@article{Bernstein2023,

title = {Evaluating \textit{E. coli} genome‐scale metabolic model accuracy with high‐throughput mutant fitness data},

author = {David B Bernstein and Batu Akkas and Morgan N Price and Adam P Arkin},

doi = {10.15252/msb.202311566},

issn = {1744-4292},

year  = {2023},

date = {2023-12-06},

journal = {Molecular Systems Biology},

volume = {19},

number = {12},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractThe Escherichia coli genome‐scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision–recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene‐protein‐reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high‐throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.},

keywords = {Applied Mathematics, Computational Theory and Mathematics, General Agricultural and Biological Sciences, General Biochemistry, General Immunology and Microbiology, Genetics and Molecular Biology, Information Systems},

pubstate = {published},

tppubtype = {article}

}

@article{Piya2023,

title = {Systematic and scalable genome-wide essentiality mapping to identify nonessential genes in phages},

author = {Denish Piya and Nicholas Nolan and Madeline L. Moore and Luis A. Ramirez Hernandez and Brady F. Cress and Ry Young and Adam P. Arkin and Vivek K. Mutalik},

editor = {Paula Jauregui},

doi = {10.1371/journal.pbio.3002416},

issn = {1545-7885},

year  = {2023},

date = {2023-12-04},

journal = {PLoS Biol},

volume = {21},

number = {12},

publisher = {Public Library of Science (PLoS)},

abstract = {Phages are one of the key ecological drivers of microbial community dynamics, function, and evolution. Despite their importance in bacterial ecology and evolutionary processes, phage genes are poorly characterized, hampering their usage in a variety of biotechnological applications. Methods to characterize such genes, even those critical to the phage life cycle, are labor intensive and are generally phage specific. Here, we develop a systematic gene essentiality mapping method scalable to new phage–host combinations that facilitate the identification of nonessential genes. As a proof of concept, we use an arrayed genome-wide CRISPR interference (CRISPRi) assay to map gene essentiality landscape in the canonical coliphages λ and P1. Results from a single panel of CRISPRi probes largely recapitulate the essential gene roster determined from decades of genetic analysis for lambda and provide new insights into essential and nonessential loci in P1. We present evidence of how CRISPRi polarity can lead to false positive gene essentiality assignments and recommend caution towards interpreting CRISPRi data on gene essentiality when applied to less studied phages. Finally, we show that we can engineer phages by inserting DNA barcodes into newly identified inessential regions, which will empower processes of identification, quantification, and tracking of phages in diverse applications.},

keywords = {General Agricultural and Biological Sciences, General Biochemistry, General Immunology and Microbiology, General Neuroscience, Genetics and Molecular Biology},

pubstate = {published},

tppubtype = {article}

}