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

Coupling metabolic footprinting and flux balance analysis to predict how single gene knockouts perturb microbial metabolism

Tese de mestrado. Biologia (Bioinformática e Biologia Computacional). Universidade de Lisboa, Faculdade de Ciências, 2012The model organisms Caenorhabditis elegans and E. coli form one of the simplest gut microbe host interaction models. Interventions in the microbe that increase the host longevity including inhibition of folate synthesis have been reported previously. To find novel single gene knockouts with an effect on lifespan, a screen of the Keio collection of E. coli was undertaken, and some of the genes found are directly involved in metabolism. The next step in those specific cases is to understand how these mutations perturb metabolism systematically, so that hypotheses can be generated. For that, I employed dynamic Flux Balance Analysis (dFBA), a constraint-based modeling technique capable of simulating the dynamics of metabolism in a batch culture and making predictions about changes in intracellular flux distribution. Since the specificities of the C. elegans lifespan experiments demand us to culture microbes in conditions differing from most of the published literature on E. coli physiology, novel data must be acquired to characterize and make dFBA simulations as realistic as possible. To do this exchange fluxes were measured using quantitative H NMR Time-Resolved Metabolic Footprinting. Furthermore, I also investigate the combination of TReF and dFBA as a tool in microbial metabolism studies. These approaches were tested by comparing wild type E. coli with one of the knockout strains found, ΔmetL, a knockout of the metL gene which encodes a byfunctional enzyme involved in aspartate and threonine metabolism. I found that the strain exhibits a slower growth rate than the wild type. Model simulation results revealed that reduced homoserine and methionine synthesis, as well as impaired sulfur and folate metabolism are the main effects of this knockout and the reasons for the growth deficiency. These results indicate that there are common mechanisms of the lifespan extension between ΔmetL and inhibition of folate biosynthesis and that the flux balance analysis/metabolic footprinting approach can help us understand the nature of these mechanisms.Os organismos modelo Caenorhabditis elegans e E. coli formam um dos modelos mais simples de interacções entre micróbio do tracto digestivo e hospedeiro. Intervenções no micróbio capazes de aumentar a longevidade do hospedeiro, incluindo inibição de síntese de folatos, foram reportadas previamente. Para encontrar novas delecções génicas do micróbio capazes de aumentar a longevidade do hospedeiro, a colecção Keio de deleções génicas de E. coli foi rastreada. Alguns dos genes encontrados participam em processos metabólicos, e nesses casos, o próximpo passo é perceber como as deleções perturbam o metabolismo sistémicamente, para gerar hipóteses. Para isso, utilizo dynamic Flux Balance Analysis (dFBA), uma técnica de modelação metabólica capaz de fazer previsões sobre alterações na distribuição intracelular de fluxos. As especificidades das experiências de tempo de vida em C.elegans obrigam-nos a trabalhar em condições diferentes das usadas na maioria da literatura publicada em fisiologia de E. coli, e para dar o máximo realismo às simulações de dFBA novos dados foram adquiridos, utilizando H NMR Time-Resolved Metabolic Footprinting para medir fluxos de troca de metabolitos entre microorganismo e meio de cultura. A combinação de TReF e dFBA como ferramenta de estudo do metabolism microbiano é também investigada. Estas abordagens foram testadas ao comparar E. coli wild-type com uma das estirpes encontradas no rastreio, ΔmetL, knockout do gene metL, que codifica um enzima bifunctional participante no metabolismo de aspartato e treonina, e que exibe uma taxa de crescimento reduzida comparativamente ao wild-type. Os resultados das simulações revelaram que os principais efeitos da deleção deste gene, e as razões para a menor taxa de crescimento observada, são a produção reduzida de homoserina e metionina e os efeitos que provoca no metabolismo de folatos e enxofre. Estes resultados indicam que há mecanismos comuns na extensão da longevidade causada por esta deleção e inibição de síntese de folatos, e que a combinação metabolic footprinting/flux balance analysis pode ajudar-nos a compreender a natureza desses mecanismos

Characterizing steady states of genome-scale metabolic networks in continuous cell cultures

We present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. More importantly, we derive a number of consequences from the model that are independent of parameter values. First, that the ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties invariant across perfusion systems. This conclusion is robust even in the presence of multi-stability, which is explained in our model by the negative feedback loop on cell growth due to toxic byproduct accumulation. Moreover, a complex landscape of steady states in continuous cell culture emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced. Thus, in order to actually reflect the expected behavior in perfusion, performance benchmarks of cell-lines and culture media should be carried out in a chemostat

Investigations on the application of complex cell models in the simulation of bioprocesses

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Why Optimal States Recruit Fewer Reactions in Metabolic Networks

The metabolic network of a living cell involves several hundreds or thousands of interconnected biochemical reactions. Previous research has shown that under realistic conditions only a fraction of these reactions is concurrently active in any given cell. This is partially determined by nutrient availability, but is also strongly dependent on the metabolic function and network structure. Here, we establish rigorous bounds showing that the fraction of active reactions is smaller (rather than larger) in metabolic networks evolved or engineered to optimize a specific metabolic task, and we show that this is largely determined by the presence of thermodynamically irreversible reactions in the network. We also show that the inactivation of a certain number of reactions determined by irreversibility can generate a cascade of secondary reaction inactivations that propagates through the network. The mathematical results are complemented with numerical simulations of the metabolic networks of the bacterium Escherichia coli and of human cells, which show, counterintuitively, that even the maximization of the total reaction flux in the network leads to a reduced number of active reactions.Comment: Contribution to the special issue in honor of John Guckenheimer on the occasion of his 65th birthda

MATHEMATICAL MODELING OF \u3ci\u3eCLOSTRIDIUM THERMOCELLUM’S\u3c/i\u3e METABOLIC RESPONSES TO ENVIRONMENTAL PERTURBATION

Clostridium thermocellum is a thermophilic anaerobe that is capable of producing ethanol directly from lignocellulosic compounds, however this organism suffers from low ethanol tolerance and low ethanol yields. In vivo mathematical modeling studies based on steady state traditional metabolic flux analysis, metabolic control analysis, transient and steady states’ flux spectrum analysis (FSA) were conducted on C. thermocellum’s central metabolism. The models were developed in Matrix Laboratory software ( MATLAB® (The Language of Technical Computing), R2008b, Version 7.7.0.471)) based on known stoichiometry from C. thermocellum pathway and known physical constraints. Growth on cellobiose from Metabolic flux analysis (MFA) and Metabolic control analysis (MCA) of wild type (WT) and ethanol adapted (EA) cells showed that, at lower than optimum exogenous ethanol levels, ethanol to acetate (E/A) ratios increased by approximately 29% in WT cells and 7% in EA cells. Sensitivity analyses of the MFA and MCA models indicated that the effects of variability in experimental data on model predictions were minimal (within ±5% differences in predictions if the experimental data varied up to ±20%). Steady state FSA model predictions showed that, an optimum hydrogen flux of ~5mM/hr in the presence of pressure equal to or above 7MPa inhibits ferrodoxin hydrogenase which causes NAD re-oxidation in the system to increase ethanol yields to about 3.5 mol ethanol/mol cellobiose

Method for finding metabolic properties based on the general growth law. Liver examples. A General framework for biological modeling

We propose a method for finding metabolic parameters of cells, organs and whole organisms, which is based on the earlier discovered general growth law. Based on the obtained results and analysis of available biological models, we propose a general framework for modeling biological phenomena and discuss how it can be used in Virtual Liver Network project. The foundational idea of the study is that growth of cells, organs, systems and whole organisms, besides biomolecular machinery, is influenced by biophysical mechanisms acting at different scale levels. In particular, the general growth law uniquely defines distribution of nutritional resources between maintenance needs and biomass synthesis at each phase of growth and at each scale level. We exemplify the approach considering metabolic properties of growing human and dog livers and liver transplants. A procedure for verification of obtained results has been introduced too. We found that two examined dogs have high metabolic rates consuming about 0.62 and 1 gram of nutrients per cubic centimeter of liver per day, and verified this using the proposed verification procedure. We also evaluated consumption rate of nutrients in human livers, determining it to be about 0.088 gram of nutrients per cubic centimeter of liver per day for males, and about 0.098 for females. This noticeable difference can be explained by evolutionary development, which required females to have greater liver processing capacity to support pregnancy. We also found how much nutrients go to biomass synthesis and maintenance at each phase of liver and liver transplant growth. Obtained results demonstrate that the proposed approach can be used for finding metabolic characteristics of cells, organs, and whole organisms, which can further serve as important inputs for many applications in biology (protein expression), biotechnology (synthesis of substances), and medicine.Comment: 20 pages, 6 figures, 4 table

Model transformation of metabolic networks using a Petri net based framework

The different modeling approaches in Systems Biology create models with different levels of detail. The transformation techniques in Petri net theory can provide a solid framework for zooming between these different levels of abstraction and refinement. This work presents a Petri net based approach to Metabolic Engineering that implements model reduction methods to reduce the complexity of large-scale metabolic networks. These methods can be complemented with kinetics inference to build dynamic models with a smaller number of parameters. The central carbon metabolism model of E. coli is used as a test-case to illustrate the application of these concepts. Model transformation is a promising mechanism to facilitate pathway analysis and dynamic modeling at the genome-scale level.(undefined

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided