การพัฒนาการคิดเชิงคำนวณของผู้เรียนชั้นมัธยมศึกษาปีที่ 4 ด้วยการปฏิบัติทางชีวสารสนเทศขั้นพื้นฐาน

Development of Computational Thinking of Tenth–Grade Students Using Basic Bioinformatics Practices   Natthasit Norasit, Pongprapan Pongsophon, Wanwipa Vongsangnak and Santichai Anuworrachai   รับบทความ: 12 มีนาคม 2566; แก้ไขบทความ: 13 ตุลาคม 2566; ยอมรับตีพิมพ์: 5 ธันวาคม 2566; ตีพิมพ์ออนไลน์: 21 ธันวาคม 2566    บทคัดย่อ วิทยาการคำนวณได้เข้ามามีบทบาทในวงการวิทยาศาสตร์อย่างมาก โดยเฉพาะอย่างยิ่งในการจัดการข้อมูลทางวิทยาศาสตร์ขนาดใหญ่ มีความซับซ้อนสูง ผู้เรียนต้องเตรียมความพร้อมในการเป็นนักวิทยาศาสตร์ในยุคแห่งข้อมูลและเทคโนโลยีโดยการมีการคิดเชิงคำนวณ ทว่ายังไม่มีแนวทางปฏิบัติที่ชัดเจนในการจัดการเรียนรู้ที่ส่งเสริมการคิดเชิงคำนวณในชั้นเรียนวิทยาศาสตร์ ดังนั้น งานวิจัยมีเป้าหมายเพื่อ 1) วัดการคิดเชิงคำนวณของผู้เรียนก่อนและหลังเรียนด้วยการปฏิบัติทางชีวสารสนเทศขั้นพื้นฐาน และ 2) ศึกษาแนวปฏิบัติที่ดีในการใช้การปฏิบัติทางชีวสารสนเทศขั้นพื้นฐานเพื่อพัฒนาการคิดเชิงคำนวณ กลุ่มตัวอย่างคือนักเรียนชั้นมัธยมศึกษาปีที่ 4 โรงเรียนสาธิตแห่งหนึ่งในกรุงเทพฯ จำนวน 32 คน ผู้วิจัยออกแบบการจัดการเรียนรู้ แบ่งเป็น 2 ช่วง ได้แก่ การจัดการเรียนรู้โดยไม่ใช้คอมพิวเตอร์และใช้คอมพิวเตอร์ เก็บข้อมูลด้วยแบบวัดการคิดเชิงคำนวณ วิเคราะห์ข้อมูลด้วยสถิติเชิงพรรณนาและทดสอบความแตกต่างระหว่างค่าเฉลี่ยสองค่าที่ได้จากกลุ่มตัวอย่างสองกลุ่มที่ไม่เป็นอิสระต่อกัน (paired t–test) พบว่า ค่าเฉลี่ยคะแนนการคิดเชิงคำนวณก่อนและหลังเรียน เท่ากับ 17.78 (SD = 4.11) และ 21.65 (SD = 2.18) แตกต่างกัน (t31, .05 = 7.08, p < .05) รวมถึงค่าเฉลี่ยคะแนนทั้ง 4 องค์ประกอบเพิ่มขึ้นอย่างมีนัยสำคัญเช่นกัน และครูผู้สอนควรจัดการเรียนรู้โดยใช้การปฏิบัติทางชีวสารสนเทศที่ท้าทายและเชื่อมโยงกับชีวิตประจำวันต่อผู้เรียนอย่างชัดแจ้งและเนื้อหาสอดคล้องกับหลักสูตรวิทยาศาสตร์ของประเทศ เพื่อการใช้และพัฒนาการคิดเชิงคำนวณอย่างต่อเนื่อง คำสำคัญ:  ชีวสารสนเทศ  การคิดเชิงคำนวณ  วิทยาการคำนวณ   Abstract One impact of computing in scientific fields and thinking processes lies in the processing of voluminous scientific data. Students therefore need to prepare themselves to confront the upcoming digital era and handle cutting–edge technology using computational thinking (CT); however, this is still absent from typical science classrooms. Hence, the purposes of this study were to 1) assess students’ CT before and after learning basic bioinformatics practices and 2) study what are good practices to incorporate bioinformatics practices to enhance students’ CT. Researchers designed four learning plans using inquiry–based learning and basic bioinformatics practices, having two parts: unplugged and plugged–in sessions. Data were collected using CT tests and analyzed using descriptive statistics and a paired t–test. The participants comprised 32 tenth–grade students in a science–technology emphasis program at a demon-stration school in Bangkok, Thailand. The results showed CT pretest and posttest mean were significantly different by 17.78 (SD = 4.11) and 21.65 (SD = 2.18), respectively (t31, .05 = 7.08, p < .05). Additionally, the development of CT was evident in the improvement of all four CT components as well, and good practices to incorporate bioinformatics practices is to use real–life bioinformatics challenges explicitly and related to the standard science curriculum to maintain engagement in and persistence of CT usage. Keywords: Bioinformatics, Computational thinking, Computing scienc

Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

<p>Abstract</p> <p>Background</p> <p>Since ancient times the filamentous fungus <it>Aspergillus oryzae </it>has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of <it>A. oryzae </it>with 12,074 annotated genes was released but the number of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other related fungi. Here we proposed the gene prediction by construction of an <it>A. oryzae </it>Expressed Sequence Tag (EST) library, sequencing and assembly. We enhanced the function assignment by our developed annotation strategy. The resulting better annotation was used to reconstruct the metabolic network leading to a genome scale metabolic model of <it>A. oryzae</it>.</p> <p>Results</p> <p>Our assembled EST sequences we identified 1,046 newly predicted genes in the <it>A. oryzae </it>genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted in assignment of new putative functions to 1,469 hypothetical proteins already present in the <it>A. oryzae </it>genome database. Using the substantially improved annotated genome we reconstructed the metabolic network of <it>A. oryzae</it>. This network contains 729 enzymes, 1,314 enzyme-encoding genes, 1,073 metabolites and 1,846 (1,053 unique) biochemical reactions. The metabolic reactions are compartmentalized into the cytosol, the mitochondria, the peroxisome and the extracellular space. Transport steps between the compartments and the extracellular space represent 281 reactions, of which 161 are unique. The metabolic model was validated and shown to correctly describe the phenotypic behavior of <it>A. oryzae </it>grown on different carbon sources.</p> <p>Conclusion</p> <p>A much enhanced annotation of the <it>A. oryzae </it>genome was performed and a genome-scale metabolic model of <it>A. oryzae </it>was reconstructed. The model accurately predicted the growth and biomass yield on different carbon sources. The model serves as an important resource for gaining further insight into our understanding of <it>A. oryzae </it>physiology.</p

The development of molecular genetics concept test for senior high school students using Rasch analysis

Developing a high-quality test item requires substantial time and effort. A well-developed item bank is conducted using rigorous development and validation procedures. This study aimed to describe the development process of molecular genetics concept test (MGCT) for senior high school students using Rasch analysis under Berkeley evaluation and assessment research (BEAR) assessment system framework. The test consists of 50 multiple-choice items to assess conceptual understanding of molecular genetics concepts. The MGCT was developed based on curriculum analysis from the Indonesian ministry of education and culture and content-validated by three content experts comprising an expert in biology, an expert in bioinformatics, and an experienced Indonesian biology teacher in a senior high school. The MGCT was then piloted to 114 students who had taught the molecular genetics unit from a senior high school to conduct the empirical validation. The results from Rasch analysis showed that the MGCT is acceptable because all items have outfit and infit mean-square values in the acceptable range of 0.7 to 1.3 and the reliability is 0.43. So, the MGCT can be used to assess the understanding of the molecular genetics concept. However, several items were too difficult to discriminate the student ability. So, future studies need to develop more this MGCT to get a more appropriate instrument

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production

SYSTEMS BIOLOGY AND METABOLIC ENGINEERING OF ARTHROSPIRA CELL FACTORIES

AbstractArthrospira are attractive candidates to serve as cell factories for production of many valuable compounds useful for food, feed, fuel and pharmaceutical industries. In connection with the development of sustainable bioprocessing, it is a challenge to design and develop efficient Arthrospira cell factories which can certify effective conversion from the raw materials (i.e. CO2 and sun light) into desired products. With the current availability of the genome sequences and metabolic models of Arthrospira, the development of Arthrospira factories can now be accelerated by means of systems biology and the metabolic engineering approach. Here, we review recent research involving the use of Arthrospira cell factories for industrial applications, as well as the exploitation of systems biology and the metabolic engineering approach for studying Arthrospira. The current status of genomics and proteomics through the development of the genome-scale metabolic model of Arthrospira, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies are discussed. At the end, the perspective and future direction on Arthrospira cell factories for industrial biotechnology are presented

Global Metabolic Changes by Bacillus Cyclic Lipopeptide Extracts on Stress Responses of Para Rubber Leaf

Changing environmental conditions can generate abiotic stress, such as the scarcity of water and exposure to chemicals. This includes biotic stress like Phytophthora palmivora infection, which causes leaf fall disease and inhibits the growth rate of para rubber seedlings, resulting in economic loss. To prevent abiotic and biotic stresses, biocontrol agents such as cyclic lipopeptides (CLPs) from Bacillus spp. have been introduced to reduce the use of chemically synthesized fungicides and fertilizers. This study aimed to use Bacillus CLP extracts as a biological agent to stimulate the plant growth system in para rubber seedlings under stress conditions compared with the exogenous plant hormone (salicylic acid, SA). CLP extracts obtained from B. subtilis PTKU12 and exogenous SA were applied to the leaves of para rubber seedlings. The extracted metabolites from each treatment were analyzed by untargeted metabolomics for metabolite identification and metabolic networks under stress responses. In both treatments, 1,702 and 979 metabolites were detected in the positive and negative ion modes of electrospray ionization, respectively. The differential analysis revealed that the accumulation of up-regulated metabolites in the treatment of CLP extracts was higher than in the exogenous SA treatment, belonging to 56 metabolic pathways. The analysis of metabolic pathways indicated that CLP extracts employed alanine, aspartate, and glutamate metabolisms for stress responses leading to plant growth promotion. These findings revealed that the metabolic network for plant growth promotion induced by BacillusCLP extracts could be considered a protective option for para rubber plantations. Doi: 10.28991/ESJ-2023-07-03-022 Full Text: PD

Revealing the beneficial effect of protease supplementation to high gravity beer fermentations using "-omics" techniques

<p>Abstract</p> <p>Background</p> <p>Addition of sugar syrups to the basic wort is a popular technique to achieve higher gravity in beer fermentations, but it results in dilution of the free amino nitrogen (FAN) content in the medium. The multicomponent protease enzyme Flavourzyme has beneficial effect on the brewer's yeast fermentation performance during high gravity fermentations as it increases the initial FAN value and results in higher FAN uptake, higher specific growth rate, higher ethanol yield and improved flavour profile.</p> <p>Results</p> <p>In the present study, transcriptome and metabolome analysis were used to elucidate the effect on the addition of the multicomponent protease enzyme Flavourzyme and its influence on the metabolism of the brewer's yeast strain Weihenstephan 34/70. The study underlines the importance of sufficient nitrogen availability during the course of beer fermentation. The applied metabolome and transcriptome analysis allowed mapping the effect of the wort sugar composition on the nitrogen uptake.</p> <p>Conclusion</p> <p>Both the transcriptome and the metabolome analysis revealed that there is a significantly higher impact of protease addition for maltose syrup supplemented fermentations, while addition of glucose syrup to increase the gravity in the wort resulted in increased glucose repression that lead to inhibition of amino acid uptake and hereby inhibited the effect of the protease addition.</p

Transcriptome landscapes of salt-susceptible rice cultivar IR29 associated with a plant growth promoting endophytic streptomyces

Plant growth-promoting endophytic (PGPE) actinomycetes have been known to enhance plant growth and mitigate plant from abiotic stresses via their PGP-traits. In this study, PGPE Streptomyces sp. GKU 895 promoted growth and alleviated salt tolerance of salt-susceptible rice cultivar IR29 by augmentation of plant weight and declined ROS after irrigation with 150 mM NaCl in a pot experiment. Transcriptome analysis of IR29 exposed to the combination of strain GKU 895 and salinity demonstrated up and downregulated differentially expressed genes (DEGs) classified by gene ontology and plant reactome. Streptomyces sp. GKU 895 induced changes in expression of rice genes including transcription factors under salt treatment which involved in growth and development, photosynthesis, plant hormones, ROS scavenging, ion transport and homeostasis, and plant–microbe interactions regarding pathogenesis- and symbiosis-related proteins. Taken together, these data demonstrate that PGPE Streptomyces sp. GKU 895 colonized and enhanced growth of rice IR29 and triggered salt tolerance phenotype. Our findings suggest that utilisation of beneficial endophytes in the saline fields could allow for the use of such marginal soils for growing rice and possibly other crops

Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis

With the development of next-generation sequencing (NGS) techniques, many software tools have emerged for the discovery of novel microRNAs (miRNAs) and for analyzing the miRNAs expression profiles. An overall evaluation of these diverse software tools is lacking. In this study, we evaluated eight software tools based on their common feature and key algorithms. Three deep-sequencing data sets were collected from different species and used to assess the computational time, sensitivity and accuracy of detecting known miRNAs as well as their capacity for predicting novel miRNAs. Our results provide useful information for researchers to facilitate their selection of the optimal software tools for miRNA analysis depending on their specific requirements, i.e. novel miRNAs discovery or miRNA expression profile analysis of sequencing data sets