An efficient model development strategy for bioprocesses based on neural networks in macroscopic balances

In the serial gray box modeling strategy, generally available knowledge, represented in the macroscopic balance, is combined naturally with neural networks, which are powerful and convenient tools to model the inaccurately known terms in the macroscopic balance. This article shows, for a typical biochemical conversion, that in the serial gray box modeling strategy the identification data only have to cover the input-output space of the inaccurately known term in the macroscopic balances and that the accurately known terms can be used to achieve reliable extrapolation. The strategy is demonstrated successfully on the modeling of the enzymatic (repeated) batch conversion of penicillin G, for which real-time results are presented. Compared with a more data-driven black box strategy, the serial gray box strategy leads to models with reliable extrapolation properties, so that with the same number of identification experiments the model can be applied to a much wider range of different conditions. Compared to a more knowledge-driven white box strategy, the serial gray box model structure is only based on readily available or easily obtainable knowledge, so that the development time of serial gray box models still may be short in a situation where there is no detailed knowledge of the system available. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 53: 549-566, 1997.     

Modeling of Sugar Crystallization through Knowledge Integration

This paper reports on the comparison of three modeling approaches that were applied to a fed batch evaporative sugar crystallization process. They are termed white box, black box, and grey box modeling strategies, which reflects the level of physical transparency and understanding of the model. White box models represent the traditional modeling approach, based on modeling by first principles. Black box models rely on recorded process data and knowledge collected during the normal process operation. Among various tools in this group an artificial neural networks (ANN) approach is adopted in this paper. The grey box model is obtained from a combination of first principles modeling, based on mass, energy and population balances, with an ANN to approximate three kinetic parameters ‐‐ crystal growth rate, nucleation rate and the agglomeration kernel. The results have shown that the hybrid modeling approach outperformed the other aforementioned modeling strategies.     

Residual stress in the adult mouse brain

This work provides direct evidence that sustained tensile stress exists in white matter of the mature mouse brain. This finding has important implications for the mechanisms of brain development, as tension in neural axons has been hypothesized to drive cortical folding in the human brain. In addition, knowledge of residual stress is required to fully understand the mechanisms behind traumatic brain injury and changes in mechanical properties due to aging and disease. To estimate residual stress in the brain, we performed serial dissection experiments on 500-mum thick coronal slices from fresh adult mouse brains and developed finite element models for these experiments. Radial cuts were made either into cortical gray matter, or through the cortex and the underlying white matter tract composed of parallel neural axons. Cuts into cortical gray matter did not open, but cuts through both layers consistently opened at the point where the cut crossed the white matter. We infer that the cerebral white matter is under considerable tension in the circumferential direction in the coronal cerebral plane, parallel to most of the neural fibers, while the cerebral cortical gray matter is in compression. The models show that the observed deformation after cutting can be caused by more growth in the gray matter than in the white matter, with the estimated tensile stress in the white matter being on the order of 100–1,000 Pa.     

Real time phase detection based online monitoring of batch fermentation processes

Industrial fermentations conducted in a batch or semi-batch mode demonstrate significant batch-to-batch variability. Current batch process monitoring strategies involve manual interpretation of highly informative but low frequency offline measurements such as concentrations of products, biomass and substrates. Fermentors are also fitted with computer interfaced instrumentation, enabling high frequency online measurements of several variables and automated techniques which can utilize this data would be desirable. Evolution of a batch fermentation, which typically uses complex medium, can be conceptualized as a sequence of several distinct metabolic phases. Monitoring of batch processes can then be achieved by detecting the phase change events, also termed as singular points (SP). In this work, we propose a novel moving window based real-time monitoring strategy for SP detection based only on online measurements. The key hypothesis of the strategy is that the statistical properties of the online data undergo a significant change around an SP. The strategy is easily implementable and does not require past data or prior knowledge of the number or time of occurrence of SPs. The efficacy of the proposed approach has been demonstrated to be superior compared to that of reported techniques for industrially relevant model organisms. The proposed approach can be used to decide offline sampling timings in real time.     

Identification of Molecular Marker and Aggressiveness for Different Groups of <Emphasis Type="Italic">Bipolaris sorokiniana</Emphasis> Isolates Causing Spot Blotch Disease in Wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

One hundred fifty-five isolates of Bipolaris sorokiniana of wheat were studied for their morphopathological characterization. These isolates were grouped in five categories--black, brown/dull black, gray cottony growth, dull white/greenish black, and white--on the basis of their growth pattern. The frequency of the black suppressed type was maximum (45.63%), whereas the white isolate displayed lowest frequency (6.96%) in the natural population. Twenty RAPD (random amplified polymorphic DNA) primers were used to observe the variability among the identified groups of B. sorokininana. From each group, eight random isolates were investigated. A total of 143 bands were amplified, out of which 107 (74.83%) were polymorphic and 36 (25.17%) were monomorphic. On an average, the total numbers of bands generated per primer were 7.15, of which 5.35 and 1.80 were polymorphic and monomorphic, respectively. Dendrograms based on molecular polymorphism unveiled a considerable amount of diversity among the isolates. Specific DNA bands were identified for selected isolates. The distinct markers appeared to be potential enough to be employed as genetic fingerprints for future strain identification and classification. The study indicated that the RAPD primers provide an easy, rapid, and simple technique for the preliminary assessment of genetic diversity among the fungal isolates.     

针刺百会穴对产后抑郁小鼠行为学改变和海马区NMDAR相关蛋白表达的影响

摘要 目的：研究针刺百会穴对产后抑郁小鼠行为学改变和海马区N-甲基-D-天冬氨酸受体（NMDAR）相关蛋白表达的影响。方法：30只C57BL/6母鼠被随机分为对照组、模型组和治疗组,每组30只。模型组和治疗组小鼠在妊娠期间通过皮下注射地塞米松磷酸钠建立产后抑郁小鼠模型,对照组小鼠皮下注射等量的生理盐水作为对照。治疗组小鼠分娩后通过针刺百会穴治疗14天,模型组和对照组小鼠不进行治疗。比较三组小鼠24 h食物消耗量和体重,黑白箱实验中白箱停留时间和黑白箱穿梭次数,以及强迫游泳实验不动状态时间和悬尾实验中悬尾不动时间。同时,通过免疫印记法检测三组小鼠海马去NMDA受体（NR2A和NR2B）、cAMP 结合蛋白(CREB) 和钙调蛋白激酶II(CaMKII) 蛋白表达水平。结果：治疗前,产后抑郁小鼠24 h食物消耗量、体重、白箱停留时间、黑白箱穿梭次数以及海马区CREB蛋白表达水平均显著低于对照组小鼠（P<0.05）,而游泳不同状态时间、悬尾不动时间和海马区NR2A、NR2B、cAMP蛋白表达水平均显著高于对照组小鼠（P<0.05）。治疗后,针刺百会穴治疗组小鼠24 h食物消耗量、体重、白箱停留时间和黑白箱穿梭次数以及海马区CREB蛋白表达水平均显著高于模型组小鼠（P<0.05）,而游泳不同状态时间、悬尾不动时间和海马区NR2A、NR2B、cAMP蛋白表达水平均显著低于模型组小鼠（P<0.05）。结论：针刺百会穴可以显著改善产后抑郁小鼠行为学情况,提高其运动能力,其可能与影响产后抑郁小鼠NMDAR相关蛋白表达有关。     

Identification in an anaerobic batch system: global sensitivity analysis, multi-start strategy and optimization criterion selection

Several mathematical models have been developed in anaerobic digestion systems and a variety of methods have been used for parameter estimation and model validation. However, structural and parametric identifiability questions are relatively seldom addressed in the reported AD modeling studies. This paper presents a 3-step procedure for the reliable estimation of a set of kinetic and stoichiometric parameters in a simplified model of the anaerobic digestion process. This procedure includes the application of global sensitivity analysis, which allows to evaluate the interaction among the identified parameters, multi-start strategy that gives a picture of the possible local minima and the selection of optimization criteria or cost functions. This procedure is applied to the experimental data collected from a lab-scale sequencing batch reactor. Two kinetic parameters and two stoichiometric coefficients are estimated and their accuracy was also determined. The classical least-squares cost function appears to be the best choice in this case study.     

Biology in a gray box: targeting the emergent properties of protein complexes: 2011 Yale Chemical Biology Symposium

At the 2011 Yale Chemical Biology Symposium, Jason Gestwicki presented a novel yet intuitive approach to drug screening. This method, which he termed "gray box" screening, targets protein complexes that have been reconstituted in vitro. Therefore, the gray box screen can achieve greater phenotypic complexity than biochemical assays but avoids the need for target identification that follows cell-based assays. Dr. Gestwicki's research group was able to use the gray box screen to identify myricetin as an inhibitor of the DnaK-DnaJ chaperone complex. This review will discuss Dr. Gestwicki's approach to identifying DnaK-DnaJ inhibitors as well as where the gray box screen fits among traditional techniques in drug discovery.     

Detailed kinetic model describing new oligosaccharides synthesis using different β-galactosidases

The production of prebiotic galactooligosaccharides (GOS) from lactose has been widely studied whereas the synthesis of new prebiotic oligosaccharides with improved properties as those derived from lactulose is receiving an increasing interest. Understanding the mechanism of enzymatic oligosaccharides synthesis from lactulose would help to improve the quality of the products in a rational way as well as to increase the production efficiency by optimally selecting the operating conditions. A detailed kinetic model describing the enzymatic transgalactosylation reaction during lactulose hydrolysis is presented here for the first time. The model was calibrated with the experimental data obtained in batch assays with two different β-galactosidases at various temperatures and concentrations of substrate. A complete system identification loop, including model selection, robust estimation of the parameters by means of a global optimization method and computation of confidence intervals was performed. The kinetic model showed a good agreement between experimental data and predictions for lactulose conversion and provided important insights into the mechanism of formation of new oligosaccharides with potential prebiotic properties.     

Negative‐assortative mating for color in wolves

There is strong negative‐assortative mating for gray and black pelage color in the iconic wolves in Yellowstone National Park. This is the first documented case of significant negative‐assortative mating in mammals and one of only a very few cases in vertebrates. Of 261 matings documented from 1995 to 2015, 63.6% were between gray and black wolves and the correlation between mates for color was –0.266. There was a similar excess of matings of both gray males × black females and black males × gray females. Using the observed frequency of negative‐assortative mating in a model with both random and negative‐assortative mating, the estimated proportion of negative‐assortative mating was 0.430. The estimated frequency of black wolves in the population from 1996 to 2014 was 0.452 and these frequencies appear stable over this 19‐year period. Using the estimated level of negative‐assortative mating, the predicted equilibrium frequency of the dominant allele was 0.278, very close to the mean value of 0.253 observed. In addition, the patterns of genotype frequencies, that is, the observed proportion of black homozygotes and the observed excess of black heterozygotes, are consistent with negative‐assortative mating. Importantly these results demonstrate that negative‐assortative mating could be entirely responsible for the maintenance of this well‐known color polymorphism.     

Viability and Acrosome Staining of Bull, Boar and Rabbit Spermatozoa

A practical and reliable staining procedure was developed to distinguish the viability and acrosomal status of bull, boar and rabbit spermatozoa. The first stain with trypan blue or Congo red is rapid and avoids artifacts. This stain is precipitated by neutral red during the 2 min required for fixation. The precipitate gives a high contrast black color, resistant to the subsequent rinsings and persists during the time required for staining the acrosome with Giemsa. Ten classes of spermatozoa are distinguished (live or dead with intact acrosomes, loose acrosomes, damaged acrosomes, no acrosome, or with no acrosome and no postacrosomal ring). The intact acrosomes are purple, the loose acrosomes are dark lavender and the damaged acrosomes are pale lavender. The anterior part of the head of live spermatozoa with no acrosome is white or light pink and the same area of dead spermatozoa is white or pale gray. The postacrosomal ring is red. The postacrosomal area of the head of live spermatozoa is white or light pink and the same part of dead spermatozoa is black, dark violet or gray. The procedure did not give satisfactory results for stallion spermatozoa.     

Can Black‐and‐White Ruffed Lemurs (Varecia variegata) Solve Object Permanence Tasks?

We examined object permanence in black‐and‐white‐ruffed lemurs (Varecia variegata) at Zoo Atlanta. A series of visible and invisible displacement tasks with suitable controls were presented to five adult subjects. Subjects performed significantly above chance on all regular tasks, except for the double invisible displacements. Subjects failed visible and invisible controls. Failure on the control trials did not appear to be because subjects used the “last box touched” strategy (subjects did not choose the last box touched significantly more than expected by chance). However, a substantial percentage of choices was made to the last box touched by the experimenter. There was no significant difference between this percentage, and the percentage of choices made to the baited box (on both visible and invisible controls), which indicates that subjects were drawn to both boxes which the experimenter visited/touched, and thus failed the controls. Based on the results from the present study, we believe that there is no evidence that black‐and‐white ruffed lemurs understand visible and invisible tasks in the traditional object permanence battery. Am. J. Primatol. 75:376‐386, 2013. © 2013 Wiley Periodicals, Inc.     

Feasible and optimal retrofitting of batch-soluble to continuous-immobilized enzyme processes applied to adsorbed enzymes

The advantages of retrofitting an in situ soluble enzyme batch process to an immobilized enzyme continuous process are contrasted against the disadvantages by means of a dimensionless feasibility/optimization analysis. The general analysis is applied to the case of an adsorbed enzyme system where a maximum in activity occurs with respect to loading. For this case, a minimum in the ratio of enzyme-carrier complex working lifetime to in situ batch process time and a maximum in the cost difference between the in situ and retrofit processes occurs with respect to loading and retrofit process conversion. For the maximization of cost difference, the analysis also suggests a criterion that can be used to determine whether the values for optimal loading and retrofit conversion will result in the retrofit being economically feasible. When infeasibility occurs, qualitative sensitivity analysis for a variety of situations points out whether a catalyst or process modification will improve feasibility the most. Apart from forming the basis for an iterative retrofit process design algorithm, the modeling approach's ability to specify optimal values of catalyst properties such as loading lends itself to defining process-specific, catalyst design "targets" would be useful for those developing immobilized enzyme preparation methodology and those investigating enzyme-carrier interactions.     

Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization

Upstream bioprocess characterization and optimization are time and resource‐intensive tasks. Regularly in the biopharmaceutical industry, statistical design of experiments (DoE) in combination with response surface models (RSMs) are used, neglecting the process trajectories and dynamics. Generating process understanding with time‐resolved, dynamic process models allows to understand the impact of temporal deviations, production dynamics, and provides a better understanding of the process variations that stem from the biological subsystem. The authors propose to use DoE studies in combination with hybrid modeling for process characterization. This approach is showcased on Escherichia coli fed‐batch cultivations at the 20L scale, evaluating the impact of three critical process parameters. The performance of a hybrid model is compared to a pure data‐driven model and the widely adopted RSM of the process endpoints. Further, the performance of the time‐resolved models to simultaneously predict biomass and titer is evaluated. The superior behavior of the hybrid model compared to the pure black‐box approaches for process characterization is presented. The evaluation considers important criteria, such as the prediction accuracy of the biomass and titer endpoints as well as the time‐resolved trajectories. This showcases the high potential of hybrid models for soft‐sensing and model predictive control.     

Individual-based modeling of phytoplankton: evaluating approaches for applying the cell quota model

Present phytoplankton models typically use a population-level (lumped) modeling (PLM) approach that assumes average properties of a population within a control volume. For modern biogeochemical models that formulate growth as a nonlinear function of the internal nutrient (e.g. Droop kinetics), this averaging assumption can introduce a significant error. Individual-based (agent-based) modeling (IBM) does not make the assumption of average properties and therefore constitutes a promising alternative for biogeochemical modeling. This paper explores the hypothesis that the cell quota (Droop) model, which predicts the population-average specific growth or cell division rate, based on the population-average nutrient cell quota, can be applied to individual algal cells and produce the same population-level results. Three models that translate the growth rate calculated using the cell quota model into discrete cell division events are evaluated, including a stochastic model based on the probability of cell division, a deterministic model based on the maturation velocity and fraction of the cell cycle completed (maturity fraction), and a deterministic model based on biomass (carbon) growth and cell size. The division models are integrated into an IBM framework (iAlgae), which combines a lumped system representation of a nutrient with an individual representation of algae. The IBM models are evaluated against a conventional PLM (because that is the traditional approach) and data from a number of steady and unsteady continuous (chemostat) and batch culture laboratory experiments. The stochastic IBM model fails the steady chemostat culture test, because it produces excessive numerical randomness. The deterministic cell cycle IBM model fails the batch culture test, because it has an abrupt drop in cell quota at division, which allows the cell quota to fall below the subsistence quota. The deterministic cell size IBM model reproduces the data and PLM results for all experiments and the model parameters (e.g. maximum specific growth rate, subsistence quota) are the same as those for the PLM. In addition, the model-predicted cell age, size (carbon) and volume distributions are consistent with those derived analytically and compare well to observations. The paper discusses and illustrates scenarios where intra-population variability in natural systems leads to differences between the IBM and PLM models.     

A semi-empirical glycosylation model of a camelid monoclonal antibody under hypothermia cell culture conditions

The impact of cell culture environment on the glycan distribution of a monoclonal antibody (mAb) has been investigated through a combination of experiments and modeling. A newly developed CHO DUXB cell line was cultivated at two levels of initial Glutamine (Gln) concentrations (0, 4 mM) and incubation temperatures of (33 and 37 °C) in batch operation mode. Hypothermia was applied either through the entire culture duration or only during the post-exponential phase. Beyond reducing cell growth and increasing productivity, hypothermia significantly altered the galactosylation index profiles as compared to control conditions. A novel semi-empirical dynamic model was proposed for elucidating the connections between the extracellular cell culture conditions to galactosylation index. The developed model is based on a simplified balance of nucleotides sugars and on the correlation between sugars’ levels to the galactosylation index (GI). The model predictions were found to be in a good agreement with the experimental data. The proposed empirical model is expected to be useful for controlling the glycoprofiles by manipulating culture conditions.     

Hybrid neural network modeling of a full-scale industrial wastewater treatment process

In recent years, hybrid neural network approaches, which combine mechanistic and neural network models, have received considerable attention. These approaches are potentially very efficient for obtaining more accurate predictions of process dynamics by combining mechanistic and neural network models in such a way that the neural network model properly accounts for unknown and nonlinear parts of the mechanistic model. In this work, a full-scale coke-plant wastewater treatment process was chosen as a model system. Initially, a process data analysis was performed on the actual operational data by using principal component analysis. Next, a simplified mechanistic model and a neural network model were developed based on the specific process knowledge and the operational data of the coke-plant wastewater treatment process, respectively. Finally, the neural network was incorporated into the mechanistic model in both parallel and serial configurations. Simulation results showed that the parallel hybrid modeling approach achieved much more accurate predictions with good extrapolation properties as compared with the other modeling approaches even in the case of process upset caused by, for example, shock loading of toxic compounds. These results indicate that the parallel hybrid neural modeling approach is a useful tool for accurate and cost-effective modeling of biochemical processes, in the absence of other reasonably accurate process models.     

兔黑素皮质素受体4(MC4R)基因多态性及其与体重及屠体性状的关联研究

以哈尔滨白兔、天府黑兔、比利时兔、齐卡巨型兔以及加利福尼亚兔5个肉兔品种共220个个体为研究对象, 采用PCR-SSCP技术和克隆测序技术进行SNPs检测和基因型的分析。结果发现兔MC4R基因237位发生转换突变(A→G), 5个品种内的AA基因型和A等位基因频率均分别高于AG基因型和G等位基因, 而哈尔滨白兔、比利时兔以及齐卡巨型兔3个品种兔的AG基因型频率和G等位基因频率明显高于天府黑兔和加利福尼亚兔。单位点基因型与屠体性状、饲料转化率以及熟肉率的最小二乘分析表明; AG基因型与兔的体重、全净膛重以及饲料转化率有显著的关系(P<0.05), 而与熟肉率的关系不显著(P>0.05)。结果表明, MC4R基因可以作为影响和控制兔体重及屠体性状的候选基因。