检索结果-内蒙古大学图书馆

Optimization of a modeling platform to predict oncogenes from genome-scale metabolic networks of non-small-cell lung cancers

引用

FEBS OPEN BIO 2021年第8期11卷 2078-2094页

作者： Wang, You-Tyun Lin, Min-Ru Chen, Wei-Chen Wu, Wu-Hsiung Wang, Feng-Sheng Natl Chung Cheng Univ Dept Chem Engn Chiayi Taiwan

Cancer cell dysregulations result in the abnormal regulation of cellular metabolic pathways. By simulating this metabolic reprogramming using constraint-based modeling approaches, oncogenes can be predicted, and this knowledge can be used in prognosis and treatment. We introduced a trilevel optimization problem describing metabolic reprogramming for inferring oncogenes. First, this study used RNA-Seq expression data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples and their healthy counterparts to reconstruct tissue-specific genome-scale metabolic models and subsequently build the flux distribution pattern that provided a measure for the oncogene inference optimization problem for determining tumorigenesis. The platform detected 45 genes for LUAD and 84 genes for LUSC that lead to tumorigenesis. A high level of differentially expressed genes was not an essential factor for determining tumorigenesis. The platform indicated that pyruvate kinase (PKM), a well-known oncogene with a low level of differential gene expression in LUAD and LUSC, had the highest fitness among the predicted oncogenes based on computation. By contrast, pyruvate kinase L/R (PKLR), an isozyme of PKM, had a high level of differential gene expression in both cancers. Phosphatidylserine synthase 1 (PTDSS1), an oncogene in LUAD, was inferred to have a low level of differential gene expression, and overexpression could significantly reduce survival probability. According to the factor analysis, PTDSS1 characteristics were close to those of the template, but they were unobvious in LUSC. Angiotensin-converting enzyme 2 (ACE2) has recently garnered widespread interest as the SARS-CoV-2 virus receptor. Moreover, we determined that ACE2 is an oncogene of LUSC but not of LUAD. The platform developed in this study can identify oncogenes with low levels of differential expression and be used to identify potential therapeutic targets for cancer treatment.

关键词： cancer cell metabolism constraint-based modeling flux balance analysis tissue-specific metabolic models trilevel optimization

来源：评论

学校读者我要写书评

暂无评论

Longitudinal flux balance analyses of a patient with episodic colonic inflammation reveals microbiome metabolic dynamics

引用

GUT MICROBES 2023年第1期15卷 2226921页

作者： Basile, Arianna Heinken, Almut Hertel, Johannes Smarr, Larry Li, Weizhong Treu, Laura Valle, Giorgio Campanaro, Stefano Thiele, Ines Univ Padua Dept Biol Via U Bassi 58-B I-35121 Padua Italy Univ Galway Sch Med Galway Ireland Univ Galway Ryan Inst Galway Ireland Univ Med Greifswald Dept Psychiat & Psychotherapy Greifswald Germany Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA USA J Craig Venter Inst La Jolla CA USA Univ Galway Discipline Microbiol Galway Ireland Univ Coll Cork APC Microbiome Ireland Cork Ireland

We report the first use of constraint-based microbial community modeling on a single individual with episodic inflammation of the gastrointestinal tract, who has a well documented set of colonic inflammatory biomarkers, as well as metagenomically-sequenced fecal time series covering seven dates over 16 months. Between the first two time steps the individual was treated with both steroids and antibiotics. Our methodology enabled us to identify numerous time-correlated microbial species and metabolites. We found that the individual's dynamical microbial ecology in the disease state led to time-varying in silico overproduction, compared to healthy controls, of more than 24 biologically important metabolites, including methane, thiamine, formaldehyde, trimethylamine N-oxide, folic acid, serotonin, histamine, and tryptamine. The microbe-metabolite contribution analysis revealed that some Dialister species changed metabolic pathways according to the inflammation phases. At the first time point, characterized by the highest levels of serum (complex reactive protein) and fecal (calprotectin) inflammation biomarkers, they produced L-serine or formate. The production of the compounds, through a cascade effect, was mediated by the interaction with pathogenic Escherichia coli strains and Desulfovibrio piger. We integrated the microbial community metabolic models of each time point with a male whole-body, organ-resolved model of human metabolism to track the metabolic consequences of dysbiosis at different body sites. The presence of D. piger in the gut microbiome influenced the sulfur metabolism with a domino effect affecting the liver. These results revealed large longitudinal variations in an individual's gut microbiome ecology and metabolite production, potentially impacting other organs in the body. Future simulations with more time points from an individual could permit us to assess how external drivers, such as diet change or medical interventions, drive microbial communi

关键词： Flux balance analysis longitudinal data inflammatory bowel diseases colonic inflammation metagenomics microbiota metabolic modeling bioinformatics constraint-based modeling personalized medicine

来源：评论

学校读者我要写书评

暂无评论

Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis

引用

BMC BIOINFORMATICS 2022年第1期23卷 226-226页

作者： Thanamit, Kulwadee Hoerhold, Franziska Oswald, Marcus Koenig, Rainer Jena Univ Hosp Inst Infect Dis & Infect Control IIMK Syst Biol Res Grp Kollegiengasse 10 D-07743 Jena Germany

Background Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by C-13 tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes. Result We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with C-13 tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods. Conclusion Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tra

关键词： Flux balance analysis Mixed-integer linear programming Bacillus subtilis Carbon source Transcriptomics constraint-based modeling Thermodynamically infeasible loops

来源：评论

学校读者我要写书评

暂无评论

SplineLearner: Generative learning system of design constraints for models represented using B-spline surfaces

引用

ADVANCED ENGINEERING INFORMATICS 2022年第0期51卷 101478-101478页

作者： Tasmektepligil, A. Alper Gunpinar, Erkan Istanbul Tech Univ Istanbul Turkey Yildiz Tech Univ Istanbul Turkey

Product design involves a computer-aided design (CAD) model with its design (dimensional) parameters. A generative design (GD) system can then be utilized to generate new designs by modifying these parameters. There is a need for a GD system to determine the visual validity of a design that is obtained after parametric modification. In this context, this paper introduces an approach to learn visual (i.e., design) constraints of a CAD model (represented using B-spline surfaces) by means of user feedbacks. A deformation technique (utilizing modification and limit curves) for B-spline surfaces is first introduced, which involves a few design (deformation) parameters. Via a generative learning process, the proposed system, SplineLearner, generates random designs, which are shown to user(s) for visual validity classifications. In a machine learning step, a mathematical model is computed that can perform prediction for a design to be valid or not. The mathematical model is also integrated into SplineLearner (after some user interactions) to prevent imbalances between the numbers of valid and invalid designs. As a proof of concept, B-spline surface models of a car body parts (hood, roof, side and trunk) are utilized, and two user studies are conducted to demonstrate the efficacy of the proposed method.

关键词： B-spline surface Design constraints Generative design Computer-aided design constraint-based modeling Machine learning

来源：评论

学校读者我要写书评

暂无评论

Human/SARS-CoV-2 genome-scale metabolic modeling to discover potential antiviral targets for COVID-19

引用

JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2022年 133卷 104273-104273页

作者： Wang, Feng-Sheng Chen, Ke-Lin Chu, Sz-Wei Natl Chung Cheng Univ Dept Chem Engn Chiayi 621301 Taiwan

Background: Coronavirus disease 2019 (COVID-19) has caused a substantial increase in mortality and economic and social disruption. The absence of US Food and Drug Administration -approved drugs for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlights the need for new therapeutic drugs to combat COVID-19. Methods: The present study proposed a fuzzy hierarchical optimization framework for identifying potential antiviral targets for COVID-19. The objectives in the decision-making problem were not only to evaluate the elimination of the virus growth, but also to minimize side effects causing treatment. The identified candidate targets could promote processes of drug discovery and development. Significant findings: Our gene-centric method revealed that dihydroorotate dehydrogenase (DHODH) inhibition could reduce viral biomass growth and metabolic deviation by 99.4% and 65.6%, respectively, and increase cell viability by 70.4%. We also identified two-target combinations that could completely block viral biomass growth and more effectively prevent metabolic deviation. We also discovered that the inhibition of two antiviral metabolites, cytidine triphosphate (CTP) and uridine-50-triphosphate (UTP), exhibits effects similar to those of molnupiravir, which is undergoing phase III clinical trials. Our predictions also indicate that CTP and UTP inhibition blocks viral RNA replication through a similar mechanism to that of molnupiravir. (c) 2022 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Flux balance analysis constraint-based modeling Computer-aided drug discovery Evolutionary optimization Bioprocess systems engineering Fuzzy optimization

来源：评论

学校读者我要写书评

暂无评论

Characterization of maximal enzyme catalytic rates in central metabolism ofArabidopsis thaliana

引用

PLANT JOURNAL 2020年第6期103卷 2168-2177页

作者： Kueken, Anika Gennermann, Kristin Nikoloski, Zoran Max Planck Inst Mol Plant Physiol Syst Biol & Math Modeling Grp Muhlenberg 1 Potsdam Germany Univ Potsdam Bioinformat Grp Inst Biochem & Biol Karl Liebknecht Str 24-25 Potsdam Germany

Availability of plant-specific enzyme kinetic data is scarce, limiting the predictive power of metabolic models and precluding identification of genetic factors of enzyme properties. Enzyme kinetic data are measuredin vitro, often under non-physiological conditions, and conclusions elicited from modeling warrant caution. Here we estimate maximalin vivocatalytic rates for 168 plant enzymes, including photosystems I and II, cytochrome-b6f complex, ATP-citrate synthase, sucrose-phosphate synthase as well as enzymes from amino acid synthesis with previously undocumented enzyme kinetic data in BRENDA. The estimations are obtained by integrating condition-specific quantitative proteomics data, maximal rates of selected enzymes, growth measurements fromArabidopsis thalianarosette with and fluxes through canonical pathways in a constraint-based model of leaf metabolism. In comparison to findings inEscherichia coli, we demonstrate weaker concordance between the plant-specificin vitroandin vivoenzyme catalytic rates due to a low degree of enzyme saturation. This is supported by the finding that concentrations of nicotinamide adenine dinucleotide (phosphate), adenosine triphosphate and uridine triphosphate, calculated based on our maximalin vivocatalytic rates, and available quantitative metabolomics data are below reportedKMvalues and, therefore, indicate undersaturation of respective enzymes. Our findings show that genome-wide profiling of enzyme kinetic properties is feasible in plants, paving the way for understanding resource allocation.

关键词： Arabidopsis thaliana constraint-based modeling enzyme catalytic rates kinetic parameter metabolic network turnover number

来源：评论

学校读者我要写书评

暂无评论

μBialSim: constraint-based Dynamic Simulation of Complex Microbiomes

引用

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2020年 8卷 574页

作者： Popp, Denny Centler, Florian UFZ Helmholtz Ctr Environm Res Dept Environm Microbiol Leipzig Germany

Microbial communities are pervasive in the natural environment, associated with many hosts, and of increasing importance in biotechnological applications. The complexity of these microbial systems makes the underlying mechanisms driving their dynamics difficult to identify. While experimental meta-OMICS techniques are routinely applied to record the inventory and activity of microbiomes over time, it remains difficult to obtain quantitative predictions based on such data. Mechanistic, quantitative mathematical modeling approaches hold the promise to both provide predictive power and shed light on cause-effect relationships driving these dynamic systems. We introduce mu bialSim (pronounced "microbial sim"), a dynamic Flux-Balance-Analysis-based (dFBA) numerical simulator which is able to predict the time course in terms of composition and activity of microbiomes containing 100s of species in batch or chemostat mode. Activity of individual species is simulated by using separate FBA models which have access to a common pool of compounds, allowing for metabolite exchange. A novel augmented forward Euler method ensures numerical accuracy by temporarily reducing the time step size when compound concentrations decrease rapidly due to high compound affinities and/or the presence of many consuming species. We present three exemplary applications of mu bialSim: a batch culture of a hydrogenotrophic archaeon, a syntrophic methanogenic biculture, and a 773-species human gut microbiome which exhibits a complex and dynamic pattern of metabolite exchange. Focusing on metabolite exchange as the main interaction type, mu bialSim allows for the mechanistic simulation of microbiomes at their natural complexity. Simulated trajectories can be used to contextualize experimental meta-OMICS data and to derive hypotheses on cause-effect relationships driving community dynamics based on scenario simulations. mu bialSim is implemented in Matlab and relies on the COBRA Toolbox or CellNetAnalyz

关键词： microbial communities metabolic modeling constraint-based modeling cross-feeding microbiome dynamics

来源：评论

学校读者我要写书评

暂无评论

Yeast has evolved to minimize protein resource cost for synthesizing amino acids

引用

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2022年第4期119卷 e2114622119-e2114622119页

作者： Chen, Yu Nielsen, Jens Chalmers Univ Technol Dept Biol & Biol Engn SE-41296 Gothenburg Sweden BioInnovat Inst DK-2200 Copenhagen N Denmark

Proteins, as essential biomolecules, account for a large fraction of cell mass, and thus the synthesis of the complete set of proteins (i.e., the proteome) represents a substantial part of the cellular resource budget. Therefore, cells might be under selective pressures to optimize the resource costs for protein synthesis, particularly the biosynthesis of the 20 proteinogenic amino acids. Previous studies showed that less energetically costly amino acids are more abundant in the proteomes of bacteria that survive under energy-limited conditions, but the energy cost of synthesizing amino acids was reported to be weakly associated with the amino acid usage in Saccharomyces cerevisiae. Here we present a modeling framework to estimate the protein cost of synthesizing each amino acid (i.e., the protein mass required for supporting one unit of amino acid biosynthetic flux) and the glucose cost (i.e., the glucose consumed per amino acid synthesized). We show that the logarithms of the relative abundances of amino acids in S. cerevisiae's proteome correlate well with the protein costs of synthesizing amino acids (Pearson's r = -0.89), which is better than that with the glucose costs (Pearson's r = -0.5). Therefore, we demonstrate that S. cerevisiae tends to minimize protein resource, rather than glucose or energy, for synthesizing amino acids.

关键词： amino acid biosynthetic cost constraint-based modeling metabolic engineering proteome constraint Saccharomyces cerevisiae

来源：评论

学校读者我要写书评

暂无评论

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

引用

FEMS YEAST RESEARCH 2022年第1期22卷 foac003-foac003页

作者： Chen, Yu Li, Feiran Nielsen, Jens Chalmers Univ Technol Dept Biol & Biol Engn SE-41296 Gothenburg Sweden BioInnovat Inst DK-2200 Copenhagen N Denmark

Historical development, recent applications, current status and future directions of yeast genome-scale metabolic models. Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.

关键词： constraint-based modeling genome-scale metabolic model metabolic engineering proteome constraints

来源：评论

学校读者我要写书评

暂无评论

Speeding up the core algorithm for the dual calculation of minimal cut sets in large metabolic networks

引用

BMC BIOINFORMATICS 2020年第1期21卷 510-510页

作者： Klamt, Steffen Mahadevan, Radhakrishnan von Kamp, Axel Max Planck Inst Dynam Complex Tech Syst Sandtorstr 1 D-39106 Magdeburg Germany Univ Toronto Dept Chem Engn & Appl Chem 200 Coll St Toronto ON M5S 3E5 Canada

Background The concept of minimal cut sets (MCS) has become an important mathematical framework for analyzing and (re)designing metabolic networks. However, the calculation of MCS in genome-scale metabolic models is a complex computational problem. The development of duality-based algorithms in the last years allowed the enumeration of thousands of MCS in genome-scale networks by solving mixed-integer linear problems (MILP). A recent advancement in this field was the introduction of the MCS2 approach. In contrast to the Farkas-lemma-based dual system used in earlier studies, the MCS2 approach employs a more condensed representation of the dual system based on the nullspace of the stoichiometric matrix, which, due to its reduced dimension, holds promise to further enhance MCS computations. Results In this work, we introduce several new variants and modifications of duality-based MCS algorithms and benchmark their effects on the overall performance. As one major result, we generalize the original MCS2 approach (which was limited to blocking the operation of certain target reactions) to the most general case of MCS computations with arbitrary target and desired regions. Building upon these developments, we introduce a new MILP variant which allows maximal flexibility in the formulation of MCS problems and fully leverages the reduced size of the nullspace-based dual system. With a comprehensive set of benchmarks, we show that the MILP with the nullspace-based dual system outperforms the MILP with the Farkas-lemma-based dual system speeding up MCS computation with an averaged factor of approximately 2.5. We furthermore present several simplifications in the formulation of constraints, mainly related to binary variables, which further enhance the performance of MCS-related MILP. However, the benchmarks also reveal that some highly condensed formulations of constraints, especially on reversible reactions, may lead to worse behavior when compared to variants with a larger n

关键词： constraint-based modeling Stoichiometric modeling Metabolic networks Metabolic engineering Computational strain design Duality Elementary modes

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：