We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For this, we generate synthetic metabolic profiles for benchmarking purposes based on a well-established model for red blood cell metabolism. A variety of data sets is generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We apply ARACNE, a mainstream transcriptional networks reverse engineering algorithm, to these data sets and observe performance comparable to that obtained in the transcriptional domain, for which the algorithm was originally designed.
Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulation…
The heterogeneity of reaction fluxes present in a metabolic network within a single flux state can be exploited to construct the so-called backbone as a reduced version of metabolism. The backbone maintains all significant fluxes producing or consuming metabolites while displaying a substantially decreased number of interconnections and, hence, it becomes a useful tool to extract primary metabolic routes. Here, we disclose the metabolic backbone of Escherichia coli using the computationally predicted fluxes which maximize the growth rate in glucose minimal medium, and we compare it with the ba…
The MetaboX library: building metabolic networks from KEGG database
在代谢组学的许多关键应用中，如毒理学或营养基因组学，人们感兴趣的是分析和检测代谢过程中的变化，通常以途径的形式表示。作为一种替代办法，更广泛的视角将使调查人员能够更好地了解不同进程中实体之间的关系。因此，将一个可能的扰动与几个已知过程联系起来是这一研究领域的一种新方法。我们建议使用的反应物，酶和代谢物的代谢网络表示。为了模拟这些系统，可以描述酶和代谢物之间的反应和关系。通过这种方式，分析某些代谢物或酶的变化对不同过程的影响更容易理解，检测和预测。结果：我们发布了MetaboX库，这是一个开源PHP框架，用于从一组化合物开发代谢网络。该库使用存储在Kyoto Encyclopedia for Genes and Genomes（KEGG）数据库中的数据，并使用其REST风格的应用程序编程接口（API）和方法来增强对KEGG Web服务返回的信息的操作。MetaboX库包括提取有关感兴趣资源（例如代谢物，反应和酶）的信息以及构建反应物网络，二分酶-代谢物和单分酶网络的方法。这些网络可以以不同的格式导出，以便使用标准工具进行数据可视化。作为一个案例研究，从糖酵解途径的一个子集建立的网络进行了描述和讨论。结论：使用这样的库的优点意味着能够用由代谢物KEGG ID的集合表示的很少的起始信息对复杂系统进行建模。
Metabolic heterogeneity is widely recognised as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression a…
Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell's capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for com…
The regulatory network of E. coli metabolism as a boolean dynamical system exhibits both homeostasis and flexibility of response
阐明细胞的大规模遗传调控网络的结构和动力学是系统生物学的重要目标。我们研究了大肠杆菌的遗传网络，调节其代谢的系统级动力学特性，并展示了它的设计如何导致生物有用的细胞特性。我们的研究使用数据库（Covert et al.，Nature 2004），包含583个基因和96个外部代谢物，它不仅描述了网络连接，还描述了每个基因节点处的布尔规则，该规则控制基因的开关作为其输入的函数。我们已经研究了如何从这个数据库构建的布尔动力系统的吸引子依赖于基因的初始条件和各种环境条件对应的缓冲最小媒体。我们发现，该系统表现出稳态，它的吸引子，原来是固定点或低周期周期，是高度不敏感的初始条件或扰动的基因配置为任何给定的固定环境。同时，当外部介质变化时，吸引子显示出很大的变化，这意味着系统对变化的环境条件具有高度灵活的响应。调节动力学作用于在改变的培养基下提高细胞生长速率。我们的研究表明，在{ \it E. coli}是分层的、模块化的，并且在很大程度上是非循环的，环境变量控制层次结构的根。这种结构使细胞对基因构型的扰动具有高度鲁棒性，并对环境变化具有高度响应性。该动力系统虽然没有接近混沌的边缘，但仍然具有稳态和响应灵活性的双重特性。
We cast the metabolism of interacting cells within a statistical mechanics framework considering both, the actual phenotypic capacities of each cell and its interaction with its neighbors. Reaction fluxes will be the components of high-dimensional spin vectors, whose values will be constrained by the stochiometry and the energy requirements of the metabolism. Within this picture, finding the phenotypic states of the population turns out to be equivalent to searching for the equilibrium states of a disordered spin model. We provide a general solution of this problem for arbitrary metabolic netw…
Alterations in the human gut bacteriome can be associated with human health issues, such as type-2 diabetes and cardiovascular disease. Both external and internal factors can drive changes in the composition and in the interactions of the human gut bacteriome, impacting negatively on the host cells. In this paper, we focus on the human gut bacteriome metabolism and we propose a two-layer network system to investigate its dynamics. Furthermore, we develop an in-silico simulation model (virtual GB), allowing us to study the impact of the metabolite exchange through molecular communications in th…
Perturbations are ubiquitous in metabolism. A central tool to understand and control their influence on metabolic networks is sensitivity analysis, which investigates how the network responds to external perturbations. We follow here a structural approach: the analysis is based on the network stoichiometry only and it does not require any quantitative knowledge of the reaction rates. We consider perturbations of reaction rates and metabolite concentrations, at equilibrium, and we investigate the responses in the network. For general metabolic systems, this paper focuses on the sign of the resp…
Cells adapt their metabolic fluxes in response to changes in the environment. We present a framework for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic fluxes via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environmental conditions by incorporating flux distributions computed through constraint-based approaches such…
13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both of these methods use metabolic reaction network models of metabolism operating at steady state, so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. A number of approaches have been taken to test the reliability of esti…
We introduce an in silico model for the initial spread of an aberrant phenotype with Warburg-like overflow metabolism within a healthy homeostatic tissue in contact with a nutrient reservoir (the blood), aimed at characterizing the role of the microenvironment for aberrant growth. Accounting for cellular metabolic activity, competition for nutrients, spatial diffusion and their feedbacks on aberrant replication and death rates, we obtain a phase portrait where distinct asymptotic whole-tissue states are found upon varying the tissue-blood turnover rate and the level of blood-borne primary nutr…
