Biology subjects

Boyle, N. R.

Publications and source records attributed to Boyle, N. R..

5 recordsLinked to original sources

bioRxiv2026

iSsus3744: A Genome-Scale Model-Guided Strategy for Rational Media Design for Cultivated Pork

Cultivated meat production is currently limited by high production costs and an incomplete understanding of cellular metabolism in agriculturally relevant species. Genome-scale metabolic models (GEMs) have successfully guided media optimization in biopharmaceutical systems but have not been widely applied to cultivated meat. In this study, we present iSsus3744, the first genome-scale metabolic reconstruction for Sus scrofa and demonstrate its application for rational media design in cultivated pork production. iSsus3744 was reconstructed using HumanGEM and Recon3D as template models and further constrained using experimentally determined biomass composition and uptake and excretion fluxes from a Duroc porcine muscle satellite cell line. The final model comprised 3,744 genes, 8,854 metabolites, and 12,248 reactions distributed across eight cellular compartments. Flux balance analysis (FBA) and flux variability analysis (FVA) were used to identify amino acids limiting cellular growth and predict media supplementation strategies. Experimental validation demonstrated that model-guided amino acid supplementation significantly improved proliferation. Supplementation with phenylalanine reduced doubling time from 31.9 hours to 17.2 hours, representing a 46% reduction, while lysine, methionine, tyrosine, leucine, and valine also improved growth performance. These results demonstrate the potential of genome-scale metabolic modeling as a powerful platform for rational media optimization in cultivated meat systems. iSsus3744 provides a foundational resource for future integration of omics, transcriptional regulation, and isotope-assisted metabolic flux analysis to further accelerate serum-free media development and cultivated meat bioprocess optimization.

systems biology↗

bioRxiv2022

Characterizing Photosynthetic Biofuel Production: Isotopically non-stationary 13C metabolic flux analysis (INST-13CMFA) on limonene producingSynechococcus sp. PCC 7002

Synechococcus sp. PCC 7002 is a unicellular cyanobacterium capable of fast growth, even under high light intensity and high salinity. These attributes along with genetic tractability make Synechococcus sp. PCC 7002 an attractive candidate for industrial scale production of specialty and commodity chemicals. One such strain produces limonene, an energy dense diesel jet fuel drop-in additive, at a titer of 4 mg/L over a four-day incubation period. In this study, we use the state-of-the-art whole-cell characterization tool, isotopically non-stationary 13C metabolic flux analysis (INST-13CMFA) to determine intracellular fluxes through the pathways of central metabolism for the limonene producing strain and wild type strain of Synechococcus sp. PCC 7002. We find similar flux distribution in the Calvin-Benson-Bassham cycle, photorespiration, oxidative pentose phosphate pathway, and reductive tricarboxylic acid cycle. The key difference between strains is observed in the production of pyruvate. The limonene producing strain displays significantly higher flux through the amphibolic pathways of phosphoenolpyruvate carboxylase and the malic enzyme to synthesize pyruvate, while the wild type strain uses pyruvate kinase in a single step. Our findings suggest that this flux distribution is a mechanism to recover a physiologically optimal ratio of ATP to NADPH. The upregulation of this amphibolic pathway may act to restore the physiological ATP:NADPH ratio that has been disturbed by limonene biosynthesis.

bioengineering↗

bioRxiv2021

Genome-Scale Metabolic Model Accurately Predicts Fermentation of Glucose by Chromochloris zofingiensis

Algae have the potential to be sources of renewable fuels and chemicals. One particular strain, Chromochloris zofingiensis, is of interest due to the co-production of triacylglycerols (TAGs) and astaxanthin, a valuable nutraceutical. To aid in future engineering efforts, we have developed the first genome-scale metabolic model on C. zofingiensis, iChr1915. This model includes 1915 genes, 3413 metabolic reactions and 2652 metabolites. We performed detailed biomass composition analysis for three growth conditions: autotrophic, mixotrophic and heterotrophic and used this data to develop biomass formation equations for each growth condition. The completed model was then used to predict flux distributions for each growth condition; interestingly, for heterotrophic growth, the model predicts the excretion of fermentation products due to overflow metabolism. We confirmed this experimentally via metabolomics of spent medium and fermentation product assays. An in silico gene essentiality analysis was performed on this model, as well as a flux variability analysis to test the production capabilities of this organism. In this work, we present the first genome scale metabolic model of C. zofingiensis and demonstrate its use predicting metabolic activity in different growth conditions, setting up a foundation for future metabolic engineering studies in this organism.

systems biology↗

bioRxiv2021

Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor

The association of the receptor binding domain (RBD) of SARS-CoV-2 viral spike with human angiotensin converting enzyme (hACE2) represents the first required step for viral entry. Amino acid changes in the RBD have been implicated with increased infectivity and potential for immune evasion. Reliably predicting the effect of amino acid changes in the ability of the RBD to interact more strongly with the hACE2 receptor can help assess the public health implications and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born electrostatic solvation, hydrogen-bonding, {pi}-{pi} packing and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity using the decomposed energy terms as descriptors. The computational base achieves an accuracy of 82.2% in terms of correctly classifying single amino-acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient r of 0.69 between predicted and experimentally calculated binding affinities. Both metrics are calculated using a 5-fold cross validation test. Our method thus sets up a framework for effectively screening binding affinity change with unknown single and multiple amino-acid changes. This can be a very valuable tool to predict host adaptation and zoonotic spillover of current and future SARS-CoV-2 variants.

biophysics↗

bioRxiv2019

MultIscale MultiObjective Systems Analysis (MIMOSA): an advanced metabolic modeling framework for complex systems

In natural environments, cells live in complex communities and experience a high degree of heterogeneity internally and in the environment. Unfortunately, most of the metabolic modeling approaches that are currently used assume ideal conditions and that each cell is identical, limiting their application to pure cultures in well-mixed vessels. Here we describe our development of MultIscale MultiObjective Systems Analysis (MIMOSA), a metabolic modeling approach that can track individual cells in both space and time, track the diffusion of nutrients and light and the interaction of cells with each other and the environment. As a proof-of concept study, we used MIMOSA to model the growth of Trichodesmium erythraeum, a filamentous diazotrophic cyanobacterium which has cells with two distinct metabolic modes. The use of MIMOSA significantly improves our ability to predictively model metabolic changes and phenotype in more complex cell cultures.

bioengineering↗