scholarly journals Towards scaling elementary flux mode computation

2019 ◽  
Vol 21 (6) ◽  
pp. 1875-1885
Author(s):  
Ehsan Ullah ◽  
Mona Yosafshahi ◽  
Soha Hassoun
Keyword(s):  
Pathway Analysis ◽  
Alternative Pathway ◽  
Solution Space ◽  
Computational Technique ◽  
Elementary Flux Mode ◽  
Scale Models ◽  
Network Compression ◽  
Genome Scale ◽  
The Impact ◽  
Flux Mode

Abstract While elementary flux mode (EFM) analysis is now recognized as a cornerstone computational technique for cellular pathway analysis and engineering, EFM application to genome-scale models remains computationally prohibitive. This article provides a review of aspects of EFM computation that elucidates bottlenecks in scaling EFM computation. First, algorithms for computing EFMs are reviewed. Next, the impact of redundant constraints, sensitivity to constraint ordering and network compression are evaluated. Then, the advantages and limitations of recent parallelization and GPU-based efforts are highlighted. The article then reviews alternative pathway analysis approaches that aim to reduce the EFM solution space. Despite advances in EFM computation, our review concludes that continued scaling of EFM computation is necessary to apply EFM to genome-scale models. Further, our review concludes that pathway analysis methods that target specific pathway properties can provide powerful alternatives to EFM analysis.

scholarly journals Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 331 ◽  
Author(s):  
Zhuangrong Huang ◽  
Seongkyu Yoon
Keyword(s):  
Cell Lines ◽  
Chinese Hamster ◽  
Parental Cell ◽  
Process Conditions ◽  
Parental Cell Line ◽  
Specific Cell ◽  
Scale Models ◽  
Metabolic Models ◽  
Genome Scale ◽  
The Impact

Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines.

scholarly journals TEX-FBA: A constraint-based method for integrating gene expression, thermodynamics, and metabolomics data into genome-scale metabolic models

2019 ◽  
Author(s):  
Vikash Pandey ◽  
Daniel Hernandez Gardiol ◽  
Anush Chiappino-Pepe ◽  
Vassily Hatzimanikatis
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Solution Space ◽  
Metabolic Model ◽  
Scale Models ◽  
Different Types ◽  
Transcriptomics Data ◽  
Metabolic Reactions ◽  
Metabolic Models ◽  
Genome Scale

AbstractA large number of genome-scale models of cellular metabolism are available for various organisms. These models include all known metabolic reactions based on the genome annotation. However, the reactions that are active are dependent on the cellular metabolic function or environmental condition. Constraint-based methods that integrate condition-specific transcriptomics data into models have been used extensively to investigate condition-specific metabolism. Here, we present a method (TEX-FBA) for modeling condition-specific metabolism that combines transcriptomics and reaction thermodynamics data to generate a thermodynamically-feasible condition-specific metabolic model. TEX-FBA is an extension of thermodynamic-based flux balance analysis (TFA), which allows the simultaneous integration of different stages of experimental data (e.g., absolute gene expression, metabolite concentrations, thermodynamic data, and fluxomics) and the identification of alternative metabolic states that maximize consistency between gene expression levels and condition-specific reaction fluxes. We applied TEX-FBA to a genome-scale metabolic model ofEscherichia coliby integrating available condition-specific experimental data and found a marked reduction in the flux solution space. Our analysis revealed a marked correlation between actual gene expression profile and experimental flux measurements compared to the one obtained from a randomly generated gene expression profile. We identified additional essential reactions from the membrane lipid and folate metabolism when we integrated transcriptomics data of the given condition on the top of metabolomics and thermodynamics data. These results show TEX-FBA is a promising new approach to study condition-specific metabolism when different types of experimental data are available.Author summaryCells utilize nutrients via biochemical reactions that are controlled by enzymes and synthesize required compounds for their survival and growth. Genome-scale models of metabolism representing these complex reaction networks have been reconstructed for a wide variety of organisms ranging from bacteria to human cells. These models comprise all possible biochemical reactions in a cell, but cells choose only a subset of reactions for their immediate needs and functions. Usually, these models allow for a large flux solution space and one can integrate experimental data in order to reduce it and potentially predict the physiology for a specific condition. We developed a method for integrating different types of omics data, such as fluxomics, transcriptomics, metabolomics into genome-scale metabolic models that reduces the flux solution space. Using gene expression data, the algorithm maximizes the consistency between the predicted and experimental flux for the reactions and predicts biologically relevant flux ranges for the remaining reactions in the network. This method is useful for determining fluxes of metabolic reactions with reduced uncertainty and suitable for performing context- and condition-specific analysis in metabolic models using different types of experimental data.

scholarly journals Inspecting the Solution Space of Genome-Scale Metabolic Models

Metabolites ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 43
Author(s):  
Seyed Babak Loghmani ◽  
Nadine Veith ◽  
Sven Sahle ◽  
Frank T. Bergmann ◽  
Brett G. Olivier ◽  
...  
Keyword(s):  
Solution Space ◽  
Flux Variability Analysis ◽  
New Approach ◽  
Complementary Method ◽  
Kinetic Information ◽  
Integrative View ◽  
Scale Models ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Genome Scale

Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results.

scholarly journals Revealing the Metabolic Alterations during Biofilm Development of Burkholderia cenocepacia Based on Genome-Scale Metabolic Modeling

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 221
Author(s):  
Ozlem Altay ◽  
Cheng Zhang ◽  
Hasan Turkez ◽  
Jens Nielsen ◽  
Mathias Uhlén ◽  
...  
Keyword(s):  
Systems Biology ◽  
Alternative Pathway ◽  
Growth Conditions ◽  
Pentose Phosphate ◽  
Burkholderia Cenocepacia ◽  
Bacterial Cells ◽  
Metabolic Remodeling ◽  
Transcriptomics Data ◽  
Biofilm Development ◽  
Genome Scale

Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that integrate experimental transcriptomics data and genome-scale metabolic models (GEMs). Here, we integrated transcriptomics data of bacterial cells grown on exponential and biofilm conditions into a manually curated GEM of B. cenocepacia. We observed substantial differences in pathway response to different growth conditions and alternative pathway susceptibility to extracellular nutrient availability. For instance, we found that blockage of the reactions was vital through the lipid biosynthesis pathways in the exponential phase and the absence of microenvironmental lysine and tryptophan are essential for survival. During biofilm development, bacteria mostly had conserved lipid metabolism but altered pathway activities associated with several amino acids and pentose phosphate pathways. Furthermore, conversion of serine to pyruvate and 2,5-dioxopentanoate synthesis are also identified as potential targets for metabolic remodeling during biofilm development. Altogether, our integrative systems biology analysis revealed the interactions between the bacteria and its microenvironment and enabled the discovery of antimicrobial targets for biofilm-related diseases.

scholarly journals Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

2021 ◽  
Vol 11 (5) ◽  
pp. 2175
Author(s):  
Oscar Danilo Montoya ◽  
Walter Gil-González ◽  
Jesus C. Hernández
Keyword(s):  
Objective Function ◽  
Reactive Power ◽  
Programming Model ◽  
Search Algorithm ◽  
Solution Space ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Solution Vector ◽  
The Impact

The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.

Biodynamic Modeling of a Pedestrian Impact With a Rigid Frontal Guard of a Utility Vehicle

2010 ◽  
Author(s):  
Rasoul Moradi ◽  
Chandrashekhar K. Thorbole ◽  
Michael McCoy ◽  
Hamid M. Lankarani
Keyword(s):  
Front Surface ◽  
Body Region ◽  
Computational Technique ◽  
Surface Model ◽  
Published Data ◽  
Factors Affecting ◽  
Light Trucks ◽  
Accident Data ◽  
Facet Surface ◽  
The Impact

Accident data reveals that in most pedestrian accidents, the pedestrian head and lower extremity are vulnerable to serious injuries. The vehicle front geometry profile as well as the impact speed are important factors affecting the pedestrian kinematics and injury potential. In the US, accident data also shows that the fatality rate for pedestrian/light trucks and vans (LTV) impact is greater than that for the pedestrian/passenger-car impact. Addition of a front guard on light trucks and sports utility vehicles to mitigate damage during off-road activity or to provide mounting points for extra lights, makes the pedestrian more vulnerable to the impact. In this paper, a computational technique is utilized to study the influence of the added front guard on the impacted pedestrian. A CAD model of a typical commercial frontal guard is developed and converted into a rigid facet model, and attached to the vehicle front. The validated standing dummy model in the MADYMO code is used to simulate a pedestrian, and the rigid facet-surface model of a pickup truck is used to generate a vehicle front surface. This computational model is validated by comparing the pedestrian kinematics with the published data. This study demonstrates that the pedestrian mid body region is more vulnerable with the addition of guard on the vehicle. The result from this study facilitates a better understanding of a guard design and its geometry profile as required to protect vulnerable road users.

scholarly journals Cofactors revisited – Predicting the impact of flavoprotein-related diseases on a genome scale

2019 ◽  
Vol 1865 (2) ◽  
pp. 360-370 ◽  
Author(s):  
Agnieszka B. Wegrzyn ◽  
Sarah Stolle ◽  
Rienk A. Rienksma ◽  
Vítor A.P. Martins dos Santos ◽  
Barbara M. Bakker ◽  
...  
Keyword(s):  
A Genome ◽  
Genome Scale ◽  
The Impact

scholarly journals Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Neeraj Sinha ◽  
Evert M. van Schothorst ◽  
Guido J. E. J. Hooiveld ◽  
Jaap Keijer ◽  
Vitor A. P. Martins dos Santos ◽  
...  
Keyword(s):  
Metabolic Flux ◽  
Transcript Levels ◽  
Proportionality Constant ◽  
Model Predictions ◽  
Physiological Information ◽  
Highly Expressed Genes ◽  
Transcriptomics Data ◽  
Genome Scale ◽  
The Impact ◽  
Constraint Based Models

Abstract Background Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values. Results Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes. Conclusion We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.

scholarly journals Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

2020 ◽  
Author(s):  
Colton J. Lloyd ◽  
Jonathan Monk ◽  
Laurence Yang ◽  
Ali Ebrahim ◽  
Bernhard O. Palsson
Keyword(s):  
Amino Acids ◽  
Growth Conditions ◽  
Anaerobic Growth ◽  
Metabolic Phenotype ◽  
E Coli ◽  
Scale Models ◽  
Protein Components ◽  
K 12 ◽  
Genome Scale ◽  
Prosthetic Groups

AbstractSustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

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