References of "Bintener, Tamara Jean Rita 50015893"
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See detailTowards the Integration of Metabolic Network Modelling and Machine Learning for the Routine Analysis of High-Throughput Patient Data
Pacheco, Maria UL; Bintener, Tamara Jean Rita UL; Sauter, Thomas UL

in Automated Reasoning for Systems Biology and Medicine (2019)

The decreasing cost of high-throughput technologies allows to consider their use in healthcare and medicine. To prepare for this upcoming revolution, the community is assembling large disease-dedicated ... [more ▼]

The decreasing cost of high-throughput technologies allows to consider their use in healthcare and medicine. To prepare for this upcoming revolution, the community is assembling large disease-dedicated datasets such as TCGA or METABRIC. These datasets will serve as references to compare new patient samples to in order to assign them to a predefined category (i.e. ‘patients associated with poor prognosis’). Some problems affecting the downstream analysis remain to be solved, the bottleneck is no longer data generation itself but the integration of the existing datasets with the present knowledge. Constraint-based modelling, that only requires the setting of a few parameters, became popular for the integration of high-throughput data in a metabolic context. Notably, context-specific building algorithms that extract a subnetwork from a reference network are largely used to study metabolic changes in various diseases. Reference networks are composed of canonical pathways while extracted subnetworks include only active pathways in the context of interest based on high-throughput data. Even though these algorithms can be part of automated pipelines, to be applied by clinicians, the model-building pipelines must be coupled to a standardized semi-automated analysis workflow based on machine learning approaches to avoid bias and reduce the cost of diagnostics. [less ▲]

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See detailIdentifying and targeting cancer-specific metabolism with network-based drug target prediction
Pacheco, Maria UL; Bintener, Tamara Jean Rita UL; Ternes, Dominik UL et al

in EBioMedicine (2019), 43(May 2019), 98-106

Background Metabolic rewiring allows cancer cells to sustain high proliferation rates. Thus, targeting only the cancer-specific cellular metabolism will safeguard healthy tissues. Methods We developed the ... [more ▼]

Background Metabolic rewiring allows cancer cells to sustain high proliferation rates. Thus, targeting only the cancer-specific cellular metabolism will safeguard healthy tissues. Methods We developed the very efficient FASTCORMICS RNA-seq workflow (rFASTCORMICS) to build 10,005 high-resolution metabolic models from the TCGA dataset to capture metabolic rewiring strategies in cancer cells. Colorectal cancer (CRC) was used as a test case for a repurposing workflow based on rFASTCORMICS. Findings Alternative pathways that are not required for proliferation or survival tend to be shut down and, therefore, tumours display cancer-specific essential genes that are significantly enriched for known drug targets. We identified naftifine, ketoconazole, and mimosine as new potential CRC drugs, which were experimentally validated. Interpretation The here presented rFASTCORMICS workflow successfully reconstructs a metabolic model based on RNA-seq data and successfully predicted drug targets and drugs not yet indicted for colorectal cancer. [less ▲]

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See detailIntegrated In Vitro and In Silico Modeling Delineates the Molecular Effects of a Synbiotic Regimen on Colorectal-Cancer-Derived Cells
Greenhalgh, Kacy UL; Ramiro Garcia, Javier UL; Heinken et al

in Cell Reports (2019), 27

By modulating the human gut microbiome, prebiotics and probiotics (combinations of which are called synbiotics) may be used to treat diseases such as colorectal cancer (CRC). Methodological limitations ... [more ▼]

By modulating the human gut microbiome, prebiotics and probiotics (combinations of which are called synbiotics) may be used to treat diseases such as colorectal cancer (CRC). Methodological limitations have prevented determining the potential combina- torial mechanisms of action of such regimens. We expanded our HuMiX gut-on-a-chip model to co-culture CRC-derived epithelial cells with a model probiotic under a simulated prebiotic regimen, and we integrated the multi-omic results with in silico metabolic modeling. In contrast to individual prebi- otic or probiotic treatments, the synbiotic regimen caused downregulation of genes involved in procarci- nogenic pathways and drug resistance, and reduced levels of the oncometabolite lactate. Distinct ratios of organic and short-chain fatty acids were produced during the simulated regimens. Treatment of primary CRC-derived cells with a molecular cocktail reflecting the synbiotic regimen attenuated self-renewal ca- pacity. Our integrated approach demonstrates the potential of modeling for rationally formulating synbi- otics-based treatments in the future. [less ▲]

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See detailPrediction of drug targets using metabolic modelling
Bintener, Tamara Jean Rita UL

Bachelor/master dissertation (2016)

Cancer, as one of the leading causes of death worldwide, is a disease characterized by the abnormal and uncontrolled proliferation of cells. Currently available anti-cancer drugs come with a variety of ... [more ▼]

Cancer, as one of the leading causes of death worldwide, is a disease characterized by the abnormal and uncontrolled proliferation of cells. Currently available anti-cancer drugs come with a variety of different side effects reducing the quality of life of cancer patients. Due to these severe side effects in anti-cancer therapy it is important to find a compromise between killing the cancer cells (efficiency) and not affecting the healthy cells (toxicity) to improve the quality of life of those patients. There exist different methods of finding new drug targets in cancer such as the in vitro development of new drugs which is very time consuming and expensive. The in silico prediction of targets, on the other hand, is fast and cost effective and allows to make a pre-selection of drug targets based on candidate genes. In this work, I propose a new workflow which implements metabolic modelling for finding metabolic drug targets in cancer. Therefore, context-specific models for cancer (including primary and metastatic melanoma) and healthy controls were reconstructed from Recon 2 (a genome scale metabolic model) using FASTCORMICS and two different expression datasets. In silico single gene deletion was performed in the models to search for potential candidate genes which are essential in cancer (reduce biomass production by 50%) but not in healthy (do not affect ATP production). In a second step, (approved) drugs targeting metabolic genes and their side effects, were extracted from the DrugBank, STITCH and SIDER through data mining and mapped to the metabolic network. A total of 65 possible drug targets have been found. These targets include genes which are known targets for chemotherapeutic agents such as the thymidylate synthase (TYMS), the fatty acid synthase (FASN) or dihydrofolate reductase (DHFR). Furthermore, two anti-cancer agents have been predicted for FASN which have already been proposed for the treatment of cancer. [less ▲]

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