Machine and deep learning meet genome-scale metabolic modeling

Zampieri, Guido; Vijayakumar, Supreeta; Yaneske, Elisabeth; Angione, Claudio

doi:10.1371/journal.pcbi.1007084

Cited by 235 publications

(175 citation statements)

References 115 publications

(156 reference statements)

Supporting

Mentioning

174

Contrasting

Order By: Relevance

“…Methods to find knockout strategies are successfully guiding metabolic engineering [60], [61], and general recent advances in mammalian cell engineering have been reviewed elsewhere [62]- [64]. Likewise, computational biology will soon need to address the lack of methods to guide genetic modulations of expression, where the considerably larger search space will likely require using metabolic modelling in combination with advanced machine or deep learning methods [65]. Our method may offer a route to find the best gene modulations (overexpression or partial knockdown) to carry out on multiple genes and towards multiple cellular objectives.…”

Section: Discussionmentioning

confidence: 99%

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Occhipinti

Hamadi²,

Kugler

et al. 2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

Self Cite

View full text Add to dashboard Cite

Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns.After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multiobjective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes.We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.

show abstract

Section: Discussionmentioning

confidence: 99%

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Occhipinti

Hamadi²,

Kugler

et al. 2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Most methods surveyed by Baltrušaitis and colleagues [89] are general or specialized for multimedia applications. The applications of multiview learning in biomedical data are just recently investigated [90,91], and there are also surveys investigating the methods to integrate heterogeneous biological and multiomics data [92,93,94,91]. However, they did not discuss the underlying machine learning principles (e.g., ERM) for multiview learning and how to use these principles for modeling multiomics data and revealing functional omics.…”

Section: Summary and Discussionmentioning

confidence: 99%

Multiview learning for understanding functional multiomics

Nguyen

Wang

2020

PLoS Comput Biol

View full text Add to dashboard Cite

The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks. To address these challenges, machine learning has been broadly applied to analyze multiomics. This review introduces multiview learning-an emerging machine learning field-and envisions its potentially powerful applications to multiomics. In particular, multiview learning is more effective than previous integrative methods for learning data's heterogeneity and revealing cross-talk patterns. Although it has been applied to various contexts, such as computer vision and speech recognition, multiview learning has not yet been widely applied to biological data-specifically, multiomics data. Therefore, this paper firstly reviews recent multiview learning methods and unifies them in a framework called multiview empirical risk minimization (MV-ERM). We further discuss the potential applications of each method to multiomics, including genomics, transcriptomics, and epigenomics, in an aim to discover the functional and mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems.

show abstract

“…Given the limited availability of in-vivo fluxomics data [90], the in-silico fluxomics simulated data provided by GEMs are very valuable and provided at lower cost than experimental techniques [91]. However, although FBA returns quantitative data, it is very important to highlight that the certainty/reliability of these predicted values will depend on the quality of the GEM that generates fluxes, and on the biological knowledge on which the model was based (that may be incomplete).…”

Section: Discussionmentioning

confidence: 99%

Dynamic simulations of microbial communities under perturbations: opportunities for microbiome engineering

García-Jiménez

Carrasco

Medina

et al. 2020

Preprint

View full text Add to dashboard Cite

Background : There are few large longitudinal microbiome studies, and fewer that include controlled, well-annotated perturbations between sampling-points. Thus, there are few opportunities to employ data-driven computational analyses of perturbed microbial communities over time. Results : Our novel computational system simulates the dynamics of microbial communities under perturbations using genome-scale metabolic models (GEMs). Perturbations include modifications to a) the nutrients available in the medium, allowing modelling of prebiotics; and/or b) the microorganisms present in the community to model, for example, probiotics or pathogen infection. These simulations generate the quantity and types of information required by MDPbiome, an AI system which builds predictive models suggesting the perturbation(s) required to engineer microbial communities to a desired state. We call this novel combination of technologies "MDPbiomeGEM"'. We demonstrate, in a Crohn’s disease microbiome, that MDPbiomeGEM correctly models the influence of both prebiotic fiber and a probiotic, resulting in a recommendation to consume inulin to recover from dysbiosis, consistent with prior biomedical knowledge. When used to model the soil microbiome's ability to degrade the herbicide Atrazine, differing recommendations arise depending on the highly variable state of the initial soil microbial composition, highlighting the relevance of both phosphate and microbes (i.e. Halobacillus sp. and H.stevensii ) in a directed microbiome engineering strategy, consistent with previously published observations. Conclusions : MDPbiomeGEM generates large volumes of longitudinal data of complex microbial communities experiencing perturbations. Machine learning on these data reveal patterns consistent with existing biological knowledge, supporting the validity of the approach. MDPbiomeGEM could save research resources by optimizing sample collection in metagenomics studies through identification of "informative" scenarios/time-points, or by predicting optimal in-vitro culture formulations for generating performant synthetic microbial communities. Finally, MDPbiomeGEM outputs include detailed information about the metabolic state of the community, which can be used to further interpret the impact of perturbations, and potentially could be used to predict novel metabolic biomarkers of a microbiome's state.

show abstract

Machine and deep learning meet genome-scale metabolic modeling

Cited by 235 publications

References 115 publications

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Multiview learning for understanding functional multiomics

Dynamic simulations of microbial communities under perturbations: opportunities for microbiome engineering

Contact Info

Product

Resources

About