characterizing material flow. Continuous capture chromatography is widely applied for biopharmaceutical continuous manufacturing. However, the ReTD behavior in these systems is still not fully understood. In this study, an ReTD model combining general rate model and two-component mobile phase modulator Langmuir model was developed for Protein A affinity chromatography under high breakthrough conditions. The model was calibrated using adsorption equilibrium experiments, protein breakthrough curves and elution curves. It was then validated through pulse injection experiments at varying protein loading phase. The results showed good agreement between model predictions and experimental results (R2 > 0.945). The exchange mechanism between the solid and liquid phases was further analyzed using confocal laser scanning microscopy images and model simulations, revealing that proteins with stronger binding affinity surpass the bound fraction to bind at the adsorption front while those with weaker affinity would exchange with the surface-bound fractions. Finally, simulations of protein distribution in the column during the interconnected loading step indicate that the exchange effect could broaden the ReTD in continuous chromatography. The model developed lays the groundwork for achieving material traceability and enables non-conforming material diversion strategies to facilitate real-time product release in continuous chromatography processes.

   Retention time distribution
   Residence time distribution
   Modeling
   Protein A affinity chromatography
   Continuous process

   @article{6,
     date = {2025/09/27},
     author = {Chen, Wu-Wei and Sun, Yan-Na and Chen, Yu-Cheng and Jungbauer, Alois and Yao, Shan-Jing and Qu, Hai-Bin and Lin, Dong-Qiang},
     title = {Mechanistic modeling of retention time distribution under high breakthrough conditions for continuous Protein A affinity capture},
     journal = {Journal of Chromatography A},
     volume = {1759},
     pages = {466227},
     keywords = {Retention time distribution<br>
     Residence time distribution<br>
     Modeling<br>
     Protein A affinity chromatography<br>
     Continuous process},
     doi = {10.1016/j.chroma.2025.466227},
     year = {2025},
   }

2024

1. 

   [5] Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter estimation method

   Yu-Xiang Yang, Zhi-Yuan Lin, Yu-Cheng Chen, Shan-Jing Yao, Dong-Qiang Lin*

   Journal of Chromatography A, 2024, 1730: 465121

   2024/08/16 JCR Q1 IF=4.0 Abs KWs DOI Bib PDF Citations 7

   Mechanistic models are powerful tools for chromatographic process development and optimization. However, hydrophobic interaction chromatography (HIC) mechanistic models lack an effective and logical parameter estimation method, especially for multi-component system. In this study, a parameter-by-parameter method for multi-component system (called as mPbP-HIC) was derived based on the retention mechanism to estimate the six parameters of the Mollerup isotherm for HIC. The linear parameters (ks,i and keq,i) and nonlinear parameters (ni and qmax,i) of the isotherm can be estimated by the linear regression (LR) and the linear approximation (LA) steps, respectively. The remaining two parameters (kp,i and kkin,i) are obtained by the inverse method (IM). The proposed method was verified with a two-component model system. The results showed that the model could accurately predict the protein elution at a loading of 10 g/L. However, the elution curve fitting was unsatisfactory for high loadings (12 g/L and 14 g/L), which is mainly attributed to the demanding experimental conditions of the LA step and the potential large estimation error of the parameter qmax. Therefore, the inverse method was introduced to further calibrate the parameter qmax, thereby reducing the estimation error and improving the curve fitting. Moreover, the simplified linear approximation (SLA) was proposed by reasonable assumption, which provides the initial guess of qmax without solving any complex matrix and avoids the problem of matrix unsolvable. In the improved mPbP-HIC method, qmax would be initialized by the SLA and finally determined by the inverse method, and this strategy was named as SLA+IM. The experimental validation showed that the improved mPbP-HIC method has a better curve fitting, and the use of SLA+IM reduces the error accumulation effect. In process optimization, the parameters estimated by the improved mPbP-HIC method provided the model with excellent predictive ability and reasonable extrapolation. In conclusion, the SLA+IM strategy makes the improved mPbP-HIC method more rational and can be easily applied to the practical separation of protein mixture, which would accelerate the process development for HIC in downstream of biopharmaceuticals.

   Hydrophobic interaction chromatography
   Multi-component system
   Parameter estimation
   Mechanistic model
   Mollerup isotherm

   @article{5,
     date = {2024/08/16},
     author = {Yang, Yu-Xiang and Lin, Zhi-Yuan and Chen, Yu-Cheng and Yao, Shan-Jing and Lin, Dong-Qiang},
     title = {Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter estimation method},
     journal = {Journal of Chromatography A},
     volume = {1730},
     pages = {465121},
     keywords = {Hydrophobic interaction chromatography<br>
     Multi-component system<br>
     Parameter estimation<br>
     Mechanistic model<br>
     Mollerup isotherm},
     doi = {10.1016/j.chroma.2024.465121},
     year = {2024},
   }

2. 

   [4] 离子交换层析分离单抗电荷异质体的模型辅助过程优化

   许茹枫, 陈煜成, 高丹, 焦静雨, 高栋, 王海彬, 姚善泾, 林东强*

   化工学报, 2024, 75(5): 1-10

   2024/05/25 Abs KWs DOI Bib PDF

   针对单抗电荷异质体分离，采用离子交换层析机理模型，预测洗脱分离行为，辅助工艺条件优化。设计了校准实验，拟合得到模型参数，模型计算与实验吻合良好，具有良好的预测能力。利用模型分析比较了不同洗脱方式，得到最优的两步阶跃洗脱方案，具有较高的收率，但发现该分离过程对盐浓度极为敏感。进一步针对第一步洗脱盐浓度进行过程稳健性约束的过程优化，发现盐浓度为105.8 mM时过程稳健性增强。经实验验证，两步阶跃洗脱收率最高可达到85.3%，稳健约束优化后第一步等度洗脱盐浓度操作区间增大为98.9 117.5 mM。结果表明，模型辅助的工艺优化可以进行复杂条件分析，促进难分离体系的分离过程优化，并能够针对过程稳健性给出合理解决方案。

   单克隆抗体
   电荷异质体
   分离
   离子交换层析
   层析模型
   过程优化

   @article{4,
     date = {2024/05/25},
     author = {许茹枫 and 陈煜成 and 高丹 and 焦静雨 and 高栋 and 王海彬 and 姚善泾 and 林东强},
     title = {离子交换层析分离单抗电荷异质体的模型辅助过程优化},
     journal = {化工学报},
     volume = {75},
     number = {5},
     pages = {1-10},
     keywords = {单克隆抗体<br>
     电荷异质体<br>
     分离<br>
     离子交换层析<br>
     层析模型<br>
     过程优化},
     doi = {10.11949/0438-1157.20231246},
     year = {2024},
   }

3. 

   [3] Parameter-by-parameter estimation method for adsorption isotherm in hydrophobic interaction chromatography

   Yu-Xiang Yang, Yu-Cheng Chen, Shan-Jing Yao, Dong-Qiang Lin*

   Journal of Chromatography A, 2024, 1716: 464638

   2024/02/08 JCR Q1 IF=4.0 Abs KWs DOI Bib PDF Citations 18

   Hydrophobic interaction chromatography (HIC) is used as a critical polishing step in the downstream processing of biopharmaceuticals. Normally the process development of HIC is a cumbersome and time-consuming task, and the mechanical models can provide a powerful tool to characterize the process, assist process design and accelerate process development. However, the current estimation of model parameters relies on the inverse method, which lacks an efficient and logical parameter estimation strategy. In this study, a parameter-by-parameter (PbP) method based on the theoretical derivation and simplifying assumptions was proposed to estimate the Mollerup isotherm parameters for HIC. The method involves three key steps: (1) linear regression (LR) to estimate the salt-protein interaction parameter and the equilibrium constant; (2) linear approximation (LA) to estimate the stoichiometric parameter and the maximum binding capacity; and (3) inverse method to estimate the protein-protein interaction parameter and the kinetic coefficient. The results indicated that the LR step should be used for dilution condition (loading factor below 5%), while the LA step should be conducted when the isotherm is in the transition or nonlinear regions. Six numerical experiments were conducted to implement the PbP method. The results demonstrated that the PbP method developed allows for the systematic estimation of HIC parameters one-by-one, effectively reducing the number of parameters required for inverse method estimation from six to two. This helps prevent non-identifiability of structural parameters. The feasibility of the PbP-HIC method was further validated by real-world experiments. Moreover, the PbP method enhances the mechanistic understanding of adsorption behavior of HIC and shows a promising application to other stoichiometric displacement model-derived isotherms.

   Hydrophobic interaction chromatography
   Parameter estimation
   Mechanistic model
   Mollerup isotherm

   @article{3,
     date = {2024/02/08},
     author = {Yang, Yu-Xiang and Chen, Yu-Cheng and Yao, Shan-Jing and Lin, Dong-Qiang},
     title = {Parameter-by-parameter estimation method for adsorption isotherm in hydrophobic interaction chromatography},
     journal = {Journal of Chromatography A},
     volume = {1716},
     pages = {464638},
     keywords = {Hydrophobic interaction chromatography<br>
     Parameter estimation<br>
     Mechanistic model<br>
     Mollerup isotherm},
     doi = {10.1016/j.chroma.2024.464638},
     year = {2024},
   }

2023

1. 

   [2] Physics-informed neural networks to solve lumped kinetic model for chromatography process

   Si-Yuan Tang, Yun-Hao Yuan, Yu-Cheng Chen, Shan-Jing Yao, Ying Wang, Dong-Qiang Lin*

   Journal of Chromatography A, 2023, 1708: 464346

   2023/10/11 JCR Q1 IF=3.8 Abs KWs DOI Bib PDF Citations 30

   Numerical method is widely used for solving the mechanistic models of chromatography process, but it is time-consuming and hard to response in real-time. Physics-informed neural network (PINN) as an emerging technology combines the structure of neural network with physics laws, and is getting noticed for solving physics problems with a balanced accuracy and calculation speed. In this research, a proof-of-concept study was carried out to apply PINN to chromatography process simulation. The PINN model structure was designed for the lumped kinetic model (LKM) with all LKM parameters. The PINN structure, training data and model complexity were optimized, and an optimal mode was obtained by adopting an in-series structure with a nonuniform training data set focusing on the breakthrough transition region. A PINN for LKM (LKM-PINN) consisting of four neural networks, 12 layers and 606 neurons was then used for the simulation of breakthrough curves of chromatography processes. The LKM parameters were trained with two breakthrough curves and used to infer the breakthrough curves at different residence times, loading concentrations and column sizes. The results were comparable to that obtained with numerical methods. With the same raw data and constraints, the average fitting error for LKM-PINN model was 0.075, which was 0.081 for numerical method. With the same initial guess, the LKM-PINN model took 160 s to complete the fitting, while the numerical method took 7 to 72 min, depending on the fitting settings. The fitting speed of LKM-PINN model was further improved to 30 s with random initial guess. Thus, the LKM-PINN model developed in this study is capable to be applied to real-time simulation and realize some of the functions of digital twin.

   Chromatography
   Lumped kinetic model
   Physics-informed neural network
   Process modeling
   Artificial neural network
   Digital twin

   @article{2,
     date = {2023/10/11},
     author = {Tang, Si-Yuan and Yuan, Yun-Hao and Chen, Yu-Cheng and Yao, Shan-Jing and Wang, Ying and Lin, Dong-Qiang},
     title = {Physics-informed neural networks to solve lumped kinetic model for chromatography process},
     journal = {Journal of Chromatography A},
     volume = {1708},
     pages = {464346},
     keywords = {Chromatography<br>
     Lumped kinetic model<br>
     Physics-informed neural network<br>
     Process modeling<br>
     Artificial neural network<br>
     Digital twin},
     doi = {10.1016/j.chroma.2023.464346},
     year = {2023},
   }

2021

1. 

   [1] Optimization and comparison of the production of galactooligosaccharides using free or immobilized Aspergillus oryzae beta-galactosidase, followed by purification using silica gel

   Geng Wang, Haidong Wang, Yucheng Chen, Xun Pei, Wanjing Sun, Lujie Liu, Fengqin Wang, Muhammad Umar Yaqoob, Wenjing Tao, Zhiping Xiao, Yuyue Jin, Shang-Tian Yang, Dongqiang Lin, Minqi Wang*

   Food Chemistry, 2021, 362: 130195

   2021/11/15 JCR Q1 IF=9.231 Abs KWs DOI Bib PDF Citations 29

   The aim of this study was to optimize and compare the production of galactooligosaccharides (GOSs) by free and cotton cloth-immobilized Aspergillus oryzae beta-galactosidase, and perform economical evaluation of production of GOSs (100%) between them. Using the response surface method, the optimal reaction time (3.9 h), initial lactose concentration (57.13%), and enzyme to lactose ratio (44.81 U/g) were obtained for the free enzyme, which provided a GOSs yield of 32.62%. For the immobilized enzyme, the optimal yield of GOSs (32.48%) was obtained under reaction time (3.09 h), initial lactose concentration (52.74%), and temperature (50.0 ). And it showed desirable reusability during five successive enzymatic reactions. The recovery rate of GOSs (100%) is 65% using silica gel filtration chromatography. The economical evaluation showed almost no difference in the manufacturing cost for the GOSs (100%) between these two systems, and that the recovery rate had a great impact on the cost.

   Galactooligosaccharides
   Optimization
   Free enzyme
   Immobilized enzyme
   Purification
   Economical evaluation

   @article{1,
     date = {2021/11/15},
     author = {Wang, Geng and Wang, Haidong and Chen, Yucheng and Pei, Xun and Sun, Wanjing and Liu, Lujie and Wang, Fengqin and Yaqoob, Muhammad Umar and Tao, Wenjing and Xiao, Zhiping and Jin, Yuyue and Yang, Shang-Tian and Lin, Dongqiang and Wang, Minqi},
     title = {Optimization and comparison of the production of galactooligosaccharides using free or immobilized Aspergillus oryzae beta-galactosidase, followed by purification using silica gel},
     journal = {Food Chemistry},
     volume = {362},
     pages = {130195},
     keywords = {Galactooligosaccharides<br>
     Optimization<br>
     Free enzyme<br>
     Immobilized enzyme<br>
     Purification<br>
     Economical evaluation},
     doi = {10.1016/j.foodchem.2021.130195},
     year = {2021},
   }