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1. md_harmonize: A Python Package for Atom-Level Harmonization of Public Metabolic Databases

   https://doi.org/10.3390/metabo13121199  

   Jin, Huan; Moseley, Hunter_N B (December 2023, Metabolites)

   A major challenge to integrating public metabolic resources is the use of different nomenclatures by individual databases. This paper presents md_harmonize, an open-source Python package for harmonizing compounds and metabolic reactions across various metabolic databases. The md_harmonize package utilizes a neighborhood-specific graph coloring method for generating a unique identifier for each compound via atom identifiers based on a compound’s chemical structure. The resulting harmonized compounds and reactions can be used for various downstream analyses, including the construction of atom-resolved metabolic networks and models for metabolic flux analysis. Parts of the md_harmonize package have been optimized using a variety of computational techniques to allow certain NP-complete problems handled by the software to be tractable for these specific use-cases. The software is available on GitHub and through the Python Package Index, with end-user documentation hosted on GitHub Pages. 

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2. Integrative teaching of metabolic modeling and flux analysis with interactive python modules

   https://doi.org/10.1002/bmb.21777  

   Kaste, Joshua_A M; Green, Antwan; Shachar‐Hill, Yair (November 2023, Biochemistry and Molecular Biology Education)

   Abstract The modeling of rates of biochemical reactions—fluxes—in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint‐based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to‐date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C‐metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands‐on computer laboratory component of a four‐day metabolic modeling workshop and participant survey results showed improvements in learners' self‐assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level. 

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3. Plant Metabolic Network 16: expansion of underrepresented plant groups and experimentally supported enzyme data

   https://doi.org/10.1093/nar/gkae991  

   Hawkins, Charles; Xue, Bo; Yasmin, Farida; Wyatt, Gabrielle; Zerbe, Philipp; Rhee, Seung Y (January 2025, Nucleic Acids Research)

   Abstract The Plant Metabolic Network (PMN) is a free online database of plant metabolism available at https://plantcyc.org. The latest release, PMN 16, provides metabolic databases representing >1200 metabolic pathways, 1.3 million enzymes, >8000 metabolites, >10 000 reactions and >15 000 citations for 155 plant and green algal genomes, as well as a pan-plant reference database called PlantCyc. This release contains 29 additional genomes compared with PMN 15, including species listed by the African Orphan Crop Consortium and nonflowering plant species. Furthermore, 52 new enzymes with experimentally supported function information have been included in this release. The single-species databases contain a combination of experimental information from the literature and computationally predicted information obtained through PMN’s database generation pipeline for a single species, while PlantCyc contains only experimental information but for any species within Viridiplantae. PMN is a comprehensive resource for querying, visualizing, analyzing and interpreting omics data with metabolic knowledge. It also serves as a useful and interactive tool for teaching plant metabolism. 

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4. Considering Metabolic Context in Enzyme Evolution and Design

   https://doi.org/10.1021/acs.biochem.5c00165  

   Dokwal, Dhiraj; Brown, Philip M; Filipowska, Karolina; Reynolds, Kimberly A (August 2025, Biochemistry)

   Enzymes are often treated as isolated molecular entities when measuring their biochemical activity and designing synthetic sequences. Yet inside the cell, enzymes interact through shared metabolite pools, physical binding, and regulatory feedback. This coordination is necessary for responsive, cohesive metabolic behavior. Moreover, these interactions place constraints on enzyme activity, specificity, abundance, and ultimately sequence. Defining these interaction-mediated constraints is important to understand metabolic evolution and to design synthetic systems. For example, the influence of the cellular and environmental milieu becomes evident when a heterologous enzyme from one species fails to function properly upon transfer to a new host. In this review we consider how cell context shapes enzyme evolution, and in turn, how variations in enzyme biochemistry, biophysics, and abundance impact cell fitness. 

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5. Computing optimal factories in metabolic networks with negative regulation

   https://doi.org/10.1093/bioinformatics/btac231  

   Krieger, Spencer; Kececioglu, John (June 2022, Bioinformatics)

   Abstract Motivation A factory in a metabolic network specifies how to produce target molecules from source compounds through biochemical reactions, properly accounting for reaction stoichiometry to conserve or not deplete intermediate metabolites. While finding factories is a fundamental problem in systems biology, available methods do not consider the number of reactions used, nor address negative regulation. Methods We introduce the new problem of finding optimal factories that use the fewest reactions, for the first time incorporating both first- and second-order negative regulation. We model this problem with directed hypergraphs, prove it is NP-complete, solve it via mixed-integer linear programming, and accommodate second-order negative regulation by an iterative approach that generates next-best factories. Results This optimization-based approach is remarkably fast in practice, typically finding optimal factories in a few seconds, even for metabolic networks involving tens of thousands of reactions and metabolites, as demonstrated through comprehensive experiments across all instances from standard reaction databases. Availability and implementation Source code for an implementation of our new method for optimal factories with negative regulation in a new tool called Odinn, together with all datasets, is available free for non-commercial use at http://odinn.cs.arizona.edu. 

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