scholarly journals On the impact of biomass composition in constraint-based flux analysis

2019 ◽  
Author(s):  
Meiyappan Lakshmanan ◽  
Sichang Long ◽  
Kok Siong Ang ◽  
Nathan Lewis ◽  
Dong-Yup Lee
Keyword(s):  
In Silico ◽  
Relevant Information ◽  
Biomass Composition ◽  
Flux Analysis ◽  
Biomass Equation ◽  
Highly Sensitive ◽  
Biomass Components ◽  
Genome Scale ◽  
The Impact ◽  
Macromolecular Composition

ABSTRACTThe biomass equation is a critical component in genome-scale metabolic models (GEMs): It is one of most widely used objective functions within constraint-based flux analysis formulation, describing cellular driving force under the growth condition. The equation accounts for the quantities of all known biomass precursors that are required for cell growth. Most often than not, published GEMs have adopted relevant information from other species to derive the biomass equation when any of the macromolecular composition is unavailable. Thus, its validity is still questionable. Here, we investigated the qualitative and quantitative aspects of biomass equations from GEMs of eight different yeast species. Expectedly, most yeast GEMs borrowed macromolecular compositions from the model yeast, Saccharomyces cerevisiae. We further confirmed that the biomass compositions could be markedly different even between phylogenetically closer species and none of the high throughput omics data such as genome, transcriptome and proteome provided a good estimate of relative amino acid abundances. Upon varying the stoichiometric coefficients of biomass components, subsequent flux simulations demonstrated how predicted in silico growth rates change with the carbon substrates used. Furthermore, the internal fluxes through individual reactions are highly sensitive to all components in the biomass equation. Overall, the current analysis clearly highlight that biomass equation need to be carefully drafted from relevant experiments, and the in silico simulation results should be appropriately interpreted to avoid any inaccuracies.

scholarly journals Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Javad Aminian-Dehkordi ◽  
Seyyed Mohammad Mousavi ◽  
Arezou Jafari ◽  
Ivan Mijakovic ◽  
Sayed-Amir Marashi
Keyword(s):  
Experimental Data ◽  
Metabolic Network ◽  
Bacillus Megaterium ◽  
In Silico ◽  
Metabolic Model ◽  
Growth Behavior ◽  
Industrial Biotechnology ◽  
Flux Analysis ◽  
A Genome ◽  
Genome Scale

AbstractBacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

scholarly journals Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an in silico Trial

2021 ◽  
pp. 193229682110123
Author(s):  
Chiara Roversi ◽  
Martina Vettoretti ◽  
Simone Del Favero ◽  
Andrea Facchinetti ◽  
Pratik Choudhary ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
In Silico ◽  
Open Loop ◽  
Random Errors ◽  
Linear Regression Models ◽  
Reference Case ◽  
Counting Error ◽  
The Impact

Background: In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control. Methods: The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes. Results: Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD ( R2>0.95), with slopes of [Formula: see text], [Formula: see text] for ∆TIR, [Formula: see text], [Formula: see text] for ∆TAR, and [Formula: see text], [Formula: see text] for ∆TBR. Conclusions: The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

Insight into the impact of EGFR L792Y/F/H mutations on sensitivity to osimertinib: an in silico study

2021 ◽  
Vol 45 (10) ◽  
pp. 4756-4765
Author(s):  
Daoxing Chen ◽  
Liting Zhang ◽  
Yanan Liu ◽  
Jiali Song ◽  
Jingwen Guo ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Study ◽  
The Impact ◽  
Insight Into

EGFR L792Y/F/H mutation makes it difficult for Osimertinib to recognize ATP pockets.

scholarly journals Personality and Dementia: an Approach to Differential Profile of the Caregiver

2011 ◽  
Vol 1 (3) ◽  
pp. 85-94
Author(s):  
Elena de Andrés-Jiménez ◽  
Rosa Mª Limiñana-Gras ◽  
Encarna Fernández-Ros
Keyword(s):  
Relevant Information ◽  
Personality Profile ◽  
Family Carers ◽  
Psychological Variables ◽  
People With Dementia ◽  
The Family ◽  
Study Population ◽  
The One ◽  
The Impact

The aim of this study is to determine the existence of a characteristic personality profile of family carers of people with dementia. The correct knowledge and use of psychological variables which affect the carer, helps to promote appropriate actions to mitigate the impact of care and improve the carer’s quality of life and likewise the one of the person cared for. The study population consists of 69 family carers of people with dementia, members of various associations and care centers. The results allow us to identify a characteristic personality profile for these carers and it reveals a specific psychological working in this sample, although we cannot directly relate it with the tasks of caring for people with this disease, this profile gives us very relevant information to pay more attention to the needs of this group. Moreover, the analysis of personality styles depends on the sex of the family carer, showing, once again, that the woman is in a situation of most vulnerability.

scholarly journals Common Treatment, Common Variant: Evolutionary Prediction of Functional Pharmacogenomic Variants

2021 ◽  
Vol 11 (2) ◽  
pp. 131
Author(s):  
Laura B. Scheinfeldt ◽  
Andrew Brangan ◽  
Dara M. Kusic ◽  
Sudhir Kumar ◽  
Neda Gharani
Keyword(s):  
In Silico ◽  
Drug Efficacy ◽  
Common Variant ◽  
Genomic Research ◽  
Whole Genome Sequencing Data ◽  
Mendelian Disease ◽  
Allele Frequency Distribution ◽  
Sequencing Data ◽  
Patient Race ◽  
The Impact

Pharmacogenomics holds the promise of personalized drug efficacy optimization and drug toxicity minimization. Much of the research conducted to date, however, suffers from an ascertainment bias towards European participants. Here, we leverage publicly available, whole genome sequencing data collected from global populations, evolutionary characteristics, and annotated protein features to construct a new in silico machine learning pharmacogenetic identification method called XGB-PGX. When applied to pharmacogenetic data, XGB-PGX outperformed all existing prediction methods and identified over 2000 new pharmacogenetic variants. While there are modest pharmacogenetic allele frequency distribution differences across global population samples, the most striking distinction is between the relatively rare putatively neutral pharmacogene variants and the relatively common established and newly predicted functional pharamacogenetic variants. Our findings therefore support a focus on individual patient pharmacogenetic testing rather than on clinical presumptions about patient race, ethnicity, or ancestral geographic residence. We further encourage more attention be given to the impact of common variation on drug response and propose a new ‘common treatment, common variant’ perspective for pharmacogenetic prediction that is distinct from the types of variation that underlie complex and Mendelian disease. XGB-PGX has identified many new pharmacovariants that are present across all global communities; however, communities that have been underrepresented in genomic research are likely to benefit the most from XGB-PGX’s in silico predictions.

scholarly journals Interphotoreceptor coupling: an evolutionary perspective

2021 ◽  
Author(s):  
Lorenzo Cangiano ◽  
Sabrina Asteriti
Keyword(s):  
Visual Information ◽  
Signal To Noise Ratio ◽  
Electrical Coupling ◽  
Physiological Role ◽  
Cellular Processes ◽  
Contact Points ◽  
Highly Sensitive ◽  
The Many ◽  
The Impact

AbstractIn the vertebrate retina, signals generated by cones of different spectral preference and by highly sensitive rod photoreceptors interact at various levels to extract salient visual information. The first opportunity for such interaction is offered by electrical coupling of the photoreceptors themselves, which is mediated by gap junctions located at the contact points of specialised cellular processes: synaptic terminals, telodendria and radial fins. Here, we examine the evolutionary pressures for and against interphotoreceptor coupling, which are likely to have shaped how coupling is deployed in different species. The impact of coupling on signal to noise ratio, spatial acuity, contrast sensitivity, absolute and increment threshold, retinal signal flow and colour discrimination is discussed while emphasising available data from a variety of vertebrate models spanning from lampreys to primates. We highlight the many gaps in our knowledge, persisting discrepancies in the literature, as well as some major unanswered questions on the actual extent and physiological role of cone-cone, rod-cone and rod-rod communication. Lastly, we point toward limited but intriguing evidence suggestive of the ancestral form of coupling among ciliary photoreceptors.

scholarly journals Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

2012 ◽  
Vol 78 (24) ◽  
pp. 8735-8742 ◽  
Author(s):  
Yilin Fang ◽  
Michael J. Wilkins ◽  
Steven B. Yabusaki ◽  
Mary S. Lipton ◽  
Philip E. Long
Keyword(s):  
Proteomic Analysis ◽  
In Silico ◽  
Field Experiments ◽  
Metabolic Model ◽  
Geobacter Metallireducens ◽  
Content Type ◽  
Proteomic Data ◽  
Subsurface Environment ◽  
A Genome ◽  
Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

scholarly journals A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Omid Oftadeh ◽  
Pierre Salvy ◽  
Maria Masid ◽  
Maxime Curvat ◽  
Ljubisa Miskovic ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Expression System ◽  
Biomass Composition ◽  
Industrial Biotechnology ◽  
Overflow Metabolism ◽  
Cellular Expression ◽  
A Genome ◽  
Wide Range ◽  
Eukaryotic Organisms ◽  
Genome Scale

AbstractEukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.

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