Machine and deep learning meet genome-scale metabolic modeling

Machine learning framework for assessment of microbial factory performance

(2019) Tolutola Oyetunde et al.   PLoS One

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism

(2019) Chiara Damiani et al.   PLoS Computational Biology

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

(2019) Laurent Heirendt et al.   Nature Protocols

Dynamic elementary mode modelling of non-steady state flux data

(2018) Abel Folch-Fortuny et al.   BMC Systems Biology

Opportunities and obstacles for deep learning in biology and medicine

(2018) Travers Ching et al.   Journal of the Royal Society Interface

Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

(2018) Daniel Machado et al.   NUCLEIC ACIDS RESEARCH

Methods for automated genome-scale metabolic model reconstruction

(2018) José P. Faria et al.   BIOCHEMICAL SOCIETY TRANSACTIONS

The poly-omics of ageing through individual-based metabolic modelling

(2018) Elisabeth Yaneske et al.   BMC BIOINFORMATICS

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models

(2018) David Heckmann et al.   Nature Communications

In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production

(2018) Annalisa Occhipinti et al.   PeerJ

Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks

(2018) Demetrius DiMucci et al.   mSystems

Disaggregating asthma: Big investigation versus big data

(2017) Danielle Belgrave et al.   JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY

Biomedical applications of cell- and tissue-specific metabolic network models

(2017) Hamideh Fouladiha et al.   JOURNAL OF BIOMEDICAL INFORMATICS

Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion

(2017) Zachary A. King et al.   METABOLIC ENGINEERING

An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features

(2017) Sutanu Nandi et al.   Molecular BioSystems

Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints

(2017) Benjamín J Sánchez et al.   Molecular Systems Biology

Why Batch Effects Matter in Omics Data, and How to Avoid Them

(2017) Wilson Wen Bin Goh et al.   TRENDS IN BIOTECHNOLOGY

The Impact of Systems Biology on Bioprocessing

(2017) Kate Campbell et al.   TRENDS IN BIOTECHNOLOGY

Single-Cell Multiomics: Multiple Measurements from Single Cells

(2017) Iain C. Macaulay et al.   TRENDS IN GENETICS

Learning Microbial Community Structures with Supervised and Unsupervised Non-negative Matrix Factorization

(2017) Yun Cai et al.   Microbiome

Advances in the integration of transcriptional regulatory information into genome-scale metabolic models

(2016) R.P. Vivek-Ananth et al.   BIOSYSTEMS

Multiplex methods provide effective integration of multi-omic data in genome-scale models

(2016) Claudio Angione et al.   BMC BIOINFORMATICS

Dimension reduction techniques for the integrative analysis of multi-omics data

(2016) Chen Meng et al.   BRIEFINGS IN BIOINFORMATICS

Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology

(2016) Ina Aretz et al.   INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

Constraint-based stoichiometric modelling from single organisms to microbial communities

(2016) Willi Gottstein et al.   Journal of the Royal Society Interface

iSCHRUNK – I n S ilico Approach to Ch aracterization and R eduction of Un certainty in the K inetic Models of Genome-scale Metabolic Networks

(2016) Stefano Andreozzi et al.   METABOLIC ENGINEERING

A Markov chain model for N-linked protein glycosylation – towards a low-parameter tool for model-driven glycoengineering

(2016) Philipp N. Spahn et al.   METABOLIC ENGINEERING

Integrative analysis of human omics data using biomolecular networks

(2016) Jonathan L. Robinson et al.   Molecular BioSystems

Principal elementary mode analysis (PEMA)

(2016) Abel Folch-Fortuny et al.   Molecular BioSystems

Deep learning for computational biology

(2016) Christof Angermueller et al.   Molecular Systems Biology

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

(2016) Stefanía Magnúsdóttir et al.   NATURE BIOTECHNOLOGY

Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

(2016) Michael K. K. Leung et al.   PROCEEDINGS OF THE IEEE

Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli

(2016) Minseung Kim et al.   Nature Communications

Multi-omic data integration enables discovery of hidden biological regularities

(2016) Ali Ebrahim et al.   Nature Communications

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming

(2016) Stephen Gang Wu et al.   PLoS Computational Biology

Metabolic Network Prediction of Drug Side Effects

(2016) Itay Shaked et al.   Cell Systems

Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow

(2016) Elizabeth Brunk et al.   Cell Systems

Principles of proteome allocation are revealed using proteomic data and genome-scale models

(2016) Laurence Yang et al.   Scientific Reports

Transcriptomic and metabolomic data integration

(2015) Rachel Cavill et al.   BRIEFINGS IN BIOINFORMATICS

Using Genome-scale Models to Predict Biological Capabilities

(2015) Edward J. O’Brien et al.   CELL

How to measure metabolic fluxes: a taxonomic guide for 13 C fluxomics

(2015) Sebastian Niedenführ et al.   CURRENT OPINION IN BIOTECHNOLOGY

Computational methods in metabolic engineering for strain design

(2015) Matthew R Long et al.   CURRENT OPINION IN BIOTECHNOLOGY

Methods for biological data integration: perspectives and challenges

(2015) Vladimir Gligorijević et al.   Journal of the Royal Society Interface

Machine learning applications in genetics and genomics

(2015) Maxwell W. Libbrecht et al.   NATURE REVIEWS GENETICS

Methods of integrating data to uncover genotype–phenotype interactions

(2015) Marylyn D. Ritchie et al.   NATURE REVIEWS GENETICS

Predictive analytics of environmental adaptability in multi-omic network models

(2015) Claudio Angione et al.   Scientific Reports

A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation

(2015) Claudio Angione et al.   ACS Synthetic Biology

Kernel methods for large-scale genomic data analysis

(2014) X. Wang et al.   BRIEFINGS IN BIOINFORMATICS

Optimizing genome-scale network reconstructions

(2014) Jonathan Monk et al.   NATURE BIOTECHNOLOGY

Constraint-based models predict metabolic and associated cellular functions

(2014) Aarash Bordbar et al.   NATURE REVIEWS GENETICS

Predicting Growth Conditions from Internal Metabolic Fluxes in an In-Silico Model of E. coli

(2014) Viswanadham Sridhara et al.   PLoS One

Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism

(2014) Daniel Machado et al.   PLoS Computational Biology

A survey of multi-view machine learning

(2013) Shiliang Sun   NEURAL COMPUTING & APPLICATIONS

The evolution of genome-scale models of cancer metabolism

(2013) Nathan E. Lewis et al.   Frontiers in Physiology

Molecular signatures from omics data: From chaos to consensus

(2012) Jaeyun Sung et al.   Biotechnology Journal

Plant genome-scale metabolic reconstruction and modelling

(2012) Cristiana Gomes de Oliveira Dal’Molin et al.   CURRENT OPINION IN BIOTECHNOLOGY

Analysis of omics data with genome-scale models of metabolism

(2012) Daniel R. Hyduke et al.   Molecular BioSystems

Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods

(2012) Nathan E. Lewis et al.   NATURE REVIEWS MICROBIOLOGY

In silico method for modelling metabolism and gene product expression at genome scale

(2012) Joshua A. Lerman et al.   Nature Communications

Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT

(2012) Rasmus Agren et al.   PLoS Computational Biology

rBioNet: A COBRA toolbox extension for reconstructing high-quality biochemical networks

(2011) S. G. Thorleifsson et al.   BIOINFORMATICS

A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology

(2011) Aarash Bordbar et al.   BMC Systems Biology

An integrated approach to characterize genetic interaction networks in yeast metabolism

(2011) Balázs Szappanos et al.   NATURE GENETICS

Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model

(2010) K. Yizhak et al.   BIOINFORMATICS

iMAT: an integrative metabolic analysis tool

(2010) Hadas Zur et al.   BIOINFORMATICS

Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks

(2010) Eytan Ruppin et al.   CURRENT OPINION IN BIOTECHNOLOGY

What is flux balance analysis?

(2010) Jeffrey D Orth et al.   NATURE BIOTECHNOLOGY

A protocol for generating a high-quality genome-scale metabolic reconstruction

(2010) Ines Thiele et al.   Nature Protocols

Decomposing complex reaction networks using random sampling, principal component analysis, and basis rotation

(2009) Christian L Barrett et al.   BMC Systems Biology

Machine learning based analyses on metabolic networks supports high-throughput knockout screens

(2008) Kitiporn Plaimas et al.   BMC Systems Biology

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

(2008) Adam M Feist et al.   NATURE BIOTECHNOLOGY