Constraints-based genome-scale metabolic simulation for systems metabolic engineering

Random mutagenesis and selection approaches used traditionally for the development of industrial strains have largely been complemented by metabolic engineering, which allows purposeful modification of metabolic and cellular characteristics by using recombinant DNA and other molecular biological techniques. As systems biology advances as a new paradigm of research thanks to the development of genome-scale computational tools and high-throughput experimental technologies including omics, systems metabolic engineering allowing modification of metabolic, regulatory and signaling networks of the cell at the systems-level is becoming possible. In silico genome-scale metabolic model and its simulation play increasingly important role in providing systematic strategies for metabolic engineering. The in silico genome-scale metabolic model is developed using genomic annotation, metabolic reactions, literature information, and experimental data. The advent of in silico genome-scale metabolic model brought about the development of various algorithms to simulate the metabolic status of the cell as a whole. In this paper, we review the algorithms developed for the system-wide simulation and perturbation of cellular metabolism, discuss the characteristics of these algorithms, and suggest future research direction.     

Comparison of metabolic states using genome-scale metabolic models

Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.     

Interpreting metabolic complexity via isotope-assisted metabolic flux analysis

Isotope-assisted metabolic flux analysis (iMFA) is a powerful method to mathematically determine the metabolic fluxome from experimental isotope labeling data and a metabolic network model. While iMFA was originally developed for industrial biotechnological applications, it is increasingly used to analyze eukaryotic cell metabolism in physiological and pathological states. In this review, we explain how iMFA estimates the intracellular fluxome, including data and network model (inputs), the optimization-based data fitting (process), and the flux map (output). We then describe how iMFA enables analysis of metabolic complexities and discovery of metabolic pathways. Our goal is to expand the use of iMFA in metabolism research, which is essential to maximizing the impact of metabolic experiments and continuing to advance iMFA and biocomputational techniques.     

On-line metabolic pathway analysis based on metabolic signal flow diagram

In this work, an integrated modeling approach based on a metabolic signal flow diagram and cellular energetics was used to model the metabolic pathway analysis for the cultivation of yeast on glucose. This approach enables us to make a clear analysis of the flow direction of the carbon fluxes in the metabolic pathways as well as of the degree of activation of a particular pathway for the synthesis of biomaterials for cell growth. The analyses demonstrate that the main metabolic pathways of Saccharomyces cerevisiae change significantly during batch culture. Carbon flow direction is toward glycolysis to satisfy the increase of requirement for precursors and energy. The enzymatic activation of TCA cycle seems to always be at normal level, which may result in the overflow of ethanol due to its limited capacity. The advantage of this approach is that it adopts both virtues of the metabolic signal flow diagram and the simple network analysis method, focusing on the investigation of the flow directions of carbon fluxes and the degree of activation of a particular pathway or reaction loop. All of the variables used in the model equations were determined on-line; the information obtained from the calculated metabolic coefficients may result in a better understanding of cell physiology and help to evaluate the state of the cell culture process.     

Genome-scale metabolic model in guiding metabolic engineering of microbial improvement

In the past few decades, despite all the significant achievements in industrial microbial improvement, the approaches of traditional random mutation and selection as well as the rational metabolic engineering based on the local knowledge cannot meet today’s needs. With rapid reconstructions and accurate in silico simulations, genome-scale metabolic model (GSMM) has become an indispensable tool to study the microbial metabolism and design strain improvements. In this review, we highlight the application of GSMM in guiding microbial improvements focusing on a systematic strategy and its achievements in different industrial fields. This strategy includes a repetitive process with four steps: essential data acquisition, GSMM reconstruction, constraints-based optimizing simulation, and experimental validation, in which the second and third steps are the centerpiece. The achievements presented here belong to different industrial application fields, including food and nutrients, biopharmaceuticals, biopolymers, microbial biofuel, and bioremediation. This strategy and its achievements demonstrate a momentous guidance of GSMM for metabolic engineering breeding of industrial microbes. More efforts are required to extend this kind of study in the meantime.     

Reducing complexity in metabolic networks: making metabolic meshes manageable

The dynamics of complex systems can be effectively analyzed by judicious use of intrinsic time constants. Order of magnitude estimation based on time constants has been used successfully to examine the dynamic behavior of complicated processes. The main goal of this paper is to introduce this approach to the analysis of complex metabolic systems. Time constants and dynamic modes of motion are defined within the context of well-established linear algebra. The order of magnitude estimation is then introduced into the systemic framework. The main goals of the analysis are: to provide improved understanding of biochemical dynamics and their physiological significance, and to yield reduced dynamic models that are physiologically realistic but tractable for practical use.     

Insights into plant metabolic networks from steady-state metabolic flux analysis

Steady-state metabolic flux analysis (MFA) is an experimental approach that allows the measurement of multiple fluxes in the core network of primary carbon metabolism. It is based on isotopic labelling experiments, and although well established in the analysis of micro-organisms, and some mammalian systems, the extension of the method to plant cells has been challenging because of the extensive subcellular compartmentation of the metabolic network. Despite this difficulty there has been substantial progress in developing robust protocols for the analysis of heterotrophic plant metabolism by steady-state MFA, and flux maps have now been published that reflect the metabolic phenotypes of excised root tips, developing embryos and cotyledons, hairy root cultures, and cell suspensions under a variety of physiological conditions. There has been a steady improvement in the quality, extent and statistical reliability of these analyses, and new information is emerging on the performance of the plant metabolic network and the contributions of specific pathways.     

代谢流量比率分析及其在代谢工程中的应用

细胞内代谢反应流量在系统理解细胞代谢特性和指导代谢工程改造等方面都起着重要的作用。由于代谢流量难以直接测量得到,在很多情况下通过跟踪稳定同位素在代谢网络中的转移并进行相应的模型计算能有效地定量代谢流量。代谢流量比率分析法能够高度体现系统的生物化学真实性、辨别细胞代谢网络的拓扑结构,并且能够相对简单快速地定量反应速率等,因此受到代谢工程研究者越来越多的重视。以下着重介绍并讨论了利用代谢物同位体分布信息分析关键代谢节点合成途径的流量比率、基于流量比率的代谢流量解析、以及应用于代谢工程等的相关原理、实验测量、数据分析、使用条件等,以期充分发挥代谢流量比率分析法的优势,并将其拓展推广至更多细胞体系的代谢特性阐明和代谢工程改造中去。     

Integration of metabolic databases for the reconstruction of genome-scale metabolic networks

Background  

Genome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources.     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.     

Probing metabolic pathways with isotopic tracers: insights from mammalian metabolic physiology

Metabolic Engineering offers an opportunity to forge a link between metabolic physiologists, working with mammalian systems and metabolic engineers. Many parallels may be drawn between the specific modification of metabolic networks to improve cellular properties and the analysis of metabolic networks in search of causes of disease. At the core of both fields is the measurement of fluxes. This issue of Metabolic Engineering highlights important topics: mammalian metabolic physiology where estimating fluxes is challenging. The challenges come from compartmentation of metabolites, from dilution of tracer by endogenous pools, and from the difficulty of sampling relevant pools. The common theme across these investigations is the use of isotopic tracers. The wide variety of tracers and tracer analysis techniques in use in mammalian metabolic physiology reflects the complexity of the systems under study. In presenting these examples from the field of mammalian metabolic physiology, our goal is to strengthen the linkages between physiologists and engineers as we develop our knowledge and appreciation of the complexity of metabolic networks.     

Obesity-related metabolic dysfunction in dogs: a comparison with human metabolic syndrome

ABSTRACT: BACKGROUND: Recently, metabolic syndrome (MS) has gained attention in human metabolic medicine given its associations with development of type 2 diabetes mellitus and cardiovascular disease. Canine obesity is associated with the development of insulin resistance, dyslipidaemia, and mild hypertension, but the authors are not aware of any existing studies examining the existence or prevalence of MS in obese dogs.Thirty-five obese dogs were assessed before and after weight loss (median percentage loss 29%, range 10-44%). The diagnostic criteria of the International Diabetes Federation were modified in order to define canine obesity-related metabolic dysfunction (ORMD), which included a measure of adiposity (using a 9-point body condition score [BCS]), systolic blood pressure, fasting plasma cholesterol, plasma triglyceride, and fasting plasma glucose. By way of comparison, total body fat mass was measured by dual-energy X-ray absorptiometry, whilst total adiponectin, fasting insulin, and high-sensitivity C-reactive protein (hsCRP) were measured using validated assays. RESULTS: Systolic blood pressure (P = 0.008), cholesterol (P = 0.003), triglyceride (P = 0.018), and fasting insulin (P < 0.001) all decreased after weight loss, whilst plasma total adiponectin increased (P = 0.001). However, hsCRP did not change with weight loss. Prior to weight loss, 7 dogs were defined as having ORMD, and there was no difference in total fat mass between these dogs and those who did not meet the criteria for ORMD. However, plasma adiponectin concentration was less (P = 0.031), and plasma insulin concentration was greater (P = 0.030) in ORMD dogs. CONCLUSIONS: In this study, approximately 20% of obese dogs suffer from ORMD, and this is characterized by hypoadiponectinaemia and hyperinsulinaemia. These studies can form the basis of further investigations to determine path genetic mechanisms and the health significance for dogs, in terms of disease associations and outcomes of weight loss.     

NMR study of rat diaphragm exposed to metabolic and compensated metabolic acidosis

When exposed to hypercapnia, several muscles deteriorate with respect to their mechanical performance. Exposure to metabolic acidosis and, perhaps surprisingly, to compensated metabolic acidosis has the same effect on the diaphragm. The mechanisms involved in these effects remain unclear. If the diaphragmatic intracellular pH (pHi) is assumed to decrease with hypercapnia, to remain unchanged during metabolic acidosis, and to increase during compensated metabolic acidosis, it would appear that different mechanisms must be responsible for the depreciation in the diaphragm's mechanical performance. The present experiments using 31P nuclear magnetic resonance (31P-NMR) spectroscopy were undertaken to determine the effect of metabolic acidosis and compensated metabolic acidosis on pHi and on high-energy phosphate metabolites in the resting rat diaphragm. A whole diaphragm was slightly stretched while being stitched onto a fiberglass mesh. The area approximated that at functional residual capacity. It was superfused in the NMR sample tube with a phosphate-free Krebs-Ringer bicarbonate solution [( HCO3-] = 6 meqO equilibrated with either 95% O2-5% CO2 or 98.75% O2-1.25% CO2). Spectra were acquired during 15-min intervals for control (30 min of normal Krebs-Ringer bicarbonate superfusate, equilibrated with 95% O2-5% CO2), for 120 min of exposure to either form of acidosis and for 60 min of recovery with normal superfusate. The pHi decreased rapidly during metabolic acidosis but did not change significantly during compensated metabolic acidosis. In both forms of acidosis, phosphocreatine declined gradually but not significantly, whereas ATP and inorganic phosphate did not change at all. The results suggest that HCO3- passes freely through the diaphragmatic sarcolemma, very much like the cardiac sarcolemma.(ABSTRACT TRUNCATED AT 250 WORDS)