Predicted threshold against forward and backward loss of balance for perturbed walking

The biomechanical mechanisms of loss of balance have been studied before for slip condition but have not been investigated for arbitrary perturbation profiles under non-slip conditions in sagittal plane. This study aimed to determine the thresholds of center of mass (COM) velocity and position relative to the base of support (BOS) that predict forward and backward loss of balance during walking with a range of BOS perturbations. Perturbations were modeled as sinusoidal BOS motions in the vertical or anterior-posterior direction or as sagittal rotation. The human body was modeled using a seven-link model. Forward dynamics alongside with dynamic optimization were used to find the thresholds of initial COM velocity for each initial COM position that would predict forward or backward loss of balance. The effects of perturbation frequency and amplitude on these thresholds were modeled based on the simulation data. Experimental data were collected from 15 able-bodied individuals and three individuals with disability during perturbed walking. The simulation results showed similarity with the stability region reported for slip and non-slip conditions. The feasible stability region shrank when the perturbation frequency and amplitude increased, especially for larger initial COM velocities. 89.5% (70.9%) and 82.4% (68.2%) of the measured COM position and velocity combinations during low (high) perturbations were located inside the simulated limits of the stability region, for able-bodied and disabled individuals, respectively. The simulation results demonstrated the effects of different perturbation levels on the stability region. The obtained stability region can be used for developing rehabilitative programs in interactive environments.     

Development of a novel MATLAB-based framework for implementing mechanical joint stability constraints within OpenSim musculoskeletal models

The Static Optimization (SO) solver in OpenSim estimates muscle activations and forces that only equilibrate applied moments. In this study, SO was enhanced through an open-access MATLAB interface, where calculated muscle activations can additionally satisfy crucial mechanical stability requirements. This Stability-Constrained SO (SCSO) is applicable to many OpenSim models and can potentially produce more biofidelic results than SO alone, especially when antagonistic muscle co-contraction is required to stabilize body joints. This hypothesis was tested using existing models and experimental data in the literature. Muscle activations were calculated by SO and SCSO for a spine model during two series of static trials (i.e. simulation 1 and 2), and also for a lower limb model (supplementary material 2). In simulation 1, symmetric and asymmetric flexion postures were compared, while in simulation 2, various external load heights were compared, where increases in load height did not change the external lumbar flexion moment, but necessitated higher EMG activations. During the tasks in simulation 1, the predicted muscle activations by SCSO demonstrated less average deviation from the EMG data (6.8% −7.5%) compared to those from SO (10.2%). In simulation 2, SO predicts constant muscle activations and forces, while SCSO predicts increases in the average activations of back and abdominal muscles that better match experimental data. Although the SCSO results are sensitive to some parameters (e.g. musculotendon stiffness), when considering the strategy of the central nervous system in distributing muscle forces and in activating antagonistic muscles, the assigned activations by SCSO are more biofidelic than SO.     

Reconstruction of tricarboxylic acid cycle in Corynebacterium glutamicum with a genome-scale metabolic network model for trans-4-hydroxyproline production

trans-4-Hydroxy- l -proline (Hyp) is an abundant component of mammalian collagen and functions as a chiral synthon for the syntheses of anti-inflammatory drugs in the pharmaceutical industry. Proline 4-hydroxylase (P4H) can catalyze the conversion of l -proline to Hyp; however, it is still challenging for the fermentative production of Hyp from glucose using P4H due to the low yield and productivity. Here, we report the metabolic engineering of Corynebacterium glutamicum for the fermentative production of Hyp by reconstructing tricarboxylic acid (TCA) cycle together with heterologously expressing the p4h gene from Dactylosporangium sp. strain RH1. In silico model-based simulation showed that α-ketoglutarate was redirected from the TCA cycle toward Hyp synthetic pathway driven by P4H when the carbon flux from succinyl-CoA to succinate descended to zero. The interruption of the TCA cycle by the deletion of sucCD-encoding the succinyl-CoA synthetase (SUCOAS) led to a 60% increase in Hyp production and had no obvious impact on the growth rate. Fine-tuning of plasmid-borne ProB* and P4H abundances led to a significant increase in the yield of Hyp on glucose. The final engineered Hyp-7 strain produced up to 21.72 g/L Hyp with a yield of 0.27 mol/mol (Hyp/glucose) and a volumetric productivity of 0.36 g·L ⁻¹·hr ⁻¹ in the shake flask fermentation. To our knowledge, this is the highest yield and productivity achieved by microbial fermentation in a glucose-minimal medium for Hyp production. This strategy provides new insights into engineering C. glutamicum by flux coupling for the fermentative production of Hyp and related products.     

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

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.     

A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins

Due to the lack of complete understanding of metabolic networks and reaction pathways, establishing a universal mechanistic model for mammalian cell culture processes remains a challenge. Contrarily, data-driven approaches for modeling these processes lack extrapolation capabilities. Hybrid modeling is a technique that exploits the synergy between the two modeling methods. Although mammalian cell cultures are among the most relevant processes in biotechnology and indeed looks ideal for hybrid modeling, their application has only been proposed but never developed in the literature. This study provides a quantitative assessment of the improvement brought by hybrid models with respect to the state-of-the-art statistical predictive models in the context of therapeutic protein production. This is illustrated using a dataset obtained from a 3.5 L fed-batch experiment. With the goal to robustly define the process design space, hybrid models reveal a superior capability to predict the time evolution of different process variables using only the initial and process conditions in comparison to the statistical models. Hybrid models not only feature more accurate prediction results but also demonstrate better robustness and extrapolation capabilities. For the future application, this study highlights the added value of hybrid modeling for model-based process optimization and design of experiments.     

Streptomycin-induced ribosome engineering complemented with fermentation optimization for enhanced production of 10-membered enediynes tiancimycin-A and tiancimycin-D

Tiancimycins (TNMs) are a group of 10-membered anthraquinone-fused enediynes, newly discovered from Streptomyces sp. CB03234. Among them, TNM-A and TNM-D have exhibited excellent antitumor performances and could be exploited as very promising warheads for the development of anticancer antibody-drug conjugates (ADCs). However, their low titers, especially TNM-D, have severely limited following progress. Therefore, the streptomycin-induced ribosome engineering was adopted in this work for strain improvement of CB03234, and a TNMs high producer S. sp. CB03234-S with the K43N mutation at 30S ribosomal protein S12 was successfully screened out. Subsequent media optimization revealed the essential effects of iodide and copper ion on the production of TNMs, while the substitution of nitrogen source could evidently promote the accumulation of TNM-D, and the ratio of produced TNM-A and TNM-D was responsive to the change of carbon and nitrogen ratio in the medium. Further amelioration of the pH control in scaled up 25 L fermentation increased the average titers of TNM-A and TNM-D up to 13.7 ± 0.3 and 19.2 ± 0.4 mg/L, respectively. The achieved over 45-fold titer improvement of TNM-A, and 109-fold total titer improvement of TNM-A and TNM-D enabled the efficient purification of over 200 mg of each target molecule from 25 L fermentation. Our efforts have demonstrated a practical strategy for titer improvement of anthraquinone-fused enediynes and set up a solid base for the pilot scale production and preclinical studies of TNMs to expedite the future development of anticancer ADC drugs.     

柴达木沙漠链霉菌S10T阿糖腺苷的大孔树脂分离工艺优化与结构鉴定

为了获得具有药用价值的活性天然产物,采用4种大孔吸附树脂对柴达木沙漠链霉菌(Streptomyces qaidemensis)S10^T发酵液进行静态吸附和解吸实验,优化分离工艺。结果显示,AB-8型树脂具有良好的吸附和解吸性能,该树脂对柴达木沙漠链霉菌S10^T发酵液中的活性天然产物吸附工艺为发酵液pH值9,吸附时间4 h,洗脱液70%甲醇溶液。经正向硅胶、反相硅胶和葡聚糖凝胶Sephadex LH-20分离得到了一个化合物,¹H-NMR和¹³C-NMR结合高分辨质谱(LC-HR-MS)鉴定该化合物为阿糖腺苷(vidarabine),是一种具有抗病毒活性的核苷类抗生素,并简单探究了其在柴达木沙漠链霉菌中的生物合成过程。     

Glucose-based optimization of CHO-cell perfusion cultures

Perfusion cultures of CHO cells producing t-PA were performed using acoustic filter cell retention. A robust off-line glucose analysis and predictive control protocol was developed to maintain the process within approximately 0.5 mM of the glucose set point, without the need for a more fallible on-line sensor. Glucose usage (the difference between the inlet and reactor glucose concentrations) provided an easily measured indicator of overall medium utilization for mapping acceptable ranges of operation, including the edge of failure. Earlier onset of perfusion with a ramping glucose set point (1.5 mM/d) resulted in improved growth and consistency during the perfusion culture start-up. At steady state, the t-PA concentration variability increased gradually with increasing glucose usage up to approximately 22 mM, then up to 24 mM the variability increased threefold. Peak t-PA concentrations of over 90 mg/L were obtained by controlling at a glucose usage of approximately 24 mM, but these t-PA levels were not sustainable for more than 3 days. A consistent t-PA concentration of 40 mg/L was obtained at a glucose usage of 21.5 mM.     

Optimization of Solutions for the One Plant Protection Problem

Plant protection problems are simulated by a system of ordinary differential equations with given initial conditions. The sensitivity and resistance of pathogen subpopulations to fungicide mixtures, fungicide weathering, plant growth, etc. are taken into consideration. The system of equations is solved numerically for each set of initial conditions and parameters of the disease and fungicide applications. Optimization algorithms were investigated and a computer program was developed for optimization of these solutions. 14 typical cases of the disease were simulated and optimized in order to determine optimal fungicide treatments. The optimized strategy for fungicide application differs considerably from the commonly used method and seems to be an important new principle in plant protection. The approach developed in this study may be useful for a wide spectrum of purposes in the simulation of leaf diseases. It may also help the biologist to decrease or pinpoint experimental work and analyze its results and is perspective for plant disease control.     

Minimizing model fitting objectives that contain spurious local minima by bootstrap restarting

Objective functions that arise when fitting nonlinear models often contain local minima that are of little significance except for their propensity to trap minimization algorithms. The standard methods for attempting to deal with this problem treat the objective function as fixed and employ stochastic minimization approaches in the hope of randomly jumping out of local minima. This article suggests a simple trick for performing such minimizations that can be employed in conjunction with most conventional nonstochastic fitting methods. The trick is to stochastically perturb the objective function by bootstrapping the data to be fit. Each bootstrap objective shares the large-scale structure of the original objective but has different small-scale structure. Minimizations of bootstrap objective functions are alternated with minimizations of the original objective function starting from the parameter values with which minimization of the previous bootstrap objective terminated. An example is presented, fitting a nonlinear population dynamic model to population dynamic data and including a comparison of the suggested method with simulated annealing. Convergence diagnostics are discussed.