Metabolic Reaction Rates Research Articles

Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality.

Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint-based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable "golden batches" in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.

Isotopically nonstationary metabolic flux analysis of plants: recent progress and future opportunities.

Metabolic flux analysis (MFA) is a valuable tool for quantifying cellular phenotypes and to guide plant metabolic engineering. By introducing stable isotopic tracers and employing mathematical models, MFA can quantify the rates of metabolic reactions through biochemical pathways. Recent applications of isotopically nonstationary MFA (INST-MFA) to plants have elucidated nonintuitive metabolism in leaves under optimal and stress conditions, described coupled fluxes for fast-growing algae, and produced a synergistic multi-organ flux map that is a first in MFA for any biological system. These insights could not be elucidated through other approaches and show the potential of INST-MFA to correct an oversimplified understanding of plant metabolism.

An exercise physiologist's guide to metabolomics.

The field of exercise physiology has undergone significant technological advancements since the pioneering works of exercise physiologists in the early to mid-20th century. Historically, the ability to detect metabolites in biofluids from exercising participants was limited to single-metabolite analyses. However, the rise of metabolomics, a discipline focused on the comprehensive analysis of metabolites within a biological system, has facilitated a more intricate understanding of metabolic pathways and networks in exercise. This review explores some of the pivotal technological and bioinformatic advancements that have propelled metabolomics to the forefront of exercise physiology research. Metabolomics offers a unique 'fingerprint' of cellular activity, offering a broader spectrum than traditional single-metabolite assays. Techniques, including mass spectrometry and nuclear magnetic resonance spectroscopy, have significantly improved the speed and sensitivity of metabolite analysis. Nonetheless, challenges persist, including study design and data interpretation issues. This review aims to serve as a guide for exercise physiologists to facilitate better research design, data analysis and interpretation within metabolomics. The potential of metabolomics in bridging the gap between genotype and phenotype is emphasised, underscoring the critical importance of careful study design and the selection of appropriate metabolomics techniques. Furthermore, the paper highlights the need to deeply understand the broader scientific context to discern meaningful metabolic changes. The emerging field of fluxomics, which seeks to quantify metabolic reaction rates, is also introduced as a promising avenue for future research.

Abstract 5352: Targeting DNA methylation in T cells to improve the efficacy of immunotherapy

Abstract T-cells are critical mediators of immunity and immunologic memory. Their cell fates are regulated in part through epigenetic mechanisms, including DNA methylation. Recent genome-wide methylation analyses have revealed dynamic alterations in the methylome at various stages of development and differentiation of T cells. At single cell level, it is not easy to simultaneously collect RNA-seq and RBBS methylation profiling. An important task is to understand the expression change of which genes and pathways are regulated by DNA methylations, especially for the ones that are associated with functional variations in the T cells from tumor microenvironment.In this study, we developed a computational approach based on our recently developed metabolic flux estimation to estimate cell-wise global DNA methylation activity level by using scRNA-seq data. We also hypothesize that the global DNA methylation activity level in one cell determines most of the DNA methylation level in gene-specific DNA methylations. Hence, the dependency between gene-specific DNA methylation and expression could be imputed by the dependency between predicted global DNA methylation input level and the gene expression. We validated our method to impute cell-wise global DNA methylation level by using four independent sets of paired gene expression and DNA methylation data. Noted, our prediction of global DNA methylation activity is from a pure metabolic perspective. We found that two metabolic reaction rates, named metabolic flux from methionine to SAM and SAM to SAH, purely predicted by using gene expression data can accurately impute DNA methylation activity in all validating data sets. Our method enables further identification of the disease/cell context specific contributor of DNA methylation, i.e., the genes high contribution to DNA methylations in each individual cell or cell groups. We applied our method on scRNA-seq data of different T cell types extracted from TME of lung, liver, and colon cancer. We have seen that exhausted T cells, especially the ones with decreased Granzymes and PRF1 are associated with increased global DNA methylation level and related genes, suggesting the potential clinical implications in targeting DNA methylation to improve the efficacy of immunotherapies. Citation Format: Pengtao Dang, Xiao Wang, Haiqi Zhu, Jia Wang, Tingbo Guo, Xinyu Zhou, Paveethran Swaminathan, Chi Zhang, Sha Cao. Targeting DNA methylation in T cells to improve the efficacy of immunotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5352.

Global determination of reaction rates and lipid turnover kinetics in Mus musculus

Metabolism is fundamental to life, but measuring metabolic reaction rates remains challenging. Here, we applied C13 fluxomics to monitor the metabolism of dietary glucose carbon in 12 tissues, 9 brain compartments, and over 1,000 metabolite isotopologues over a 4-day period. The rates of 85 reactions surrounding central carbon metabolism are determined with elementary metabolite unit (EMU) modeling. Lactate oxidation, not glycolysis, occurs at a comparable pace with the tricarboxylic acid cycle (TCA), supporting lactate as the primary fuel. We expand the EMU framework to track and quantify metabolite flows across tissues. Specifically, multi-organ EMU simulation of uridine metabolism shows that tissue-blood exchange, not synthesis, controls nucleotide homeostasis. In contrast, isotopologue fingerprinting and kinetic analyses reveal the brown adipose tissue (BAT) having the highest palmitate synthesis activity but no apparent contribution to circulation, suggesting a tissue-autonomous synthesis-to-burn mechanism. Together, this study demonstrates the utility of dietary fluxomics for kinetic mapping invivo and provides a rich resource for elucidating inter-organ metabolic cross talk.

Models and molecular mechanisms for trade-offs in the context of metabolism.

Accumulating evidence for trade-offs involving metabolic traits has demonstrated their importance in the evolution of organisms. Metabolic models with different levels of complexity have already been considered when investigating mechanisms that explain various metabolic trade-offs. Here we provide a systematic review of modelling approaches that have been used to study and explain trade-offs between: (i) the kinetic properties of individual enzymes, (ii) rates of metabolic reactions, (iii) the rate and yield of metabolic pathways and networks, (iv) different metabolic objectives in single organisms and in metabolic communities, and (v) metabolic concentrations. In providing insights into the mechanisms underlying these five types of metabolic trade-offs obtained from constraint-based metabolic modelling, we emphasize the relationship of metabolic trade-offs to the classical black box Y-model that provides a conceptual explanation for resource acquisition-allocation trade-offs. In addition, we identify several pressing concerns and offer a perspective for future research in the identification and manipulation of metabolic trade-offs by relying on the toolbox provided by constraint-based metabolic modelling for single organisms and microbial communities.

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease.

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

Acceleration of enzymatic catalysis by active hydrodynamic fluctuations

The cellular milieu is teeming with biochemical nano-machines whose activity is a strong source of correlated non-thermal fluctuations termed active noise. Essential elements of this circuitry are enzymes, catalysts that speed up the rate of metabolic reactions by orders of magnitude, thereby making life possible. Here, we examine the possibility that active noise in the cell, or in vitro, affects enzymatic catalytic rate by accelerating or decelerating the crossing rate of energy barriers during the reaction. Considering hydrodynamic perturbations induced by biochemical activity as a source of active noise, we evaluate their impact on the enzymatic cycle using a combination of analytic and numerical methods. Our estimates show that the fast component of the active noise spectrum may significantly enhance the turnover rate of enzymes, while reactions remain practically unaffected by the slow noise spectrum. Revisiting the physics of barrier crossing under the influence of active hydrodynamic fluctuations suggests that the biochemical activity of macromolecules such as enzymes is coupled to active noise. Thus, we propose that enzymatic catalysis is a collective, many-body process in which enzymes may affect each other’s activity via long-range hydrodynamic interaction, with potential impact on biochemical networks in living and artificial systems alike.

Pool size measurements improve precision of flux estimates but increase sensitivity to unmodeled reactions outside the core network in isotopically nonstationary metabolic flux analysis (INST-MFA).

Metabolic flux analysis (MFA) involves model-based estimation of metabolic reaction rates (i.e., fluxes) and, in some cases, metabolite content (i.e., pool sizes) from experimental measurements. Applying MFA to biological data helps determine the fate of substrates and the activity of specific pathways within metabolic networks. However, reliably estimating fluxes by using simplified "core" models to predict the dynamics of larger metabolic networks remains a challenge. One point of uncertainty relates to the advantages and potential pitfalls of including pool size measurements as experimental inputs for isotopically nonstationary MFA (INST-MFA). Here, we directly assessed the role of pool sizes using various core models and simulated datasets. To investigate the effects of pool size measurements on INST-MFA, we assessed the accuracy and precision of flux estimates obtained using different subsets of data (e.g., with or without pool size measurements) and simple network models that either matched or differed from the true network. The inclusion of pool size measurements provided incremental improvements to the precision of the flux estimates. However, adding pool size measurements increased the sensitivity of the flux solution to unmodeled reactions outside the core network. These results were confirmed using a large Escherichia coli model that is representative of realistic metabolic networks examined in MFA studies. Our findings indicate that accurate flux estimates can be obtained in the absence of pool size measurements, even when using core models that lack full network coverage. Addition of pool size measurements to INST-MFA datasets may reveal the activity of non-core reactions that influence the labeling dynamics and therefore necessitate network expansion in order to reconcile all available data to the model. Our findings also emphasize the key role that goodness-of-fit testing plays in assessing the quality of model fits obtained with INST-MFA.

Robustifying Experimental Tracer Design for13C-Metabolic Flux Analysis.

13C metabolic flux analysis (MFA) has become an indispensable tool to measure metabolic reaction rates (fluxes) in living organisms, having an increasingly diverse range of applications. Here, the choice of the13C labeled tracer composition makes the difference between an information-rich experiment and an experiment with only limited insights. To improve the chances for an informative labeling experiment, optimal experimental design approaches have been devised for13C-MFA, all relying on some a priori knowledge about the actual fluxes. If such prior knowledge is unavailable, e.g., for research organisms and producer strains, existing methods are left with a chicken-and-egg problem. In this work, we present a general computational method, termed robustified experimental design (R-ED), to guide the decision making about suitable tracer choices when prior knowledge about the fluxes is lacking. Instead of focusing on one mixture, optimal for specific flux values, we pursue a sampling based approach and introduce a new design criterion, which characterizes the extent to which mixtures are informative in view of all possible flux values. The R-ED workflow enables the exploration of suitable tracer mixtures and provides full flexibility to trade off information and cost metrics. The potential of the R-ED workflow is showcased by applying the approach to the industrially relevant antibiotic producer Streptomyces clavuligerus, where we suggest informative, yet economic labeling strategies.

Thirty years development of ¹³C metabolic flux analysis: a review

¹³C metabolic flux analysis (¹³C-MFA) enables the precise quantification of intracellular metabolic reaction rates by analyzing the distribution of mass isotopomers of proteinogenic amino acids or intracellular metabolites through ¹³C labeling experiments. ¹³C-MFA has received much attention as it can help systematically understand cellular metabolic characteristics, guide metabolic engineering design and gain mechanistic insights into pathophysiology. This article reviews the advances of ¹³C-MFA in the past 30 years and discusses its potential and future perspective, with a focus on its application in industrial biotechnology and biomedicine.

Inhibitory Effect of Soybean Seed Extracts on Human Salivary Amylase: An <i>in vitro</i> Study

Enzymes are biological catalysts that accelerate the rate of metabolic reactions. Amylases are the class of hydrolase, split the (1-4) glycosidic bonds of polysaccharides into glucose or maltose. Salivary and pancreatic α-amylases share 92 % of analogy in their catalytic region. In diabetic condition, α-amylases become a major challenge in managing postprandial hyperglycemia. Natural inhibitors have been well-known to treat diabetes and were accounted to contain compounds with anti α-amylase activity. Soybean has been reported to have anti-diabetic activity because of its rich phytochemical content. The aim of the present study is to examine the in vitro inhibitory effects of aqueous, methanol and acid methanol extracts on the salivary amylase activity. Soybean seeds were procured from the Chikkapet market of Tumakuru, Karnataka State, India. Salivary amylase activity was estimated by the DNS method using maltose as standard. Amylase activity was found to be high at pH of 6.8 and a temperature of 37𫌱C. Amylase inhibition exhibited by different solvent extracts of soybean has been recorded (92% in aqueous extract, 93% in methanol extract, 98% in acid methanol extract) and inhibition concentration (aqueous, 45.73 μg; methanol, 34.26 μg; acid methanol, 18.20 μg).Total polyphenolic contents of various solvent extracts of soybean (0.29% in aqueous extract, 0.25% in methanol extracts and 0.27 % in acid methanol) might be the reason for the significant inhibition of amylase activity. Consequently, the results obtained in the present investigation can be considered as useful in controlling the digestion of complex sugars among the diabetic population by influencing the catalytic activity of salivary amylase.

Computational Modeling of Fructose Metabolism and Development in NAFLD.

Non-alcohol fatty liver disease (NAFLD) is a common disorder that has increased in prevalence 20-fold over the last three decades. It covers a spectrum of conditions resulting from excess lipid accumulation in the liver without alcohol abuse. Among all the risk factors, over-consumption of fructose has been repeatedly reported in both clinical and experimental studies to be highly associated with the development of NAFLD. However, studying in vivo systems is complicated, time consuming and expensive. A detailed kinetic model of fructose metabolism was constructed to investigate the metabolic mechanisms whereby fructose consumption can induce dyslipidaemia associated with NAFLD and to explore whether the pathological conditions can be reversed during the early stages of disease. The model contains biochemical components and reactions identified from the literature, including ~120 parameters, 25 variables, and 25 first order differential equations. Three scenarios were presented to demonstrate the behavior of the model. Scenario one predicts the acute effects of a change in carbohydrate input in lipid profiles. The results present progressive triglyceride accumulations in the liver and plasma for three diets. The rate of accumulation was greater in the fructose diet than that of the mixed or glucose only models. Scenario two explores the variability of metabolic reaction rate within the general population. Sensitivity analysis reveals that hepatic triglyceride concentration is most sensitive to the rate constant of pyruvate kinase and fructokinase. Scenario three tests the effect of one specific inhibitor that might be potentially administered. The simulations of fructokinase suppression provide a good model for potentially reversing simple steatosis induced by high fructose consumption, which can be corroborated by experimental studies. The predictions in these three scenarios suggest that the model is robust and it has sufficient detail to present the kinetic relationship between fructose and lipid in the liver.

Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth

The current trends of crop yield improvements are not expected to meet the projected rise in demand. Genomic selection uses molecular markers and machine learning to identify superior genotypes with improved traits, such as growth. Plant growth directly depends on rates of metabolic reactions which transform nutrients into the building blocks of biomass. Here, we predict growth of Arabidopsis thaliana accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. We demonstrate that, comparing to classical genomic selection on the available data sets for 67 accessions, our approach improves the prediction accuracy for growth within and across nitrogen environments by 32.6% and 51.4%, respectively, and from optimal nitrogen to low carbon environment by 50.4%. Therefore, integration of molecular markers into metabolic models offers an approach to predict traits directly related to metabolism, and its usefulness in breeding can be examined by gathering matching datasets in crops.

Modeling and Exploiting Microbial Temperature Response

Temperature is an important parameter in bioprocesses, influencing the structure and functionality of almost every biomolecule, as well as affecting metabolic reaction rates. In industrial biotechnology, the temperature is usually tightly controlled at an optimum value. Smart variation of the temperature to optimize the performance of a bioprocess brings about multiple complex and interconnected metabolic changes and is so far only rarely applied. Mathematical descriptions and models facilitate a reduction in complexity, as well as an understanding, of these interconnections. Starting in the 19th century with the “primal” temperature model of Svante Arrhenius, a variety of models have evolved over time to describe growth and enzymatic reaction rates as functions of temperature. Data-driven empirical approaches, as well as complex mechanistic models based on thermodynamic knowledge of biomolecular behavior at different temperatures, have been developed. Even though underlying biological mechanisms and mathematical models have been well-described, temperature as a control variable is only scarcely applied in bioprocess engineering, and as a conclusion, an exploitation strategy merging both in context has not yet been established. In this review, the most important models for physiological, biochemical, and physical properties governed by temperature are presented and discussed, along with application perspectives. As such, this review provides a toolset for future exploitation perspectives of temperature in bioprocess engineering.

Hyperpolarized product selective saturating‐excitations for determination of changes in metabolic reaction rates in real‐time

Investigation of hyperpolarized substrate metabolism has been showing utility in real-time determination of in-cell and in vivo enzymatic activities. Intracellular reaction rates may vary during the course of a measurement, even on the very short time scales of visibility on hyperpolarized MR, due to many factors such as the availability of the substrate and co-factors in the intracellular space. Despite this potential variation, the kinetic analysis of hyperpolarized signals typically assumes that the same rate constant (and in many cases, the same rate) applies throughout the course of the reaction as observed via the build-up and decay of the hyperpolarized signals. We demonstrate here an acquisition approach that can null the need for such an assumption and enable the detection of instantaneous changes in the rate of the reaction during an ex vivo hyperpolarized investigation, (i.e. in the course of the decay of one hyperpolarized substrate dose administered to a viable tissue sample ex vivo). This approach utilizes hyperpolarized product selective saturating-excitation pulses. Similar pulses have been previously utilized in vivo for spectroscopic imaging. However, we show here favorable consequences to kinetic rate determinations in the preparations used. We implement this acquisition strategy for studies on perfused tissue slices and develop a theory that explains why this particular approach enables the determination of changes in enzymatic rates that are monitored via the chemical conversions of hyperpolarized substrates. Real-time changes in intracellular reaction rates are demonstrated in perfused brain, liver, and xenograft breast cancer tissue slices and provide another potential differentiation parameter for tissue characterization.

The Effect of Oxygen Limitation on a Xylophagous Insect's Heat Tolerance Is Influenced by Life-Stage Through Variation in Aerobic Scope and Respiratory Anatomy.

Temperature has a profound impact on insect fitness and performance via metabolic, enzymatic or chemical reaction rate effects. However, oxygen availability can interact with these thermal responses in complex and often poorly understood ways, especially in hypoxia-adapted species. Here we test the hypothesis that thermal limits are reduced under low oxygen availability – such as might happen when key life-stages reside within plants – but also extend this test to attempt to explain that the magnitude of the effect of hypoxia depends on variation in key respiration-related parameters such as aerobic scope and respiratory morphology. Using two life-stages of a xylophagous cerambycid beetle, Cacosceles (Zelogenes) newmannii we assessed oxygen-limitation effects on metabolic performance and thermal limits. We complement these physiological assessments with high-resolution 3D (micro-computed tomography scan) morphometry in both life-stages. Results showed that although larvae and adults have similar critical thermal maxima (CTmax) under normoxia, hypoxia reduces metabolic rate in adults to a greater extent than it does in larvae, thus reducing aerobic scope in the former far more markedly. In separate experiments, we also show that adults defend a tracheal oxygen (critical) setpoint more consistently than do larvae, indicated by switching between discontinuous gas exchange cycles (DGC) and continuous respiratory patterns under experimentally manipulated oxygen levels. These effects can be explained by the fact that the volume of respiratory anatomy is positively correlated with body mass in adults but is apparently size-invariant in larvae. Thus, the two life-stages of C. newmannii display key differences in respiratory structure and function that can explain the magnitude of the effect of hypoxia on upper thermal limits.

Bayesian metabolic flux analysis reveals intracellular flux couplings.

MotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary information Supplementary data are available at Bioinformatics online.

The Role of Pool Size Measurements in Improving Flux Estimations in Non-Stationary Metabolic Flux Analysis

Over the past several decades, Metabolic Flux Analysis (MFA) has gained importance in the biomedical and pharmaceutical sciences. MFA is based on a computational procedure used to determine metabolic reaction rates (fluxes) and in some cases metabolite concentrations (pool sizes) within metabolic pathways where direct observation and measurement of intracellular function is not feasible. Applying 13C MFA to biological data, such as that in liver metabolism, helps to determine, for example, the fate of glucose in regulatory metabolic pathways under physiological and pathophysiological steady state conditions of a cell. However, designing models with reliable flux estimations remains a challenge. A debate that still persists is whether metabolite pool size measurements added as constraints in optimization algorithms improve metabolic flux predictions. We studied the role of pool sizes using simple model networks with total of 8 and 9 metabolites. To investigate the effect of pool size constraints on flux estimations, we set up a series of scenarios varying inputs into the model for several metabolites (such as information on pool size measurements and noise levels in the mass isotopomer distribution (MID) labeling data). Our findings indicate that in models when a full set of MID labeling data (as oppose to just M0 labeling data) is available, pool sizes become dispensable in flux estimations. Moreover, in biological networks where a full set of labeling data for certain metabolites is accessible but the exact model network is unknown, flux estimations are more reliable if pool sizes are not included as model constraints.

In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production.

BackgroundRhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including Pseudomonas aeruginosa. However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications.MethodsWe investigate in silico the metabolic capabilities of P. putida for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlA and RhlB) from P. aeruginosa into a genome-scale model of P. putida. This pipeline combines machine learning methods with multi-omic modelling, and drives the engineered P. putida model toward an optimal production and export of rhamnolipids out of the membrane.ResultsWe identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids in P. putida.ConclusionsWe anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis of P. putida toward maximization of biosurfactant production.