Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements,pitfalls and solution paths

Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four-dimensional antibody-producing CHO fed-batch process are compared. Overall, 33 experiments are performed in the fractional factorial four-dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling.     

Machine learning in protein structure prediction

Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the increasing “neuralization” of structure prediction pipelines, whereby computations previously based on energy models and sampling procedures are replaced by neural networks. The extraction of physical contacts from the evolutionary record; the distillation of sequence–structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks. Cumulatively, this transformation has resulted in algorithms that can now predict single protein domains with a median accuracy of 2.1 Å, setting the stage for a foundational reconfiguration of the role of biomolecular modeling within the life sciences.     

基于生态认知的校园雨水花园设计

生态认知视角下的校园雨水花园设计旨在探讨生态语境下校园景观的教育作用。环境教育中的无意识教育方式是通过环境的感染力潜移默化影响受育者,无意识教育方式为受育者提供的开放性、自主性和互动性的教育途径更能深刻影响受育者的认知。基于生态认知的校园雨水花园设计以无意识环境教育思想为指导,运用生态显露设计,以可视化雨水路径刻画雨水生态特征,以满足花园功能、技术和艺术要求且实现可视化雨水路径的空间设计与艺术品质,创造雨水花园的张力场效应和感染力,如空间秩序、材质对比、色彩运用以及空间的愉悦体验等,以促进生态的无意识教育     

THE RELATIONSHIP BETWEEN EVOLUTIONARY BIOLOGY AND RELIGION

Belief in creationism and intelligent design is widespread and gaining significance in a number of countries. This article examines the characteristics of science and of religions and the possible relationship between science and religion. I argue that creationism is sometimes best seen not as a misconception but as a worldview. In such instances, the most to which a science educator (whether in school, college or university) can normally aspire is to ensure that students with creationist beliefs understand the scientific position. In the short term, the scientific worldview is unlikely to supplant a creationist one for students who are firm creationists. We can help students to find their evolutionary biology courses interesting and intellectually challenging without their being threatening. Effective teaching in this area can help students not only learn about the theory of evolution but better appreciate the way science is done, the procedures by which scientific knowledge accumulates, the limitations of science, and the ways in which scientific knowledge differs from other forms of knowledge.     

Rational design of TNFα binding proteins based on the de novo designed protein DS119

De novo protein design offers templates for engineering tailor‐made protein functions and orthogonal protein interaction networks for synthetic biology research. Various computational methods have been developed to introduce functional sites in known protein structures. De novo designed protein scaffolds provide further opportunities for functional protein design. Here we demonstrate the rational design of novel tumor necrosis factor alpha (TNFα) binding proteins using a home‐made grafting program AutoMatch. We grafted three key residues from a virus 2L protein to a de novo designed small protein, DS119, with consideration of backbone flexibility. The designed proteins bind to TNFα with micromolar affinities. We further optimized the interface residues with RosettaDesign and significantly improved the binding capacity of one protein Tbab1‐4. These designed proteins inhibit the activity of TNFα in cellular luciferase assays. Our work illustrates the potential application of the de novo designed protein DS119 in protein engineering, biomedical research, and protein sequence‐structure‐function studies.     

Model-based analysis on the relationship of signal quality to real-time extraction of information in bioprocesses

Quality by design (QbD) is a current structured approach to design processes yielding a quality product. Knowledge and process understanding cannot be achieved without proper experimental data; hence requirements for measurement error and frequency of measurement of bioprocess variables have to be defined. In this contribution, a model-based approach is used to investigate impact factors on calculated rates to predict the obtainable information from real-time measurements (= signal quality). Measurement error, biological activity, and averaging window (= period of observation) were identified as biggest impact factors on signal quality. Moreover, signal quality has been set in context with a quantifiable measure using statistical error testing, which can be used as a benchmark for process analytics and exploitation of data. Results have been validated with data from an E. coli batch process. This approach is useful to get an idea which process dynamics can be observed with a given bioprocess setup and sampling strategy beforehand.     

Facile conversion of RNA aptamers to modular fluorescent sensors with tunable detection wavelengths

A GTP aptamer was converted to a modular fluorescent GTP sensor by conjugation of RRE (Rev responsive element) RNA and successive complex formation with a fluorophore-modified Rev peptide. Structural changes associated with substrate binding in the RNA aptamer were successfully transduced into changes in fluorescence intensity because of the modular structure of ribonucleopeptides. A simple modular strategy involving conjugation of a fluorophore-modified ribonucleopeptide to the stem region of an RNA aptamer deduced from secondary structural information helps produce fluorescent sensors, which allow tuning of excitation and detection wavelengths through the replacement of the fluorophore at the N-terminal of the Rev peptide.     

Evaluation of Medium Components by Plackett-Burman Statistical Design for Lipase Production by Candida rugosa and Kinetic Modeling

Lipase production by Candida rugosa was carried out in submerged fermentation. Plackett-Burman statistical experimental design was applied to evaluate the fermentation medium components. The effect of twelve medium components was studied in sixteen experimental trials. Glucose, olive oil, peptone and FeCl3?6H2O were found to have more significance on lipase production by Candida rugosa. Maximum lipase activity of 3.8 u mL-1 was obtained at 50 h of fermentation period. The fermentation was carried out at optimized temperature of 30oC, initial pH of 6.8 and shaking speed of 120 r/min. Unstructured kinetic models were used to simulate the experimental data. Logistic model, Luedeking-Piret model and modified Luedeking-Piret model were found suitable to efficiently predict the cell mass, lipase production and glucose consumption respectively with high determination coefficient(R2). From the estimated values of the Luedeking-Piret kinetic model parameters, α and β, it was found that the lipase production by Candida rugosa is growth associated.     

生物化学教学中采用案例教学法之案例设计与分析

案例教学法是被普遍采用的一种教学方式,将其应用于生物化学教学中,并用图解的形式给出案例的归纳与分析,使抽象难懂、错综复杂、生涩冷僻的教学内容变得直观明了。加深了学生对教学内容的理解,增强了课堂教学的趣味性与学生学习的主动性,同时提高了学生分析问题和解决问题的能力。