Analysis of metabolic flux using dynamic labeling and metabolic modeling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms which control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches having been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences are reviewed and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed

Metabolic modeling and analysis of the metabolic switch in Streptomyces coelicolor

Background The transition from exponential to stationary phase in Streptomyces coelicolor is accompanied by a major metabolic switch and results in a strong activation of secondary metabolism. Here we have explored the underlying reorganization of the metabolome by combining computational predictions based on constraint-based modeling and detailed transcriptomics time course observations. Results We reconstructed the stoichiometric matrix of S. coelicolor, including the major antibiotic biosynthesis pathways, and performed flux balance analysis to predict flux changes that occur when the cell switches from biomass to antibiotic production. We defined the model input based on observed fermenter culture data and used a dynamically varying objective function to represent the metabolic switch. The predicted fluxes of many genes show highly significant correlation to the time series of the corresponding gene expression data. Individual mispredictions identify novel links between antibiotic production and primary metabolism. Conclusion Our results show the usefulness of constraint-based modeling for providing a detailed interpretation of time course gene expression data

Incorporating expression data in metabolic modeling: a case study of lactate dehydrogenase

Integrating biological information from different sources to understand cellular processes is an important problem in systems biology. We use data from mRNA expression arrays and chemical kinetics to formulate a metabolic model relevant to K562 erythroleukemia cells. MAP kinase pathway activation alters the expression of metabolic enzymes in K562 cells. Our array data show changes in expression of lactate dehydrogenase (LDH) isoforms after treatment with phorbol 12-myristate 13-acetate (PMA), which activates MAP kinase signaling. We model the change in lactate production which occurs when the MAP kinase pathway is activated, using a non-equilibrium, chemical-kinetic model of homolactic fermentation. In particular, we examine the role of LDH isoforms, which catalyze the conversion of pyruvate to lactate. Changes in the isoform ratio are not the primary determinant of the production of lactate. Rather, the total concentration of LDH controls the lactate concentration.Comment: In press, Journal of Theoretical Biology. 27 pages, 9 figure

Metabolic modeling for the microbiome

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Spatial Metabolic Modeling and Analysis

Over the summer of 2021, I (Matthew Cummings) and one other undergraduate student worked closely with an Assistant Professor and Graduate Researcher for the NSF-Simons Center for Quantitative Biology at Northwestern University. As a team, we extensively researched how manipulation of a set of partial differential equations could effect the reaction pathway for a set of chemical reactions (within a cell) involving 1,2-propendiol, propenaldehyde, and 1-proponal. Analyzing how manipulation could effect these reaction pathways has many applications that could be used in many industries, including cosmetic, perfume, air care, cleaning, and more. In my presentation, I will talk about our team\u27s findings and how we came about them as well as these applications.https://ecommons.udayton.edu/stander_posters/3524/thumbnail.jp

Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, here the predicted fluxes achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. We suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data.Comment: 57 pages, 14 figures; submitted to PLoS Computational Biology; source code available at http://github.com/ebogart/fluxtools and http://github.com/ebogart/multiscale_c4_sourc

METABOLIC MODELING OF MULTISPECIES MICROBIAL BIOFILMS

Biofilms are ubiquitous in medical, environmental, and engineered microbial systems. The majority of naturally occurring microbes grow as mixed species biofilms. These complicated biofilm consortia are comprised of many cell phenotypes with complex interactions and self-organized into three-dimensional structures. Approximately 2% of the US population suffers from non-healing chronic wounds infected by a combination of commensal and pathogenic bacteria whereas about 500,000 cases of Clostridium difficile infections (CDI) are reported annually. These polymicrobial infections are often resilient to antibiotic treatment due to the nutrient-rich environments and species interactions that promote community stability and robustness. This thesis focusses on developing metabolic modeling framework to study the interactions and the spatial/temporal organizations in the biofilms. The modeling framework is based on integrating genome scale metabolic reconstructions of considered species in this work, with the nutrient uptake kinetics to predict the species abundances, growth rates and byproduct secretions. The spatiotemporal modeling framework accounts for the nutrient concentration gradients in the biofilm system. Spatiotemporal biofilm metabolic models were formulated by combining genome scale metabolic reconstructions of considered species with uptake kinetics for available nutrients and reaction-diffusion type equations for species biomass, supplied substrates and synthesized metabolic byproducts. The resulting partial differential equations embedded with linear programs were discretized in the space and integrated using a dynamic flux balance method. This framework was used to calculate the spatial and temporal variations in the species, nutrient and byproduct concentrations in biofilms. This framework was used to study the species organization and dynamics in chronic wound infections, CDI and environmental biofilms. The chronic wound biofilm model was comprising of two most dominant species, Pseudomonas aeruginosa and Staphylococcus aureus. The CDI biofilm model was comprising of representative species from three most common phyla in gut Bacteroidetes thetaiotaomicron, Faecalibacterium prausnitzii, Escherichia coli and pathogen C. difficile. The simulation results were used to study the interspecies interactions, the spatial partitioning in the biofilms and important crossfeeding relationships within the community. These predictions would be useful in devising effective antibiotic treatment strategies to cure the biofilm infections associated with chronic wounds and C. difficile. The environmental biofilm model for cyanobacteria and heterotrophs was developed and validated with the experimental results, this model was used to evaluate the community dynamics under extreme environmental conditions The second modeling framework considered biofilm as a well-mixed homogenous system at steady state. Steady state in silico community models were formulated by combining genome scale metabolic reconstructions of the considered species. The community models were solved using SteadyCom method. This method uses community flux balance analysis to calculate the relative abundance of each species with an objective of maximizing the community growth rate. A 12 species chronic wound community metabolic model covering 74% of 16S rDNA pyrosequencing reads of dominant genera from 2,963 chronic wound patients was developed. The community model was used to predict species abundances averaged across this large patient population. The simulation results from this study were used to identify putative mutualistic interactions between bacteria that could be targeted to enhance treatment efficacy. The frameworks developed in this thesis would be useful in developing patient/disease specific therapeutic treatments

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome‐scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network‐level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field