Mathematical modeling of proteome constraints within metabolism

Genome-scale metabolic models (GEMs) are widely used to predict phenotypes with the aid of constraint-based modeling. In order to improve the predictive power of these models, there have been many efforts on imposing biological constraints, among which proteome constraints are of particular interest. Here we describe the concept of proteome constraints and review proteome-constrained GEMs, as well as their advantages and applications. In addition, we discuss a key issue in the field, i.e., low coverage of enzyme-specific turnover rates, and subsequently provide a few solutions to solve it. We end with a discussion on the trade-off between model complexity and capability

Advances in constraint-based models: methods for improved predictive power based on resource allocation constraints

The concept of metabolic models with resource allocation constraints has been around for over a decade and has clear advantages even when implementation is relatively rudimentary. Nonetheless, the number of organisms for which such a model is reconstructed is low. Various approaches exist, from coarse-grained consideration of enzyme usage to finegrained description of protein translation. These approaches are reviewed here, with a particular focus on user-friendly solutions that can introduce resource allocation constraints to metabolic models of any organism. The availability of kcat data is a major hurdle, where recent advances might help to fill in the numerous gaps that exist for this data, especially for nonmodel organisms

Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth

The tumor microenvironment is composed of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells’ preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer-associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme-constrained models for accurate prediction of metabolism in cells and tumor microenvironments

Deep learning-based k(cat) prediction enables improved enzyme-constrained model reconstruction

Enzyme turnover numbers (k(cat)) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured k(cat) data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput k(cat) prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture k(cat) changes for mutated enzymes and identify amino acid residues with a strong impact on k(cat) values. We applied this approach to predict genome-scale k(cat) values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted k(cat) values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale

A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism

The interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and its malfunction has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer, and neurological disorders. Therefore, unraveling the role of nutrients as signaling molecules and metabolites together with their interconnectivity may provide a deeper understanding of how these conditions occur. Both signaling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition, current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism in yeast cells, we developed a hybrid model, combining a Boolean module, describing the main pathways of glucose and nitrogen signaling, and an enzyme-constrained model accounting for the central carbon metabolism of Saccharomyces cerevisiae, using a regulatory network as a link. The resulting hybrid model was able to capture a diverse utalization of isoenzymes and to our knowledge outperforms constraint-based models in the prediction of individual enzymes for both respiratory and mixed metabolism. The model showed that during fermentation, enzyme utilization has a major contribution in governing protein allocation, while in low glucose conditions robustness and control are prioritized. In addition, the model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression, as well as regulatory effects associated with lifespan increase during caloric restriction. Overall, we show that our hybrid model provides a comprehensive framework for the study of the non-trivial effects of the interplay between signaling and metabolism, suggesting connections between the Snf1 signaling pathways and processes that have been related to chronological lifespan of yeast cells

Constraint-based modeling of yeast metabolism and protein secretion

Yeasts are extensively exploited as cell factories for producing alcoholic beverages, biofuels, bio-pharmaceutical proteins, and other value-added chemicals. To improve the performance of yeast cell factories, it is necessary to understand their metabolism. Genome-scale metabolic models (GEMs) have been widely used to study cellular metabolism systematically. However, GEMs for yeast species have not been equally developed. GEMs for the well-studied yeasts such as Saccharomyces cerevisiae have been updated several times, while most of the other yeast species have no available GEM. Additionally, classical GEMs only account for the metabolic reactions, which limits their usage to study complex phenotypes that are not controlled by metabolism alone. Thus, other biological processes can be integrated with GEMs to fulfill diverse research purposes. \ua0In this thesis, the GEM for S. cerevisiae was updated to the latest version Yeast8, which serves as the basic model for the remaining work of the thesis including two dimensions: 1) Yeast8 was used as a template for generating GEMs of other yeast species/strains, and 2) Yeast8 was expanded to account for more biological processes. Regarding the first dimension, strain-specific GEMs for 1,011 S. cerevisiae isolates from diverse origins and species-specific GEMs for 343 yeast/fungi species were generated. These GEMs enabled explore the phenotypic diversity of the single species from diverse ecological and geographical origins, and evolution tempo among diverse yeast species. Regarding the second dimension, other biological processes were formulated within Yeast8. Firstly, Yeast8 was expanded to account for enzymatic constraints, resulting in enzyme-constrained GEMs (ecGEMs). Secondly, Yeast8 was expanded to the model CofactorYeast by accounting for enzyme cofactors such as metal ions, which was used to simulate the interaction between metal ions and metabolism, and the cellular responses to metal ion limitation. Lastly, Yeast8 was expanded to include the protein synthesis and secretion processes, named as pcSecYeast. pcSecYeast was used to simulate the competition of the recombinant protein with the native secretory-pathway-processed proteins. Besides that, pcSecYeast enabled the identification of overexpression targets for improving recombinant protein production. \ua0When developing these complex models, issues were identified among which the lack of enzyme turnover rates, i.e., kcatvalues, needs to be solved. Accordingly, a machine learning method for kcat prediction and automated incorporation into GEMs were developed, facilitating the generation of functional ecGEMs in a large scale

Proceso de liquidación técnica y financiera de obras públicas en un proyecto especial de la región Lambayeque

La presente tesis tiene como objetivo explicar el efecto de la liquidación técnica y la liquidación financiera en las obras públicas en un proyecto especial de la región Lambayeque al año 2022, para lo cual se aplicó una investigación básica, con enfoque cualitativo, de nivel descriptivo y diseño fenomenológico. Esta investigación aplicó entrevistas como instrumentos de recolección de datos a 05 colaboradores del proyecto especial de la región Lambayeque. Las conclusiones indican que se demostró que existen variaciones presupuestales entre el expediente técnico y la ejecución real en campo, con pocos ingenieros que dominan el tema de las liquidaciones financieras, debido a la falta de capacitaciones. Los resultados arrojan que cuando existen esas variaciones, tanto en planillas, bienes y servicios, se han dado a veces soluciones incompatibles con la normativa, incluso con la misma directiva, lo cual es necesario para conocer el monto real de la ejecución de la obra, es decir, su costo real

A systems biology understanding of protein constraints in the metabolism of budding yeasts

Fermentation technologies, such as bread making and production of alcoholic beverages, have been crucial for development of humanity throughout history. Saccharomyces cerevisiae provides a natural platform for this, due to its capability to transform sugars into ethanol. This, and other yeasts, are now used for production of pharmaceuticals, including insulin and artemisinic acid, flavors, fragrances, nutraceuticals, and fuel precursors. In this thesis, different systems biology methods were developed to study interactions between metabolism, enzymatic capabilities, and regulation of gene expression in budding yeasts. In paper I, a study of three different yeast species (S. cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus), exposed to multiple conditions, was carried out to understand their adaptation to environmental stress. Paper II revises the use of genome-scale metabolic models (GEMs) for the study and directed engineering of diverse yeast species. Additionally, 45 GEMs for different yeasts were collected, analyzed, and tested. In paper III, GECKO 2.0, a toolbox for integration of enzymatic constraints and proteomics data into GEMs, was developed and used for reconstruction of enzyme-constrained models (ecGEMs) for three yeast species and model organisms. Proteomics data and ecGEMs were used to further characterize the impact of environmental stress over metabolism of budding yeasts. On paper IV, gene engineering targets for increased accumulation of heme in S. cerevisiae cells were predicted with an ecGEM. Predictions were experimentally validated, yielding a 70-fold increase in intracellular heme. The prediction method was systematized and applied to the production of 102 chemicals in S. cerevisiae (Paper V). Results highlighted general principles for systems metabolic engineering and enabled understanding of the role of protein limitations in bio-based chemical production. Paper VI presents a hybrid model integrating an enzyme-constrained metabolic network, coupled to a gene regulatory model of nutrient-sensing mechanisms in S. cerevisiae. This model improves prediction of protein expression patterns while providing a rational connection between metabolism and the use of nutrients from the environment.This thesis demonstrates that integration of multiple systems biology approaches is valuable for understanding the connection of cell physiology at different levels, and provides tools for directed engineering of cells for the benefit of society