bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.08.06.606923

MMINT: a Metabolic Model Interactive Network Tool for the exploration and comparative visualisation of metabolic networks

Molina Ortiz, J. P.·Morgan, M. M.·Paten, A. M.·Warden, A. C.·Kilby, P.
View original source ↗Export metadata ↓

Abstract

Genome-scale metabolic models (GEMs) are essential tools in systems and synthetic biology, enabling the mathematical simulation of metabolic pathways encoded in genomes to predict phenotypes. The complexity of GEMs, however, can often limit the interpretation and comparison of their outputs. Here, we present MMINT (Metabolic Modelling Interactive Network Tool), designed to facilitate the exploration and comparison of metabolic networks. MMINT employs GEM networks and flux solutions derived from Constraint Based Analysis (e.g. Flux Balance Analysis) to create interactive visualizations. This tool allows for seamless toggling of source and target metabolites, network decluttering, enabling exploration and comparison of flux solutions by highlighting similarities and differences between metabolic states, which enhances the identification of mechanistic drivers of phenotypes. We demonstrate MMINTs capabilities using the Pyrococcus furiosus GEM, showcasing its application in distinguishing the metabolic drivers of acetate- and ethanol-producing phenotypes. By providing an intuitive and responsive model-exploration experience, MMINT addresses the need for a tool that simplifies the interpretation of GEM outputs and supports the discovery of novel metabolic engineering strategies. MMINT is available at https://doi.org/10.6084/m9.figshare.26409328 Graphical abstractMMINT functionalities provide an intuitive and responsive model-exploration experience, enabling flux solution comparison and the identification of metabolic drivers of phenotypes O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/606923v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@163d42eorg.highwire.dtl.DTLVardef@ff2922org.highwire.dtl.DTLVardef@1e5881aorg.highwire.dtl.DTLVardef@4a8bf0_HPS_FORMAT_FIGEXP M_FIG C_FIG

Explore related subjects

synthetic biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Molina Ortiz, J. P., Morgan, M. M., Paten, A. M., Warden, A. C., Kilby, P.. 2024-08-07. MMINT: a Metabolic Model Interactive Network Tool for the exploration and comparative visualisation of metabolic networks. https://doi.org/10.1101/2024.08.06.606923

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Living electronic transistors with tunable conductivity

Electroactive bacteria, like Shewanella oneidensis, can couple the oxidation of organic electron donors to the reduction of external conductive surfaces, such as minerals and electrodes, by utilizing multiheme cytochromes to carry charge from within the cell to external surfaces. Additionally, multiheme cytochromes facilitate gateable, long-distance (micrometer-scale) redox conduction along the outer membrane and across multiple cells bridging electrodes. While electroactive microbes are being used to develop bioelectrochemical devices, there have been limited efforts to use synthetic biology to exert additional control over microbes serving as device components. Thus, this work implements an optogenetic biofilm patterning gene circuit and a small molecule sensor in S. oneidensis to simultaneously control cell deposition and cytochrome expression. This allows for photolithographic patterning of biofilms possessing tunable electrical properties controlled with small molecules. This system demonstrates tunable electrochemical activity, redox conduction, intrinsic biofilm conductivity, and negative differential transconductance as a function of cytochrome expression. Additionally, temperature-dependent measurements of this tunable biofilm conduction reveal changes in activation energy as a function of cytochrome expression. Through this combination of synthetic biology and electrochemistry, simultaneous control over biofilm geometry and conductivity sheds light on fundamental microbial electron transport processes, and it enables the construction of living electronic devices.

synthetic biology↗
bioRxiv2026

Evolutionary stabilisation of stressful metabolism via integrated biocomputing and essential-gene metabolic locking circuits

Synthetic genetic circuits enable microbial differentiation from growth to production, yet metabolic burden, imbalance and toxicity frequently drive strain degeneration. Yeast strains engineered to produce different terpene products exhibited divergent genetic responses to metabolic stresses, but commonly underwent progressive loss of induction of synthetic GAL regulatory circuits, either across the entire population or within subpopulations. Using di- and tri-input biocomputing circuits, the essential glutamine synthetase gene GLN1 was coupled to GAL induction, thereby enabling stabilisation and evolutionary adaptation of the synthetic genetic circuits and stressful heterologous terpene synthetic pathways. The integrated biocomputing and metabolic coupling circuit systems not only prevent strain degeneration but also enable interrogation of non-degenerative evolutionary shifts, providing a platform for metabolic engineering optimisation.

synthetic biology↗
bioRxiv2026

Unbiased and scalable reduction of diverse bacterial genomes

The genome is a complex, integrated system where the functions and regulatory interactions of its many components remain poorly understood. Genome minimization aims to reduce genomic complexity by removing non-essential elements to reveal the fundamental building blocks of cellular life. However, current minimization strategies are often slow and species-specific due to a reliance on prior information, and limited to producing single, isolated strains, which obscures the diverse ways a genome can adapt to large-scale DNA removal. Here we show the development and application of Stochastic Lineage-based Iterative Minimization (SLIM) a modular, high-throughput platform for unbiased genome reduction across phylogenetically diverse bacteria. We apply SLIM to generate a library of genome-reduced Escherichia coli lineages. We then interrogate the lineages, identifying both universal and lineage-specific transcriptional and translational reprogramming in response to deletions. We demonstrate that these expression dynamics drive environment-dependent fitness, allowing us to pinpoint a single gene deletion in one genome-reduced lineage as the driver of a measurable environmental growth defect. Beyond E. coli, we successfully deploy SLIM in phylogenetically distinct bacterial taxa to rapidly reduce the genomes of Shigella flexneri and Pseudomonas putida, distinct genus and order respectively from E. coli, without species-specific optimization. Our results establish a scalable, generalizable framework for navigating the vast landscape of minimized genomes, providing a powerful new tool for functional discovery and the rational design of synthetic genomic chassis.

synthetic biology↗