Structure Of Metabolic Networks Research Articles

Understanding metabolic diversification in plants: branchpoints in the evolution of specialized metabolism.

Plants are chemical engineers par excellence. Collectively they make a vast array of structurally diverse specialized metabolites. The raw materials for building new pathways (genes encoding biosynthetic enzymes) are commonly recruited directly or indirectly from primary metabolism. Little is known about how new metabolic pathways and networks evolve in plants, or what key nodes contribute to branches that lead to the biosynthesis of diverse chemicals. Here we review the molecular mechanisms underlying the generation of biosynthetic branchpoints. We also consider examples in which new metabolites are formed through the joining of precursor molecules arising from different biosynthetic routes, a scenario that greatly increases both the diversity and complexity of specialized metabolism. Given the emerging importance of metabolic gene clustering in helping to identify new enzymes and pathways, we further cover the significance of biosynthetic gene clusters in relation to metabolic networks and dedicated biosynthetic pathways. In conclusion, an improved understanding of the branchpoints between metabolic pathways will be key in order to be able to predict and illustrate the complex structure of metabolic networks and to better understand the plasticity of plant metabolism. This article is part of the theme issue 'The evolution of plant metabolism'.

The metabolic domestication syndrome of budding yeast

Cellular metabolism evolves through changes in the structure and quantitative states of metabolic networks. Here, we explore the evolutionary dynamics of metabolic states by focusing on the collection of metabolite levels, the metabolome, which captures key aspects of cellular physiology. Using a phylogenetic framework, we profiled metabolites in 27 populations of nine budding yeast species, providing a graduated view of metabolic variation across multiple evolutionary time scales. Metabolite levels evolve more rapidly and independently of changes in the metabolic network's structure, providing complementary information to enzyme repertoire. Although metabolome variation accumulates mainly gradually over time, it is profoundly affected by domestication. We found pervasive signatures of convergent evolution in the metabolomes of independently domesticated clades of Saccharomyces cerevisiae. Such recurring metabolite differences between wild and domesticated populations affect a substantial part of the metabolome, including rewiring of the TCA cycle and several amino acids that influence aroma production, likely reflecting adaptation to human niches. Overall, our work reveals previously unrecognized diversity in central metabolism and the pervasive influence of human-driven selection on metabolite levels in yeasts.

Network-wide thermodynamic constraints shape NAD(P)H cofactor specificity of biochemical reactions

The ubiquitous coexistence of the redox cofactors NADH and NADPH is widely considered to facilitate an efficient operation of cellular redox metabolism. However, it remains unclear what shapes the NAD(P)H specificity of specific redox reactions. Here, we present a computational framework to analyze the effect of redox cofactor swaps on the maximal thermodynamic potential of a metabolic network and use it to investigate key aspects of redox cofactor redundancy in Escherichia coli. As one major result, our analysis suggests that evolved NAD(P)H specificities are largely shaped by metabolic network structure and associated thermodynamic constraints enabling thermodynamic driving forces that are close or even identical to the theoretical optimum and significantly higher compared to random specificities. Furthermore, while redundancy of NAD(P)H is clearly beneficial for thermodynamic driving forces, a third redox cofactor would require a low standard redox potential to be advantageous. Our approach also predicts trends of redox-cofactor concentration ratios and could facilitate the design of optimal redox cofactor specificities.

Functional convergence in slow-growing microbial communities arises from thermodynamic constraints.

The dynamics of microbial communities is complex, determined by competition for metabolic substrates and cross-feeding of byproducts. Species in the community grow by harvesting energy from chemical reactions that transform substrates to products. In many anoxic environments, these reactions are close to thermodynamic equilibrium and growth is slow. To understand the community structure in these energy-limited environments, we developed a microbial community consumer-resource model incorporating energetic and thermodynamic constraints on an interconnected metabolic network. The central element of the model is product inhibition, meaning that microbial growth may be limited not only by depletion of metabolic substrates but also by accumulation of products. We demonstrate that these additional constraints on microbial growth cause a convergence in the structure and function of the community metabolic network-independent of species composition and biochemical details-providing a possible explanation for convergence of community function despite taxonomic variation observed in many natural and industrial environments. Furthermore, we discovered that the structure of community metabolic network is governed by the thermodynamic principle of maximum free energy dissipation. Our results predict the decrease of functional convergence in faster growing communities, which we validate by analyzing experimental data from anaerobic digesters. Overall, the work demonstrates how universal thermodynamic principles may constrain community metabolism and explain observed functional convergence in microbial communities.

Unveiling the dark side of guard cell metabolism

Evidence suggests that guard cells have higher rate of phosphoenolpyruvate carboxylase (PEPc)-mediated dark CO2 assimilation than mesophyll cells. However, it is unknown which metabolic pathways are activated following dark CO2 assimilation in guard cells. Furthermore, it remains unclear how the metabolic fluxes throughout the tricarboxylic acid (TCA) cycle and associated pathways are regulated in illuminated guard cells. Here we carried out a13C–HCO3 labelling experiment in tobacco guard cells harvested under continuous dark or during the dark-to-light transition to elucidate principles of metabolic dynamics downstream of CO2 assimilation. Most metabolic changes were similar between dark-exposed and illuminated guard cells. However, illumination altered the metabolic network structure of guard cells and increased the 13C-enrichment in sugars and metabolites associated to the TCA cycle. Sucrose was labelled in the dark, but light exposure increased the 13C-labelling and leads to more drastic reductions in the content of this metabolite. Fumarate was strongly labelled under both dark and light conditions, while illumination increased the 13C-enrichment in pyruvate, succinate and glutamate. Only one 13C was incorporated into malate and citrate in either dark or light conditions. Our results indicate that several metabolic pathways are redirected following PEPc-mediated CO2 assimilation in the dark, including gluconeogenesis and the TCA cycle. We further showed that the PEPc-mediated CO2 assimilation provides carbons for gluconeogenesis, the TCA cycle and glutamate synthesis and that previously stored malate and citrate are used to underpin the specific metabolic requirements of illuminated guard cells.

Extracting functionally accurate context-specific models of Atlantic salmon metabolism

Constraint-based models (CBMs) are used to study metabolic network structure and function in organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture differences in reaction activities, which, in turn, determine metabolic capabilities, between cell types, tissues, environments, or other conditions. Only a subset of a CBM’s metabolic reactions and capabilities are likely to be active in any given context, and several methods have therefore been developed to extract context-specific models from generic CBMs through integration of omics data. We tested the ability of six model extraction methods (MEMs) to create functionally accurate context-specific models of Atlantic salmon using a generic CBM (SALARECON) and liver transcriptomics data from contexts differing in water salinity (life stage) and dietary lipids. Three MEMs (iMAT, INIT, and GIMME) outperformed the others in terms of functional accuracy, which we defined as the extracted models’ ability to perform context-specific metabolic tasks inferred directly from the data, and one MEM (GIMME) was faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling better captures salmon metabolism. Thus, we demonstrate that results from human studies also hold for a non-mammalian animal and major livestock species.

Metagenomic Analysis of Microbial Alliances for Efficient Degradation of PHE: Microbial Community Structure and Reconstruction of Metabolic Network.

Polycyclic aromatic hydrocarbons are a widespread organic pollutant worldwide. In this study, a highly efficient phenanthrene (PHE)-degrading microbial community was enriched from oil extraction soil, which could degrade 500 mg/L PHE within 4 days. Using 16S rRNA sequencing, the dominant bacteria in this community at the phylum level were found to be Proteobacteria, Actinobacteria, and Firmicutes. Metagenomic annotation of genes revealed the metabolic pathways and the contribution of different bacteria to the degradation process. Pseudomonadaceae contributed multiple functional genes in the degradation process. This study revealed the functional genes, metabolic pathways, and microbial interactions of the microbial community, which are expected to provide guidance for practical management.

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme levels through mechanistic relations, rendering them essential for understanding, predicting and optimizing the behaviour of living organisms. However, due to the lack of kinetic data, traditional kinetic modelling often yields only a few or no kinetic models with desirable dynamical properties, making the analysis unreliable and computationally inefficient. We present REKINDLE (Reconstruction of Kinetic Models using Deep Learning), a deep-learning-based framework for efficiently generating kinetic models with dynamic properties matching the ones observed in cells. We showcase REKINDLE’s capabilities to navigate through the physiological states of metabolism using small numbers of data with significantly lower computational requirements. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in biotechnology and health.

Urban spatial structure features in Qinling mountain area based on ecological network analysis-case study of Shangluo City

Since the 20th century, the world is facing two major problems: the serious deterioration of the ecological environment and the accelerated decline of living quality of people residing in the urban environment. Urban development policies, ecological patterns, production and consumption modes have an important impact on global environmental changes that threaten the future living of mankind. The focus of urban space construction has also turned to the fields of ecological security, energy development, resource management and urban ecological virtuous circulation. Investigating urban spatial structure features from the perspective of urban metabolic system is important for ecological security and resource management. Shangluo urban space in Qinling area is used as the research object and an analysis system of urban spatial structure based on ecological network analysis is proposed in this study. First, an ecological network model of urban space was constructed from the research perspective of ecological network analysis and the system metabolic level was evaluated using metabolic scale, intensity, and efficiency. Second, the system structure of urban metabolic network and its functional relations were analyzed. On this basis, interactive relationships among different components in the metabolic system were explored via symbiosis index and symbiotic, competitive, predatory, and control relations. Meanwhile, the ecological network analysis method is used to construct an analysis system for urban spatial structure in the Qinling mountain area. Finally, the analysis system of urban spatial structure in the Qinling mountain area is applied to examine the urban spatial structure of Shangluo, Qinling Mountains, China. Results showed that resource utilization efficiency of Shangluo City presents a rising trend and the city is located in a strong structural center in the regional space. Predatory and control relations played a dominant role among ecological relations in the urban metabolic structure, and the urban space presented a clear uniaxially extended spatial structure. Sensitivity of internal environment was extremely evident, and the natural spatial structure was an important basis for urban space development in Shangluo. The urban spatial structure analysis system in this research is constructed with the three aspects of “metabolic level”, “ecological relationship” and “compartment weight” of the spatial structure information, which solves the problem that the conventional spatial structure analysis system is difficult to quantify the ecological network information, and it provides a new idea to explain the internal component interaction mechanism and spatial structure composition of the urban system. The results of this study can provide not only useful reference for urban spatial structure analysis of underdeveloped ecological capital areas in China but also a basis for sustainable development planning of urban spaces.

Trade-offs predicted by metabolic network structure give rise to evolutionary specialization and phenotypic diversification.

Mitigating trade-offs between different resource-utilization functions is key to an organism’s ecological and evolutionary success. These trade-offs often reflect metabolic constraints with a complex molecular underpinning; therefore, their consequences for evolutionary processes have remained elusive. Here, we investigate how metabolic architecture induces resource-utilization constraints and how these constraints, in turn, elicit evolutionary specialization and diversification. Guided by the metabolic network structure of the bacterium Lactococcus cremoris, we selected two carbon sources (fructose and galactose) with predicted coutilization constraints. By evolving L. cremoris on either fructose, galactose, or a mix of both sugars, we imposed selection favoring divergent metabolic specializations or coutilization of both resources, respectively. Phenotypic characterization revealed the evolution of either fructose or galactose specialists in the single-sugar treatments. In the mixed-sugar regime, we observed adaptive diversification: both specialists coexisted, and no generalist evolved. Divergence from the ancestral phenotype occurred at key pathway junctions in the central carbon metabolism. Fructose specialists evolved mutations in the fbp and pfk genes that appear to balance anabolic and catabolic carbon fluxes. Galactose specialists evolved increased expression of pgmA (the primary metabolic bottleneck of galactose metabolism) and silencing of ptnABCD (the main glucose transporter) and ldh (regulator/enzyme of downstream carbon metabolism). Overall, our study shows how metabolic network architecture and historical contingency serve to predict targets of selection and inform the functional interpretation of evolved mutations. The elucidation of the relationship between molecular constraints and phenotypic trade-offs contributes to an integrative understanding of evolutionary specialization and diversification.

The hidden simplicity of metabolic networks is revealed by multireaction dependencies.

Understanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceived from their structure alone.

Cysteine and iron accelerate the formation of ribose-5-phosphate, providing insights into the evolutionary origins of the metabolic network structure.

The structure of the metabolic network is highly conserved, but we know little about its evolutionary origins. Key for explaining the early evolution of metabolism is solving a chicken–egg dilemma, which describes that enzymes are made from the very same molecules they produce. The recent discovery of several nonenzymatic reaction sequences that topologically resemble central metabolism has provided experimental support for a “metabolism first” theory, in which at least part of the extant metabolic network emerged on the basis of nonenzymatic reactions. But how could evolution kick-start on the basis of a metal catalyzed reaction sequence, and how could the structure of nonenzymatic reaction sequences be imprinted on the metabolic network to remain conserved for billions of years? We performed an in vitro screening where we add the simplest components of metabolic enzymes, proteinogenic amino acids, to a nonenzymatic, iron-driven reaction network that resembles glycolysis and the pentose phosphate pathway (PPP). We observe that the presence of the amino acids enhanced several of the nonenzymatic reactions. Particular attention was triggered by a reaction that resembles a rate-limiting step in the oxidative PPP. A prebiotically available, proteinogenic amino acid cysteine accelerated the formation of RNA nucleoside precursor ribose-5-phosphate from 6-phosphogluconate. We report that iron and cysteine interact and have additive effects on the reaction rate so that ribose-5-phosphate forms at high specificity under mild, metabolism typical temperature and environmental conditions. We speculate that accelerating effects of amino acids on rate-limiting nonenzymatic reactions could have facilitated a stepwise enzymatization of nonenzymatic reaction sequences, imprinting their structure on the evolving metabolic network.

Channeling Anabolic Side Products toward the Production of Nonessential Metabolites: Stable Malate Production in Synechocystis sp. PCC6803.

Powered by (sun)light to oxidize water, cyanobacteria can directly convert atmospheric CO2 into valuable carbon-based compounds and meanwhile release O2 to the atmosphere. As such, cyanobacteria are promising candidates to be developed as microbial cell factories for the production of chemicals. Nevertheless, similar to other microbial cell factories, engineered cyanobacteria may suffer from production instability. The alignment of product formation with microbial fitness is a valid strategy to tackle this issue. We have described previously the “FRUITS” algorithm for the identification of metabolites suitable to be coupled to growth (i.e., side products in anabolic reactions) in the model cyanobacterium Synechocystis. sp PCC6803. However, the list of candidate metabolites identified using this algorithm can be somewhat limiting, due to the inherent structure of metabolic networks. Here, we aim at broadening the spectrum of candidate compounds beyond the ones predicted by FRUITS, through the conversion of a growth-coupled metabolite to downstream metabolites via thermodynamically favored conversions. We showcase the feasibility of this approach for malate production using fumarate as the growth-coupled substrate in Synechocystis mutants. A final titer of ∼1.2 mM was achieved for malate during photoautotrophic batch cultivations. Under prolonged continuous cultivation, the most efficient malate-producing strain can maintain its productivity for at least 45 generations, sharply contrasting with other producing Synechocystis strains engineered with classical approaches. Our study also opens a new possibility for extending the stable production concept to derivatives of growth-coupled metabolites, increasing the list of suitable target compounds.

Pyridoxal and α-Ketoglutarate Independently Improve Function of Saccharomyces cerevisiae Thi5 in the Metabolic Network of Salmonella enterica.

Microbial metabolism is often considered modular, but metabolic engineering studies have shown that transferring pathways, or modules, between organisms is not always straightforward. The Thi5-dependent pathway(s) for synthesis of the pyrimidine moiety of thiamine from Saccharomyces cerevisiae and Legionella pneumophila functioned differently when incorporated into the metabolic network of Salmonella enterica. Function of Thi5 from Saccharomyces cerevisiae (ScThi5) required modification of the underlying metabolic network, while LpThi5 functioned with the native network. Here we probe the metabolic requirements for heterologous function of ScThi5 and report strong genetic and physiological evidence for a connection between alpha-ketoglutarate (αKG) levels and ScThi5 function. The connection was built with two classes of genetic suppressors linked to metabolic flux or metabolite pool changes. Further, direct modulation of nitrogen assimilation through nutritional or genetic modification implicated αKG levels in Thi5 function. Exogenous pyridoxal similarly improved ScThi5 function in S. enterica. Finally, directly increasing αKG and PLP with supplementation improved function of both ScThi5 and relevant variants of Thi5 from Legionella pneumophila (LpThi5). The data herein suggest structural differences between ScThi5 and LpThi5 impact their level of function in vivo and implicate αKG in supporting function of the Thi5 pathway when placed in the heterologous metabolic network of S. enterica. IMPORTANCE Thiamine biosynthesis is a model metabolic node that has been used to extend our understanding of metabolic network structure and individual enzyme function. The requirements for in vivo function of the Thi5-dependent pathway found in Legionella and yeast are poorly characterized. Here we suggest that αKG modulates function of the Thi5 pathway in S. enterica and provide evidence that structural variation between ScThi5 and LpThi5 contributes to their functional differences in a Salmonella enterica host.

Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome‐scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence‐level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.

Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure

Uncovering the general principles that govern the structure of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries can help illuminate this problem by enabling the exploration of chemical reaction universes that are constrained by general mathematical rules. Here, we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We developed a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET), to study string chemistries using tools from the field of stoichiometric constraint-based modeling. In addition to exploring the topological characteristics of different string chemistry networks, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental nutrients. We found that the composition of these minimal metabolic networks was influenced more strongly by the metabolites in the biomass reaction than the identities of the environmental nutrients. This finding has important implications for the reconstruction of organismal metabolic networks and could help us better understand the rise and evolution of biochemical organization. More generally, our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.

Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models

Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology. In principle, we can perform a hand-on checking (Manual Curation). However, this is a challenging task due to the high number of combinations of pairs of nodes (possible metabolic reactions). The CPTML linear model obtained using the LDA algorithm is able to discriminate nodes (metabolites) with the correct assignation of reactions from incorrect nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series. In this work, we used Combinatorial Perturbation Theory and Machine Learning techniques to seek a CPTML model for MRNs >40 organisms compiled by Barabasis' group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms. Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens the door to the study of MRNs of multiple organisms using PTML models.

The metabolic origins of non-photorespiratory CO2 release during photosynthesis: a metabolic flux analysis.

Respiration in the light (RL) releases CO2 in photosynthesizing leaves and is a phenomenon that occurs independently from photorespiration. Since RL lowers net carbon fixation, understanding RL could help improve plant carbon-use efficiency and models of crop photosynthesis. Although RL was identified more than 75 years ago, its biochemical mechanisms remain unclear. To identify reactions contributing to RL, we mapped metabolic fluxes in photosynthesizing source leaves of the oilseed crop and model plant camelina (Camelina sativa). We performed a flux analysis using isotopic labeling patterns of central metabolites during 13CO2 labeling time course, gas exchange, and carbohydrate production rate experiments. To quantify the contributions of multiple potential CO2 sources with statistical and biological confidence, we increased the number of metabolites measured and reduced biological and technical heterogeneity by using single mature source leaves and quickly quenching metabolism by directly injecting liquid N2; we then compared the goodness-of-fit between these data and data from models with alternative metabolic network structures and constraints. Our analysis predicted that RL releases 5.2 μmol CO2 g-1 FW h-1 of CO2, which is relatively consistent with a value of 9.3 μmol CO2 g-1 FW h-1 measured by CO2 gas exchange. The results indicated that ≤10% of RL results from TCA cycle reactions, which are widely considered to dominate RL. Further analysis of the results indicated that oxidation of glucose-6-phosphate to pentose phosphate via 6-phosphogluconate (the G6P/OPP shunt) can account for >93% of CO2 released by RL.

The Contribution of Metabolomics to Systems Biology: Current Applications Bridging Genotype and Phenotype in Plant Science.

Metabolomics is a valuable approach used to acquire comprehensive information about the set of metabolites in a cell or tissue, enabling a functional screen of the cellular activities in biological systems. Although metabolomics provides a more immediate and dynamic picture of phenotypes in comparison to the other omics, it is also the most complicated to measure because no single analytical technology can capture the extraordinary complexity of metabolite diversity in terms of structure and physical properties. Metabolomics has been extensively employed for a wide range of applications in plant science, which will be described in detail in this chapter. Among them, metabolomics is used for discriminating patterns of plant responses to genetic and environmental perturbations, as diagnostics and prediction tool to elucidate the function of genes for important and complex agronomic traits in crop species, and flux measurements are used to dissect the structure and regulatory properties of metabolic networks.

IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks.

Nanoparticles are useful antimicrobial drug-release systems, but some nanoparticles also exhibit antibacterial activity. However, investigation of their antibacterial activity is a difficult and slow process due to the numerous combinations of nanoparticle size, shape, and composition vs. biological tests, assay organisms, and multiple activity parameters to be measured. Additionally, the overuse of antibiotics has led to the emergence of resistant bacterial strains with different metabolic networks. Computational models may speed up this process, but the models reported to date do not to consider all the previous factors, and the data sources are dispersed and not curated. Thus, herein, we used an information fusion, perturbation-theory machine learning (IFPTML) approach, which is introduced by us for the first time, to fit a model for the discovery of antibacterial nanoparticles. The dataset studied had 15 classes of nanoparticles (1-100 nm) with most cases in the range of 1-50 nm vs. >20 pathogenic bacteria species with different metabolic networks. The nanoparticles studied included metal nanoparticles of Au, Ag, and Cu; oxide nanoparticles of Zn, Cu, La, Al, Fe, Sn, Ti, Cd, and Si; and metal salt nanoparticles of CuI and CdS. We used the SOFT.PTML software (our own application) with a user-friendly interface for the IFPTML calculations and a control statistics package. Using SOFT.PTML, we found a linear logistic regression equation that could model 4 biological activity parameters using only 8 variables with χ2 = 2265.75, p-level <0.05, sensitivity, Sn = 79.4, and specificity, Sp = 99.3, for 3213 cases (nanoparticle-bacteria pairs) in the training series. The model had Sn = 80.8 and Sp = 99.3 for 2114 cases in the external validation series. We also developed a random forest non-linear model with higher values of Sn and Sp = 98-99% in the training/validation series, although it was more complicated to use. SOFT.PTML has been demonstrated to be a useful tool for the analysis of complex data in nanotechnology. We also introduced a new anabolism-catabolism unbalance index of metabolic networks to reveal the biological connotation of the IFPTML predictions for antibacterial nanoparticles. These new models open a new door for the discovery of NPs vs. new bacterial species and strains with different topological structures of their metabolic networks.