The landscape of tiered regulation of breast cancer cell metabolism

Katzir, Rotem; Polat, Ibrahim H.; Harel, Michal; Katz, Shir; Foguet, Carles; Selivanov, Vitaly A.; Sabatier, Philippe; Cascante, Marta; Geiger, Tamar; Ruppin, Eytan

doi:10.1038/s41598-019-54221-y

Cited by 18 publications

(22 citation statements)

References 55 publications

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“…Finally, we applied COSMOS to a public breast cancer dataset including transcriptomics and fluxomics measurements (Katzir et al , 2019) to connect signaling directly with metabolic flux estimation, instead of metabolite abundance measurements as done in the previous cases. We performed a differential analysis of transcript abundance and flux values between tumor cells cultured with and without glutamine.…”

Section: Resultsmentioning

confidence: 99%

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Causal integration of multi‐omics data with prior knowledge to generate mechanistic hypotheses

Dugourd

Kuppe

Sciacovelli

et al. 2021

Molecular Systems Biology

105

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Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

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Section: Resultsmentioning

confidence: 99%

“…Multi‐omics experimental data for breast cancer cell lines was obtained from (Katzir et al , 2019). The authors performed experimental measurements on the MCF7 cell line under normal growth conditions, glutamine deprivation, and oligomycin supplementation.…”

Section: Methodsmentioning

confidence: 99%

Causal integration of multi‐omics data with prior knowledge to generate mechanistic hypotheses

Dugourd

Kuppe

Sciacovelli

et al. 2021

Molecular Systems Biology

105

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“…With the recent increased availability of multiple, powerful omics techniques (that is, genomics, transcriptomics, proteomics, and metabolomics), a key emerging challenge is the integration of different omics platforms. Several methods have been developed for multi-omics integration using machine and deep learning techniques [133], including SVM [134,135], KNN [136,137], NMF [138], PCA [139] and CNN [140], for example, for cancer subtype and survival prediction [141][142][143] and for prediction of drug response [143,144], the paucity of studies systematically comparing different multi-omics integration methods is a serious bottleneck in the advancement of this field. Such systematic comparison was recently performed for a subset of the multi-omics techniques aimed at the prediction of tumor subtype [145].…”

Section: Integrating ML Into Systems Biologymentioning

confidence: 99%

Incorporating Machine Learning into Established Bioinformatics Frameworks

Auslander

Gussow

Koonin

2021

IJMS

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The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

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“…A comprehensive metabolic model can also improve conventional wisdom regarding the cell metabolism by making important corrections, such as gap-filling reactions suggested by genome-wide metabolic models [12,13]. When required to interfere with cell functioning, a metabolic model can contribute to discovering new strategies [11,14,15], and to organize the disparate accumulated information into a coherent body of practical knowledge [3,16]. In a complex system such as the metabolic network of the cell, a model helps to think (and calculate) logically about what components and interactions are important and what other components can be neglected [17].…”

Section: Introductionmentioning

confidence: 99%

“…However, one of the adverse consequences of this is that the constraint-based models are generally poor at extrapolating outside the imposed experimental conditions. These models are also weak in accommodating sequential changes in the conditions, i.e., in the fed-batch regime, which are frequently occurring in bioprocesses, as well as describing medical cases related to cancer cell metabolism [15].…”

mentioning

confidence: 99%

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Yasemi¹,

Jolicœur²

2021

Processes

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Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

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The landscape of tiered regulation of breast cancer cell metabolism

Cited by 18 publications

References 55 publications

Causal integration of multi‐omics data with prior knowledge to generate mechanistic hypotheses

Causal integration of multi‐omics data with prior knowledge to generate mechanistic hypotheses

Incorporating Machine Learning into Established Bioinformatics Frameworks

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

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