An in-silico approach to predict and exploit synthetic lethality in cancer metabolism

Apaolaza, Iñigo; José‐Eneriz, Edurne San; Tobalina, Luis; Miranda, Estíbaliz; Gárate, Leire; Agirre, Xabier; Prósper, Felipe; Planes, Francisco J.

doi:10.1038/s41467-017-00555-y

Cited by 44 publications

(60 citation statements)

References 40 publications

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“…A Minimal cut sets Extended functionality by integration with CellNetAnalyzer 147, 148 , and new algorithms for genetic MCSs 68 . A Flux variability analysis Increased computational efficiency 103 .…”

Section: Flux Balance Analysis and Its Variantsmentioning

confidence: 99%

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

et al. 2019

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COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods. ]); 61 | The MUST sets are the sets of reactions that must increase or decrease their flux in order to achieve the desired phenotype in the mutant strain. As shown in Figure 6, the first order MUST sets are MustU and MustL while second order MUST sets are denoted as MustUU, MustLL, and MustUL. After parameters and constraints are defined, the functions findMustL and findMustU are run to determine the mustU and mustL sets, respectively. Define an ID of the run with:Each time the MUST sets are determined, folders are generated to read inputs and store outputs, i.e., reports. These folders are located in the directory defined by the uniquely defined runID.62 | In order to find the first order MUST sets, constraints should be defined: >> constrOpt = struct('rxnList', {{'EX_gluc', 'R75', 'EX_suc'}}, 'values', [-100; 0; 155.5]); 63 | The first order MUST set MustL is determined by running: >> [mustLSet, pos_mustL] = findMustL(model, minFluxesW, maxFluxesW, ... 'constrOpt', constrOpt, 'runID', runID);If runID is set to 'TestoptForceL', a folder TestoptForceL is created, in which two additional folders InputsMustL and OutputsMustL are created. The InputsMustL folder contains all the inputs required to run the function findMustL, while the OutputsMustL folder contains the mustL set found and a report that summarises all the inputs and outputs. In order to maintain a chronological order of computational experiments, the report is timestamped.64 | Display the reactions that belong to the mustL set using: >> disp(mustLSet) 65 | The first order MUST set MustU is determined by running: >> [mustUSet, pos_mustU] = findMustU(model, minFluxesW, maxFluxesW, ... 'constrOpt', constrOpt, 'runID', runID);...

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Section: Flux Balance Analysis and Its Variantsmentioning

confidence: 99%

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

et al. 2019

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“…This has been widely explored to develop treatment options with respect to cancer-specific drug targets [26][27][28]. Additionally, COBRA modeling approaches have enabled in identification of robust cancer-specific synthetic lethal interactions [29,30].…”

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confidence: 99%

Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma

et al. 2018

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Retinoblastoma (RB) is a childhood eye cancer. Currently, chemotherapy, local therapy, and enucleation are the main ways in which these tumors are managed. The present work is the first study that uses constraint‐based reconstruction and analysis approaches to identify and explain RB‐specific survival strategies, which are RB tumor specific. Importantly, our model‐specific secretion profile is also found in RB1‐depleted human retinal cells in vitro and suggests that novel biomarkers involved in lipid metabolism may be important. Finally, RB‐specific synthetic lethals have been predicted as lipid and nucleoside transport proteins that can aid in novel drug target development.

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“…Our group recently developed a novel COBRA method to find cancer-specific metabolic essential genes. 5 We showed that our approach presents several advantages with respect to existing approaches in the literature. 6 Firstly, our approach returns more objective and unbiased results, since gene expression data is mapped onto the reference metabolic network, avoiding the use of context-specific metabolic reconstructions, which take heuristic decisions to reconcile omics data and add unnecessary noise.…”

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confidence: 86%

COBRA methods and metabolic drug targets in cancer

Apaolaza

José‐Eneriz

Agirre

et al. 2017

Molecular & Cellular Oncology

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An in-silico approach to predict and exploit synthetic lethality in cancer metabolism

Cited by 44 publications

References 40 publications

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma

COBRA methods and metabolic drug targets in cancer

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