Journal articles: 'FBA modeling'

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Gage, Nicholas A., Timothy J. Lewis, and Janine P. Stichter. "Functional Behavioral Assessment-Based Interventions for Students with or at Risk for Emotional and/or Behavioral Disorders in School: A Hierarchical Linear Modeling Meta-Analysis." Behavioral Disorders 37, no. 2 (February 2012): 55–77. http://dx.doi.org/10.1177/019874291203700201.

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Of the myriad practices currently utilized for students with disabilities, particularly students with or at risk for emotional and/or behavioral disorder (EBD), functional behavior assessment (FBA) is a practice with an emerging solid research base. However, the FBA research base relies on single-subject design (SSD) and synthesis has relied on literature review or analyses using nonparametric effect size calculations. This study was designed to examine the omnibus effect that FBA-based interventions have on problem behaviors for students with or at risk for EBD in schools using a hierarchical linear modeling meta-analytic approach to SSD synthesis. Based on a sample of 69 FBA studies, 146 subjects, and 206 outcome graphs, results indicated that, overall, FBA-based interventions reduced problem behavior by an average of 70.5% and that the procedure was effective across all student characteristics. Differences of effectiveness were evident between functional analysis and descriptive assessment procedures. Findings of this study suggest that FBA-based interventions for students with or at risk for EBD are an effective approach for the reduction of problem behaviors.

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van Pelt-KleinJan, Eunice, Daan H. de Groot, and Bas Teusink. "Understanding FBA Solutions under Multiple Nutrient Limitations." Metabolites 11, no. 5 (April 21, 2021): 257. http://dx.doi.org/10.3390/metabo11050257.

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Genome-scale stoichiometric modeling methods, in particular Flux Balance Analysis (FBA) and variations thereof, are widely used to investigate cell metabolism and to optimize biotechnological processes. Given (1) a metabolic network, which can be reconstructed from an organism’s genome sequence, and (2) constraints on reaction rates, which may be based on measured nutrient uptake rates, FBA predicts which reactions maximize an objective flux, usually the production of cell components. Although FBA solutions may accurately predict the metabolic behavior of a cell, the actual flux predictions are often hard to interpret. This is especially the case for conditions with many constraints, such as for organisms growing in rich nutrient environments: it remains unclear why a certain solution was optimal. Here, we rationalize FBA solutions by explaining for which properties the optimal combination of metabolic strategies is selected. We provide a graphical formalism in which the selection of solutions can be visualized; we illustrate how this perspective provides a glimpse of the logic that underlies genome-scale modeling by applying our formalism to models of various sizes.

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Katebi, Ataur R., and Robert L. Jernigan. "Modeling Complex between FBA and TIM: Functional Motions of FBA and TIM are Preserved in their Complex." Biophysical Journal 104, no. 2 (January 2013): 402a. http://dx.doi.org/10.1016/j.bpj.2012.11.2243.

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Covert, Markus W., Nan Xiao, Tiffany J. Chen, and Jonathan R. Karr. "Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli." Bioinformatics 24, no. 18 (July 10, 2008): 2044–50. http://dx.doi.org/10.1093/bioinformatics/btn352.

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AbstractMotivation: The effort to build a whole-cell model requires the development of new modeling approaches, and in particular, the integration of models for different types of processes, each of which may be best described using different representation. Flux-balance analysis (FBA) has been useful for large-scale analysis of metabolic networks, and methods have been developed to incorporate transcriptional regulation (regulatory FBA, or rFBA). Of current interest is the integration of these approaches with detailed models based on ordinary differential equations (ODEs).Results: We developed an approach to modeling the dynamic behavior of metabolic, regulatory and signaling networks by combining FBA with regulatory Boolean logic, and ordinary differential equations. We use this approach (called integrated FBA, or iFBA) to create an integrated model of Escherichia coli which combines a flux-balance-based, central carbon metabolic and transcriptional regulatory model with an ODE-based, detailed model of carbohydrate uptake control. We compare the predicted Escherichia coli wild-type and single gene perturbation phenotypes for diauxic growth on glucose/lactose and glucose/glucose-6-phosphate with that of the individual models. We find that iFBA encapsulates the dynamics of three internal metabolites and three transporters inadequately predicted by rFBA. Furthermore, we find that iFBA predicts different and more accurate phenotypes than the ODE model for 85 of 334 single gene perturbation simulations, as well for the wild-type simulations. We conclude that iFBA is a significant improvement over the individual rFBA and ODE modeling paradigms.Availability: All MATLAB files used in this study are available at http://www.simtk.org/home/ifba/.Contact: covert@stanford.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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Ohno, Satoshi, Saori Uematsu, and Shinya Kuroda. "Quantitative metabolic fluxes regulated by trans-omic networks." Biochemical Journal 479, no. 6 (March 31, 2022): 787–804. http://dx.doi.org/10.1042/bcj20210596.

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Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.

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Zorrilla, Francisco, Filip Buric, Kiran R. Patil, and Aleksej Zelezniak. "metaGEM: reconstruction of genome scale metabolic models directly from metagenomes." Nucleic Acids Research 49, no. 21 (October 6, 2021): e126-e126. http://dx.doi.org/10.1093/nar/gkab815.

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Abstract Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

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Karlsen, Emil, Marianne Gylseth, Christian Schulz, and Eivind Almaas. "A study of a diauxic growth experiment using an expanded dynamic flux balance framework." PLOS ONE 18, no. 1 (January 6, 2023): e0280077. http://dx.doi.org/10.1371/journal.pone.0280077.

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Flux balance analysis (FBA) remains one of the most used methods for modeling the entirety of cellular metabolism, and a range of applications and extensions based on the FBA framework have been generated. Dynamic flux balance analysis (dFBA), the expansion of FBA into the time domain, still has issues regarding accessibility limiting its widespread adoption and application, such as a lack of a consistently rigid formalism and tools that can be applied without expert knowledge. Recent work has combined dFBA with enzyme-constrained flux balance analysis (decFBA), which has been shown to greatly improve accuracy in the comparison of computational simulations and experimental data, but such approaches generally do not take into account the fact that altering the enzyme composition of a cell is not an instantaneous process. Here, we have developed a decFBA method that explicitly takes enzyme change constraints (ecc) into account, decFBAecc. The resulting software is a simple yet flexible framework for using genome-scale metabolic modeling for simulations in the time domain that has full interoperability with the COBRA Toolbox 3.0. To assess the quality of the computational predictions of decFBAecc, we conducted a diauxic growth fermentation experiment with Escherichia coli BW25113 in glucose minimal M9 medium. The comparison of experimental data with dFBA, decFBA and decFBAecc predictions demonstrates how systematic analyses within a fixed constraint-based framework can aid the study of model parameters. Finally, in explaining experimentally observed phenotypes, our computational analysis demonstrates the importance of non-linear dependence of exchange fluxes on medium metabolite concentrations and the non-instantaneous change in enzyme composition, effects of which have not previously been accounted for in constraint-based analysis.

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Ramakrishna, Ramprasad, Jeremy S. Edwards, Andrew McCulloch, and Bernhard O. Palsson. "Flux-balance analysis of mitochondrial energy metabolism: consequences of systemic stoichiometric constraints." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 280, no. 3 (March 1, 2001): R695—R704. http://dx.doi.org/10.1152/ajpregu.2001.280.3.r695.

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Mitochondrial metabolism is a critical component in the functioning and maintenance of cellular organs. The stoichiometry of biochemical reaction networks imposes constraints on mitochondrial function. A modeling framework, flux-balance analysis (FBA), was used to characterize the optimal flux distributions for maximal ATP production in the mitochondrion. The model predicted the expected ATP yields for glucose, lactate, and palmitate. Genetic defects that affect mitochondrial functions have been implicated in several human diseases. FBA can characterize the metabolic behavior due to genetic deletions at the metabolic level, and the effect of mutations in the tricarboxylic acid (TCA) cycle on mitochondrial ATP production was simulated. The mitochondrial ATP production is severely affected by TCA-cycle mutations. In addition, the model predicts the secretion of TCA-cycle intermediates, which is observed in clinical studies of mitochondriopathies such as those associated with fumarase deficiency. The model provides a systemic perspective to characterize the effect of stoichiometric constraints and specific metabolic fluxes on mitochondrial function.

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Olivier, Brett G., and Frank T. Bergmann. "The Systems Biology Markup Language (SBML) Level 3 Package: Flux Balance Constraints." Journal of Integrative Bioinformatics 12, no. 2 (June 1, 2015): 660–90. http://dx.doi.org/10.1515/jib-2015-269.

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Summary Constraint-based modeling is a well established modelling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size, genome scale models are typically analysed using constraint-based optimization techniques. One widely used method is Flux Balance Analysis (FBA) which, for example, requires a modelling description to include: the definition of a stoichiometric matrix, an objective function and bounds on the values that fluxes can obtain at steady state.The Flux Balance Constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modelling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. The FBC package establishes a base level for the unambiguous exchange of genome-scale, constraint-based models, that can be built upon by the community to meet future needs (e. g. by extending it to cover dynamic FBC models).

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Ravi, Sudharshan, and Rudiyanto Gunawan. "ΔFBA—Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data." PLOS Computational Biology 17, no. 11 (November 10, 2021): e1009589. http://dx.doi.org/10.1371/journal.pcbi.1009589.

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Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences.

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Sarkar, Debolina, Marine Landa, Anindita Bandyopadhyay, Himadri B. Pakrasi, Jonathan P. Zehr, and Costas D. Maranas. "Elucidation of trophic interactions in an unusual single-cell nitrogen-fixing symbiosis using metabolic modeling." PLOS Computational Biology 17, no. 5 (May 7, 2021): e1008983. http://dx.doi.org/10.1371/journal.pcbi.1008983.

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Marine nitrogen-fixing microorganisms are an important source of fixed nitrogen in oceanic ecosystems. The colonial cyanobacterium Trichodesmium and diatom symbionts were thought to be the primary contributors to oceanic N2 fixation until the discovery of the unusual uncultivated symbiotic cyanobacterium UCYN-A (Candidatus Atelocyanobacterium thalassa). UCYN-A has atypical metabolic characteristics lacking the oxygen-evolving photosystem II, the tricarboxylic acid cycle, the carbon-fixation enzyme RuBisCo and de novo biosynthetic pathways for a number of amino acids and nucleotides. Therefore, it is obligately symbiotic with its single-celled haptophyte algal host. UCYN-A receives fixed carbon from its host and returns fixed nitrogen, but further insights into this symbiosis are precluded by both UCYN-A and its host being uncultured. In order to investigate how this syntrophy is coordinated, we reconstructed bottom-up genome-scale metabolic models of UCYN-A and its algal partner to explore possible trophic scenarios, focusing on nitrogen fixation and biomass synthesis. Since both partners are uncultivated and only the genome sequence of UCYN-A is available, we used the phylogenetically related Chrysochromulina tobin as a proxy for the host. Through the use of flux balance analysis (FBA), we determined the minimal set of metabolites and biochemical functions that must be shared between the two organisms to ensure viability and growth. We quantitatively investigated the metabolic characteristics that facilitate daytime N2 fixation in UCYN-A and possible oxygen-scavenging mechanisms needed to create an anaerobic environment to allow nitrogenase to function. This is the first application of an FBA framework to examine the tight metabolic coupling between uncultivated microbes in marine symbiotic communities and provides a roadmap for future efforts focusing on such specialized systems.

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Basharat, Zarrin, Kanwal Khan, Khurshid Jalal, Sulaiman Mohammed Alnasser, Sania Majeed, and Marium Zehra. "Inferring Therapeutic Targets in Candida albicans and Possible Inhibition through Natural Products: A Binding and Physiological Based Pharmacokinetics Snapshot." Life 12, no. 11 (October 30, 2022): 1743. http://dx.doi.org/10.3390/life12111743.

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Despite being responsible for invasive infections, fungal pathogens have been underrepresented in computer aided therapeutic target mining and drug design. Excess of Candida albicans causes candidiasis, causative of thrush and vaginal infection due to off-balance. In this study, we attempted to mine drug targets (n = 46) using a subtractive proteomic approach in this pathogenic yeast and screen natural products with inhibition potential against fructose-bisphosphate aldolase (FBA) of the C. albicans. The top compound selected on the basis of best docking score from traditional Indian medicine/Ayurvedic library was (4-Hydroxybenzyl)thiocarbamic acid, from the ZINC FBA inhibitor library was ZINC13507461 (IUPAC name: [(2R)-2-hydroxy-3-phosphonooxypropyl] (9E,12E)-octadeca-9,12-dienoate), and from traditional Tibetan medicine/Sowa rigpa was Chelerythrine (IUPAC name: 1,2-Dimethoxy-12-methyl-9H-[1,3]benzodioxolo[5,6-c]phenanthridin-12-ium), compared to the control (2E)-1-(4-nitrophenyl)-2-[(4-nitrophenyl)methylidene]hydrazine. No Ames toxicity was predicted for prioritized compounds while control depicted this toxicity. (4-Hydroxybenzyl)thiocarbamic acid showed hepatotoxicity, while Chelerythrine depicted hERG inhibition, which can lead to QT syndrome, so we recommend ZINC13507461 for further testing in lab. Pharmacological based pharmacokinetic modeling revealed that it has low bioavailability and hence, absorption in healthy state. In cirrhosis and renal impairment, absorption and plasma accumulation increased so we recommend further investigation into this occurrence and recommend high dosage in further tests to increase bioavailability.

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Koblitz, Julia, Sabine Will, S. Riemer, Thomas Ulas, Meina Neumann-Schaal, and Dietmar Schomburg. "The Metano Modeling Toolbox MMTB: An Intuitive, Web-Based Toolbox Introduced by Two Use Cases." Metabolites 11, no. 2 (February 17, 2021): 113. http://dx.doi.org/10.3390/metabo11020113.

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Genome-scale metabolic models are of high interest in a number of different research fields. Flux balance analysis (FBA) and other mathematical methods allow the prediction of the steady-state behavior of metabolic networks under different environmental conditions. However, many existing applications for flux optimizations do not provide a metabolite-centric view on fluxes. Metano is a standalone, open-source toolbox for the analysis and refinement of metabolic models. While flux distributions in metabolic networks are predominantly analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization also allow a metabolite-centric view on flux distributions. In addition, we present MMTB (Metano Modeling Toolbox), a web-based toolbox for metabolic modeling including a user-friendly interface to Metano methods. MMTB assists during bottom-up construction of metabolic models by integrating reaction and enzymatic annotation data from different databases. Furthermore, MMTB is especially designed for non-experienced users by providing an intuitive interface to the most commonly used modeling methods and offering novel visualizations. Additionally, MMTB allows users to upload their models, which can in turn be explored and analyzed by the community. We introduce MMTB by two use cases, involving a published model of Corynebacterium glutamicum and a newly created model of Phaeobacter inhibens.

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Zoledowska, Sabina, Luana Presta, Marco Fondi, Francesca Decorosi, Luciana Giovannetti, Alessio Mengoni, and Ewa Lojkowska. "Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria." Microorganisms 7, no. 4 (April 5, 2019): 101. http://dx.doi.org/10.3390/microorganisms7040101.

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Understanding plant–microbe interactions is crucial for improving plants’ productivity and protection. Constraint-based metabolic modeling is one of the possible ways to investigate the bacterial adaptation to different ecological niches and may give insights into the metabolic versatility of plant pathogenic bacteria. We reconstructed a raw metabolic model of the emerging plant pathogenic bacterium Pectobacterium parmentieri SCC3193 with the use of KBase. The model was curated by using inParanoind and phenotypic data generated with the use of the OmniLog system. Metabolic modeling was performed through COBRApy Toolbox v. 0.10.1. The curated metabolic model of P. parmentieri SCC3193 is highly reliable, as in silico obtained results overlapped up to 91% with experimental data on carbon utilization phenotypes. By mean of flux balance analysis (FBA), we predicted the metabolic adaptation of P. parmentieri SCC3193 to two different ecological niches, relevant for the persistence and plant colonization by this bacterium: soil and the rhizosphere. We performed in silico gene deletions to predict the set of essential core genes for this bacterium to grow in such environments. We anticipate that our metabolic model will be a valuable element for defining a set of metabolic targets to control infection and spreading of this plant pathogen.

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DE, RAJAT K., and NAMRATA TOMAR. "MODELING THE OPTIMAL CENTRAL CARBON METABOLIC PATHWAYS UNDER FEEDBACK INHIBITION USING FLUX BALANCE ANALYSIS." Journal of Bioinformatics and Computational Biology 10, no. 06 (October 18, 2012): 1250019. http://dx.doi.org/10.1142/s0219720012500199.

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Metabolism is a complex process for energy production for cellular activity. It consists of a cascade of reactions that form a highly branched network in which the product of one reaction is the reactant of the next reaction. Metabolic pathways efficiently produce maximal amount of biomass while maintaining a steady-state behavior. The steady-state activity of such biochemical pathways necessarily incorporates feedback inhibition of the enzymes. This observation motivates us to incorporate feedback inhibition for modeling the optimal activity of metabolic pathways using flux balance analysis (FBA). We demonstrate the effectiveness of the methodology on a synthetic pathway with and without feedback inhibition. Similarly, for the first time, the Central Carbon Metabolic (CCM) pathways of Saccharomyces cerevisiae and Homo sapiens have been modeled and compared based on the above understanding. The optimal pathway, which maximizes the amount of the target product(s), is selected from all those obtained by the proposed method. For this, we have observed the concentration of the product inhibited enzymes of CCM pathway and its influence on its corresponding metabolite/substrate. We have also studied the concentration of the enzymes which are responsible for the synthesis of target products. We further hypothesize that an optimal pathway would opt for higher flux rate reactions. In light of these observations, we can say that an optimal pathway should have lower enzyme concentration and higher flux rates. Finally, we demonstrate the superiority of the proposed method by comparing it with the extreme pathway analysis.

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Isarankura Na Ayudhya, Nattapat, Kobkul Laoteng, Yuanda Song, Asawin Meechai, and Wanwipa Vongsangnak. "Metabolic traits specific for lipid-overproducing strain of Mucor circinelloides WJ11 identified by genome-scale modeling approach." PeerJ 7 (June 7, 2019): e7015. http://dx.doi.org/10.7717/peerj.7015.

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The genome-scale metabolic model of a lipid-overproducing strain of Mucor circinelloides WJ11 was developed. The model (iNI1159) contained 1,159 genes, 648 EC numbers, 1,537 metabolites, and 1,355 metabolic reactions, which were localized in different compartments of the cell. Using flux balance analysis (FBA), the iNI1159 model was validated by predicting the specific growth rate. The metabolic traits investigated by phenotypic phase plane analysis (PhPP) showed a relationship between the nutrient uptake rate, cell growth, and the triacylglycerol production rate, demonstrating the strength of the model. A putative set of metabolic reactions affecting the lipid-accumulation process was identified when the metabolic flux distributions under nitrogen-limited conditions were altered by performing fast flux variability analysis (fastFVA) and relative flux change. Comparative analysis of the metabolic models of the lipid-overproducing strain WJ11 (iNI1159) and the reference strain CBS277.49 (iWV1213) using both fastFVA and coordinate hit-and-run with rounding (CHRR) showed that the flux distributions between these two models were significantly different. Notably, a higher flux distribution through lipid metabolisms such as lanosterol, zymosterol, glycerolipid and fatty acids biosynthesis in iNI1159 was observed, leading to an increased lipid production when compared to iWV1213. In contrast, iWV1213 exhibited a higher flux distribution across carbohydrate and amino acid metabolisms and thus generated a high flux for biomass production. This study demonstrated that iNI1159 is an effective predictive tool for the pathway engineering of oleaginous strains for the production of diversified oleochemicals with industrial relevance.

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Panikov, Nicolai S. "Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges." Microorganisms 9, no. 11 (November 14, 2021): 2352. http://dx.doi.org/10.3390/microorganisms9112352.

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This review is a part of the SI ‘Genome-Scale Modeling of Microorganisms in the Real World’. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life behaviors of microorganisms, such as cell proliferation, dormancy, and mortality; balanced and unbalanced growth; steady-state and transient processes; primary and secondary metabolism; stress responses; etc. Constraint-based metabolic reconstructions were successfully started two decades ago as FBA, followed by more advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances. The most essential deficiencies are: (i) an inadequate account of environmental conditions, such as various degrees of nutrients limitation and other factors shaping phenotypes; (ii) a failure to simulate the adaptive changes of MMCC (MacroMolecular Cell Composition) in response to the fluctuating environment; (iii) the misinterpretation of the SGR (Specific Growth Rate) as either a fixed constant parameter of the model or independent factor affecting the conditional expression of macromolecules; (iv) neglecting stress resistance as an important objective function; and (v) inefficient experimental verification of GEM against simple growth (constant MMCC and SGR) data. Finally, we propose several ways to improve GEMs, such as replacing the outdated Monod equation with the SCM (Synthetic Chemostat Model) that establishes the quantitative relationships between primary and secondary metabolism, growth rate and stress resistance, process kinetics, and cell composition.

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Simeonidis, Evangelos, Ettore Murabito, Kieran Smallbone, and Hans V. Westerhoff. "Why does yeast ferment? A flux balance analysis study." Biochemical Society Transactions 38, no. 5 (September 24, 2010): 1225–29. http://dx.doi.org/10.1042/bst0381225.

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Advances in biological techniques have led to the availability of genome-scale metabolic reconstructions for yeast. The size and complexity of such networks impose limits on what types of analyses one can perform. Constraint-based modelling overcomes some of these restrictions by using physicochemical constraints to describe the potential behaviour of an organism. FBA (flux balance analysis) highlights flux patterns through a network that serves to achieve a particular objective and requires a minimal amount of data to make quantitative inferences about network behaviour. Even though FBA is a powerful tool for system predictions, its general formulation sometimes results in unrealistic flux patterns. A typical example is fermentation in yeast: ethanol is produced during aerobic growth in excess glucose, but this pattern is not present in a typical FBA solution. In the present paper, we examine the issue of yeast fermentation against respiration during growth. We have studied a number of hypotheses from the modelling perspective, and novel formulations of the FBA approach have been tested. By making the observation that more respiration requires the synthesis of more mitochondria, an energy cost related to mitochondrial synthesis is added to the FBA formulation. Results, although still approximate, are closer to experimental observations than earlier FBA analyses, at least on the issue of fermentation.

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Henson, Michael A. "Genome-scale modelling of microbial metabolism with temporal and spatial resolution." Biochemical Society Transactions 43, no. 6 (November 27, 2015): 1164–71. http://dx.doi.org/10.1042/bst20150146.

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Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated.

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Parikh, Kiran B., and Tushar M. Patel. "Parametric Optimization of Fin-Tube Type Evaporator Using FEA-DOE Hybrid Modeling." Indian Journal of Applied Research 3, no. 8 (October 1, 2011): 242–45. http://dx.doi.org/10.15373/2249555x/aug2013/78.

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Ankit D. Oza, Ankit D. Oza, and Prof Tushar M. Patel Prof. Tushar M. Patel. "Parametric Optimization of Gravity Die Casting Process Using FEA-DOE Hybrid Modeling." Indian Journal of Applied Research 3, no. 7 (October 1, 2011): 193–98. http://dx.doi.org/10.15373/2249555x/july2013/60.

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Gottstein, Willi, Brett G. Olivier, Frank J. Bruggeman, and Bas Teusink. "Constraint-based stoichiometric modelling from single organisms to microbial communities." Journal of The Royal Society Interface 13, no. 124 (November 2016): 20160627. http://dx.doi.org/10.1098/rsif.2016.0627.

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Microbial communities are ubiquitously found in Nature and have direct implications for the environment, human health and biotechnology. The species composition and overall function of microbial communities are largely shaped by metabolic interactions such as competition for resources and cross-feeding. Although considerable scientific progress has been made towards mapping and modelling species-level metabolism, elucidating the metabolic exchanges between microorganisms and steering the community dynamics remain an enormous scientific challenge. In view of the complexity, computational models of microbial communities are essential to obtain systems-level understanding of ecosystem functioning. This review discusses the applications and limitations of constraint-based stoichiometric modelling tools, and in particular flux balance analysis (FBA). We explain this approach from first principles and identify the challenges one faces when extending it to communities, and discuss the approaches used in the field in view of these challenges. We distinguish between steady-state and dynamic FBA approaches extended to communities. We conclude that much progress has been made, but many of the challenges are still open.

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Fleming, A. E., D. Dalley, R. H. Bryant, G. R. Edwards, and P. Gregorini. "Modelling feeding strategies to improve milk production, rumen function and discomfort of the early lactation dairy cow supplemented with fodder beet." Journal of Agricultural Science 158, no. 4 (May 2020): 313–25. http://dx.doi.org/10.1017/s0021859620000593.

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AbstractFeeding fodder beet (FB) to dairy cows in early lactation has recently been adopted by New Zealand dairy producers despite limited definition of feeding and grazing management practices that may prevent acute and sub-acute ruminal acidosis (SARA). This modelling study aimed to characterize changes of rumen pH, milk production and total discomfort from FB and define practical feeding strategies of a mixed herbage and FB diet. The deterministic, dynamic and mechanistic model MINDY was used to compare a factorial arrangement of FB allowance (FBA), herbage allowance (HA) and time of allocation. The FBA were 0, 2, 4 or 7 kg dry matter (DM)/cow/day (0FB, 2FB, 4FB and 7FB, respectively) and HA were 18, 24 or 48 kg DM/cow/day above ground. All combinations were offered either in the morning or afternoon or split across two equal meals. Milk production from 2FB diets was similar to 0FB but declined by 4 and 16% when FB increased to 4 and 7 kg DM, respectively. MINDY predicted that 7FB would result in SARA and that rumen conditions were sub-optimal even at moderate FBA (pH < 5.6 for 160 and 90 min/day, 7FB and 4FB respectively). Pareto front analysis identified the best compromise between high milk production and low total discomfort was achieved by splitting the 2FB diet into two equal meals fed each day with 48 kg DM herbage. However, due to low milk response and high risk of acidosis, it is concluded that FB is a poor supplement for lactating dairy cows.

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Kostareva, Olga, Ilya Kolyadenko, Andrey Ulitin, Victoria Ekimova, Stanislav Evdokimov, Maria Garber, Svetlana Tishchenko, and Azat Gabdulkhakov. "Fab Fragment of VHH-Based Antibody Netakimab: Crystal Structure and Modeling Interaction with Cytokine IL-17A." Crystals 9, no. 3 (March 26, 2019): 177. http://dx.doi.org/10.3390/cryst9030177.

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Interleukin 17A (IL-17A) is a proinflammatory cytokine produced by Th17 cells. Antibody BCD-085 (netakimab) against human IL-17A is one of the new inhibitors of this cytokine. In netakimab, the VH domain is replaced by the VHH domain of Lama glama possessing a long complementarity determining region (CDR-H3) in its heavy chain. Here we demonstrate the high affinity of IL-17A to the Fab fragment of netakimab and to its integral part, the VHH domain. We have determined the crystal structure of the Fab fragment of netakimab at 1.9 Å resolution. High variability in the orientation of light and heavy chains of the Fab fragment of netakimab was shown, which is determined by the peculiarity of the structural organization of the CDR-H3. As the high conformational plasticity of the molecule hampers modeling the Fab fragment of netakimab complexed to IL-17A, we have carried out modeling the complex between the antigen and the integral part of the Fab fragment, the VHH domain. We explain the high netakimab Fab fragment affinity for IL-17A by a large number of protein–protein contacts due to additional interactions between CDR-H3 and the cytokine dimer.

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Sørensen, Megan E. S., Duncan D. Cameron, Michael A. Brockhurst, and A. Jamie Wood. "Metabolic constraints for a novel symbiosis." Royal Society Open Science 3, no. 3 (March 2016): 150708. http://dx.doi.org/10.1098/rsos.150708.

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Ancient evolutionary events are difficult to study because their current products are derived forms altered by millions of years of adaptation. The primary endosymbiotic event formed the first photosynthetic eukaryote resulting in both plants and algae, with vast consequences for life on Earth. The evolutionary time that passed since this event means the dominant mechanisms and changes that were required are obscured. Synthetic symbioses such as the novel interaction between Paramecium bursaria and the cyanobacterium Synechocystis PC6803, recently established in the laboratory, permit a unique window on the possible early trajectories of this critical evolutionary event. Here, we apply metabolic modelling, using flux balance analysis (FBA), to predict the metabolic adaptations necessary for this previously free-living symbiont to transition to the endosymbiotic niche. By enforcing reciprocal nutrient trading, we are able to predict the most efficient exchange nutrients for both host and symbiont. During the transition from free-living to obligate symbiosis, it is likely that the trading parameters will change over time, which leads in our model to discontinuous changes in the preferred exchange nutrients. Our results show the applicability of FBA modelling to ancient evolutionary transitions driven by metabolic exchanges, and predict how newly established endosymbioses, governed by conflict, will differ from a well-developed one that has reached a mutual-benefit state.

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Servadio, Michela, Louk J. M. J. Vanderschuren, and Viviana Trezza. "Modeling autism-relevant behavioral phenotypes in rats and mice." Behavioural Pharmacology 26, no. 6 (September 2015): 522–40. http://dx.doi.org/10.1097/fbp.0000000000000163.

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Sanabria, Federico, Jazmin I. Acosta, Peter R. Killeen, Janet L. Neisewander, and Lewis A. Bizo. "Modeling the effects of fluoxetine on food-reinforced behavior." Behavioural Pharmacology 19, no. 1 (February 2008): 61–70. http://dx.doi.org/10.1097/fbp.0b013e3282f3df9b.

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Kruger, Nicholas J., and R. George Ratcliffe. "Fluxes through plant metabolic networks: measurements, predictions, insights and challenges." Biochemical Journal 465, no. 1 (December 12, 2014): 27–38. http://dx.doi.org/10.1042/bj20140984.

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Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

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Labhsetwar, Piyush, Marcelo C. R. Melo, John A. Cole, and Zaida Luthey-Schulten. "Population FBA predicts metabolic phenotypes in yeast." PLOS Computational Biology 13, no. 9 (September 8, 2017): e1005728. http://dx.doi.org/10.1371/journal.pcbi.1005728.

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Sufka, Kenneth J., Matthew W. Feltenstein, Jason E. Warnick, Edmund O. Acevedo, Heather E. Webb, and Courtney M. Cartwright. "Modeling the anxiety???depression continuum hypothesis in domestic fowl chicks." Behavioural Pharmacology 17, no. 8 (December 2006): 681–89. http://dx.doi.org/10.1097/fbp.0b013e3280115fac.

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Bouchon, P., and D. L. Pyle. "Modelling Oil Absorption During Post-Frying Cooling." Food and Bioproducts Processing 83, no. 4 (December 2005): 261–72. http://dx.doi.org/10.1205/fbp.05114.

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Bouchon, P., and D. L. Pyle. "Modelling Oil Absorption During Post-Frying Cooling." Food and Bioproducts Processing 83, no. 4 (December 2005): 253–60. http://dx.doi.org/10.1205/fbp.05115.

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Wachutka, Gerhard. "Tailored modeling: A way to the 'virtual microtransducer fab'?" Sensors and Actuators A: Physical 47, no. 1-3 (March 1995): 603–12. http://dx.doi.org/10.1016/0924-4247(94)00971-j.

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Bagchi, Sugato, Robert J. Baseman, Andrew Davenport, Ramesh Natarajan, Noam Slonim, and Sholom Weiss. "Data analytics and stochastic modeling in a semiconductor fab." Applied Stochastic Models in Business and Industry 26, no. 1 (January 2010): 1–27. http://dx.doi.org/10.1002/asmb.828.

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Gao, Chao, Yang Zou, Jie Zhou, Yan Liu, Wenjuan Liu, Yao Cai, and Chengliang Sun. "Influence of Etching Trench on Keff2 of Film Bulk Acoustic Resonator." Micromachines 13, no. 1 (January 8, 2022): 102. http://dx.doi.org/10.3390/mi13010102.

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As radio-frequency (RF) communication becomes more ubiquitous globally, film bulk acoustic resonators (FBAR) have attracted great attention for their superior performance. One of the key parameters of an FBAR, the effective electromechanical coupling coefficient (Keff2), has a great influence on the bandwidth of RF filters. In this work, we propose a feasible method to tune the Keff2 of the FBAR by etching the piezoelectric material to form a trench around the active area of the FBAR. The influence of the position of the etching trench on the Keff2 of the FBAR was investigated by 3D finite element modeling and experimental fabricating. Meanwhile, a theoretical electrical model was presented to test and verify the simulated and measured results. The Keff2 of the FBAR tended to be reduced when the distance between the edge of the top electrode and the edge of the trench was increased, but the Q value of the FBAR was not degraded. This work provides a new possibility for tuning the Keff2 of resonators to meet the requirements of different filter bandwidths.

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AJAM NOROUZI, Hosein, and Farshid VAZIN. "Modelling of the Faba bean (Vicia faba L.) Sprouting Reaction to Temperature in Farm Condition." Notulae Botanicae Horti Agrobotanici Cluj-Napoca 39, no. 2 (November 21, 2011): 179. http://dx.doi.org/10.15835/nbha3926198.

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The main goal of this study is to developa Faba bean plant sprouting model that includes a cardinal temperature determination and a required biologic day for Faba bean sprouting ( or to determine the Faba bean sprouting reaction to temperature). For this purpose, in a farm test, 4 cultivars of Faba bean plants (Barekat, Saraziri, Eraghi and Gavi) were cultivated on 11 sowing dates (one in each month ) and the required days to their sprouting were recorded. Beta, curve, quadratic and Sawtooth models were used to describe relationship between the sprouting rate and the temperature . To select the best model, it has been used an average root mean square deviation (RMSD), an R square (R2), simple linear regression coefficients (a and b) and a correlation coefficient (r). The results showed that the Faba bean sprouting reaction to temperature was better described by a Piecewise function. The estimation of the cardinal temperature by a Piecewise function indicated that the base temperature varies from 0.98C to 1.61C, and the optimum temperature varies from 24.99C to 28.8C for the different cultivars of Faba bean plants, but the ceiling temperature for all the cultivars was estimated at 35 degree centigrade. Also, the required biologic day estimation for sprouting as determined by the Piecewise model showed a significant difference between the cultivars, as the Gavi cultivars (which are smaller) germinated after 6.65 days (maximum rate is 0.09). For the other cultivars, the required biologic day fell between 8.60 and 9.31 days. The obtained data can be used to predict Faba bean sprouting in different temperature conditions.

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Schwartz, Jean-Marc, Michael Barber, and Zita Soons. "Metabolic flux prediction in cancer cells with altered substrate uptake." Biochemical Society Transactions 43, no. 6 (November 27, 2015): 1177–81. http://dx.doi.org/10.1042/bst20150149.

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Proliferating cells, such as cancer cells, are known to have an unusual metabolism, characterized by an increased rate of glycolysis and amino acid metabolism. Our understanding of this phenomenon is limited but could potentially be used in order to develop new therapies. Computational modelling techniques, such as flux balance analysis (FBA), have been used to predict fluxes in various cell types, but remain of limited use to explain the unusual metabolic shifts and altered substrate uptake in human cancer cells. We implemented a new flux prediction method based on elementary modes (EMs) and structural flux (StruF) analysis and tested them against experimentally measured flux data obtained from 13C-labelling in a cancer cell line. We assessed the quality of predictions using different objective functions along with different techniques in normalizing a metabolic network with more than one substrate input. Results show a good correlation between predicted and experimental values and indicate that the choice of cellular objective critically affects the quality of predictions. In particular, lactate gives an excellent correlation and correctly predicts the high flux through glycolysis, matching the observed characteristics of cancer cells. In contrast with FBA, which requires a priori definition of all uptake rates, often hard to measure, atomic StruFs (aStruFs) are able to predict uptake rates of multiple substrates.

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杨, 仁建. "Time/Space Separation Based FOA-Elman Modeling for Non-Linear Distributed Parameter Processes—Spatial-Temporal Modeling." Computer Science and Application 09, no. 02 (2019): 328–38. http://dx.doi.org/10.12677/csa.2019.92038.

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Athimulam, A., S. Kumaresan, D. C. Y. Foo, M. R. Sarmidi, and R. A. Aziz. "Modelling and Optimization of Eurycoma longifolia Water Extract Production." Food and Bioproducts Processing 84, no. 2 (June 2006): 139–49. http://dx.doi.org/10.1205/fbp.06004.

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Shaw, Emma, and Victor Sparrow. "Modeling acoustic impedance and atmospheric absorption around airports using high-fidelity weather data." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 264, no. 1 (June 24, 2022): 104–10. http://dx.doi.org/10.3397/nc-2022-698.

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It is known that environmental factors have an impact on the propagation of noise and as such, the integration of high-fidelity meteorological data into noise modeling programs has given hope for improved predictions of outdoor noise level, particularly in the field of aircraft noise. Through the use of these high-fidelity weather data sets taken over a specific region surrounding an airport, there is an ability to calculate adjusted coefficients for the acoustic impedance and atmospheric absorption of the environment in external programs. These new calculations offer insight into whether more formal noise modeling programs will benefit from the use of these high-fidelity meteorological files and how the accuracy of future aircraft noise predictions will be affected. [This research was funded by the U.S. Federal Aviation Administration Office of Environment and Energy through ASCENT, the FAA Center of Excellence for Alternative Jet Fuels and the Environment, project 62 through FAA Award Number 13-C-AJFE-PSU under the supervision of Chris Hobbs. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA.]

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Cornetta, Gianluca, Francisco Javier Mateos, Abdellah Touhafi, and Gabriel-Miro Muntean. "Modelling and Simulation of a Cloud Platform for Sharing Distributed Digital Fabrication Resources." Computers 8, no. 2 (June 12, 2019): 47. http://dx.doi.org/10.3390/computers8020047.

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Fabrication as a Service (FaaS) is a new concept developed within the framework of the NEWTON Horizon 2020 project. It is aimed at empowering digital fabrication laboratories (Fab Labs) by providing hardware and software wrappers to expose numerically-controlled expensive fabrication equipment as web services. More specifically, FaaS leverages cloud and IoT technologies to enable a wide learning community to have remote access to these labs’ computer-controlled tools and equipment over the Internet. In such context, the fabrication machines can be seen as networked resources distributed over a wide geographical area. These resources can communicate through machine-to-machine protocols and a centralized cloud infrastructure and can be digitally monitored and controlled through programmatic interfaces relying on REST APIs. This paper introduces FaaS in the context of Fab Lab challenges and describes FaaS deployment within NEWTON Fab Labs, part of the NEWTON European Horizon 2020 project on technology enhanced learning. The NEWTON Fab Labs architecture is described in detail targeting software, hardware and network architecture. The system has been extensively load-tested simulating real use-case scenarios and it is presently in production. In particular, this paper shows how the measured data has been used to build a simulation model to estimate system performance and identify possible bottlenecks. The measurements performed show that the platform delays exhibit a tail distribution with Pareto-like behaviour; this finding has been used to build a simple mathematical model and a simulator on top of CloudSim to estimate the latencies of the critical paths of the NEWTON Fab Lab platform under several load conditions.

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He, Feng Lan, Ming Dong, and Zhi Gang Wu. "Continuous Modeling of Re-Entrant Manufacturing Systems." Advanced Materials Research 148-149 (October 2010): 595–600. http://dx.doi.org/10.4028/www.scientific.net/amr.148-149.595.

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This paper presents a continuous modeling approach for multi-product re-entrant manufacturing systems. First, a generic continuous model for single-product re-entrant manufacturing systems is elaborated. Second, the concept of re-entrant factor is introduced to the model, which considers the influences of the re-entrant degree. Then, the continuous model is extended from single-product re-entrant manufacturing systems to multi-product re-entrant manufacturing systems in which the product priority is also taken into consideration. Finally, the proposed continuous model is validated through the Mini-Fab case study.

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Tsang, K. H., N. J. Samsatli, and N. Shah. "Modelling and Planning Optimization of a Complex Flu Vaccine Facility." Food and Bioproducts Processing 84, no. 2 (June 2006): 123–34. http://dx.doi.org/10.1205/fbp.05001.

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Chen, James C., Shu Jen Hu, Yu Hsin Chen, C. L. Yang, Cheng Ju Sun, and C. W. Chen. "Facility Planning for TFT-LCD Array Fab." Key Engineering Materials 419-420 (October 2009): 641–44. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.641.

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Facility planning is crucial to the performance of array fabs in Thin Film Transistor - Liquid Crystal Display (TFT-LCD) industry. In order to avoid costly changes and modifications of fab layouts at the installation or production stages, the designers should carefully evaluate design alternatives and then select the best one during the design stage. A TFT-LCD manufacturing process consists of four basic processes: array, color filter, cell, and module. This research proposes an Array Fab Design Procedure (AFDP) to conduct quick calculations to develop and evaluate initial design alternatives for TFT-LCD array fabs. A series of practical formulae are presented to sequentially determine the related design parameters. The proposed AFDP provides a basis for rapid modeling and evaluation of array fab designs. AFDP is developed in Microsoft Visual Basic and data from real array fabs are used to demonstrate its effectiveness and efficiency. Results indicate that AFDP can quickly calculate the related fab design parameters. “Job shop with small bays” leads to the least average sheet moving distance. With AFDP as a tool, fab designers can evaluate design alternatives and conduct what-if analysis in the initial phase of fab design.

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Juchem, Jasper, Amélie Chevalier, Kevin Dekemele, and Mia Loccufier. "First order plus fractional diffusive delay modeling: Interconnected discrete systems." Fractional Calculus and Applied Analysis 24, no. 5 (October 1, 2021): 1535–58. http://dx.doi.org/10.1515/fca-2021-0064.

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Abstract This paper presents a novel First Order Plus Fractional Diffusive Delay (FOPFDD) model, capable of modeling delay dominant systems with high accuracy. The novelty of the FOPFDD is the Fractional Diffusive Delay (FDD) term, an exponential delay of non-integer order α, i.e. e −(Ls) α in Laplace domain. The special cases of α = 0.5 and α = 1 have already been investigated thoroughly. In this work α is generalized to any real number in the interval ]0, 1[. For α = 0.5, this term appears in the solution of distributed diffusion systems, which will serve as a source of inspiration for this work. Both frequency and time domain are investigated. However, regarding the latter, no closed-form expression of the inverse Laplace transform of the FDD can be found for all α, so numerical tools are used to obtain an impulse response of the FDD. To establish the algorithm, several properties of the FDD term have been proven: firstly, existence of the term, secondly, invariance of the time integral of the impulse response, and thirdly, dependency of the impulse response’s energy on α. To conclude, the FOPFDD model is fitted to several delay-dominant, diffusive-like resistors-capacitors (RC) circuits to show the increased modeling accuracy compared to other state-of-the-art models found in literature. The FOPFDD model outperforms the other approximation models in accurately tracking frequency response functions as well as in mimicking the peculiar delay/diffusive-like time responses, coming from the interconnection of a large number of discrete subsystems. The fractional character of the FOPFDD makes it an ideal candidate for an approximate model to these large and complex systems with only a few parameters.

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Villacampa, Yolanda, Francisco José Navarro-González, Gabriela Hernández, Juan Laddaga, and Adriana Confalone. "Modelling Faba Bean (Vicia faba L.) Biomass Production for Sustainability of Agricultural Systems of Pampas." Sustainability 12, no. 23 (November 24, 2020): 9829. http://dx.doi.org/10.3390/su12239829.

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The Pampas region is characterized by a high complexity in its productive system planning and faces the challenge of satisfying future food demands, as well as reducing the environmental impact of the activity. Climate change affects crops and farmers should use species capable of adapting to the changed climate. Among these species, faba bean (Vicia faba L.) cv. ‘Alameda’ has shown good adaptation to weather variability and, as a winter legume, it can help maintain the sustainability of agricultural systems in the area. The main purpose of this research was to select the models which describe the production characteristics of the ‘Alameda’ bean by using the least number of variables. Experimental and agrometeorological data from the cultivation of the ‘Alameda’ in Azul, Buenos Aires province, Argentina were used to generate mathematical models. Several modelling methodologies have been applied to study the production characteristics of the faba bean. The prediction of the models generated was analyzed by randomly disturbing the experimental data and analyzing the magnitude of the errors produced. The models obtained will be useful for predicting the biomass production of the faba bean cv. ‘Alameda’ grown in the agroclimatic conditions of Azul, Buenos Aires province, Argentina.

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Hwee, Chia Jing, Farouq Jamil, Akhmal Sidek, Zaidi Jaafar, Radzuan Junin, Shaziera Omar, Fadzlin Hasani, Aizuddin Supee, and Mohd Hariri Arifin. "Subsurface geological model of Malay basin using free air anomaly." IOP Conference Series: Earth and Environmental Science 880, no. 1 (October 1, 2021): 012007. http://dx.doi.org/10.1088/1755-1315/880/1/012007.

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Abstract The aim of gravity survey is to assist in the detection and delineation of subsurface geological features such as salt domes and faults. In this study, free air anomaly (FAA) data was adopted for mapping and modelling process to delineate subsurface geological features and basement depth in Malay Basin. FAA is the measured gravity anomaly after a free air correction is applied, and it is used for elevation correction. The data of FAA in this study is obtained from Earth Gravitational Model (EGM) 2008 released by the National Geospatial-Intelligence Agency (NGA)-EGM Development Team. Oasis Montaj software was used in the mapping and modelling process whereby the base map which constructed by the Oasis Montaj is used to form the FAA map of Malay Basin. Typically, the positive anomaly is associated with the high-density intrusion at the base of the crust, while in contrast (negative anomaly), it is related to the sedimentary basin in the upper crust. On top of that, the regional-residual anomaly, total horizontal derivative (THD) and 3D Euler Deconvolution enhanced maps were produced and interpreted to acquire comprehensive insight of subsurface geological features. To conclude, this study showed 5% deviation as compared to previous reported works and the deepest basement depth encountered is 14.5 km.

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Moreira, R., F. Chenlo, L. Chaguri, and G. Vázquez. "Mathematical Modelling of the Drying Kinetics of Chestnut (Castanea Sativa Mill.)." Food and Bioproducts Processing 83, no. 4 (December 2005): 306–14. http://dx.doi.org/10.1205/fbp.05035.

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Gu, Yiqun, John W. Crawford, D. Ramanee Peiris, Cornelius Grashoff, James W. McNicol, and Bruce Marshall. "Modelling faba bean production in an uncertain future climate." Agricultural and Forest Meteorology 79, no. 4 (May 1996): 289–300. http://dx.doi.org/10.1016/0168-1923(95)02285-6.

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Barletta, M., A. Gisario, and S. Guarino. "Modelling of Fluidized Bed Degreasing (FBD) process by ANNs." International Journal of Surface Science and Engineering 2, no. 3/4 (2008): 294. http://dx.doi.org/10.1504/ijsurfse.2008.020500.

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Journal articles on the topic 'FBA modeling'

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