A Systems Biology Approach to Iron Metabolism

Iron is critical to the survival of almost all living organisms. However, inappropriately low or high levels of iron are detrimental and contribute to a wide range of diseases. Recent advances in the study of iron metabolism have revealed multiple intricate pathways that are essential to the maintenance of iron homeostasis. Further, iron regulation involves processes at several scales, ranging from the subcellular to the organismal. This complexity makes a systems biology approach crucial, with its enabling technology of computational models based on a mathematical description of regulatory systems. Systems biology may represent a new strategy for understanding imbalances in iron metabolism and their underlying cause

A Boolean probabilistic model of metabolic adaptation to oxygen in relation to iron homeostasis and oxidative stress

<p>Abstract</p> <p>Background</p> <p>In aerobically grown cells, iron homeostasis and oxidative stress are tightly linked processes implicated in a growing number of diseases. The deregulation of iron homeostasis due to gene defects or environmental stresses leads to a wide range of diseases with consequences for cellular metabolism that remain poorly understood. The modelling of iron homeostasis in relation to the main features of metabolism, energy production and oxidative stress may provide new clues to the ways in which changes in biological processes in a normal cell lead to disease.</p> <p>Results</p> <p>Using a methodology based on probabilistic Boolean modelling, we constructed the first model of yeast iron homeostasis including oxygen-related reactions in the frame of central metabolism. The resulting model of 642 elements and 1007 reactions was validated by comparing simulations with a large body of experimental results (147 phenotypes and 11 metabolic flux experiments). We removed every gene, thus generating <it>in silico </it>mutants. The simulations of the different mutants gave rise to a remarkably accurate qualitative description of most of the experimental phenotype (overall consistency > 91.5%). A second validation involved analysing the anaerobiosis to aerobiosis transition. Therefore, we compared the simulations of our model with different levels of oxygen to experimental metabolic flux data. The simulations reproducted accurately ten out of the eleven metabolic fluxes. We show here that our probabilistic Boolean modelling strategy provides a useful description of the dynamics of a complex biological system. A clustering analysis of the simulations of all <it>in silico </it>mutations led to the identification of clear phenotypic profiles, thus providing new insights into some metabolic response to stress conditions. Finally, the model was also used to explore several new hypothesis in order to better understand some unexpected phenotypes in given mutants.</p> <p>Conclusions</p> <p>All these results show that this model, and the underlying modelling strategy, are powerful tools for improving our understanding of complex biological problems.</p

Efficient Simulation and Parametrization of Stochastic Petri Nets in SystemC: A Case study from Systems Biology

Stochastic Petri nets (SPN) are a form of Petri net where the transitions fire after a probabilistic and randomly determined delay. They are adopted in a wide range of appli- cations thanks to their capability of incorporating randomness in the models and taking into account possible fluctuations and environmental noise. In Systems Biology, they are becoming a reference formalism to model metabolic networks, in which the noise due to molecule interactions in the environment plays a crucial role. Some frameworks have been proposed to implement and dynamically simulate SPN. Nevertheless, they do not allow for automatic model parametrization, which is a crucial task to identify the network configurations that lead the model to satisfy temporal properties of the model. This paper presents a framework that synthesizes the SPN models into SystemC code. The framework allows the user to formally define the network properties to be observed and to automatically extrapolate, thorough Assertion-based Verification (ABV), the parameter configurations that lead the network to satisfy such properties. We applied the framework to implement and simulate a complex biological network, i.e., the purine metabolism, with the aim of reproducing the metabolomics data obtained in-vitro from naive lymphocytes and autoreactive T cells implicated in the induction of experimental autoimmune disorders

Endomicroscopic and transcriptomic analysis of impaired barrier function and malabsorption in environmental enteropathy

Introduction: Environmental enteropathy (EE) is associated with growth failure, micronutrient malabsorption and impaired responses to oral vaccines. We set out to define cellular mechanisms of impaired barrier function in EE and explore protective mechanisms. Methods: We studied 49 adults with environmental enteropathy in Lusaka, Zambia using confocal laser endomicroscopy (CLE); histology, immunohistochemistry and mRNA sequencing of small intestinal biopsies; and correlated these with plasma lipopolysaccharide (LPS) and a zinc uptake test. Results: CLE images (median 134 for each study) showed virtually ubiquitous small intestinal damage. Epithelial defects, imaged by histology and claudin 4 immunostaining, were predominantly seen at the tips of villi and corresponded with leakage imaged in vivo by CLE. In multivariate analysis, circulating log-transformed LPS was correlated with cell shedding events (β = 0.83; P = 0.035) and with serum glucagon-like peptide-2 (β = -0.13; P = 0.007). Zinc uptake from a test dose of 25mg was attenuated in 30/47 (64%) individuals and in multivariate analysis was reduced by HIV, but positively correlated with GLP-2 (β = 2.72; P = 0.03). There was a U-shaped relationship between circulating LPS and villus surface area. Transcriptomic analysis identified 23 differentially expressed genes in severe enteropathy, including protective peptides and proteins. Conclusions: Confocal endomicroscopy, claudin 4 immunostaining and histology identify epithelial defects which are probably sites of bacterial translocation, in the presence of which increased epithelial surface area increases the burden of translocation. GLP 2 and other protective peptides may play an important role in mucosal protection in EE

On functional module detection in metabolic networks

Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models

Transferrin changes in haemodialysed patients

Transferrin (Tf) is a glycoprotein responsible for iron transport in the human body. Physiologically in reaction with Concanavalin A, Tf occurs in four distinct variants Tf1, Tf2, Tf3 (apo-Tf) and Tf4. It was reported recently that Tf is changing, particularly during acute phase response, taking place among others in end-stage renal disease. In this study, we wanted to find the answer to three main questions: firstly, how Tf is changing in patients treated with maintenance haemodialysis (mHD), secondly, whether there are any Tf changes in the course of mHD treatment, and thirdly, what factors can affect Tf microheterogeneity in these patients. Studies were performed on 80 haemodialysed patients and 21 healthy volunteers. The Tf concentration was determined by the rocket immunoelectrophoresis, and its microheterogeneity was assessed by the ConA crossed immunoaffinity electrophoresis. During the annual observation of the distribution of the Tf variants, we have found both changes of the percentage contents of all Tf variants in the whole Tf concentration and a significant decrease in Tf2, Tf3 and Tf4 serum concentrations. Moreover, we found that decrease in the renal function, duration of mHD, and inflammation may contribute to these above-mentioned changes, which are probably the factors that should be taken into account when explaining the mechanisms of persistence of anaemia in haemodialysed patients

Roles of microRNA on cancer cell metabolism

Advanced studies of microRNAs (miRNAs) have revealed their manifold biological functions, including control of cell proliferation, cell cycle and cell death. However, it seems that their roles as key regulators of metabolism have drawn more and more attention in the recent years. Cancer cells display increased metabolic autonomy in comparison to non-transformed cells, taking up nutrients and metabolizing them in pathways that support growth and proliferation. MiRNAs regulate cell metabolic processes through complicated mechanisms, including directly targeting key enzymes or transporters of metabolic processes and regulating transcription factors, oncogenes / tumor suppressors as well as multiple oncogenic signaling pathways. MiRNAs like miR-375, miR-143, miR-14 and miR-29b participate in controlling cancer cell metabolism by regulating the expression of genes whose protein products either directly regulate metabolic machinery or indirectly modulate the expression of metabolic enzymes, serving as master regulators, which will hopefully lead to a new therapeutic strategy for malignant cancer. This review focuses on miRNA regulations of cancer cell metabolism,including glucose uptake, glycolysis, tricarboxylic acid cycle and insulin production, lipid metabolism and amino acid biogenesis, as well as several oncogenic signaling pathways. Furthermore, the challenges of miRNA-based strategies for cancer diagnosis, prognosis and therapeutics have been discussed