| 1. |
- Sanchez Barja, Benjamin José, 1988, et al.
(författare)
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Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints
- 2017
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Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 13:8, s. Article no 935 -
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.
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| 2. |
- Robinson, Jonathan, 1986, et al.
(författare)
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An atlas of human metabolism
- 2020
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Ingår i: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 13:624
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.
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| 3. |
- Brunk, Elizabeth, et al.
(författare)
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Recon3D enables a three-dimensional view of gene variation in human metabolism
- 2018
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Ingår i: Nature Biotechnology. - : Springer Science and Business Media LLC. - 1087-0156 .- 1546-1696. ; 36:3, s. 272-281
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.
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| 4. |
- Garcia, Manuel, 1973, et al.
(författare)
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BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data
- 2014
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 42:W1, s. W175-W181
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Tidskriftsartikel (refereegranskat)abstract
- Analysis of large data sets using computational and mathematical tools have become a central part of biological sciences. Large amounts of data are being generated each year from different biological research fields leading to a constant development of software and algorithms aimed to deal with the increasing creation of information. The BioMet Toolbox 2.0 integrates a number of functionalities in a user-friendly environment enabling the user to work with biological data in a web interface. The unique and distinguishing feature of the BioMet Toolbox 2.0 is to provide a web user interface to tools for metabolic pathways and omics analysis developed under different platform-dependent environments enabling easy access to these computational tools.
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| 5. |
- Meimetis, Nikolaos, et al.
(författare)
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AutoTransOP: translating omics signatures without orthologue requirements using deep learning
- 2024
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Ingår i: NPJ systems biology and applications. - 2056-7189. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP, a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts-most importantly without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist.
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| 6. |
- Meimetis, Nikolaos, et al.
(författare)
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Inference of drug off-target effects on cellular signaling using interactome-based deep learning
- 2024
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Ingår i: iScience. - 2589-0042. ; 27:4
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Tidskriftsartikel (refereegranskat)abstract
- Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors’ activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.
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| 7. |
- Nilsson, Avlant, 1985, et al.
(författare)
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Artificial neural networks enable genome-scale simulations of intracellular signaling
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor-activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r = 0.98) and the effects of gene knockouts (r = 0.8). We stimulate macrophages with 59 different ligands, with and without the addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r = 0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling. Many diseases are caused by disruptions to the network of biochemical reactions that allow cells to respond to external signals. Here Nilsson et al develop a method to simulate cellular signaling using artificial neural networks to predict cellular responses and activities of signaling molecules.
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| 8. |
- Nilsson, Avlant, 1985, et al.
(författare)
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Complex I is bypassed during high intensity exercise
- 2019
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Human muscles are tailored towards ATP synthesis. When exercising at high work rates muscles convert glucose to lactate, which is less nutrient efficient than respiration. There is hence a trade-off between endurance and power. Metabolic models have been developed to study how limited catalytic capacity of enzymes affects ATP synthesis. Here we integrate an enzyme-constrained metabolic model with proteomics data from muscle fibers. We find that ATP synthesis is constrained by several enzymes. A metabolic bypass of mitochondrial complex I is found to increase the ATP synthesis rate per gram of protein compared to full respiration. To test if this metabolic mode occurs in vivo, we conduct a high resolved incremental exercise tests for five subjects. Their gas exchange at different work rates is accurately reproduced by a whole-body metabolic model incorporating complex I bypass. The study therefore shows how proteome allocation influences metabolism during high intensity exercise.
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| 9. |
- Nilsson, Avlant, 1985
(författare)
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Constraint-based modeling of metabolism - interpreting predictions of growth and ATP synthesis in human and yeast
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Growth is the primary objective of the cell. Diseases arise when cells diverge from a healthy growth-pattern. An increased understanding of cellular growth may thus be translated into improved human health. The cell requires materials and free energy (in the form of ATP) in order to grow, metabolism supplies the cell with this. The rate of metabolism is ultimately constrained by the biophysical properties of the metabolic enzymes. Interactions between the constraints and the growth-objective gives rise to metabolic trade-offs, e.g. between ATP synthesis from respiration and fermentation. We can gain quantitative insight into these processes by simulating metabolism using mathematical models. In this thesis I simulated the metabolism of four biological systems: the infant, cancer, yeast and muscle. The simulations demonstrated how a shift in metabolic strategy may increase the rates of ATP synthesis and growth. These increased metabolic rates come at the expense of decreased resource efficiency, i.e. ATP produced per carbon consumed. The effect was primarily caused by the low catalytic efficiency of the respiratory enzyme complexes I and V. By shifting from respiratory to fermentative ATP synthesis, the cell was able to bypass these constraints. An intermediate strategy involved bypassing only complex I. The phenomenon was experimentally corroborated in the working muscle, and it is the native state of the yeast Saccharomyces cerevisiae (which lacks complex I). The differences in efficiency between the different metabolic pathways also explained why cells grow faster on some carbon sources, e.g. the specific growth rate for yeast is higher on glucose than on ethanol. These models were extended to predict the world-record running-speeds at different distances, by taking the sizes of the body’s nutrient-deposits into account. A metabolic strategy employed by cancer cells involved excretion of the amino acid glutamate. The simulations showed a mechanistic relation to catabolism of branched-chain amino acids and the localization of amino acid metabolism to different cellular compartments. By experimentally inhibiting glutamate excretion using an off-the-shelf drug (sulfasalazine), the growth rate of a cancer cell line was reduced. The metabolic modeling involved integration of various types of data and thus demonstrated the potential to unify knowledge from different studies and domains. This exposed contradictory claims in literature and highlighted knowledge-gaps that need to be filled to further improve human health.
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| 10. |
- Nilsson, Avlant, 1985, et al.
(författare)
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Genome scale metabolic modeling of cancer
- 2017
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Ingår i: Metabolic Engineering. - : Elsevier BV. - 1096-7176 .- 1096-7184. ; 43, s. 103-112
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Forskningsöversikt (refereegranskat)abstract
- Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome. Cancer specific models of metabolism have also been generated by reducing the number of reactions in the generic model based on high throughput expression data, e.g. transcriptomics and proteomics. Targets for drugs and bio markers for diagnostics have been identified using these models. They have also been used as scaffolds for analysis of high throughput data to allow mechanistic interpretation of changes in expression. Finally, GEMs allow quantitative flux predictions using flux balance analysis (FBA). Here we critically review the requirements for successful FBA simulations of cancer cells and discuss the symmetry between the methods used for modeling of microbial and cancer metabolism. GEMs have great potential for translational research on cancer and will therefore become of increasing importance in the future. © 2017
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