Teaching cellular metabolism using metabolic model simulations

Chaves, Gabriel Luz; Batista, Raquel Salgado; Cunha, Josivan de Sousa; Altmann, Daniel Lossa; Silva, A. J. da

doi:10.1016/j.ece.2021.12.001

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…(Gargalo et al, 2022) discussed the need to develop an on-line and open-source educational computer-aided platform/simulator as a part of preparing students for Industry 4.0 applications. (Chaves et al, 2022) presented how the open source metabolic model simulation OPTFLUX can be used as a digital tool in teaching chemical engineers about cell metabolism.…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

Digital tools in chemical engineering education: The needs and the desires

Udugama

Atkins

Bayer

et al. 2023

Education for Chemical Engineers

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Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

Digital tools in chemical engineering education: The needs and the desires

Udugama

Atkins

Bayer

et al. 2023

Education for Chemical Engineers

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“…there. Students responded positively to using OptFlux as an adaptive questionnaire [11]. Under the concept of "teaching factory", Mourtzis et al proposed a hybrid model, which has been proven in practice.…”

Section: Related Workmentioning

confidence: 99%

Research on Recommendation Model of College English MOOC based on Hybrid Recommendation Algorithm

Ding¹,

Jiang²

2023

IJACSA

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Establishing a reasonable and efficient compulsory education balance index system is very important to boost the all-around of compulsory education development, and then realize the course recommendation for students with different attributes. Based on this, the research aimed at the problems in college English education and evaluation, aimed to establish a college English MOOC education and evaluation system based on the improved neural network recommendation algorithm. The research first constructed the college English MOOC education and evaluation data elements, and then established a genetic algorithm improved neural network algorithm (BP Neural Network Optimization Algorithm Based on Genetic Algorithm, GA-BP), and finally analyzed the effect of the assembled model. These results show that the fitness of the GA-BP model reaches the set expectation when the evolutionary algebra reaches 10 times, and its fitness is 0.6. The corresponding threshold and weight are obtained, and the threshold and weight are substituted into the model. After repeated iterative training, the model finally reached an error of 10-3 when it was trained 12 times, and the expected accuracy was achieved. The R value of each set hovered around 0.97, and the fitting degree was high, which showed that the GA-BP model proposed in the study had a better fitting degree. The difference between the expected value and the output value is mainly distributed in the [-0.08083, 0.06481] interval. To sum up, the GA-BP model proposed in the study has an excellent effect on college English education and evaluation. This evaluation model has a faster learning rate and a higher prediction accuracy and more stable performance.

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“…Although previous studies have described and provided resources for teaching kinetic metabolic modeling, 14 FBA, 3,15 MFA, 16,17 and MCA, [18][19][20] there are not any published and freely available instructional resources for introducing these toolsets to learners in an integrative and interactive fashion. Moreover, although papers and books exist describing how to experimentally approach 13C-MFA [21][22][23][24] or the theoretical background behind the technique, 25,26 we are not aware of any dedicated and published educationally focused resources for introducing learners to the theoretical background behind label-assisted MFA.…”

Section: Introductionmentioning

confidence: 99%

Integrative teaching of metabolic modeling and flux analysis with interactive python modules

Kaste

Green

Shachar‐Hill

2023

Biochem Molecular Bio Educ

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The modeling of rates of biochemical reactions—fluxes—in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint‐based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to‐date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C‐metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands‐on computer laboratory component of a four‐day metabolic modeling workshop and participant survey results showed improvements in learners' self‐assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.

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Teaching cellular metabolism using metabolic model simulations

Cited by 7 publications

References 24 publications

Digital tools in chemical engineering education: The needs and the desires

Digital tools in chemical engineering education: The needs and the desires

Research on Recommendation Model of College English MOOC based on Hybrid Recommendation Algorithm

Integrative teaching of metabolic modeling and flux analysis with interactive python modules

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