Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossilbased materials. However, they are generally difficult to optimize due to their unsteady-state operation modes and stochastic behaviours. Furthermore, biological systems are highly complex, therefore plantmodel mismatch is often present. To address the aforementioned challenges we propose a Reinforcement learning based optimization strategy for batch processes.In this work we applied the Policy Gradient method from batch-to-batch to update a control policy parametrized by a recurrent neural network. We assume that a preliminary process model is available, which is exploited to obtain a preliminary optimal control policy. Subsequently, this policy is updated based on measurements from the true plant. The capabilities of our proposed approach were tested on three case studies (one of which is nonsmooth) using a more complex process model for the true system embedded with adequate process disturbance. Lastly, we discussed advantages and disadvantages of this strategy compared against current existing approaches such as nonlinear model predictive control.

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Phosphoglycerate mutase 1 (PGAM1) is a glycolytic enzyme that coordinates glycolysis and biosynthesis to promote cancer growth via its metabolic activity. Here, we report the discovery of a non-metabolic function of PGAM1 in promoting cancer metastasis. A proteomic study identified α-smooth muscle actin (ACTA2) as a PGAM1-associated protein. PGAM1 modulated actin filaments assembly, cell motility and cancer cell migration via directly interacting with ACTA2, which was independent of its metabolic activity. The enzymatically inactive H186R mutant retained its association with ACTA2, whereas 201-210 amino acids deleted PGAM1 mutant lost the interaction with ACTA2 regardless of intact metabolic activity. Importantly, PGAM1 knockdown decreased metastatic potential of breast cancer cells in vivo and PGAM1 and ACTA2 were jointly associated with the prognosis of breast cancer patients. Together, this study provided the first evidence revealing a non-metabolic function of PGAM1 in promoting cell migration, and gained new insights into the role of PGAM1 in cancer progression.

HeLa cells treated with celastrol, a natural compound with inhibitive effect on proteasome, exhibited increase in apoptotic rate and characteristics of apoptosis. To clarify the signal network activated by celastrol to induce apoptosis, both the direct target proteins and undirect target proteins of celastrol were searched in the present study. Proteasome catalytic subunit β1 was predicted by computational analysis to be a possible direct target of celastrol and confirmed by checking direct effect of celastrol on the activity of recombinant human proteasome subunit β1 in vitro. Undirect target-related proteins of celastrol were searched using proteomic studies including two-dimensional electrophoresis (2-DE) analysis and iTRAQ-based LC-MS analysis. Possible target-related proteins of celastrol such as endoplasmic reticulum protein 29 (ERP29) and mitochondrial import receptor Tom22 (TOM22) were found by 2-DE analysis of total cellular protein expression profiles. Further study showed that celastrol induced ER stress and ER stress inhibitor could ameliorate cell death induced by celastrol. Celastrol induced translocation of Bax into the mitochondria, which might be related to the upregulation of BH-3-only proteins such as BIM and the increase in the expression level of TOM22. To further search possible target-related proteins of celastrol in ER and ER-related fractions, iTRAQ-based LC-MS method was use to analyze protein expression profiles of ER/microsomal vesicles-riched fraction of cells with or without celastrol treatment. Based on possible target-related proteins found in both 2-DE analysis and iTRAQ-based LC-MS analysis, protein–protein interaction (PPI) network was established using bioinformatic analysis. The important role of glycogen synthase kinase-3β (GSK3β) in the signal cascades of celastrol was suggested. Pretreatment of LiCL, an inhibitor of GSK3β, could significantly ameliorate apoptosis induced by celastrol. On the basis of the results of the present study, possible signal network of celastrol activated by celastrol leading to apoptosis was predicted.

Model‐based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low‐quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics‐based and data‐driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high‐quality data by correcting raw process measurements via a physics‐based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data‐driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re‐fitting the simple kinetic model (soft sensor) using the data‐driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed‐batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open‐loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.

Dynamic simulation is a valuable tool to assist the scale-up and transition of biofuel production from laboratory scale to potential industrial implementation. In the present study two dynamic models are constructed, based on the Aiba equation, the improved Lambert–Beer's law and the Arrhenius equation. The aims are to simulate the effects of incident light intensity, light attenuation and temperature upon the photo-autotrophic growth and the hydrogen production of the nitrogen-fixing cyanobacterium Cyanothece sp. ATCC 51142. The results are based on experimental data derived from an experimental setup using two different geometries of laboratory scale photobioreactors: tubular and flat-plate. All of the model parameters are determined by an advanced parameter estimation methodology and subsequently verified by sensitivity analysis. The optimal temperature and light intensity facilitating biohydrogen production in the absence of light attenuation have been determined computationally to be 34 °C and 247 μmol m− 2 s− 1, respectively, whereas for cyanobacterial biomass production they are 37 °C and 261 μmol m− 2 s− 1, respectively. Biomass concentration higher than 0.8 g L− 1 is also demonstrated to significantly enhance the light attenuation effect, which in turn inducing photolimitation phenomena. At a higher biomass concentration (3.5 g L− 1), cyanobacteria are unable to activate photosynthesis to maintain their lives in a photo-autotrophic growth culture, and biohydrogen production is significantly inhibited due to the severe light attenuation

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Dynamic modeling is an important tool to gain better understanding of complex bioprocesses and to determine optimal operating conditions for process control. Currently, two modeling methodologies have been applied to biosystems: kinetic modeling, which necessitates deep mechanistic knowledge, and artificial neural networks (ANN), which in most cases cannot incorporate process uncertainty. The goal of this study is to introduce an alternative modeling strategy, namely Gaussian processes (GP), which incorporates uncertainty but does not require complicated kinetic information. To test the performance of this strategy, GPs were applied to model microalgae growth and lutein production based on existing experimental datasets and compared against the results of previous ANNs. Furthermore, a dynamic optimization under uncertainty is performed, avoiding over-optimistic optimization outside of the model's validity. The results show that GPs possess comparable prediction capabilities to ANNs for long-term dynamic bioprocess modeling, while accounting for model uncertainty. This strongly suggests their potential applications in bioprocess systems engineering.