Towards the routine use of <i>in silico</i> screenings for drug discovery using metabolic modelling

Bintener, Tamara Jean Rita; Pacheco, Maria Pires; Sauter, Thomas

doi:10.1042/bst20190867

Cited by 14 publications

(13 citation statements)

References 172 publications

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“…Gene knock-out consists of blocking all the reactions associated with a specific gene and assesses its effect on the objective function of the model, i.e., biomass production. The genes that significantly reduce growth are considered as essential and, therefore, as potential drug targets [ 132 ].…”

Section: Metabolic Approaches To Study the Cross-talks Between Cafmentioning

confidence: 99%

Mapping the Metabolic Networks of Tumor Cells and Cancer-Associated Fibroblasts

Karta

Bossicard

Kotzamanis

et al. 2021

Cells

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Metabolism is considered to be the core of all cellular activity. Thus, extensive studies of metabolic processes are ongoing in various fields of biology, including cancer research. Cancer cells are known to adapt their metabolism to sustain high proliferation rates and survive in unfavorable environments with low oxygen and nutrient concentrations. Hence, targeting cancer cell metabolism is a promising therapeutic strategy in cancer research. However, cancers consist not only of genetically altered tumor cells but are interwoven with endothelial cells, immune cells and fibroblasts, which together with the extracellular matrix (ECM) constitute the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), which are linked to poor prognosis in different cancer types, are one important component of the TME. CAFs play a significant role in reprogramming the metabolic landscape of tumor cells, but how, and in what manner, this interaction takes place remains rather unclear. This review aims to highlight the metabolic landscape of tumor cells and CAFs, including their recently identified subtypes, in different tumor types. In addition, we discuss various in vitro and in vivo metabolic techniques as well as different in silico computational tools that can be used to identify and characterize CAF–tumor cell interactions. Finally, we provide our view on how mapping the complex metabolic networks of stromal-tumor metabolism will help in finding novel metabolic targets for cancer treatment.

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Section: Metabolic Approaches To Study the Cross-talks Between Cafmentioning

confidence: 99%

Mapping the Metabolic Networks of Tumor Cells and Cancer-Associated Fibroblasts

Karta

Bossicard

Kotzamanis

et al. 2021

Cells

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“…As Hi-C-LSTM models the dependence of sequence on 3D conformation, it enables us to perform in-silico deletion, insertion and reversal of certain genomic loci and observe changes in the resulting Hi-C contact map. In-silico knockout experiments have gained prominence lately, mainly in intercepting signal flows in signaling pathways [45] and drug discovery [46,47,48]. A Hi-C in-silico manipulation tool is of great value it enables researchers to identify the importance and influence of any genomic locus of interest to 3D chromatin conformation.…”

Section: Hi-c-lstm Enables In-silico Knockout Experimentsmentioning

confidence: 99%

Hi-C-LSTM: Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation

Dsouza

Maslova

Al-Jibury

et al. 2021

Preprint

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Despite the availability of chromatin conformation capture experiments, understanding the relationship between regulatory elements and conformation remains a challenge. We propose Hi-C-LSTM, a method that produces low dimensional latent representations that summarize intra-chromosomal Hi-C contacts via a recurrent long short-term memory (LSTM) neural network model. We find that these representations contain all the information needed to recreate the original Hi-C matrix with high accuracy, outperforming existing methods. These representations enable the identification of a variety of conformation-defining genomic elements, including nuclear compartments and conformation-related transcription factors. They furthermore enable in-silico perturbation experiments that measure the influence of cis-regulatory elements on conformation.

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“…As Hi-C-LSTM models the dependence of sequence on 3D conformation, it enables us to perform in-silico deletion, insertion and reversal of certain genomic loci and observe changes in the resulting Hi-C contact map. In-silico knockout experiments have gained prominence lately, mainly in intercepting signal flows in signaling pathways [46] and drug discovery [47][48][49]. A Hi-C in-silico manipulation tool is of great value it enables researchers to identify the importance and influence of any genomic locus of interest to 3D chromatin conformation.…”

Section: Hi-c-lstm Enables In-silico Knockout Experimentsmentioning

confidence: 99%

Hi-C-LSTM: Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of 3D genome conformation

Dsouza

Maslova

Al-Jibury

et al. 2021

Preprint

View full text Add to dashboard Cite

Despite the availability of chromatin conformation capture experiments, discerning the relationship between the 1D genome and 3D conformation remains a challenge, which limits our understanding of their affect on gene expression and disease. We propose Hi-C-LSTM, a method that produces low-dimensional latent representations that summarize intra-chromosomal Hi-C contacts via a recurrent long short-term memory (LSTM) neural network model. We find that these representations contain all the information needed to recreate the original Hi-C matrix with high accuracy, outperforming existing methods. These representations enable the identification of a variety of conformation-defining genomic elements, including nuclear compartments and conformation-related transcription factors. They furthermore enable in-silico perturbation experiments that measure the influence of cis-regulatory elements on conformation.

show abstract

Towards the routine use of in silico screenings for drug discovery using metabolic modelling

Cited by 14 publications

References 172 publications

Mapping the Metabolic Networks of Tumor Cells and Cancer-Associated Fibroblasts

Mapping the Metabolic Networks of Tumor Cells and Cancer-Associated Fibroblasts

Hi-C-LSTM: Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation

Hi-C-LSTM: Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of 3D genome conformation

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