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1.
Nat Commun ; 12(1): 4790, 2021 08 09.
Artigo em Inglês | MEDLINE | ID: mdl-34373465

RESUMO

Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.


Assuntos
Genoma , Engenharia Metabólica , Redes e Vias Metabólicas , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Biomassa , Biotecnologia , Simulação por Computador , Escherichia coli/genética , Regulação Fúngica da Expressão Gênica , Glucose , Modelos Biológicos , Fenótipo , Termodinâmica
2.
Elife ; 102021 08 03.
Artigo em Inglês | MEDLINE | ID: mdl-34340747

RESUMO

The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.


Assuntos
Desenho de Fármacos , Descoberta de Drogas/métodos , Reposicionamento de Medicamentos , Preparações Farmacêuticas/metabolismo , Animais , Antimetabólitos Antineoplásicos/química , Antimetabólitos Antineoplásicos/metabolismo , Antivirais/química , Antivirais/farmacologia , COVID-19/tratamento farmacológico , Bases de Dados de Produtos Farmacêuticos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/etiologia , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/metabolismo , Fluoruracila/química , Fluoruracila/metabolismo , Humanos , Preparações Farmacêuticas/química , Fluxo de Trabalho
3.
PLoS Comput Biol ; 17(7): e1009140, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34292935

RESUMO

The metabolic capabilities of the species and the local environment shape the microbial interactions in a community either through the exchange of metabolic products or the competition for the resources. Cells are often arranged in close proximity to each other, creating a crowded environment that unevenly reduce the diffusion of nutrients. Herein, we investigated how the crowding conditions and metabolic variability among cells shape the dynamics of microbial communities. For this, we developed CROMICS, a spatio-temporal framework that combines techniques such as individual-based modeling, scaled particle theory, and thermodynamic flux analysis to explicitly incorporate the cell metabolism and the impact of the presence of macromolecular components on the nutrients diffusion. This framework was used to study two archetypical microbial communities (i) Escherichia coli and Salmonella enterica that cooperate with each other by exchanging metabolites, and (ii) two E. coli with different production level of extracellular polymeric substances (EPS) that compete for the same nutrients. In the mutualistic community, our results demonstrate that crowding enhanced the fitness of cooperative mutants by reducing the leakage of metabolites from the region where they are produced, avoiding the resource competition with non-cooperative cells. Moreover, we also show that E. coli EPS-secreting mutants won the competition against the non-secreting cells by creating less dense structures (i.e. increasing the spacing among the cells) that allow mutants to expand and reach regions closer to the nutrient supply point. A modest enhancement of the relative fitness of EPS-secreting cells over the non-secreting ones were found when the crowding effect was taken into account in the simulations. The emergence of cell-cell interactions and the intracellular conflicts arising from the trade-off between growth and the secretion of metabolites or EPS could provide a local competitive advantage to one species, either by supplying more cross-feeding metabolites or by creating a less dense neighborhood.


Assuntos
Biologia Computacional/métodos , Interações Microbianas/fisiologia , Microbiota/fisiologia , Modelos Biológicos , Escherichia coli/metabolismo , Escherichia coli/fisiologia , Salmonella enterica/metabolismo , Salmonella enterica/fisiologia , Análise Espaço-Temporal
4.
PLoS Comput Biol ; 17(7): e1009158, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34292941

RESUMO

Microorganisms are frequently organized into crowded structures that affect the nutrients diffusion. This reduction in metabolite diffusion could modify the microbial dynamics, meaning that computational methods for studying microbial systems need accurate ways to model the crowding conditions. We previously developed a computational framework, termed CROMICS, that incorporates the effect of the (time-dependent) crowding conditions on the spatio-temporal modeling of microbial communities, and we used it to demonstrate the crowding influence on the community dynamics. To further identify scenarios where crowding should be considered in microbial modeling, we herein applied and extended CROMICS to simulate several environmental conditions that could potentially boost or dampen the crowding influence in biofilms. We explore whether the nutrient supply (rich- or low-nutrient media), the cell-packing configuration (square or hexagonal spherical cell arrangement), or the cell growing conditions (planktonic state or biofilm) modify the crowding influence on the growth of Escherichia coli. Our results indicate that the growth rate, the abundance and appearance time of different cell phenotypes as well as the amount of by-products secreted to the medium are sensitive to some extent to the local crowding conditions in all scenarios tested, except in rich-nutrient media. Crowding conditions enhance the formation of nutrient gradient in biofilms, but its effect is only appreciated when cell metabolism is controlled by the nutrient limitation. Thus, as soon as biomass (and/or any other extracellular macromolecule) accumulates in a region, and cells occupy more than 14% of the volume fraction, the crowding effect must not be underestimated, as the microbial dynamics start to deviate from the ideal/expected behaviour that assumes volumeless cells or when a homogeneous (reduced) diffusion is applied in the simulation. The modeling and simulation of the interplay between the species diversity (cell shape and metabolism) and the environmental conditions (nutrient quality, crowding conditions) can help to design effective strategies for the optimization and control of microbial systems.


Assuntos
Biofilmes , Biologia Computacional/métodos , Interações Microbianas/fisiologia , Microbiota/fisiologia , Modelos Biológicos , Escherichia coli/fisiologia
5.
Bioinformatics ; 2021 May 18.
Artigo em Inglês | MEDLINE | ID: mdl-34003971

RESUMO

MOTIVATION: Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in biosynthetic pathway design is the extraction of novel metabolic pathways from big networks that integrate known biological, as well as novel, predicted biotransformations. However, the efficient analysis and the navigation of big biochemical networks remain a challenge. RESULTS: Here, we propose the construction of searchable graph representations of metabolic networks. Each reaction is decomposed into pairs of reactants and products, and each pair is assigned a weight, which is calculated from the number of conserved atoms between the reactant and the product molecule. We test our method on a biochemical network that spans 6,546 known enzymatic reactions to show how our approach elegantly extracts biologically relevant metabolic pathways from biochemical networks, and how the proposed network structure enables the application of efficient graph search algorithms that improve navigation and pathway identification in big metabolic networks. The weighted reactant-product pairs of an example network and the corresponding graph search algorithm are available online. The proposed method extracts metabolic pathways fast and reliably from big biochemical networks, which is inherently important for all applications involving the engineering of metabolic networks. AVAILABILITY: https://github.com/EPFL-LCSB/nicepath. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

6.
Metab Eng ; 66: 191-203, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33895366

RESUMO

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.


Assuntos
Escherichia coli , Modelos Biológicos , Escherichia coli/genética , Engenharia Metabólica , Análise do Fluxo Metabólico , Redes e Vias Metabólicas/genética
7.
BMC Bioinformatics ; 22(1): 134, 2021 Mar 20.
Artigo em Inglês | MEDLINE | ID: mdl-33743594

RESUMO

BACKGROUND: Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc reduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models. However, it is important to study how network complexity affects conclusions derived from large-scale kinetic models built around consistently reduced models before we can apply them to study biological systems. RESULTS: We reduced the iJO1366 Escherichia Coli genome-scale metabolic reconstruction systematically to build three stoichiometric models of different size. Since the reduced models are expansions around the core subsystems for which the reduction was performed, the models are nested. We present a method for scaling up the flux profile and the concentration vector reference steady-states from the smallest model to the larger ones, whilst preserving maximum equivalency. Populations of kinetic models, preserving similarity in kinetic parameters, were built around the reference steady-states and their metabolic sensitivity coefficients (MSCs) were computed. The MSCs were sensitive to the model complexity. We proposed a metric for measuring the sensitivity of MSCs to these structural changes. CONCLUSIONS: We proposed for the first time a workflow for scaling up the size of kinetic models while preserving equivalency between the kinetic models. Using this workflow, we demonstrate that model complexity in terms of networks size has significant impact on sensitivity characteristics of kinetic models. Therefore, it is essential to account for the effects of network complexity when constructing kinetic models. The presented metric for measuring MSC sensitivity to structural changes can guide modelers and experimentalists in improving model quality and guide synthetic biology and metabolic engineering. Our proposed workflow enables the testing of the suitability of a kinetic model for answering certain study-specific questions. We argue that the model-based metabolic design targets that are common across models of different size are of higher confidence, while those that are different could be the objective of investigations for model improvement.


Assuntos
Escherichia coli , Engenharia Metabólica , Modelos Biológicos , Algoritmos , Escherichia coli/genética , Cinética , Redes e Vias Metabólicas
8.
Nat Commun ; 12(1): 1760, 2021 03 19.
Artigo em Inglês | MEDLINE | ID: mdl-33741955

RESUMO

Plant natural products (PNPs) and their derivatives are important but underexplored sources of pharmaceutical molecules. To access this untapped potential, the reconstitution of heterologous PNP biosynthesis pathways in engineered microbes provides a valuable starting point to explore and produce novel PNP derivatives. Here, we introduce a computational workflow to systematically screen the biochemical vicinity of a biosynthetic pathway for pharmaceutical compounds that could be produced by derivatizing pathway intermediates. We apply our workflow to the biosynthetic pathway of noscapine, a benzylisoquinoline alkaloid (BIA) with a long history of medicinal use. Our workflow identifies pathways and enzyme candidates for the production of (S)-tetrahydropalmatine, a known analgesic and anxiolytic, and three additional derivatives. We then construct pathways for these compounds in yeast, resulting in platforms for de novo biosynthesis of BIA derivatives and demonstrating the value of cheminformatic tools to predict reactions, pathways, and enzymes in synthetic biology and metabolic engineering.


Assuntos
Produtos Biológicos/metabolismo , Vias Biossintéticas/genética , Biologia Computacional/métodos , Engenharia Metabólica/métodos , Noscapina/metabolismo , Saccharomyces cerevisiae/metabolismo , Alcaloides/biossíntese , Benzilisoquinolinas/metabolismo , Noscapina/química , Plantas/genética , Plantas/metabolismo , Saccharomyces cerevisiae/genética , Software
9.
STAR Protoc ; 2(1): 100280, 2021 Mar 19.
Artigo em Inglês | MEDLINE | ID: mdl-33532729

RESUMO

Targeted identification of cellular processes responsible for a phenotype is of major importance in guiding efforts in bioengineering and medicine. Genome-scale metabolic models (GEMs) are widely used to integrate various types of omics data and study the cellular physiology under different conditions. Here, we present PhenoMapping, a protocol that uses GEMs, omics, and phenotypic data to map cellular processes and observed phenotypes. PhenoMapping also classifies genes as conditionally and unconditionally essential and guides a comprehensive curation of GEMs. For complete details on the use and execution of this protocol, please refer to Stanway et al. (2019) and Krishnan et al. (2020).

10.
Proc Natl Acad Sci U S A ; 118(8)2021 02 23.
Artigo em Inglês | MEDLINE | ID: mdl-33602812

RESUMO

Diauxie, or the sequential consumption of carbohydrates in bacteria such as Escherichia coli, has been hypothesized to be an evolutionary strategy which allows the organism to maximize its instantaneous specific growth-giving the bacterium a competitive advantage. Currently, the computational techniques used in industrial biotechnology fall short of explaining the intracellular dynamics underlying diauxic behavior. In particular, the understanding of the proteome dynamics in diauxie can be improved. We developed a robust iterative dynamic method based on expression- and thermodynamically enabled flux models to simulate the kinetic evolution of carbohydrate consumption and cellular growth. With minimal modeling assumptions, we couple kinetic uptakes, gene expression, and metabolic networks, at the genome scale, to produce dynamic simulations of cell cultures. The method successfully predicts the preferential uptake of glucose over lactose in E. coli cultures grown on a mixture of carbohydrates, a manifestation of diauxie. The simulated cellular states also show the reprogramming in the content of the proteome in response to fluctuations in the availability of carbon sources, and it captures the associated time lag during the diauxie phenotype. Our models suggest that the diauxic behavior of cells is the result of the evolutionary objective of maximization of the specific growth of the cell. We propose that genetic regulatory networks, such as the lac operon in E. coli, are the biological implementation of a robust control system to ensure optimal growth.


Assuntos
Escherichia coli/crescimento & desenvolvimento , Escherichia coli/metabolismo , Redes e Vias Metabólicas , Modelos Biológicos , Acetatos/metabolismo , Enzimas/metabolismo , Escherichia coli/citologia , Proteínas de Escherichia coli/metabolismo , Regulação Bacteriana da Expressão Gênica , Genoma Bacteriano , Glucose/metabolismo , Cinética , Lactose/metabolismo , Termodinâmica
11.
Nat Commun ; 11(1): 3757, 2020 07 23.
Artigo em Inglês | MEDLINE | ID: mdl-32703980

RESUMO

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

12.
Nat Commun ; 11(1): 2821, 2020 06 04.
Artigo em Inglês | MEDLINE | ID: mdl-32499584

RESUMO

Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells.


Assuntos
Genoma Humano , Metabolismo/genética , Modelos Biológicos , Biomassa , Vias Biossintéticas , Meios de Cultura , Humanos , Redes e Vias Metabólicas/genética , Reprodutibilidade dos Testes , Estatística como Assunto , Termodinâmica
13.
ACS Synth Biol ; 9(6): 1479-1482, 2020 06 19.
Artigo em Inglês | MEDLINE | ID: mdl-32421310

RESUMO

The ATLAS of Biochemistry is a repository of both known and novel predicted biochemical reactions between biological compounds listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG). ATLAS was originally compiled based on KEGG 2015, though the number of KEGG reactions has increased by almost 20 percent since then. Here, we present an updated version of ATLAS created from KEGG 2018 using an increased set of generalized reaction rules. Furthermore, we improved the accuracy of the enzymes that are predicted for catalyzing novel reactions. ATLAS now contains ∼150 000 reactions, out of which 96% are novel. In this report, we present detailed statistics on the updated ATLAS and highlight the improvements with regard to the previous version. Most importantly, 107 reactions predicted in the original ATLAS are now known to KEGG, which validates the predictive power of our approach. The updated ATLAS is available at https://lcsb-databases.epfl.ch/atlas.


Assuntos
Bases de Dados Factuais , Enzimas/metabolismo , Redes e Vias Metabólicas
15.
Biotechnol Biofuels ; 13: 33, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32140178

RESUMO

Background: Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. Results: In this work, we developed kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand. Conclusions: The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.

17.
Nat Commun ; 11(1): 30, 2020 01 13.
Artigo em Inglês | MEDLINE | ID: mdl-31937763

RESUMO

Systems biology has long been interested in models capturing both metabolism and expression in a cell. We propose here an implementation of the metabolism and expression model formalism (ME-models), which we call ETFL, for Expression and Thermodynamics Flux models. ETFL is a hierarchical model formulation, from metabolism to RNA synthesis, that allows simulating thermodynamics-compliant intracellular fluxes as well as enzyme and mRNA concentration levels. ETFL formulates a mixed-integer linear problem (MILP) that enables both relative and absolute metabolite, protein, and mRNA concentration integration. ETFL is compatible with standard MILP solvers and does not require a non-linear solver, unlike the previous state of the art. It also accounts for growth-dependent parameters, such as relative protein or mRNA content. We present ETFL along with its validation using results obtained from a well-characterized E. coli model. We show that ETFL is able to reproduce proteome-limited growth. We also subject it to several analyses, including the prediction of feasible mRNA and enzyme concentrations and gene essentiality.


Assuntos
Modelos Biológicos , Biologia de Sistemas/métodos , Termodinâmica , Biomassa , Escherichia coli/metabolismo , Regulação Bacteriana da Expressão Gênica , Engenharia Metabólica , Metabolômica , Proteoma/metabolismo , Proteômica , RNA/biossíntese , RNA Mensageiro/metabolismo , Software
18.
Cell Host Microbe ; 27(2): 290-306.e11, 2020 02 12.
Artigo em Inglês | MEDLINE | ID: mdl-31991093

RESUMO

To survive and proliferate in diverse host environments with varying nutrient availability, the obligate intracellular parasite Toxoplasma gondii reprograms its metabolism. We have generated and curated a genome-scale metabolic model (iTgo) for the fast-replicating tachyzoite stage, harmonized with experimentally observed phenotypes. To validate the importance of four metabolic pathways predicted by the model, we have performed in-depth in vitro and in vivo phenotyping of mutant parasites including targeted metabolomics and CRISPR-Cas9 fitness screening of all known metabolic genes. This led to unexpected insights into the remarkable flexibility of the parasite, addressing the dependency on biosynthesis or salvage of fatty acids (FAs), purine nucleotides (AMP and GMP), a vitamin (pyridoxal-5P), and a cofactor (heme) in both the acute and latent stages of infection. Taken together, our experimentally validated metabolic network leads to a deeper understanding of the parasite's biology, opening avenues for the development of therapeutic intervention against apicomplexans.


Assuntos
Ácidos Graxos/metabolismo , Heme/metabolismo , Toxoplasma/metabolismo , Vitamina B 6/metabolismo , Animais , Repetições Palindrômicas Curtas Agrupadas e Regularmente Espaçadas , Biologia Computacional , Desenvolvimento de Medicamentos/tendências , Genômica , Estágios do Ciclo de Vida/fisiologia , Redes e Vias Metabólicas , Metabolômica , Camundongos , Fenótipo , Toxoplasma/genética
19.
PLoS Comput Biol ; 15(12): e1007536, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31815929

RESUMO

Kinetic models of metabolism can be constructed to predict cellular regulation and devise metabolic engineering strategies, and various promising computational workflows have been developed in recent years for this. Due to the uncertainty in the kinetic parameter values required to build kinetic models, these workflows rely on ensemble modeling (EM) principles for sampling and building populations of models describing observed physiologies. Sensitivity coefficients from metabolic control analysis (MCA) of kinetic models can provide important insight about cellular control around a given physiological steady state. However, despite considering populations of kinetic models and their model outputs, current approaches do not provide adequate tools for statistical inference. To derive conclusions from model outputs, such as MCA sensitivity coefficients, it is necessary to rank/compare populations of variables with each other. Currently existing workflows consider confidence intervals (CIs) that are derived independently for each comparable variable. Hence, it is important to derive simultaneous CIs for the variables that we wish to rank/compare. Herein, we used an existing large-scale kinetic model of Escherichia Coli metabolism to present how univariate CIs can lead to incorrect conclusions, and we present a new workflow that applies three different multivariate statistical approaches. We use the Bonferroni and the exact normal methods to build symmetric CIs using the normality assumptions. We then suggest how bootstrapping can compute asymmetric CIs whilst relaxing this normality assumption. We conclude that the Bonferroni and the exact normal methods can provide simple and efficient ways for constructing reliable CIs, with the exact normal method favored over the Bonferroni when the compared variables present dependencies. Bootstrapping, despite its significantly higher computational cost, is recommended when comparing non-normal distributions of variables. Additionally, we show how the Bonferroni method can readily be used to estimate required sample numbers to attain a certain CI size.


Assuntos
Escherichia coli/metabolismo , Modelos Biológicos , Algoritmos , Células/metabolismo , Biologia Computacional , Simulação por Computador , Intervalos de Confiança , Cinética , Metabolismo , Biologia de Sistemas , Incerteza
20.
Cell ; 179(5): 1112-1128.e26, 2019 11 14.
Artigo em Inglês | MEDLINE | ID: mdl-31730853

RESUMO

Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development.


Assuntos
Genoma de Protozoário , Estágios do Ciclo de Vida/genética , Fígado/metabolismo , Fígado/parasitologia , Plasmodium berghei/crescimento & desenvolvimento , Plasmodium berghei/genética , Alelos , Amino Açúcares/biossíntese , Animais , Culicidae/parasitologia , Eritrócitos/parasitologia , Ácido Graxo Sintases/metabolismo , Ácidos Graxos/metabolismo , Técnicas de Inativação de Genes , Genótipo , Modelos Biológicos , Mutação/genética , Parasitos/genética , Parasitos/crescimento & desenvolvimento , Fenótipo , Plasmodium berghei/metabolismo , Ploidias , Reprodução
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