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Sample records for cellular metabolic networks

  1. Optimal flux patterns in cellular metabolic networks

    NASA Astrophysics Data System (ADS)

    Almaas, Eivind

    2007-06-01

    The availability of whole-cell-level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate the metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30 000 random cellular environments. The distribution of reaction fluxes is heavy tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations has relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reactions are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central carbon metabolic pathways for the sample of random environments.

  2. Optimal flux patterns in cellular metabolic networks

    SciTech Connect

    Almaas, E

    2007-01-20

    The availability of whole-cell level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30,000 random cellular environments. The distribution of reaction fluxes is heavy-tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations have relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reaction are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central-carbon metabolic pathways for the sample of random environments.

  3. Epigenetics and Cellular Metabolism

    PubMed Central

    Xu, Wenyi; Wang, Fengzhong; Yu, Zhongsheng; Xin, Fengjiao

    2016-01-01

    Living eukaryotic systems evolve delicate cellular mechanisms for responding to various environmental signals. Among them, epigenetic machinery (DNA methylation, histone modifications, microRNAs, etc.) is the hub in transducing external stimuli into transcriptional response. Emerging evidence reveals the concept that epigenetic signatures are essential for the proper maintenance of cellular metabolism. On the other hand, the metabolite, a main environmental input, can also influence the processing of epigenetic memory. Here, we summarize the recent research progress in the epigenetic regulation of cellular metabolism and discuss how the dysfunction of epigenetic machineries influences the development of metabolic disorders such as diabetes and obesity; then, we focus on discussing the notion that manipulating metabolites, the fuel of cell metabolism, can function as a strategy for interfering epigenetic machinery and its related disease progression as well. PMID:27695375

  4. Epigenetics and Cellular Metabolism

    PubMed Central

    Xu, Wenyi; Wang, Fengzhong; Yu, Zhongsheng; Xin, Fengjiao

    2016-01-01

    Living eukaryotic systems evolve delicate cellular mechanisms for responding to various environmental signals. Among them, epigenetic machinery (DNA methylation, histone modifications, microRNAs, etc.) is the hub in transducing external stimuli into transcriptional response. Emerging evidence reveals the concept that epigenetic signatures are essential for the proper maintenance of cellular metabolism. On the other hand, the metabolite, a main environmental input, can also influence the processing of epigenetic memory. Here, we summarize the recent research progress in the epigenetic regulation of cellular metabolism and discuss how the dysfunction of epigenetic machineries influences the development of metabolic disorders such as diabetes and obesity; then, we focus on discussing the notion that manipulating metabolites, the fuel of cell metabolism, can function as a strategy for interfering epigenetic machinery and its related disease progression as well.

  5. Exact quantification of cellular robustness in genome-scale metabolic networks

    PubMed Central

    Gerstl, Matthias P.; Klamt, Steffen; Jungreuthmayer, Christian; Zanghellini, Jürgen

    2016-01-01

    Motivation: Robustness, the ability of biological networks to uphold their functionality in spite of perturbations, is a key characteristic of all living systems. Although several theoretical approaches have been developed to formalize robustness, it still eludes an exact quantification. Here, we present a rigorous and quantitative approach for the structural robustness of metabolic networks by measuring their ability to tolerate random reaction (or gene) knockouts. Results: In analogy to reliability theory, based on an explicit consideration of all possible knockout sets, we exactly quantify the probability of failure for a given network function (e.g. growth). This measure can be computed if the network’s minimal cut sets (MSCs) are known. We show that even in genome-scale metabolic networks the probability of (network) failure can be reliably estimated from MSCs with lowest cardinalities. We demonstrate the applicability of our theory by analyzing the structural robustness of multiple Enterobacteriaceae and Blattibacteriaceae and show a dramatically low structural robustness for the latter. We find that structural robustness develops from the ability to proliferate in multiple growth environments consistent with experimentally found knowledge. Conclusion: The probability of (network) failure provides thus a reliable and easily computable measure of structural robustness and redundancy in (genome-scale) metabolic networks. Availability and implementation: Source code is available under the GNU General Public License at https://github.com/mpgerstl/networkRobustnessToolbox. Contact: juergen.zanghellini@boku.ac.at Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26543173

  6. Cellular iron metabolism.

    PubMed

    Ponka, P

    1999-03-01

    Iron is essential for oxidation-reduction catalysis and bioenergetics, but unless appropriately shielded, iron plays a key role in the formation of toxic oxygen radicals that can attack all biological molecules. Hence, specialized molecules for the acquisition, transport (transferrin), and storage (ferritin) of iron in a soluble nontoxic form have evolved. Delivery of iron to most cells, probably including those of the kidney, occurs following the binding of transferrin to transferrin receptors on the cell membrane. The transferrin-receptor complexes are then internalized by endocytosis, and iron is released from transferrin by a process involving endosomal acidification. Cellular iron storage and uptake are coordinately regulated post-transcriptionally by cytoplasmic factors, iron-regulatory proteins 1 and 2 (IRP-1 and IRP-2). Under conditions of limited iron supply, IRP binding to iron-responsive elements (present in 5' untranslated region of ferritin mRNA and 3' untranslated region of transferrin receptor mRNA) blocks ferritin mRNA translation and stabilizes transferrin receptor mRNA. The opposite scenario develops when iron in the transit pool is plentiful. Moreover, IRP activities/levels can be affected by various forms of "oxidative stress" and nitric oxide. The kidney also requires iron for metabolic processes, and it is likely that iron deficiency or excess can cause disturbed function of kidney cells. Transferrin receptors are not evenly distributed throughout the kidney, and there is a cortical-to-medullary gradient in heme biosynthesis, with greatest activity in the cortex and least in the medulla. This suggests that there are unique iron/heme metabolism features in some kidney cells, but the specific aspects of iron and heme metabolism in the kidney are yet to be explained.

  7. Attractor Metabolic Networks

    PubMed Central

    De la Fuente, Ildefonso M.; Cortes, Jesus M.; Pelta, David A.; Veguillas, Juan

    2013-01-01

    Background The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity. Methodology/Principal Findings In order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network. Conclusions/Significance We have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency

  8. Cellular compartmentalization of secondary metabolism

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Fungal secondary metabolism is often considered apart from the essential housekeeping functions of the cell. However, there are clear links between fundamental cellular metabolism and the biochemical pathways leading to secondary metabolite synthesis. Besides utilizing key biochemical precursors sh...

  9. Nanostructured cellular networks.

    PubMed

    Moriarty, P; Taylor, M D R; Brust, M

    2002-12-01

    Au nanocrystals spin-coated onto silicon from toluene form cellular networks. A quantitative statistical crystallography analysis shows that intercellular correlations drive the networks far from statistical equilibrium. Spin-coating from hexane does not produce cellular structure, yet a strong correlation is retained in the positions of nanocrystal aggregates. Mechanisms based on Marangoni convection alone cannot account for the variety of patterns observed, and we argue that spinodal decomposition plays an important role in foam formation.

  10. Mathematical Modeling of Cellular Metabolism.

    PubMed

    Berndt, Nikolaus; Holzhütter, Hermann-Georg

    2016-01-01

    Cellular metabolism basically consists of the conversion of chemical compounds taken up from the extracellular environment into energy (conserved in energy-rich bonds of organic phosphates) and a wide array of organic molecules serving as catalysts (enzymes), information carriers (nucleic acids), and building blocks for cellular structures such as membranes or ribosomes. Metabolic modeling aims at the construction of mathematical representations of the cellular metabolism that can be used to calculate the concentration of cellular molecules and the rates of their mutual chemical interconversion in response to varying external conditions as, for example, hormonal stimuli or supply of essential nutrients. Based on such calculations, it is possible to quantify complex cellular functions as cellular growth, detoxification of drugs and xenobiotic compounds or synthesis of exported molecules. Depending on the specific questions to metabolism addressed, the methodological expertise of the researcher, and available experimental information, different conceptual frameworks have been established, allowing the usage of computational methods to condense experimental information from various layers of organization into (self-) consistent models. Here, we briefly outline the main conceptual frameworks that are currently exploited in metabolism research. PMID:27557541

  11. Mathematical Modeling of Cellular Metabolism.

    PubMed

    Berndt, Nikolaus; Holzhütter, Hermann-Georg

    2016-01-01

    Cellular metabolism basically consists of the conversion of chemical compounds taken up from the extracellular environment into energy (conserved in energy-rich bonds of organic phosphates) and a wide array of organic molecules serving as catalysts (enzymes), information carriers (nucleic acids), and building blocks for cellular structures such as membranes or ribosomes. Metabolic modeling aims at the construction of mathematical representations of the cellular metabolism that can be used to calculate the concentration of cellular molecules and the rates of their mutual chemical interconversion in response to varying external conditions as, for example, hormonal stimuli or supply of essential nutrients. Based on such calculations, it is possible to quantify complex cellular functions as cellular growth, detoxification of drugs and xenobiotic compounds or synthesis of exported molecules. Depending on the specific questions to metabolism addressed, the methodological expertise of the researcher, and available experimental information, different conceptual frameworks have been established, allowing the usage of computational methods to condense experimental information from various layers of organization into (self-) consistent models. Here, we briefly outline the main conceptual frameworks that are currently exploited in metabolism research.

  12. Elements of the cellular metabolic structure

    PubMed Central

    De la Fuente, Ildefonso M.

    2015-01-01

    A large number of studies have demonstrated the existence of metabolic covalent modifications in different molecular structures, which are able to store biochemical information that is not encoded by DNA. Some of these covalent mark patterns can be transmitted across generations (epigenetic changes). Recently, the emergence of Hopfield-like attractor dynamics has been observed in self-organized enzymatic networks, which have the capacity to store functional catalytic patterns that can be correctly recovered by specific input stimuli. Hopfield-like metabolic dynamics are stable and can be maintained as a long-term biochemical memory. In addition, specific molecular information can be transferred from the functional dynamics of the metabolic networks to the enzymatic activity involved in covalent post-translational modulation, so that determined functional memory can be embedded in multiple stable molecular marks. The metabolic dynamics governed by Hopfield-type attractors (functional processes), as well as the enzymatic covalent modifications of specific molecules (structural dynamic processes) seem to represent the two stages of the dynamical memory of cellular metabolism (metabolic memory). Epigenetic processes appear to be the structural manifestation of this cellular metabolic memory. Here, a new framework for molecular information storage in the cell is presented, which is characterized by two functionally and molecularly interrelated systems: a dynamic, flexible and adaptive system (metabolic memory) and an essentially conservative system (genetic memory). The molecular information of both systems seems to coordinate the physiological development of the whole cell. PMID:25988183

  13. Cellular energy metabolism

    SciTech Connect

    Glaser, M.

    1991-06-01

    Studies have been carried out on adenylate kinase which is an important enzyme in determining the concentrations of the adenine nucleotides. An efficient method has been developed to clone mutant adenylate kinase genes in E. coli. Site-specific mutagenesis of the wild type gene also has been used to obtain forms of adenylate kinase with altered amino acids. The wild type and mutant forms of adenylate kinase have been overexpressed and large quantities were readily isolated. The kinetic and fluorescence properties of the different forms of adenylate kinase were characterized. This has led to a new model for the location of the AMP and ATP bindings sites on the enzyme and a proposal for the mechanism of substrate inhibition. Crystals of the wild type enzyme were obtained that diffract to at least 2.3 {angstrom} resolution. Experiments were also initiated to determine the function of adenylate kinase in vivo. In one set of experiments, E. coli strains with mutations in adenylate kinase showed large changes in cellular nucleotides after reaching the stationary phase in a low phosphate medium. This was caused by selective proteolytic degradation of the mutant adenylate kinase caused by phosphate starvation.

  14. Regulation of cellular metabolism: programming and maintaining metabolic homeostasis.

    PubMed

    Wilson, David F

    2013-12-01

    Mitochondrial oxidative phosphorylation is programmed to set and maintain metabolic homeostasis. This is accomplished through an intrinsic program that determines the metabolic [ATP]/[ADP]/[Pi], where [Pi] is the concentration of inorganic phosphate (energy state) and maintains it through a bidirectional sensory/signaling control network that reaches every aspect of cellular metabolism. The program sets the energy state with high precision (to better than one part in 10(9)) and can respond to transient changes in energy demand (ATP use) to more than 100 times the resting rate. Epigenetic and environmental factors are able to "fine tune" the programmed set point over a narrow range to meet the special needs associated with cell differentiation and chronic changes in metabolic requirements. The result is robust, across platform control of metabolism, essential to cellular differentiation and the evolution of complex organisms.

  15. Cellular compartmentalization of secondary metabolism

    PubMed Central

    Kistler, H. Corby; Broz, Karen

    2015-01-01

    Fungal secondary metabolism is often considered apart from the essential housekeeping functions of the cell. However, there are clear links between fundamental cellular metabolism and the biochemical pathways leading to secondary metabolite synthesis. Besides utilizing key biochemical precursors shared with the most essential processes of the cell (e.g., amino acids, acetyl CoA, NADPH), enzymes for secondary metabolite synthesis are compartmentalized at conserved subcellular sites that position pathway enzymes to use these common biochemical precursors. Co-compartmentalization of secondary metabolism pathway enzymes also may function to channel precursors, promote pathway efficiency and sequester pathway intermediates and products from the rest of the cell. In this review we discuss the compartmentalization of three well-studied fungal secondary metabolite biosynthetic pathways for penicillin G, aflatoxin and deoxynivalenol, and summarize evidence used to infer subcellular localization. We also discuss how these metabolites potentially are trafficked within the cell and may be exported. PMID:25709603

  16. Targeting cellular metabolism to improve cancer therapeutics.

    PubMed

    Zhao, Y; Butler, E B; Tan, M

    2013-03-07

    The metabolic properties of cancer cells diverge significantly from those of normal cells. Energy production in cancer cells is abnormally dependent on aerobic glycolysis. In addition to the dependency on glycolysis, cancer cells have other atypical metabolic characteristics such as increased fatty acid synthesis and increased rates of glutamine metabolism. Emerging evidence shows that many features characteristic to cancer cells, such as dysregulated Warburg-like glucose metabolism, fatty acid synthesis and glutaminolysis are linked to therapeutic resistance in cancer treatment. Therefore, targeting cellular metabolism may improve the response to cancer therapeutics and the combination of chemotherapeutic drugs with cellular metabolism inhibitors may represent a promising strategy to overcome drug resistance in cancer therapy. Recently, several review articles have summarized the anticancer targets in the metabolic pathways and metabolic inhibitor-induced cell death pathways, however, the dysregulated metabolism in therapeutic resistance, which is a highly clinical relevant area in cancer metabolism research, has not been specifically addressed. From this unique angle, this review article will discuss the relationship between dysregulated cellular metabolism and cancer drug resistance and how targeting of metabolic enzymes, such as glucose transporters, hexokinase, pyruvate kinase M2, lactate dehydrogenase A, pyruvate dehydrogenase kinase, fatty acid synthase and glutaminase can enhance the efficacy of common therapeutic agents or overcome resistance to chemotherapy or radiotherapy.

  17. MSAT and cellular hybrid networking

    NASA Technical Reports Server (NTRS)

    Baranowsky, Patrick W., II

    1993-01-01

    Westinghouse Electric Corporation is developing both the Communications Ground Segment and the Series 1000 Mobile Phone for American Mobile Satellite Corporation's (AMSC's) Mobile Satellite (MSAT) system. The success of the voice services portion of this system depends, to some extent, upon the interoperability of the cellular network and the satellite communication circuit switched communication channels. This paper will describe the set of user-selectable cellular interoperable modes (cellular first/satellite second, etc.) provided by the Mobile Phone and described how they are implemented with the ground segment. Topics including roaming registration and cellular-to-satellite 'seamless' call handoff will be discussed, along with the relevant Interim Standard IS-41 Revision B Cellular Radiotelecommunications Intersystem Operations and IOS-553 Mobile Station - Land Station Compatibility Specification.

  18. Quantitative Analysis of Cellular Metabolic Dissipative, Self-Organized Structures

    PubMed Central

    de la Fuente, Ildefonso Martínez

    2010-01-01

    One of the most important goals of the postgenomic era is understanding the metabolic dynamic processes and the functional structures generated by them. Extensive studies during the last three decades have shown that the dissipative self-organization of the functional enzymatic associations, the catalytic reactions produced during the metabolite channeling, the microcompartmentalization of these metabolic processes and the emergence of dissipative networks are the fundamental elements of the dynamical organization of cell metabolism. Here we present an overview of how mathematical models can be used to address the properties of dissipative metabolic structures at different organizational levels, both for individual enzymatic associations and for enzymatic networks. Recent analyses performed with dissipative metabolic networks have shown that unicellular organisms display a singular global enzymatic structure common to all living cellular organisms, which seems to be an intrinsic property of the functional metabolism as a whole. Mathematical models firmly based on experiments and their corresponding computational approaches are needed to fully grasp the molecular mechanisms of metabolic dynamical processes. They are necessary to enable the quantitative and qualitative analysis of the cellular catalytic reactions and also to help comprehend the conditions under which the structural dynamical phenomena and biological rhythms arise. Understanding the molecular mechanisms responsible for the metabolic dissipative structures is crucial for unraveling the dynamics of cellular life. PMID:20957111

  19. Cellular metabolism and disease: what do metabolic outliers teach us?

    PubMed Central

    DeBerardinis, Ralph J.; Thompson, Craig B.

    2012-01-01

    An understanding of metabolic pathways based solely on biochemistry textbooks would underestimate the pervasive role of metabolism in essentially every aspect of biology. It is evident from recent work that many human diseases involve abnormal metabolic states – often genetically programmed – that perturb normal physiology and lead to severe tissue dysfunction. Understanding these metabolic outliers is now a crucial frontier in disease-oriented research. This review discusses the broad impact of metabolism in cellular function, how modern concepts of metabolism can inform our understanding of common diseases like cancer, and considers the prospects of developing new metabolic approaches to disease treatment. PMID:22424225

  20. Immunometabolism: Cellular Metabolism Turns Immune Regulator.

    PubMed

    Loftus, Róisín M; Finlay, David K

    2016-01-01

    Immune cells are highly dynamic in terms of their growth, proliferation, and effector functions as they respond to immunological challenges. Different immune cells can adopt distinct metabolic configurations that allow the cell to balance its requirements for energy, molecular biosynthesis, and longevity. However, in addition to facilitating immune cell responses, it is now becoming clear that cellular metabolism has direct roles in regulating immune cell function. This review article describes the distinct metabolic signatures of key immune cells, explains how these metabolic setups facilitate immune function, and discusses the emerging evidence that intracellular metabolism has an integral role in controlling immune responses. PMID:26534957

  1. Control of fluxes in metabolic networks.

    PubMed

    Basler, Georg; Nikoloski, Zoran; Larhlimi, Abdelhalim; Barabási, Albert-László; Liu, Yang-Yu

    2016-07-01

    Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism. PMID:27197218

  2. Compartmentalization of the Edinburgh Human Metabolic Network

    PubMed Central

    2010-01-01

    Background Direct in vivo investigation of human metabolism is complicated by the distinct metabolic functions of various sub-cellular organelles. Diverse micro-environments in different organelles may lead to distinct functions of the same protein and the use of different enzymes for the same metabolic reaction. To better understand the complexity in the human metabolism, a compartmentalized human metabolic network with integrated sub-cellular location information is required. Results We extended the previously reconstructed Edinburgh Human Metabolic Network (EHMN) [Ma, et al. Molecular Systems Biology, 3:135, 2007] by integrating the sub-cellular location information for the reactions, adding transport reactions and refining the protein-reaction relationships based on the location information. Firstly, protein location information was obtained from Gene Ontology and complemented by a Swiss-Prot location keywords search. Then all the reactions in EHMN were assigned to a location based on the protein-reaction relationships to get a preliminary compartmentalized network. We investigated the localized sub-networks in each pathway to identify gaps and isolated reactions by connectivity analysis and refined the location information based on information from literature. As a result, location information for hundreds of reactions was revised and hundreds of incorrect protein-reaction relationships were corrected. Over 1400 transport reactions were added to link the location specific metabolic network. To validate the network, we have done pathway analysis to examine the capability of the network to synthesize or degrade certain key metabolites. Compared with a previously published human metabolic network (Human Recon 1), our network contains over 1000 more reactions assigned to clear cellular compartments. Conclusions By combining protein location information, network connectivity analysis and manual literature search, we have reconstructed a more complete

  3. Cellular metabolism and macrophage functional polarization.

    PubMed

    Zhu, Linnan; Zhao, Qingjie; Yang, Tao; Ding, Wenjun; Zhao, Yong

    2015-01-01

    Macrophages are a functionally heterogeneous cell population that is mainly shaped by a variety of microenvironmental stimuli. Interferon γ (IFN-γ), interleukin-1β (IL-1β), and lipopolysaccharide (LPS) induce a classical activation of macrophages (M1), whereas IL-4 and IL-13 induce an alternative activation program in macrophages (M2). Reprogramming of intracellular metabolisms is required for the proper polarization and functions of activated macrophages. Similar to the Warburg effect observed in tumor cells, M1 macrophages increase glucose consumption and lactate release and decreased oxygen consumption rate. In comparison, M2 macrophages mainly employ oxidative glucose metabolism pathways. In addition, fatty acids, vitamins, and iron metabolisms are also related to macrophage polarization. However, detailed metabolic pathways involved in macrophages have remained elusive. Understanding the bidirectional interactions between cellular metabolism and macrophage functions in physiological and pathological situations and the regulatory pathways involved may offer novel therapies for macrophage-associated diseases.

  4. Complement-Mediated Regulation of Metabolism and Basic Cellular Processes.

    PubMed

    Hess, Christoph; Kemper, Claudia

    2016-08-16

    Complement is well appreciated as a critical arm of innate immunity. It is required for the removal of invading pathogens and works by directly destroying them through the activation of innate and adaptive immune cells. However, complement activation and function is not confined to the extracellular space but also occurs within cells. Recent work indicates that complement activation regulates key metabolic pathways and thus can impact fundamental cellular processes, such as survival, proliferation, and autophagy. Newly identified functions of complement include a key role in shaping metabolic reprogramming, which underlies T cell effector differentiation, and a role as a nexus for interactions with other effector systems, in particular the inflammasome and Notch transcription-factor networks. This review focuses on the contributions of complement to basic processes of the cell, in particular the integration of complement with cellular metabolism and the potential implications in infection and other disease settings. PMID:27533012

  5. Cellular Metabolism of Unnatural Sialic Acid Precursors

    PubMed Central

    Pham, Nam D.; Fermaintt, Charles S.; Rodriguez, Andrea C.; McCombs, Janet E.; Nischan, Nicole; Kohler, Jennifer J.

    2015-01-01

    Carbohydrates, in addition to their metabolic functions, serve important roles as receptors, ligands, and structural molecules for diverse biological processes. Insight into carbohydrate biology and mechanisms has been aided by metabolic oligosaccharide engineering (MOE). In MOE, unnatural carbohydrate analogs with novel functional groups are incorporated into cellular glycoconjugates and used to probe biological systems. While MOE has expanded knowledge of carbohydrate biology, limited metabolism of unnatural carbohydrate analogs restricts its use. Here we assess metabolism of SiaDAz, a diazirine-modified analog of sialic acid, and its cell-permeable precursor, Ac4ManNDAz. We show that the efficiency of Ac4ManNDAz and SiaDAz metabolism depends on cell type. Our results indicate that different cell lines can have different metabolic roadblocks in the synthesis of cell surface SiaDAz. These findings point to roles for promiscuous intracellular esterases, kinases, and phosphatases during unnatural sugar metabolism and provide guidance for ways to improve MOE. PMID:25957566

  6. Micromechanics of cellularized biopolymer networks

    PubMed Central

    Jones, Christopher A. R.; Cibula, Matthew; Feng, Jingchen; Krnacik, Emma A.; McIntyre, David H.; Levine, Herbert; Sun, Bo

    2015-01-01

    Collagen gels are widely used in experiments on cell mechanics because they mimic the extracellular matrix in physiological conditions. Collagen gels are often characterized by their bulk rheology; however, variations in the collagen fiber microstructure and cell adhesion forces cause the mechanical properties to be inhomogeneous at the cellular scale. We study the mechanics of type I collagen on the scale of tens to hundreds of microns by using holographic optical tweezers to apply pN forces to microparticles embedded in the collagen fiber network. We find that in response to optical forces, particle displacements are inhomogeneous, anisotropic, and asymmetric. Gels prepared at 21 °C and 37 °C show qualitative difference in their micromechanical characteristics. We also demonstrate that contracting cells remodel the micromechanics of their surrounding extracellular matrix in a strain- and distance-dependent manner. To further understand the micromechanics of cellularized extracellular matrix, we have constructed a computational model which reproduces the main experiment findings. PMID:26324923

  7. Global Self-Organization of the Cellular Metabolic Structure

    PubMed Central

    De La Fuente, Ildefonso M.; Martínez, Luis; Pérez-Samartín, Alberto L.; Ormaetxea, Leire; Amezaga, Cristian; Vera-López, Antonio

    2008-01-01

    Background Over many years, it has been assumed that enzymes work either in an isolated way, or organized in small catalytic groups. Several studies performed using “metabolic networks models” are helping to understand the degree of functional complexity that characterizes enzymatic dynamic systems. In a previous work, we used “dissipative metabolic networks” (DMNs) to show that enzymes can present a self-organized global functional structure, in which several sets of enzymes are always in an active state, whereas the rest of molecular catalytic sets exhibit dynamics of on-off changing states. We suggested that this kind of global metabolic dynamics might be a genuine and universal functional configuration of the cellular metabolic structure, common to all living cells. Later, a different group has shown experimentally that this kind of functional structure does, indeed, exist in several microorganisms. Methodology/Principal Findings Here we have analyzed around 2.500.000 different DMNs in order to investigate the underlying mechanism of this dynamic global configuration. The numerical analyses that we have performed show that this global configuration is an emergent property inherent to the cellular metabolic dynamics. Concretely, we have found that the existence of a high number of enzymatic subsystems belonging to the DMNs is the fundamental element for the spontaneous emergence of a functional reactive structure characterized by a metabolic core formed by several sets of enzymes always in an active state. Conclusions/Significance This self-organized dynamic structure seems to be an intrinsic characteristic of metabolism, common to all living cellular organisms. To better understand cellular functionality, it will be crucial to structurally characterize these enzymatic self-organized global structures. PMID:18769681

  8. Lipid Droplets And Cellular Lipid Metabolism

    PubMed Central

    Walther, Tobias C.; Farese, Robert V.

    2013-01-01

    Among organelles, lipid droplets (LDs) uniquely constitute a hydrophobic phase in the aqueous environment of the cytosol. Their hydrophobic core of neutral lipids stores metabolic energy and membrane components, making LDs hubs for lipid metabolism. In addition, LDs are implicated in a number of other cellular functions, ranging from protein storage and degradation to viral replication. These processes are functionally linked to many physiological and pathological conditions, including obesity and related metabolic diseases. Despite their important functions and nearly ubiquitous presence in cells, many aspects of LD biology are unknown. In the past few years, the pace of LD investigation has increased, providing new insights. Here, we review the current knowledge of LD cell biology and its translation to physiology. PMID:22524315

  9. Promise and Reality in the Expanding Field of Network Interaction Analysis: Metabolic Networks

    PubMed Central

    Bazzani, Susanna

    2014-01-01

    In the last few decades, metabolic networks revealed their capabilities as powerful tools to analyze the cellular metabolism. Many research fields (eg, metabolic engineering, diagnostic medicine, pharmacology, biochemistry, biology and physiology) improved the understanding of the cell combining experimental assays and metabolic network-based computations. This process led to the rise of the “systems biology” approach, where the theory meets experiments and where two complementary perspectives cooperate in the study of biological phenomena. Here, the reconstruction of metabolic networks is presented, along with established and new algorithms to improve the description of cellular metabolism. Then, advantages and limitations of modeling algorithms and network reconstruction are discussed. PMID:24812497

  10. Approximating the stabilization of cellular metabolism by compartmentalization.

    PubMed

    Fürtauer, Lisa; Nägele, Thomas

    2016-06-01

    Biochemical regulation in compartmentalized metabolic networks is highly complex and non-intuitive. This is particularly true for cells of higher plants showing one of the most compartmentalized cellular structures across all kingdoms of life. The interpretation and testable hypothesis generation from experimental data on such complex systems is a challenging step in biological research and biotechnological applications. While it is known that subcellular compartments provide defined reaction spaces within a cell allowing for the tight coordination of complex biochemical reaction sequences, its role in the coordination of metabolic signals during metabolic reprogramming due to environmental fluctuations is less clear. In the present study, we numerically analysed the effects of environmental fluctuations in a subcellular metabolic network with regard to the stability of an experimentally observed steady state in the genetic model plant Arabidopsis thaliana. Applying a method for kinetic parameter normalization, several millions of probable enzyme kinetic parameter constellations were simulated and evaluated with regard to the stability information of the metabolic homeostasis. Information about the stability of the metabolic steady state was derived from real parts of eigenvalues of Jacobian matrices. Our results provide evidence for a differential stabilizing contribution of different subcellular compartments. We could identify stabilizing and destabilizing network components which we could classify according to their subcellular localization. The findings prove that a highly dynamic interplay between intracellular compartments is preliminary for an efficient stabilization of a metabolic homeostasis after environmental perturbation. Further, our results provide evidence that feedback-inhibition originating from the cytosol and plastid seem to stabilize the sucrose homeostasis more efficiently than vacuolar control. In summary, our results indicate stabilizing and

  11. Alterations of a Cellular Cholesterol Metabolism Network Are a Molecular Feature of Obesity-Related Type 2 Diabetes and Cardiovascular Disease.

    PubMed

    Ding, Jingzhong; Reynolds, Lindsay M; Zeller, Tanja; Müller, Christian; Lohman, Kurt; Nicklas, Barbara J; Kritchevsky, Stephen B; Huang, Zhiqing; de la Fuente, Alberto; Soranzo, Nicola; Settlage, Robert E; Chuang, Chia-Chi; Howard, Timothy; Xu, Ning; Goodarzi, Mark O; Chen, Y-D Ida; Rotter, Jerome I; Siscovick, David S; Parks, John S; Murphy, Susan; Jacobs, David R; Post, Wendy; Tracy, Russell P; Wild, Philipp S; Blankenberg, Stefan; Hoeschele, Ina; Herrington, David; McCall, Charles E; Liu, Yongmei

    2015-10-01

    Obesity is linked to type 2 diabetes (T2D) and cardiovascular diseases; however, the underlying molecular mechanisms remain unclear. We aimed to identify obesity-associated molecular features that may contribute to obesity-related diseases. Using circulating monocytes from 1,264 Multi-Ethnic Study of Atherosclerosis (MESA) participants, we quantified the transcriptome and epigenome. We discovered that alterations in a network of coexpressed cholesterol metabolism genes are a signature feature of obesity and inflammatory stress. This network included 11 BMI-associated genes related to sterol uptake (↑LDLR, ↓MYLIP), synthesis (↑SCD, FADS1, HMGCS1, FDFT1, SQLE, CYP51A1, SC4MOL), and efflux (↓ABCA1, ABCG1), producing a molecular profile expected to increase intracellular cholesterol. Importantly, these alterations were associated with T2D and coronary artery calcium (CAC), independent from cardiometabolic factors, including serum lipid profiles. This network mediated the associations between obesity and T2D/CAC. Several genes in the network harbored C-phosphorus-G dinucleotides (e.g., ABCG1/cg06500161), which overlapped Encyclopedia of DNA Elements (ENCODE)-annotated regulatory regions and had methylation profiles that mediated the associations between BMI/inflammation and expression of their cognate genes. Taken together with several lines of previous experimental evidence, these data suggest that alterations of the cholesterol metabolism gene network represent a molecular link between obesity/inflammation and T2D/CAC.

  12. Molecular processes in cellular arsenic metabolism

    SciTech Connect

    Thomas, David J.

    2007-08-01

    Elucidating molecular processes that underlie accumulation, metabolism and binding of iAs and its methylated metabolites provides a basis for understanding the modes of action by which iAs acts as a toxin and a carcinogen. One approach to this problem is to construct a conceptual model that incorporates available information on molecular processes involved in the influx, metabolism, binding and efflux of arsenicals in cells. This conceptual model is initially conceived as a non-quantitative representation of critical molecular processes that can be used as a framework for experimental design and prediction. However, with refinement and incorporation of additional data, the conceptual model can be expressed in mathematical terms and should be useful for quantitative estimates of the kinetic and dynamic behavior of iAs and its methylated metabolites in cells. Development of a quantitative model will be facilitated by the availability of tools and techniques to manipulate molecular processes underlying transport of arsenicals across cell membranes or expression and activity of enzymes involved in methylation of arsenicals. This model of cellular metabolism might be integrated into more complex pharmacokinetic models for systemic metabolism of iAs and its methylated metabolites. It may also be useful in development of biologically based dose-response models describing the toxic and carcinogenic actions of arsenicals.

  13. A nexus for cellular homeostasis: the interplay between metabolic and signal transduction pathways.

    PubMed

    Gomes, Ana P; Blenis, John

    2015-08-01

    In multicellular organisms, individual cells have evolved to sense external and internal cues in order to maintain cellular homeostasis and survive under different environmental conditions. Cells efficiently adjust their metabolism to reflect the abundance of nutrients, energy and growth factors. The ability to rewire cellular metabolism between anabolic and catabolic processes is crucial for cells to thrive. Thus, cells have developed, through evolution, metabolic networks that are highly plastic and tightly regulated to meet the requirements necessary to maintain cellular homeostasis. The plasticity of these cellular systems is tightly regulated by complex signaling networks that integrate the intracellular and extracellular information. The coordination of signal transduction and metabolic pathways is essential in maintaining a healthy and rapidly responsive cellular state.

  14. Remote Energy Monitoring System via Cellular Network

    NASA Astrophysics Data System (ADS)

    Yunoki, Shoji; Tamaki, Satoshi; Takada, May; Iwaki, Takashi

    Recently, improvement on power saving and cost efficiency by monitoring the operation status of various facilities over the network has gained attention. Wireless network, especially cellular network, has advantage in mobility, coverage, and scalability. On the other hand, it has disadvantage of low reliability, due to rapid changes in the available bandwidth. We propose a transmission control scheme based on data priority and instantaneous available bandwidth to realize a highly reliable remote monitoring system via cellular network. We have developed our proposed monitoring system and evaluated the effectiveness of our scheme, and proved it reduces the maximum transmission delay of sensor status to 1/10 compared to best effort transmission.

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

    PubMed

    Tomar, Namrata; De, Rajat K

    2013-05-25

    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.

  16. Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

    PubMed

    Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay

    2013-01-01

    The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more

  17. The widespread role of non-enzymatic reactions in cellular metabolism

    PubMed Central

    Keller, Markus A; Piedrafita, Gabriel; Ralser, Markus

    2015-01-01

    Enzymes shape cellular metabolism, are regulated, fast, and for most cases specific. Enzymes do not however prevent the parallel occurrence of non-enzymatic reactions. Non-enzymatic reactions were important for the evolution of metabolic pathways, but are retained as part of the modern metabolic network. They divide into unspecific chemical reactivity and specific reactions that occur either exclusively non-enzymatically as part of the metabolic network, or in parallel to existing enzyme functions. Non-enzymatic reactions resemble catalytic mechanisms as found in all major enzyme classes and occur spontaneously, small molecule (e.g. metal-) catalyzed or light-induced. The frequent occurrence of non-enzymatic reactions impacts on stability and metabolic network structure, and has thus to be considered in the context of metabolic disease, network modeling, biotechnology and drug design. PMID:25617827

  18. Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

    PubMed Central

    2011-01-01

    Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis. PMID:22784571

  19. Metabolic disease network and its implication for disease comorbidity

    NASA Astrophysics Data System (ADS)

    Lee, Deok-Sun; Oltvai, Zoltan; Christakis, Nicholas; Barabasi, Albert-Laszlo

    2008-03-01

    Given that most diseases are the result of the breakdown of some cellular processes, a key aim of modern medicine is to establish the relationship between disease phenotypes and the various disruptions in the underlying cellular networks. Here we show that our current understanding of the structure of the human metabolic network can provide insight into potential relationships among often distinct disease phenotypes. Using the known enzyme-disease associations, we construct a human metabolic disease network in which nodes are diseases and two diseases are linked if the enzymes associated with them catalyze adjacent metabolic reactions. We find that the more connected a disease is, the higher is its prevalence and the chance that it is associated with a high mortality. The results indicate that the cellular network-level relationships between metabolic pathways and the associated disease provide insights into disease comorbidity, with potential important consequences on disease diagnosis and prevention.

  20. Inferring cellular networks – a review

    PubMed Central

    Markowetz, Florian; Spang, Rainer

    2007-01-01

    In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations. PMID:17903286

  1. Genome-scale metabolic network reconstruction.

    PubMed

    Fondi, Marco; Liò, Pietro

    2015-01-01

    Bacterial metabolism is an important source of novel products/processes for everyday life and strong efforts are being undertaken to discover and exploit new usable substances of microbial origin. Computational modeling and in silico simulations are powerful tools in this context since they allow the exploration and a deeper understanding of bacterial metabolic circuits. Many approaches exist to quantitatively simulate chemical reaction fluxes within the whole microbial metabolism and, regardless of the technique of choice, metabolic model reconstruction is the first step in every modeling pipeline. Reconstructing a metabolic network consists in drafting the list of the biochemical reactions that an organism can carry out together with information on cellular boundaries, a biomass assembly reaction, and exchange fluxes with the external environment. Building up models able to represent the different functional cellular states is universally recognized as a tricky task that requires intensive manual effort and much additional information besides genome sequence. In this chapter we present a general protocol for metabolic reconstruction in bacteria and the main challenges encountered during this process. PMID:25343869

  2. [Cellular metabolism, temperature and brain injury].

    PubMed

    Geeraerts, T; Vigué, B

    2009-04-01

    Brain temperature is strongly linked to brain metabolic rate. In the brain, energy metabolism is mainly oxidative. The oxidative metabolism and heat production are therefore strongly related. In normal conditions, heat production consecutive to brain energy metabolism is counterbalanced by heat loss, by using a complex heat exchange system. After major cerebral injuries as subarachnoid haemorrhage or traumatic brain injury, cerebral temperature can often exceed systemic temperature. Moreover, brain temperature can vary independently to systemic temperature, making difficult the prediction of brain temperature from other central temperatures. Mitochondrial dysfunction is probably the corner stone of these post-injury perturbations of brain temperature. Understanding of this phenomenon remains however not complete. PMID:19303246

  3. ATM couples replication stress and metabolic reprogramming during cellular senescence

    PubMed Central

    Aird, Katherine M.; Worth, Andrew J.; Snyder, Nathaniel W.; Lee, Joyce V.; Sivanand, Sharanya; Liu, Qin; Blair, Ian A.; Wellen, Kathryn E.; Zhang, Rugang

    2015-01-01

    Summary Replication stress induced by nucleotide deficiency plays an important role in cancer initiation. Replication stress in primary cells typically activates the cellular senescence tumor suppression mechanism. Senescence bypass correlates with development of cancer, a disease characterized by metabolic reprogramming. However, the role of metabolic reprogramming in cellular response to replication stress is unknown. Here we report that ATM plays a central role in regulating cellular response to replication stress by shifting cellular metabolism. ATM inactivation bypasses senescence induced by replication stress triggered by nucleotide deficiency. This was due to restoration of dNTP levels through both upregulation of the pentose phosphate pathway via increased G6PD activity and enhanced glucose and glutamine consumption. These phenotypes were mediated by a coordinated suppression of p53 and upregulation of c-MYC downstream of ATM inactivation. Our data indicate that ATM status couples replication stress and metabolic reprogramming during senescence. PMID:25937285

  4. Quantitation of cellular metabolic fluxes of methionine.

    PubMed

    Shlomi, Tomer; Fan, Jing; Tang, Baiqing; Kruger, Warren D; Rabinowitz, Joshua D

    2014-02-01

    Methionine is an essential proteogenic amino acid. In addition, it is a methyl donor for DNA and protein methylation and a propylamine donor for polyamine biosynthesis. Both the methyl and propylamine donation pathways involve metabolic cycles, and methods are needed to quantitate these cycles. Here, we describe an analytical approach for quantifying methionine metabolic fluxes that accounts for the mixing of intracellular and extracellular methionine pools. We observe that such mixing prevents isotope tracing experiments from reaching the steady state due to the large size of the media pools and hence precludes the use of standard stationary metabolic flux analysis. Our approach is based on feeding cells with (13)C methionine and measuring the isotope-labeling kinetics of both intracellular and extracellular methionine by liquid chromatography-mass spectrometry (LC-MS). We apply this method to quantify methionine metabolism in a human fibrosarcoma cell line and study how methionine salvage pathway enzyme methylthioadenosine phosphorylase (MTAP), frequently deleted in cancer, affects methionine metabolism. We find that both transmethylation and propylamine transfer fluxes amount to roughly 15% of the net methionine uptake, with no major changes due to MTAP deletion. Our method further enables the quantification of flux through the pro-tumorigenic enzyme ornithine decarboxylase, and this flux increases 2-fold following MTAP deletion. The analytical approach used to quantify methionine metabolic fluxes is applicable for other metabolic systems affected by mixing of intracellular and extracellular metabolite pools.

  5. Pathway knockout and redundancy in metabolic networks.

    PubMed

    Min, Yong; Jin, Xiaogang; Chen, Ming; Pan, Zhengzheng; Ge, Ying; Chang, Jie

    2011-02-01

    The robustness and stability of complex cellular networks is often attributed to the redundancy of components, including genes, enzymes and pathways. Estimation of redundancy is still an open question in systems biology. Current theoretical tools to measure redundancy have various strengths and shortcomings in providing a comprehensive description of metabolic networks. Specially, there is a lack of effective measures to cover different perturbation situations. Here we present a pathway knockout algorithm to improve quantitative measure of redundancy in metabolic networks grounded on the elementary flux mode (EFM) analysis. The proposed redundancy measure is based on the average ratio of remaining EFMs after knockout of one EFM in the unperturbed state. We demonstrated with four example systems that our algorithm overcomes limits of previous measures, and provides additional information about redundancy in the situation of targeted attacks. Additionally, we compare existing enzyme knockout and our pathway knockout algorithm by the mean-field analysis, which provides mathematical expression for the average ratio of remaining EFMs after both types of knockout. Our results prove that multiple-enzymes knockout does not always yield more information than single-enzyme knockout for evaluating redundancy. Indeed, pathway knockout considers additional effects of structural asymmetry. In the metabolic networks of amino acid anabolism in Escherichia coli and human hepatocytes, and the central metabolism in human erythrocytes, we validate our mean-field solutions and prove the capacity of pathway knockout algorithm. Moreover, in the E. coli model the two sub-networks synthesizing amino acids that are essential and those that are non-essential for humans are studied separately. In contrast to previous studies, we find that redundancy of two sub-networks is similar with each other, and even sub-networks synthesizing essential amino acids can be more redundant.

  6. WD40 proteins propel cellular networks.

    PubMed

    Stirnimann, Christian U; Petsalaki, Evangelia; Russell, Robert B; Müller, Christoph W

    2010-10-01

    Recent findings indicate that WD40 domains play central roles in biological processes by acting as hubs in cellular networks; however, they have been studied less intensely than other common domains, such as the kinase, PDZ or SH3 domains. As suggested by various interactome studies, they are among the most promiscuous interactors. Structural studies suggest that this property stems from their ability, as scaffolds, to interact with diverse proteins, peptides or nucleic acids using multiple surfaces or modes of interaction. A general scaffolding role is supported by the fact that no WD40 domain has been found with intrinsic enzymatic activity despite often being part of large molecular machines. We discuss the WD40 domain distributions in protein networks and structures of WD40-containing assemblies to demonstrate their versatility in mediating critical cellular functions.

  7. Autophagy: A Critical Regulator of Cellular Metabolism and Homeostasis

    PubMed Central

    Ryter, Stefan W.; Cloonan, Suzanne M.; Choi, Augustine M. K.

    2013-01-01

    Autophagy is a dynamic process by which cytosolic material, including organelles, proteins, and pathogens, are sequestered into membrane vesicles called autophagosomes, and then delivered to the lysosome for degradation. By recycling cellular components, this process provides a mechanism for adaptation to starvation. The regulation of autophagy by nutrient signals involves a complex network of proteins that include mammalian target of rapamycin, the class III phosphatidylinositol-3 kinase/Beclin 1 complex, and two ubiquitin-like conjugation systems. Additionally, autophagy, which can be induced by multiple forms of chemical and physical stress, including endoplasmic reticulum stress, and hypoxia, plays an integral role in the mammalian stress response. Recent studies indicate that, in addition to bulk assimilation of cytosol, autophagy may proceed through selective pathways that target distinct cargoes to autophagosomes. The principle homeostatic functions of autophagy include the selective clearance of aggregated protein to preserve proteostasis, and the selective removal of dysfunctional mitochondria (mitophagy). Additionally, autophagy plays a central role in innate and adaptive immunity, with diverse functions such as regulation of inflammatory responses, antigen presentation, and pathogen clearance. Autophagy can preserve cellular function in a wide variety of tissue injury and disease states, however, maladaptive or pro-pathogenic outcomes have also been described. Among the many diseases where autophagy may play a role include proteopathies which involve aberrant accumulation of proteins (e.g., neurodegenerative disorders), infectious diseases, and metabolic disorders such as diabetes and metabolic syndrome. Targeting the autophagy pathway and its regulatory components may eventually lead to the development of therapeutics. PMID:23708729

  8. Sestrins orchestrate cellular metabolism to attenuate aging

    PubMed Central

    Karin, Michael

    2013-01-01

    Summary The Sestrins constitute a family of evolutionarily-conserved stress-inducible proteins that suppress oxidative stress and regulate adenosine monophosphate-dependent protein kinase (AMPK)-mammalian target of rapamycin (mTOR) signaling. By virtue of these activities, the Sestrins serve as important regulators of metabolic homeostasis. Accordingly, inactivation of Sestrin genes in invertebrates resulted in diverse metabolic pathologies, including oxidative damage, fat accumulation, mitochondrial dysfunction and muscle degeneration that resemble accelerated tissue aging. Likewise, Sestrin deficiencies in mice led to accelerated diabetic progression upon obesity. Further investigation of Sestrin function and regulation should provide new insights into age-associated metabolic diseases, such as diabetes, myopathies and cancer. PMID:24055102

  9. Using cellular network diagrams to interpret large-scale datasets: past progress and future challenges

    NASA Astrophysics Data System (ADS)

    Karp, Peter D.; Latendresse, Mario; Paley, Suzanne

    2011-03-01

    Cellular networks are graphs of molecular interactions within the cell. Thanks to the confluence of genome sequencing and bioinformatics, scientists are now able to reconstruct cellular network models for more than 1,000 organisms. A variety of bioinformatics tools have been developed to support the visualization and navigation of cellular network data. Another important application is the use of cellular network diagrams to visualize and interpret large-scale datasets, such as gene-expression data. We present the Cellular Overview, a network visualization tool developed at SRI International (SRI) to support visualization, navigation, and interpretation of large-scale datasets on metabolic networks. Different variations of the diagram have been generated algorithmically for more than 1,000 organisms. We discuss the graphical design of the diagram and its interactive capabilities.

  10. Translation Factors Specify Cellular Metabolic State.

    PubMed

    Mata, Juan

    2016-08-16

    In this issue of Cell Reports, Shah et al. present evidence that a subcomplex of the eIF3 translation initiation factor regulates translation of mRNAs encoding components of the mitochondrial electron transport chain and glycolytic enzymes, thus linking translational control with energy metabolism. PMID:27533178

  11. Is cancer a disease of abnormal cellular metabolism?

    PubMed Central

    DeBerardinis, Ralph J.

    2009-01-01

    In the 1920s, Otto Warburg observed that tumor cells consume a large amount of glucose, much more than normal cells, and convert most of it to lactic acid. This phenomenon, now known as the ‘Warburg effect,’ is the foundation of one of the earliest general concepts of cancer: that a fundamental disturbance of cellular metabolic activity is at the root of tumor formation and growth. In the ensuing decades, as it became apparent that abnormalities in chromosomes and eventually individual genes caused cancer, the ‘metabolic’ model of cancer lost a good deal of its appeal, even as emerging technologies were exploiting the Warburg effect clinically to detect tumors in vivo. We now know that tumor suppressors and proto-oncogenes influence metabolism, and that mutations in these genes can promote a metabolic phenotype supporting cell growth and proliferation. Thus, these advances have unified aspects of the metabolic and genetic models of cancer, and have stimulated a renewed interest in the role of cellular metabolism in tumorigenesis. This review reappraises the notion that dysregulated cellular metabolism is a key feature of cancer, and discusses some metabolic issues that have escaped scrutiny over the years and now deserve closer attention. PMID:18941420

  12. MANET: tracing evolution of protein architecture in metabolic networks

    PubMed Central

    Kim, Hee Shin; Mittenthal, Jay E; Caetano-Anollés, Gustavo

    2006-01-01

    Background Cellular metabolism can be characterized by networks of enzymatic reactions and transport processes capable of supporting cellular life. Our aim is to find evolutionary patterns and processes embedded in the architecture and function of modern metabolism, using information derived from structural genomics. Description The Molecular Ancestry Network (MANET) project traces evolution of protein architecture in biomolecular networks. We describe metabolic MANET, a database that links information in the Structural Classification of Proteins (SCOP), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and phylogenetic reconstructions depicting the evolution of protein fold architecture. Metabolic MANET literally 'paints' the ancestries of enzymes derived from rooted phylogenomic trees directly onto over one hundred metabolic subnetworks, enabling the study of evolutionary patterns at global and local levels. An initial analysis of painted subnetworks reveals widespread enzymatic recruitment and an early origin of amino acid metabolism. Conclusion MANET maps evolutionary relationships directly and globally onto biological networks, and can generate and test hypotheses related to evolution of metabolism. We anticipate its use in the study of other networks, such as signaling and other protein-protein interaction networks. PMID:16854231

  13. Cellular recurrent deep network for image registration

    NASA Astrophysics Data System (ADS)

    Alam, M.; Vidyaratne, L.; Iftekharuddin, Khan M.

    2015-09-01

    Image registration using Artificial Neural Network (ANN) remains a challenging learning task. Registration can be posed as a two-step problem: parameter estimation and actual alignment/transformation using the estimated parameters. To date ANN based image registration techniques only perform the parameter estimation, while affine equations are used to perform the actual transformation. In this paper, we propose a novel deep ANN based image rigid registration that combines parameter estimation and transformation as a simultaneous learning task. Our previous work shows that a complex universal approximator known as Cellular Simultaneous Recurrent Network (CSRN) can successfully approximate affine transformations with known transformation parameters. This study introduces a deep ANN that combines a feed forward network with a CSRN to perform full rigid registration. Layer wise training is used to pre-train feed forward network for parameter estimation and followed by a CSRN for image transformation respectively. The deep network is then fine-tuned to perform the final registration task. Our result shows that the proposed deep ANN architecture achieves comparable registration accuracy to that of image affine transformation using CSRN with known parameters. We also demonstrate the efficacy of our novel deep architecture by a performance comparison with a deep clustered MLP.

  14. Cellular automata modelling of biomolecular networks dynamics.

    PubMed

    Bonchev, D; Thomas, S; Apte, A; Kier, L B

    2010-01-01

    The modelling of biological systems dynamics is traditionally performed by ordinary differential equations (ODEs). When dealing with intracellular networks of genes, proteins and metabolites, however, this approach is hindered by network complexity and the lack of experimental kinetic parameters. This opened the field for other modelling techniques, such as cellular automata (CA) and agent-based modelling (ABM). This article reviews this emerging field of studies on network dynamics in molecular biology. The basics of the CA technique are discussed along with an extensive list of related software and websites. The application of CA to networks of biochemical reactions is exemplified in detail by the case studies of the mitogen-activated protein kinase (MAPK) signalling pathway, the FAS-ligand (FASL)-induced and Bcl-2-related apoptosis. The potential of the CA method to model basic pathways patterns, to identify ways to control pathway dynamics and to help in generating strategies to fight with cancer is demonstrated. The different line of CA applications presented includes the search for the best-performing network motifs, an analysis of importance for effective intracellular signalling and pathway cross-talk. PMID:20373215

  15. Acidosis induces reprogramming of cellular metabolism to mitigate oxidative stress

    PubMed Central

    2013-01-01

    Background A variety of oncogenic and environmental factors alter tumor metabolism to serve the distinct cellular biosynthetic and bioenergetic needs present during oncogenesis. Extracellular acidosis is a common microenvironmental stress in solid tumors, but little is known about its metabolic influence, particularly when present in the absence of hypoxia. In order to characterize the extent of tumor cell metabolic adaptations to acidosis, we employed stable isotope tracers to examine how acidosis impacts glucose, glutamine, and palmitate metabolism in breast cancer cells exposed to extracellular acidosis. Results Acidosis increased both glutaminolysis and fatty acid β-oxidation, which contribute metabolic intermediates to drive the tricarboxylic acid cycle (TCA cycle) and ATP generation. Acidosis also led to a decoupling of glutaminolysis and novel glutathione (GSH) synthesis by repressing GCLC/GCLM expression. We further found that acidosis redirects glucose away from lactate production and towards the oxidative branch of the pentose phosphate pathway (PPP). These changes all serve to increase nicotinamide adenine dinucleotide phosphate (NADPH) production and counter the increase in reactive oxygen species (ROS) present under acidosis. The reduced novel GSH synthesis under acidosis may explain the increased demand for NADPH to recycle existing pools of GSH. Interestingly, acidosis also disconnected novel ribose synthesis from the oxidative PPP, seemingly to reroute PPP metabolites to the TCA cycle. Finally, we found that acidosis activates p53, which contributes to both the enhanced PPP and increased glutaminolysis, at least in part, through the induction of G6PD and GLS2 genes. Conclusions Acidosis alters the cellular metabolism of several major metabolites, which induces a significant degree of metabolic inflexibility. Cells exposed to acidosis largely rely upon mitochondrial metabolism for energy generation to the extent that metabolic intermediates are

  16. Interplay of drug metabolizing enzymes with cellular transporters.

    PubMed

    Böhmdorfer, Michaela; Maier-Salamon, Alexandra; Riha, Juliane; Brenner, Stefan; Höferl, Martina; Jäger, Walter

    2014-11-01

    Many endogenous and xenobiotic substances and their metabolites are substrates for drug metabolizing enzymes and cellular transporters. These proteins may not only contribute to bioavailability of molecules but also to uptake into organs and, consequently, to overall elimination. The coordinated action of uptake transporters, metabolizing enzymes, and efflux pumps, therefore, is a precondition for detoxification and elimination of drugs. As the understanding of the underlying mechanisms is important to predict alterations in drug disposal, adverse drug reactions and, finally, drug-drug interactions, this review illustrates the interplay between selected uptake/efflux transporters and phase I/II metabolizing enzymes.

  17. Torsins Are Essential Regulators of Cellular Lipid Metabolism.

    PubMed

    Grillet, Micheline; Dominguez Gonzalez, Beatriz; Sicart, Adria; Pöttler, Maria; Cascalho, Ana; Billion, Karolien; Hernandez Diaz, Sergio; Swerts, Jef; Naismith, Teresa V; Gounko, Natalia V; Verstreken, Patrik; Hanson, Phyllis I; Goodchild, Rose E

    2016-08-01

    Torsins are developmentally essential AAA+ proteins, and mutation of human torsinA causes the neurological disease DYT1 dystonia. They localize in the ER membranes, but their cellular function remains unclear. We now show that dTorsin is required in Drosophila adipose tissue, where it suppresses triglyceride levels, promotes cell growth, and elevates membrane lipid content. We also see that human torsinA at the inner nuclear membrane is associated with membrane expansion and elevated cellular lipid content. Furthermore, the key lipid metabolizing enzyme, lipin, is mislocalized in dTorsin-KO cells, and dTorsin increases levels of the lipin substrate, phosphatidate, and reduces the product, diacylglycerol. Finally, genetic suppression of dLipin rescues dTorsin-KO defects, including adipose cell size, animal growth, and survival. These findings identify that torsins are essential regulators of cellular lipid metabolism and implicate disturbed lipid biology in childhood-onset DYT1 dystonia. PMID:27453503

  18. A Wireless Communications Laboratory on Cellular Network Planning

    ERIC Educational Resources Information Center

    Dawy, Z.; Husseini, A.; Yaacoub, E.; Al-Kanj, L.

    2010-01-01

    The field of radio network planning and optimization (RNPO) is central for wireless cellular network design, deployment, and enhancement. Wireless cellular operators invest huge sums of capital on deploying, launching, and maintaining their networks in order to ensure competitive performance and high user satisfaction. This work presents a lab…

  19. Biconnectivity of the cellular metabolism: A cross-species study and its implication for human diseases

    PubMed Central

    Kim, P.; Lee, D.-S.; Kahng, B.

    2015-01-01

    The maintenance of stability during perturbations is essential for living organisms, and cellular networks organize multiple pathways to enable elements to remain connected and communicate, even when some pathways are broken. Here, we evaluated the biconnectivity of the metabolic networks of 506 species in terms of the clustering coefficients and the largest biconnected components (LBCs), wherein a biconnected component (BC) indicates a set of nodes in which every pair is connected by more than one path. Via comparison with the rewired networks, we illustrated how biconnectivity in cellular metabolism is achieved on small and large scales. Defining the biconnectivity of individual metabolic compounds by counting the number of species in which the compound belonged to the LBC, we demonstrated that biconnectivity is significantly correlated with the evolutionary age and functional importance of a compound. The prevalence of diseases associated with each metabolic compound quantifies the compounds vulnerability, i.e., the likelihood that it will cause a metabolic disorder. Moreover, the vulnerability depends on both the biconnectivity and the lethality of the compound. This fact can be used in drug discovery and medical treatments. PMID:26490723

  20. Profiling metabolic networks to study cancer metabolism.

    PubMed

    Hiller, Karsten; Metallo, Christian M

    2013-02-01

    Cancer is a disease of unregulated cell growth and survival, and tumors reprogram biochemical pathways to aid these processes. New capabilities in the computational and bioanalytical characterization of metabolism have now emerged, facilitating the identification of unique metabolic dependencies that arise in specific cancers. By understanding the metabolic phenotype of cancers as a function of their oncogenic profiles, metabolic engineering may be applied to design synthetically lethal therapies for some tumors. This process begins with accurate measurement of metabolic fluxes. Here we review advanced methods of quantifying pathway activity and highlight specific examples where these approaches have uncovered potential opportunities for therapeutic intervention.

  1. Cellular metabolic and autophagic pathways: traffic control by redox signaling.

    PubMed

    Dodson, Matthew; Darley-Usmar, Victor; Zhang, Jianhua

    2013-10-01

    It has been established that the key metabolic pathways of glycolysis and oxidative phosphorylation are intimately related to redox biology through control of cell signaling. Under physiological conditions glucose metabolism is linked to control of the NADH/NAD redox couple, as well as providing the major reductant, NADPH, for thiol-dependent antioxidant defenses. Retrograde signaling from the mitochondrion to the nucleus or cytosol controls cell growth and differentiation. Under pathological conditions mitochondria are targets for reactive oxygen and nitrogen species and are critical in controlling apoptotic cell death. At the interface of these metabolic pathways, the autophagy-lysosomal pathway functions to maintain mitochondrial quality and generally serves an important cytoprotective function. In this review we will discuss the autophagic response to reactive oxygen and nitrogen species that are generated from perturbations of cellular glucose metabolism and bioenergetic function.

  2. Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling

    PubMed Central

    Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R.; Jijakli, Kenan; Salehi-Ashtiani, Kourosh

    2014-01-01

    Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates. PMID:25540776

  3. Peroxisomes: a Nexus for Lipid Metabolism and Cellular Signaling

    PubMed Central

    Lodhi, Irfan J.; Semenkovich, Clay F.

    2014-01-01

    Peroxisomes are often dismissed as the cellular hoi polloi, relegated to cleaning up reactive oxygen chemical debris discarded by other organelles. However, their functions extend far beyond hydrogen peroxide metabolism. Peroxisomes are intimately associated with lipid droplets and mitochondria, and their ability to carry out fatty acid oxidation and lipid synthesis, especially the production of ether lipids, may be critical for generating cellular signals required for normal physiology. Here we review the biology of peroxisomes and their potential relevance to human disorders including cancer, obesity-related diabetes, and degenerative neurologic disease. PMID:24508507

  4. Randomizing Genome-Scale Metabolic Networks

    PubMed Central

    Samal, Areejit; Martin, Olivier C.

    2011-01-01

    Networks coming from protein-protein interactions, transcriptional regulation, signaling, or metabolism may appear to have “unusual” properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the randomization of the network using edge exchange generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles of randomized metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations with the help of Markov Chain Monte Carlo (MCMC) and show that they allow one to approach the properties of biological metabolic networks. The implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints. PMID:21779409

  5. "Biomoléculas": cellular metabolism didactic software

    NASA Astrophysics Data System (ADS)

    Menghi, M. L.; Novella, L. P.; Siebenlist, M. R.

    2007-11-01

    "Biomoléculas" is a software that deals with topics such as the digestion, cellular metabolism and excretion of nutrients. It is a pleasant, simple and didactic guide, made by and for students. In this program, each biomolecule (carbohydrates, lipids and proteins) is accompanied until its degradation and assimilation by crossing and interrelating the different metabolic channels to finally show the destination of the different metabolites formed and the way in which these are excreted. It is used at present as a teaching-learning process tool by the chair of Physiology and Biophysics at the Facultad de Ingeniería - Universidad Nacional de Entre Ríos.

  6. The Impact of Non-Enzymatic Reactions and Enzyme Promiscuity on Cellular Metabolism during (Oxidative) Stress Conditions

    PubMed Central

    Piedrafita, Gabriel; Keller, Markus A; Ralser, Markus

    2015-01-01

    Cellular metabolism assembles in a structurally highly conserved, but functionally dynamic system, known as the metabolic network. This network involves highly active, enzyme-catalyzed metabolic pathways that provide the building blocks for cell growth. In parallel, however, chemical reactivity of metabolites and unspecific enzyme function give rise to a number of side products that are not part of canonical metabolic pathways. It is increasingly acknowledged that these molecules are important for the evolution of metabolism, affect metabolic efficiency, and that they play a potential role in human disease—age-related disorders and cancer in particular. In this review we discuss the impact of oxidative and other cellular stressors on the formation of metabolic side products, which originate as a consequence of: (i) chemical reactivity or modification of regular metabolites; (ii) through modifications in substrate specificity of damaged enzymes; and (iii) through altered metabolic flux that protects cells in stress conditions. In particular, oxidative and heat stress conditions are causative of metabolite and enzymatic damage and thus promote the non-canonical metabolic activity of the cells through an increased repertoire of side products. On the basis of selected examples, we discuss the consequences of non-canonical metabolic reactivity on evolution, function and repair of the metabolic network. PMID:26378592

  7. Metabolic constraint-based refinement of transcriptional regulatory networks.

    PubMed

    Chandrasekaran, Sriram; Price, Nathan D

    2013-01-01

    There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10(-172)), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10(-14)) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to

  8. Metabolic constraint-based refinement of transcriptional regulatory networks.

    PubMed

    Chandrasekaran, Sriram; Price, Nathan D

    2013-01-01

    There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10(-172)), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10(-14)) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to

  9. Cellular metabolism in colorectal carcinogenesis: Influence of lifestyle, gut microbiome and metabolic pathways.

    PubMed

    Hagland, Hanne R; Søreide, Kjetil

    2015-01-28

    The interconnectivity between diet, gut microbiota and cell molecular responses is well known; however, only recently has technology allowed the identification of strains of microorganisms harbored in the gastrointestinal tract that may increase susceptibility to cancer. The colonic environment appears to play a role in the development of colon cancer, which is influenced by the human metabolic lifestyle and changes in the gut microbiome. Studying metabolic changes at the cellular level in cancer be useful for developing novel improved preventative measures, such as screening through metabolic breath-tests or treatment options that directly affect the metabolic pathways responsible for the carcinogenicity.

  10. Stress-induced rearrangements of cellular networks: Consequences for protection and drug design.

    PubMed

    Szalay, Máté S; Kovács, István A; Korcsmáros, Tamás; Böde, Csaba; Csermely, Péter

    2007-07-31

    The complexity of the cells can be described and understood by a number of networks such as protein-protein interaction, cytoskeletal, organelle, signalling, gene transcription and metabolic networks. All these networks are highly dynamic producing continuous rearrangements in their links, hubs, network-skeleton and modules. Here we describe the adaptation of cellular networks after various forms of stress causing perturbations, congestions and network damage. Chronic stress decreases link-density, decouples or even quarantines modules, and induces an increased competition between network hubs and bridges. Extremely long or strong stress may induce a topological phase transition in the respective cellular networks, which switches the cell to a completely different mode of cellular function. We summarize our initial knowledge on network restoration after stress including the role of molecular chaperones in this process. Finally, we discuss the implications of stress-induced network rearrangements in diseases and ageing, and propose therapeutic approaches both to increase the robustness and help the repair of cellular networks. PMID:17433306

  11. Does habitat variability really promote metabolic network modularity?

    PubMed

    Takemoto, Kazuhiro

    2013-01-01

    The hypothesis that variability in natural habitats promotes modular organization is widely accepted for cellular networks. However, results of some data analyses and theoretical studies have begun to cast doubt on the impact of habitat variability on modularity in metabolic networks. Therefore, we re-evaluated this hypothesis using statistical data analysis and current metabolic information. We were unable to conclude that an increase in modularity was the result of habitat variability. Although horizontal gene transfer was also considered because it may contribute for survival in a variety of environments, closely related to habitat variability, and is known to be positively correlated with network modularity, such a positive correlation was not concluded in the latest version of metabolic networks. Furthermore, we demonstrated that the previously observed increase in network modularity due to habitat variability and horizontal gene transfer was probably due to a lack of available data on metabolic reactions. Instead, we determined that modularity in metabolic networks is dependent on species growth conditions. These results may not entirely discount the impact of habitat variability and horizontal gene transfer. Rather, they highlight the need for a more suitable definition of habitat variability and a more careful examination of relationships of the network modularity with horizontal gene transfer, habitats, and environments.

  12. Consistency analysis of metabolic correlation networks

    PubMed Central

    Müller-Linow, Mark; Weckwerth, Wolfram; Hütt, Marc-Thorsten

    2007-01-01

    Background Metabolic correlation networks are derived from the covariance of metabolites in replicates of metabolomics experiments. They constitute an interesting intermediate between topology (i.e. the system's architecture defined by the set of reactions between metabolites) and dynamics (i.e. the metabolic concentrations observed as fluctuations around steady-state values in the metabolic network). Results Here we analyze, how such a correlation network changes over time, and compare the relative positions of metabolites in the correlation networks with those in established metabolic networks derived from genome databases. We find that network similarity indeed decreases with an increasing time difference between these networks during a day/night course and, counter intuitively, that proximity of metabolites in the correlation network is no indicator of proximity of the metabolites in the metabolic network. Conclusion The organizing principles of correlation networks are distinct from those of metabolic reaction maps. Time courses of correlation networks may in the future prove an important data source for understanding these organizing principles. PMID:17892579

  13. An online system for metabolic network analysis

    PubMed Central

    Cicek, Abdullah Ercument; Qi, Xinjian; Cakmak, Ali; Johnson, Stephen R.; Han, Xu; Alshalwi, Sami; Ozsoyoglu, Zehra Meral; Ozsoyoglu, Gultekin

    2014-01-01

    Metabolic networks have become one of the centers of attention in life sciences research with the advancements in the metabolomics field. A vast array of studies analyzes metabolites and their interrelations to seek explanations for various biological questions, and numerous genome-scale metabolic networks have been assembled to serve for this purpose. The increasing focus on this topic comes with the need for software systems that store, query, browse, analyze and visualize metabolic networks. PathCase Metabolomics Analysis Workbench (PathCaseMAW) is built, released and runs on a manually created generic mammalian metabolic network. The PathCaseMAW system provides a database-enabled framework and Web-based computational tools for browsing, querying, analyzing and visualizing stored metabolic networks. PathCaseMAW editor, with its user-friendly interface, can be used to create a new metabolic network and/or update an existing metabolic network. The network can also be created from an existing genome-scale reconstructed network using the PathCaseMAW SBML parser. The metabolic network can be accessed through a Web interface or an iPad application. For metabolomics analysis, steady-state metabolic network dynamics analysis (SMDA) algorithm is implemented and integrated with the system. SMDA tool is accessible through both the Web-based interface and the iPad application for metabolomics analysis based on a metabolic profile. PathCaseMAW is a comprehensive system with various data input and data access subsystems. It is easy to work with by design, and is a promising tool for metabolomics research and for educational purposes. Database URL: http://nashua.case.edu/PathwaysMAW/Web PMID:25267793

  14. Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.

    PubMed

    Sohn, Insoo; Liu, Huaping; Ansari, Nirwan

    2015-01-01

    An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction. PMID:26167934

  15. Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning

    PubMed Central

    Sohn, Insoo; Liu, Huaping; Ansari, Nirwan

    2015-01-01

    An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction. PMID:26167934

  16. Dynamic optimization of metabolic networks coupled with gene expression.

    PubMed

    Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

    2015-01-21

    The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle.

  17. Tensegrity II. How structural networks influence cellular information processing networks

    NASA Technical Reports Server (NTRS)

    Ingber, Donald E.

    2003-01-01

    The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.

  18. Cellular hallmarks reveal restricted aerobic metabolism at thermal limits

    PubMed Central

    Neves, Aitana; Busso, Coralie; Gönczy, Pierre

    2015-01-01

    All organisms live within a given thermal range, but little is known about the mechanisms setting the limits of this range. We uncovered cellular features exhibiting signature changes at thermal limits in Caenorhabditis elegans embryos. These included changes in embryo size and shape, which were also observed in Caenorhabditis briggsae, indicating evolutionary conservation. We hypothesized that such changes could reflect restricted aerobic capacity at thermal limits. Accordingly, we uncovered that relative respiration in C. elegans embryos decreases at the thermal limits as compared to within the thermal range. Furthermore, by compromising components of the respiratory chain, we demonstrated that the reliance on aerobic metabolism is reduced at thermal limits. Moreover, embryos thus compromised exhibited signature changes in size and shape already within the thermal range. We conclude that restricted aerobic metabolism at the thermal limits contributes to setting the thermal range in a metazoan organism. DOI: http://dx.doi.org/10.7554/eLife.04810.001 PMID:25929283

  19. Cellular hallmarks reveal restricted aerobic metabolism at thermal limits.

    PubMed

    Neves, Aitana; Busso, Coralie; Gönczy, Pierre

    2015-05-01

    All organisms live within a given thermal range, but little is known about the mechanisms setting the limits of this range. We uncovered cellular features exhibiting signature changes at thermal limits in Caenorhabditis elegans embryos. These included changes in embryo size and shape, which were also observed in Caenorhabditis briggsae, indicating evolutionary conservation. We hypothesized that such changes could reflect restricted aerobic capacity at thermal limits. Accordingly, we uncovered that relative respiration in C. elegans embryos decreases at the thermal limits as compared to within the thermal range. Furthermore, by compromising components of the respiratory chain, we demonstrated that the reliance on aerobic metabolism is reduced at thermal limits. Moreover, embryos thus compromised exhibited signature changes in size and shape already within the thermal range. We conclude that restricted aerobic metabolism at the thermal limits contributes to setting the thermal range in a metazoan organism.

  20. Cellular metabolism as a basis for immune privilege

    PubMed Central

    Newell, M Karen; Villalobos-Menuey, Elizabeth; Schweitzer, Susan C; Harper, Mary-Ellen; Camley, Robert E

    2006-01-01

    We hypothesize that the energy strategy of a cell is a key factor for determining how, or if, the immune system interacts with that cell. Cells have a limited number of metabolic states, in part, depending on the type of fuels the cell consumes. Cellular fuels include glucose (carbohydrates), lipids (fats), and proteins. We propose that the cell's ability to switch to, and efficiently use, fat for fuel confers immune privilege. Additionally, because uncoupling proteins are involved in the fat burning process and reportedly in protection from free radicals, we hypothesize that uncoupling proteins play an important role in immune privilege. Thus, changes in metabolism (caused by oxidative stresses, fuel availability, age, hormones, radiation, or drugs) will dictate and initiate changes in immune recognition and in the nature of the immune response. This has profound implications for controlling the symptoms of autoimmune diseases, for preventing graft rejection, and for targeting tumor cells for destruction. PMID:16545119

  1. Metabolism and evolution: A comparative study of reconstructed genome-level metabolic networks

    NASA Astrophysics Data System (ADS)

    Almaas, Eivind

    2008-03-01

    The availability of high-quality annotations of sequenced genomes has made it possible to generate organism-specific comprehensive maps of cellular metabolism. Currently, more than twenty such metabolic reconstructions are publicly available, with the majority focused on bacteria. A typical metabolic reconstruction for a bacterium results in a complex network containing hundreds of metabolites (nodes) and reactions (links), while some even contain more than a thousand. The constrain-based optimization approach of flux-balance analysis (FBA) is used to investigate the functional characteristics of such large-scale metabolic networks, making it possible to estimate an organism's growth behavior in a wide variety of nutrient environments, as well as its robustness to gene loss. We have recently completed the genome-level metabolic reconstruction of Yersinia pseudotuberculosis, as well as the three Yersinia pestis biovars Antiqua, Mediaevalis, and Orientalis. While Y. pseudotuberculosis typically only causes fever and abdominal pain that can mimic appendicitis, the evolutionary closely related Y. pestis strains are the aetiological agents of the bubonic plague. In this presentation, I will discuss our results and conclusions from a comparative study on the evolution of metabolic function in the four Yersiniae networks using FBA and related techniques, and I will give particular focus to the interplay between metabolic network topology and evolutionary flexibility.

  2. Molecular and Cellular Bases of Iron Metabolism in Humans.

    PubMed

    Milto, I V; Suhodolo, I V; Prokopieva, V D; Klimenteva, T K

    2016-06-01

    Iron is a microelement with the most completely studied biological functions. Its wide dissemination in nature and involvement in key metabolic pathways determine the great importance of this metal for uni- and multicellular organisms. The biological role of iron is characterized by its indispensability in cell respiration and various biochemical processes providing normal functioning of cells and organs of the human body. Iron also plays an important role in the generation of free radicals, which under different conditions can be useful or damaging to biomolecules and cells. In the literature, there are many reviews devoted to iron metabolism and its regulation in pro- and eukaryotes. Significant progress has been achieved recently in understanding molecular bases of iron metabolism. The purpose of this review is to systematize available data on mechanisms of iron assimilation, distribution, and elimination from the human body, as well as on its biological importance and on the major iron-containing proteins. The review summarizes recent ideas about iron metabolism. Special attention is paid to mechanisms of iron absorption in the small intestine and to interrelationships of cellular and extracellular pools of this metal in the human body.

  3. Signatures of Arithmetic Simplicity in Metabolic Network Architecture

    PubMed Central

    Riehl, William J.; Krapivsky, Paul L.; Redner, Sidney; Segrè, Daniel

    2010-01-01

    Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that properties similar to those predicted for the artificial chemistry hold also for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity. PMID:20369010

  4. Viperin Regulates Cellular Lipid Metabolism during Human Cytomegalovirus Infection

    PubMed Central

    Seo, Jun-Young; Cresswell, Peter

    2013-01-01

    Human cytomegalovirus (HCMV) has been shown to induce increased lipogenesis in infected cells, and this is believed to be required for proper virion envelopment. We show here that this increase is a consequence of the virus-induced redistribution of the host protein viperin to mitochondria and its capacity to interact with and block the function of the mitochondrial trifunctional protein (TFP), the enzyme that mediates fatty acid-β-oxidation. The resulting decrease in cellular ATP levels activates the enzyme AMP-activated protein kinase (AMPK), which induces expression of the glucose transporter GLUT4, resulting in increased glucose import and translocation to the nucleus of the glucose-regulated transcription factor ChREBP. This induces increased transcription of genes encoding lipogenic enzymes, increased lipid synthesis and lipid droplet accumulation, and generation of the viral envelope. Viperin-dependent lipogenesis is required for optimal production of infectious virus. We show that all of these metabolic outcomes can be replicated by direct targeting of viperin to mitochondria in the absence of HCMV infection, and that the motif responsible for Fe-S cluster binding by viperin is essential. The data indicate that viperin is the major effector underlying the ability of HCMV to regulate cellular lipid metabolism. PMID:23935494

  5. Realization problem of multi-layer cellular neural networks.

    PubMed

    Ban, Jung-Chao; Chang, Chih-Hung

    2015-10-01

    This paper investigates whether the output space of a multi-layer cellular neural network can be realized via a single layer cellular neural network in the sense of the existence of finite-to-one map from one output space to the other. Whenever such realization exists, the phenomena exhibited in the output space of the revealed single layer cellular neural network is at most a constant multiple of the phenomena exhibited in the output space of the original multi-layer cellular neural network. Meanwhile, the computation complexity of a single layer system is much less than the complexity of a multi-layer system. Namely, one can trade the precision of the results for the execution time. We remark that a routine extension of the proposed methodology in this paper can be applied to the substitution of hidden spaces although the detailed illustration is omitted.

  6. Shunting inhibitory cellular neural networks with chaotic external inputs

    NASA Astrophysics Data System (ADS)

    Akhmet, M. U.; Fen, M. O.

    2013-06-01

    Taking advantage of external inputs, it is shown that shunting inhibitory cellular neural networks behave chaotically. The analysis is based on the Li-Yorke definition of chaos. Appropriate illustrations which support the theoretical results are depicted.

  7. Shunting inhibitory cellular neural networks with chaotic external inputs.

    PubMed

    Akhmet, M U; Fen, M O

    2013-06-01

    Taking advantage of external inputs, it is shown that shunting inhibitory cellular neural networks behave chaotically. The analysis is based on the Li-Yorke definition of chaos. Appropriate illustrations which support the theoretical results are depicted.

  8. Transcription factor networks regulating hepatic fatty acid metabolism.

    PubMed

    Karagianni, Panagiota; Talianidis, Iannis

    2015-01-01

    Tight regulation of lipid levels is critical for cellular and organismal homeostasis, not only in terms of energy utilization and storage, but also to prevent potential toxicity. The liver utilizes a set of hepatic transcription factors to regulate the expression of genes implicated in all aspects of lipid metabolism including catabolism, transport, and synthesis. In this article, we will review the main transcriptional mechanisms regulating the expression of genes involved in hepatic lipid metabolism. The principal regulatory pathways are composed of simple modules of transcription factor crosstalks, which correspond to building blocks of more complex regulatory networks. These transcriptional networks contribute to the regulation of proper lipid homeostasis in parallel to posttranslational mechanisms and end product-mediated modulation of lipid metabolizing enzymes. This article is part of a Special Issue entitled Linking transcription to physiology in lipodomics.

  9. Extracting insight from noisy cellular networks.

    PubMed

    Landry, Christian R; Levy, Emmanuel D; Abd Rabbo, Diala; Tarassov, Kirill; Michnick, Stephen W

    2013-11-21

    Network biologists attempt to extract meaningful relationships among genes or their products from very noisy data. We argue that what we categorize as noisy data may sometimes reflect noisy biology and therefore may shield a hidden meaning about how networks evolve and how matter is organized in the cell. We present practical solutions, based on existing evolutionary and biophysical concepts, through which our understanding of cell biology can be enormously enriched. PMID:24267884

  10. Extracting insight from noisy cellular networks.

    PubMed

    Landry, Christian R; Levy, Emmanuel D; Abd Rabbo, Diala; Tarassov, Kirill; Michnick, Stephen W

    2013-11-21

    Network biologists attempt to extract meaningful relationships among genes or their products from very noisy data. We argue that what we categorize as noisy data may sometimes reflect noisy biology and therefore may shield a hidden meaning about how networks evolve and how matter is organized in the cell. We present practical solutions, based on existing evolutionary and biophysical concepts, through which our understanding of cell biology can be enormously enriched.

  11. [Caloric restriction: about its positive metabolic effects and cellular impact].

    PubMed

    Ortiz-Bautista, Raúl Julián; Aguilar-Salinas, Carlos Alberto; Monroy-Guzmán, Adriana

    2013-01-01

    Caloric restriction, as a 30 to 60% decrease of ad libitum balanced caloric intake, without malnutrition, is the non-genetic strategy that has consistently extended the average and maximum lifespan of most living beings, and it has been tested from unicellular organisms like yeast Saccharomyces cerevisiae to Rhesus primates. In addition, various genetic and pharmacological caloric restriction models have shown to protect against cancer, cardiovascular and neurodegenerative diseases. Primate studies suggest that this intervention delays the onset of age-related diseases; in humans, it has physiological, biochemical and metabolic effects decreasing diabetes and cardiovascular disease risk factor. Although currently the mechanism by which caloric restriction has its positive effects at the cellular level is unknown, it has been reported to decrease oxidative stress and increase in mitochondrial biogenesis.

  12. [Caloric restriction: about its positive metabolic effects and cellular impact].

    PubMed

    Ortiz-Bautista, Raúl Julián; Aguilar-Salinas, Carlos Alberto; Monroy-Guzmán, Adriana

    2013-01-01

    Caloric restriction, as a 30 to 60% decrease of ad libitum balanced caloric intake, without malnutrition, is the non-genetic strategy that has consistently extended the average and maximum lifespan of most living beings, and it has been tested from unicellular organisms like yeast Saccharomyces cerevisiae to Rhesus primates. In addition, various genetic and pharmacological caloric restriction models have shown to protect against cancer, cardiovascular and neurodegenerative diseases. Primate studies suggest that this intervention delays the onset of age-related diseases; in humans, it has physiological, biochemical and metabolic effects decreasing diabetes and cardiovascular disease risk factor. Although currently the mechanism by which caloric restriction has its positive effects at the cellular level is unknown, it has been reported to decrease oxidative stress and increase in mitochondrial biogenesis. PMID:25125067

  13. Modelling chronotaxicity of cellular energy metabolism to facilitate the identification of altered metabolic states.

    PubMed

    Lancaster, Gemma; Suprunenko, Yevhen F; Jenkins, Kirsten; Stefanovska, Aneta

    2016-01-01

    Altered cellular energy metabolism is a hallmark of many diseases, one notable example being cancer. Here, we focus on the identification of the transition from healthy to abnormal metabolic states. To do this, we study the dynamics of energy production in a cell. Due to the thermodynamic openness of a living cell, the inability to instantaneously match fluctuating supply and demand in energy metabolism results in nonautonomous time-varying oscillatory dynamics. However, such oscillatory dynamics is often neglected and treated as stochastic. Based on experimental evidence of metabolic oscillations, we show that changes in metabolic state can be described robustly by alterations in the chronotaxicity of the corresponding metabolic oscillations, i.e. the ability of an oscillator to resist external perturbations. We also present a method for the identification of chronotaxicity, applicable to general oscillatory signals and, importantly, apply this to real experimental data. Evidence of chronotaxicity was found in glycolytic oscillations in real yeast cells, verifying that chronotaxicity could be used to study transitions between metabolic states. PMID:27483987

  14. Modelling chronotaxicity of cellular energy metabolism to facilitate the identification of altered metabolic states.

    PubMed

    Lancaster, Gemma; Suprunenko, Yevhen F; Jenkins, Kirsten; Stefanovska, Aneta

    2016-08-03

    Altered cellular energy metabolism is a hallmark of many diseases, one notable example being cancer. Here, we focus on the identification of the transition from healthy to abnormal metabolic states. To do this, we study the dynamics of energy production in a cell. Due to the thermodynamic openness of a living cell, the inability to instantaneously match fluctuating supply and demand in energy metabolism results in nonautonomous time-varying oscillatory dynamics. However, such oscillatory dynamics is often neglected and treated as stochastic. Based on experimental evidence of metabolic oscillations, we show that changes in metabolic state can be described robustly by alterations in the chronotaxicity of the corresponding metabolic oscillations, i.e. the ability of an oscillator to resist external perturbations. We also present a method for the identification of chronotaxicity, applicable to general oscillatory signals and, importantly, apply this to real experimental data. Evidence of chronotaxicity was found in glycolytic oscillations in real yeast cells, verifying that chronotaxicity could be used to study transitions between metabolic states.

  15. Modelling chronotaxicity of cellular energy metabolism to facilitate the identification of altered metabolic states

    PubMed Central

    Lancaster, Gemma; Suprunenko, Yevhen F.; Jenkins, Kirsten; Stefanovska, Aneta

    2016-01-01

    Altered cellular energy metabolism is a hallmark of many diseases, one notable example being cancer. Here, we focus on the identification of the transition from healthy to abnormal metabolic states. To do this, we study the dynamics of energy production in a cell. Due to the thermodynamic openness of a living cell, the inability to instantaneously match fluctuating supply and demand in energy metabolism results in nonautonomous time-varying oscillatory dynamics. However, such oscillatory dynamics is often neglected and treated as stochastic. Based on experimental evidence of metabolic oscillations, we show that changes in metabolic state can be described robustly by alterations in the chronotaxicity of the corresponding metabolic oscillations, i.e. the ability of an oscillator to resist external perturbations. We also present a method for the identification of chronotaxicity, applicable to general oscillatory signals and, importantly, apply this to real experimental data. Evidence of chronotaxicity was found in glycolytic oscillations in real yeast cells, verifying that chronotaxicity could be used to study transitions between metabolic states. PMID:27483987

  16. Organising metabolic networks: Cycles in flux distributions.

    PubMed

    Kritz, Maurício Vieira; Trindade Dos Santos, Marcelo; Urrutia, Sebastián; Schwartz, Jean-Marc

    2010-08-01

    Metabolic networks are among the most widely studied biological systems. The topology and interconnections of metabolic reactions have been well described for many species. This is, however, not sufficient to understand how their activity is regulated in living organisms. These descriptions depict a static set of possible chains of reactions, with no information about the dynamic activity of reaction fluxes. Cyclic structures are thought to play a central role in the homeostasis of biological systems and in their resilience to a changing environment. In this work, we present a methodology to help investigating dynamic fluxes associated to biochemical reactions in metabolic networks. We introduce an algorithm for partitioning fluxes between cyclic and acyclic sub-networks, adapted from an algorithm initially developed to study fluxes in trophic networks. Using this algorithm, we analyse three metabolic systems: the central metabolism of wild type and a deletion mutant of Escherichia coli, erythrocyte metabolism and the central metabolism of the bacterium Methylobacterium extorquens. This methodology unveils the role of cycles in driving and maintaining metabolic fluxes under perturbations in these examples, and may be used to further investigate and understand the organisational invariance of biological systems.

  17. A new small-world network created by Cellular Automata

    NASA Astrophysics Data System (ADS)

    Ruan, Yuhong; Li, Anwei

    2016-08-01

    In this paper, we generate small-world networks by the Cellular Automaton based on starting with one-dimensional regular networks. Besides the common properties of small-world networks with small average shortest path length and large clustering coefficient, the small-world networks generated in this way have other properties: (i) The edges which are cut in the regular network can be controlled that whether the edges are reconnected or not, and (ii) the number of the edges of the small-world network model equals the number of the edges of the original regular network. In other words, the average degree of the small-world network model equals to the average degree of the original regular network.

  18. Lin28 enhances tissue repair by reprogramming cellular metabolism

    PubMed Central

    Shyh-Chang, Ng; Zhu, Hao; de Soysa, T. Yvanka; Shinoda, Gen; Seligson, Marc T.; Tsanov, Kaloyan M.; Nguyen, Liem; Asara, John M.; Cantley, Lewis C.; Daley, George Q.

    2014-01-01

    SUMMARY Regeneration capacity declines with age, but why juvenile organisms show enhanced tissue repair remains unexplained. Lin28a, a highly-conserved RNA binding protein expressed during embryogenesis, plays roles in development, pluripotency and metabolism. To determine if Lin28a might influence tissue repair in adults, we engineered the reactivation of Lin28a expression in several models of tissue injury. Lin28a reactivation improved hair regrowth by promoting anagen in hair follicles, and accelerated regrowth of cartilage, bone and mesenchyme after ear and digit injuries. Lin28a inhibits let-7 microRNA biogenesis; however let-7 repression was necessary but insufficient to enhance repair. Lin28a bound to and enhanced the translation of mRNAs for several metabolic enzymes, thereby increasing glycolysis and oxidative phosphorylation (OxPhos). Lin28a-mediated enhancement of tissue repair was negated by OxPhos inhibition, whereas a pharmacologically-induced increase in OxPhos enhanced repair. Thus, Lin28a enhances tissue repair in some adult tissues by reprogramming cellular bioenergetics. PMID:24209617

  19. Optimal Prediction by Cellular Signaling Networks

    NASA Astrophysics Data System (ADS)

    Becker, Nils B.; Mugler, Andrew; ten Wolde, Pieter Rein

    2015-12-01

    Living cells can enhance their fitness by anticipating environmental change. We study how accurately linear signaling networks in cells can predict future signals. We find that maximal predictive power results from a combination of input-noise suppression, linear extrapolation, and selective readout of correlated past signal values. Single-layer networks generate exponential response kernels, which suffice to predict Markovian signals optimally. Multilayer networks allow oscillatory kernels that can optimally predict non-Markovian signals. At low noise, these kernels exploit the signal derivative for extrapolation, while at high noise, they capitalize on signal values in the past that are strongly correlated with the future signal. We show how the common motifs of negative feedback and incoherent feed-forward can implement these optimal response functions. Simulations reveal that E. coli can reliably predict concentration changes for chemotaxis, and that the integration time of its response kernel arises from a trade-off between rapid response and noise suppression.

  20. Synthetic lethal metabolic targeting of cellular senescence in cancer therapy.

    PubMed

    Dörr, Jan R; Yu, Yong; Milanovic, Maja; Beuster, Gregor; Zasada, Christin; Däbritz, J Henry M; Lisec, Jan; Lenze, Dido; Gerhardt, Anne; Schleicher, Katharina; Kratzat, Susanne; Purfürst, Bettina; Walenta, Stefan; Mueller-Klieser, Wolfgang; Gräler, Markus; Hummel, Michael; Keller, Ulrich; Buck, Andreas K; Dörken, Bernd; Willmitzer, Lothar; Reimann, Maurice; Kempa, Stefan; Lee, Soyoung; Schmitt, Clemens A

    2013-09-19

    Activated oncogenes and anticancer chemotherapy induce cellular senescence, a terminal growth arrest of viable cells characterized by S-phase entry-blocking histone 3 lysine 9 trimethylation (H3K9me3). Although therapy-induced senescence (TIS) improves long-term outcomes, potentially harmful properties of senescent tumour cells make their quantitative elimination a therapeutic priority. Here we use the Eµ-myc transgenic mouse lymphoma model in which TIS depends on the H3K9 histone methyltransferase Suv39h1 to show the mechanism and therapeutic exploitation of senescence-related metabolic reprogramming in vitro and in vivo. After senescence-inducing chemotherapy, TIS-competent lymphomas but not TIS-incompetent Suv39h1(-) lymphomas show increased glucose utilization and much higher ATP production. We demonstrate that this is linked to massive proteotoxic stress, which is a consequence of the senescence-associated secretory phenotype (SASP) described previously. SASP-producing TIS cells exhibited endoplasmic reticulum stress, an unfolded protein response (UPR), and increased ubiquitination, thereby targeting toxic proteins for autophagy in an acutely energy-consuming fashion. Accordingly, TIS lymphomas, unlike senescence models that lack a strong SASP response, were more sensitive to blocking glucose utilization or autophagy, which led to their selective elimination through caspase-12- and caspase-3-mediated endoplasmic-reticulum-related apoptosis. Consequently, pharmacological targeting of these metabolic demands on TIS induction in vivo prompted tumour regression and improved treatment outcomes further. These findings unveil the hypercatabolic nature of TIS that is therapeutically exploitable by synthetic lethal metabolic targeting.

  1. Mass-balanced randomization of metabolic networks

    PubMed Central

    Basler, Georg; Ebenhöh, Oliver; Selbig, Joachim; Nikoloski, Zoran

    2011-01-01

    Motivation: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem. Results: We first review the shortcomings of the existing generic sampling scheme—switch randomization—and explain its unsuitability for application to metabolic networks. We then devise a novel polynomial-time algorithm for randomizing metabolic networks under the (bio)chemical constraint of mass balance. The tractability of our method follows from the concept of mass equivalence classes, defined on the representation of compounds in the vector space over chemical elements. We finally demonstrate the uniformity of the proposed method on seven genome-scale metabolic networks, and empirically validate the theoretical findings. The proposed method allows a biologically meaningful estimation of significance for metabolic network properties. Contact: basler@mpimp-golm.mpg.de; nikoloski@mpimp-golm.mpg.de Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:21436128

  2. Personal communication in traditional cellular networks

    NASA Astrophysics Data System (ADS)

    Neuer, Ellwood I.

    1996-01-01

    The purpose of this paper is to describe the flow of calls through the mobile network as it applies to the operation of Basic and Enhanced Services. Included in the discussion is the overall network layout, the physical connections between the network entities, and the signaling protocols which allow the entities to be integrated. The specific functionality of the applications and services are not detailed as the specific implementation varies from vendor to vendor and from service provider to service provider. The Enhanced Services Platform is installed in a service providers network in order to offer mobile subscribers services and applications which would otherwise not be available. The service providers' objective is to increase revenue/subscriber, increase subscriber loyalty/decrease churn, and build competitive advantages through differentiation. The services provided on the Enhanced Services platform can be viewed as either Basic or Enhanced. For the purpose of this paper, Basic Services refers to Numeric Paging, Call Answering, and Voice Messaging while Enhanced Services refers to FAX Messaging, One Number Service, Voice Dialing and other Voice Recognition applications, Information Services including FAX on Demand, and Automated Call Routing.

  3. Reconstruction and applications of consensus yeast metabolic network based on RNA sequencing.

    PubMed

    Zhao, Yuqi; Wang, Yanjie; Zou, Lei; Huang, Jingfei

    2016-04-01

    One practical application of genome-scale metabolic reconstructions is to interrogate multispecies relationships. Here, we report a consensus metabolic model in four yeast species (Saccharomyces cerevisiae, S. paradoxus, S. mikatae, and S. bayanus) by integrating metabolic network simulations with RNA sequencing (RNA-seq) datasets. We generated high-resolution transcriptome maps of four yeast species through de novo assembly and genome-guided approaches. The transcriptomes were annotated and applied to build the consensus metabolic network, which was verified using independent RNA-seq experiments. The expression profiles reveal that the genes involved in amino acid and lipid metabolism are highly coexpressed. The diverse phenotypic characteristics, such as cellular growth and gene deletions, can be simulated using the metabolic model. We also explored the applications of the consensus model in metabolic engineering using yeast-specific reactions and biofuel production as examples. Similar strategies will benefit communities studying genome-scale metabolic networks of other organisms. PMID:27239440

  4. Increasing cellular coverage within integrated terrestrial/satellite mobile networks

    NASA Technical Reports Server (NTRS)

    Castro, Jonathan P.

    1995-01-01

    When applying the hierarchical cellular concept, the satellite acts as giant umbrella cell covering a region with some terrestrial cells. If a mobile terminal traversing the region arrives to the border-line or limits of a regular cellular ground service, network transition occurs and the satellite system continues the mobile coverage. To adequately assess the boundaries of service of a mobile satellite system an a cellular network within an integrated environment, this paper provides an optimized scheme to predict when a network transition may be necessary. Under the assumption of a classified propagation phenomenon and Lognormal shadowing, the study applies an analytical approach to estimate the location of a mobile terminal based on a reception of the signal strength emitted by a base station.

  5. Template learning of cellular neural network using genetic programming.

    PubMed

    Radwan, Elsayed; Tazaki, Eiichiro

    2004-08-01

    A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions. According to the lattice Cellular Neural Network architecture, Genetic Programming will be used in deriving the Cloning Template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.

  6. Designed Proteins To Modulate Cellular Networks

    PubMed Central

    Cortajarena, Aitziber L.; Liu, Tina Y.; Hochstrasser, Mark; Regan, Lynne

    2012-01-01

    A major challenge of protein design is to create useful new proteins that interact specifically with biological targets in living cells. Such binding modules have many potential applications, including the targeted perturbation of protein networks. As a general approach to create such modules, we designed a library with approximately 109 different binding specificities based on a small 3-tetratricopeptide repeat (TPR) motif framework. We employed a novel strategy, based on split GFP reassembly, to screen the library for modules with the desired binding specificity. Using this approach, we identified modules that bind tightly and specifically to Dss1, a small human protein that interacts with the tumor suppressor protein BRCA2. We showed that these modules also bind the yeast homologue of Dss1, Sem1. Furthermore, we demonstrated that these modules inhibit Sem1 activity in yeast. This strategy will be generally applicable to make novel genetically encoded tools for systems/synthetic biology applications. PMID:20020775

  7. Cellular metabolism regulates contact sites between vacuoles and mitochondria.

    PubMed

    Hönscher, Carina; Mari, Muriel; Auffarth, Kathrin; Bohnert, Maria; Griffith, Janice; Geerts, Willie; van der Laan, Martin; Cabrera, Margarita; Reggiori, Fulvio; Ungermann, Christian

    2014-07-14

    Emerging evidence suggests that contact sites between different organelles form central hubs in the coordination of cellular physiology. Although recent work has emphasized the crucial role of the endoplasmic reticulum in interorganellar crosstalk, the cooperative behavior of other organelles is largely unexplored. Here, we identify a contact site named vCLAMP (vacuole and mitochondria patch) that integrates mitochondria with the lysosome-like vacuole and thus the endocytic pathway. vCLAMPs depend on the vacuolar HOPS tethering complex subunit Vps39/Vam6 and the Rab GTPase Ypt7, which also participate in membrane fusion at the vacuole. Intriguingly, vCLAMPs are located proximal to the ER-mitochondria encounter structure (ERMES) complexes, and an increase in vCLAMPs can rescue the growth defect of ERMES mutants. Importantly, the persistence of vCLAMPs is regulated by phosphorylation of Vps39 and is strongly reduced during respiratory growth. The identification of this organelle contact site reveals a physical and metabolic interconnection between the endocytic pathway and mitochondria.

  8. Phosphatidylinositol 3,5-bisphosphate: metabolism and cellular functions.

    PubMed

    Michell, Robert H; Heath, Victoria L; Lemmon, Mark A; Dove, Stephen K

    2006-01-01

    Polyphosphoinositides (PPIn) are low-abundance membrane phospholipids that each bind to a distinctive set of effector proteins and, thereby, regulate a characteristic suite of cellular processes. Major functions of phosphatidylinositol 3,5-bisphosphate [PtdIns(3,5)P(2)] are in membrane and protein trafficking, and in pH control in the endosome-lysosome axis. Recently identified PtdIns(3,5)P(2) effectors include a family of novel beta-propeller proteins, for which we propose the name PROPPINs [for beta-propeller(s) that binds PPIn], and possibly proteins of the epsin and CHMP (charged multi-vesicular body proteins) families. All eukaryotes, with the exception of some pathogenic protists and microsporidians, possess proteins needed for the formation, metabolism and functions of PtdIns(3,5)P(2). The importance of PtdIns(3,5)P(2) for normal cell function is underscored by recent evidence for its involvement in mammalian cell responses to insulin and for PtdIns(3,5)P(2) dysfunction in the human genetic conditions X-linked myotubular myopathy, Type-4B Charcot-Marie-Tooth disease and fleck corneal dystrophy.

  9. Network signatures of cellular immortalization in human lymphoblastoid cell lines

    SciTech Connect

    Shim, Sung-Mi; Jung, So-Young; Nam, Hye-Young; Kim, Hye-Ryun; Lee, Mee-Hee; Kim, Jun-Woo; Han, Bok-Ghee; Jeon, Jae-Pil

    2013-11-15

    Highlights: •We identified network signatures of LCL immortalization from transcriptomic profiles. •More than 41% of DEGs are possibly regulated by miRNAs in LCLs. •MicroRNA target genes in LCLs are involved in apoptosis and immune-related functions. •This approach is useful to find functional miRNA targets in specific cell conditions. -- Abstract: Human lymphoblastoid cell line (LCL) has been used as an in vitro cell model in genetic and pharmacogenomic studies, as well as a good model for studying gene expression regulatory machinery using integrated genomic analyses. In this study, we aimed to identify biological networks of LCL immortalization from transcriptomic profiles of microRNAs and their target genes in LCLs. We first selected differentially expressed genes (DEGs) and microRNAs (DEmiRs) between early passage LCLs (eLCLs) and terminally differentiated late passage LCLs (tLCLs). The in silico and correlation analysis of these DEGs and DEmiRs revealed that 1098 DEG–DEmiR pairs were found to be positively (n = 591 pairs) or negatively (n = 507 pairs) correlated with each other. More than 41% of DEGs are possibly regulated by miRNAs in LCL immortalizations. The target DEGs of DEmiRs were enriched for cellular functions associated with apoptosis, immune response, cell death, JAK–STAT cascade and lymphocyte activation while non-miRNA target DEGs were over-represented for basic cell metabolisms. The target DEGs correlated negatively with miR-548a-3p and miR-219-5p were significantly associated with protein kinase cascade, and the lymphocyte proliferation and apoptosis, respectively. In addition, the miR-106a and miR-424 clusters located in the X chromosome were enriched in DEmiR–mRNA pairs for LCL immortalization. In this study, the integrated transcriptomic analysis of LCLs could identify functional networks of biologically active microRNAs and their target genes involved in LCL immortalization.

  10. Metabolic networks are almost nonfractal: a comprehensive evaluation.

    PubMed

    Takemoto, Kazuhiro

    2014-08-01

    Network self-similarity or fractality are widely accepted as an important topological property of metabolic networks; however, recent studies cast doubt on the reality of self-similarity in the networks. Therefore, we perform a comprehensive evaluation of metabolic network fractality using a box-covering method with an earlier version and the latest version of metabolic networks and demonstrate that the latest metabolic networks are almost self-dissimilar, while the earlier ones are fractal, as reported in a number of previous studies. This result may be because the networks were randomized because of an increase in network density due to database updates, suggesting that the previously observed network fractality was due to a lack of available data on metabolic reactions. This finding may not entirely discount the importance of self-similarity of metabolic networks. Rather, it highlights the need for a more suitable definition of network fractality and a more careful examination of self-similarity of metabolic networks.

  11. Metabolic networks are almost nonfractal: A comprehensive evaluation

    NASA Astrophysics Data System (ADS)

    Takemoto, Kazuhiro

    2014-08-01

    Network self-similarity or fractality are widely accepted as an important topological property of metabolic networks; however, recent studies cast doubt on the reality of self-similarity in the networks. Therefore, we perform a comprehensive evaluation of metabolic network fractality using a box-covering method with an earlier version and the latest version of metabolic networks and demonstrate that the latest metabolic networks are almost self-dissimilar, while the earlier ones are fractal, as reported in a number of previous studies. This result may be because the networks were randomized because of an increase in network density due to database updates, suggesting that the previously observed network fractality was due to a lack of available data on metabolic reactions. This finding may not entirely discount the importance of self-similarity of metabolic networks. Rather, it highlights the need for a more suitable definition of network fractality and a more careful examination of self-similarity of metabolic networks.

  12. Constructing a fish metabolic network model

    PubMed Central

    2010-01-01

    We report the construction of a genome-wide fish metabolic network model, MetaFishNet, and its application to analyzing high throughput gene expression data. This model is a stepping stone to broader applications of fish systems biology, for example by guiding study design through comparison with human metabolism and the integration of multiple data types. MetaFishNet resources, including a pathway enrichment analysis tool, are accessible at http://metafishnet.appspot.com. PMID:21114829

  13. Natural Products as Tools for Defining How Cellular Metabolism Influences Cellular Immune and Inflammatory Function during Chronic Infection

    PubMed Central

    Lovelace, Erica S.; Polyak, Stephen J.

    2015-01-01

    Chronic viral infections like those caused by hepatitis C virus (HCV) and human immunodeficiency virus (HIV) cause disease that establishes an ongoing state of chronic inflammation. While there have been tremendous improvements towards curing HCV with directly acting antiviral agents (DAA) and keeping HIV viral loads below detection with antiretroviral therapy (ART), there is still a need to control inflammation in these diseases. Recent studies indicate that many natural products like curcumin, resveratrol and silymarin alter cellular metabolism and signal transduction pathways via enzymes such as adenosine monophosphate kinase (AMPK) and mechanistic target of rapamycin (mTOR), and these pathways directly influence cellular inflammatory status (such as NF-κB) and immune function. Natural products represent a vast toolkit to dissect and define how cellular metabolism controls cellular immune and inflammatory function. PMID:26633463

  14. Millimeter-Wave Evolution for 5G Cellular Networks

    NASA Astrophysics Data System (ADS)

    Sakaguchi, Kei; Tran, Gia Khanh; Shimodaira, Hidekazu; Nanba, Shinobu; Sakurai, Toshiaki; Takinami, Koji; Siaud, Isabelle; Strinati, Emilio Calvanese; Capone, Antonio; Karls, Ingolf; Arefi, Reza; Haustein, Thomas

    Triggered by the explosion of mobile traffic, 5G (5th Generation) cellular network requires evolution to increase the system rate 1000 times higher than the current systems in 10 years. Motivated by this common problem, there are several studies to integrate mm-wave access into current cellular networks as multi-band heterogeneous networks to exploit the ultra-wideband aspect of the mm-wave band. The authors of this paper have proposed comprehensive architecture of cellular networks with mm-wave access, where mm-wave small cell basestations and a conventional macro basestation are connected to Centralized-RAN (C-RAN) to effectively operate the system by enabling power efficient seamless handover as well as centralized resource control including dynamic cell structuring to match the limited coverage of mm-wave access with high traffic user locations via user-plane/control-plane splitting. In this paper, to prove the effectiveness of the proposed 5G cellular networks with mm-wave access, system level simulation is conducted by introducing an expected future traffic model, a measurement based mm-wave propagation model, and a centralized cell association algorithm by exploiting the C-RAN architecture. The numerical results show the effectiveness of the proposed network to realize 1000 times higher system rate than the current network in 10 years which is not achieved by the small cells using commonly considered 3.5 GHz band. Furthermore, the paper also gives latest status of mm-wave devices and regulations to show the feasibility of using mm-wave in the 5G systems.

  15. Estimating the size of the solution space of metabolic networks

    PubMed Central

    Braunstein, Alfredo; Mulet, Roberto; Pagnani, Andrea

    2008-01-01

    Background Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA) has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium) the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations. Results In this work we propose a novel algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints. Using a technique derived from the fields of statistical physics and information theory we designed a message-passing algorithm to estimate the size of the affine space containing all possible steady-state flux distributions of metabolic networks. The algorithm, based on the well known Bethe approximation, can be used to approximately compute the volume of a non full-dimensional convex polytope in high dimensions. We first compare the accuracy of the predictions with an exact algorithm on small random metabolic networks. We also verify that the predictions of the algorithm match closely those of Monte Carlo based methods in the case of the Red Blood Cell metabolic network. Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism. Finally we analyze the statistical properties of the average fluxes of the reactions in the E. coli metabolic network. Conclusion We propose a novel efficient

  16. Role of nitric oxide in cellular iron metabolism.

    PubMed

    Kim, Sangwon; Ponka, Prem

    2003-03-01

    Iron regulatory proteins (IRP1 and IRP2) control the synthesis of transferrin receptors (TfR) and ferritin by binding to iron-responsive elements (IREs) which are located in the 3' untranslated region (UTR) and the 5' UTR of their respective mRNAs. Cellular iron levels affect binding of IRPs to IREs and consequently expression of TfR and ferritin. Moreover, NO*, a redox species of nitric oxide that interacts primarily with iron, can activate IRP1 RNA-binding activity resulting in an increase in TfR mRNA levels. We have shown that treatment of RAW 264.7 cells (a murine macrophage cell line) with NO+ (nitrosonium ion, which causes S-nitrosylation of thiol groups) resulted in a rapid decrease in RNA-binding of IRP2, followed by IRP2 degradation, and these changes were associated with a decrease in TfR mRNA levels. Moreover, we demonstrated that stimulation of RAW 264.7 cells with lipopolysaccharide (LPS) and interferon-gamma (IFN-gamma) increased IRP1 binding activity, whereas RNA-binding of IRP2 decreased and was followed by a degradation of this protein. Furthermore, the decrease of IRP2 binding/protein levels was associated with a decrease in TfR mRNA levels in LPS/IFN-gamma-treated cells, and these changes were prevented by inhibitors of inducible nitric oxide synthase. These results suggest that NO+-mediated degradation of IRP2 plays a major role in iron metabolism during inflammation.

  17. Maize metabolic network construction and transcriptome analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A framework for understanding the synthesis and catalysis of metabolites and other biochemicals by proteins is crucial for unraveling the physiology of cells. To create such a framework for Zea mays ssp. mays (maize), we developed MaizeCyc a metabolic network of enzyme catalysts, proteins, carbohydr...

  18. Dissecting Germ Cell Metabolism through Network Modeling

    PubMed Central

    Whitmore, Leanne S.; Ye, Ping

    2015-01-01

    Metabolic pathways are increasingly postulated to be vital in programming cell fate, including stemness, differentiation, proliferation, and apoptosis. The commitment to meiosis is a critical fate decision for mammalian germ cells, and requires a metabolic derivative of vitamin A, retinoic acid (RA). Recent evidence showed that a pulse of RA is generated in the testis of male mice thereby triggering meiotic commitment. However, enzymes and reactions that regulate this RA pulse have yet to be identified. We developed a mouse germ cell-specific metabolic network with a curated vitamin A pathway. Using this network, we implemented flux balance analysis throughout the initial wave of spermatogenesis to elucidate important reactions and enzymes for the generation and degradation of RA. Our results indicate that primary RA sources in the germ cell include RA import from the extracellular region, release of RA from binding proteins, and metabolism of retinal to RA. Further, in silico knockouts of genes and reactions in the vitamin A pathway predict that deletion of Lipe, hormone-sensitive lipase, disrupts the RA pulse thereby causing spermatogenic defects. Examination of other metabolic pathways reveals that the citric acid cycle is the most active pathway. In addition, we discover that fatty acid synthesis/oxidation are the primary energy sources in the germ cell. In summary, this study predicts enzymes, reactions, and pathways important for germ cell commitment to meiosis. These findings enhance our understanding of the metabolic control of germ cell differentiation and will help guide future experiments to improve reproductive health. PMID:26367011

  19. Metabolic Network Modeling of Microbial Communities

    PubMed Central

    Biggs, Matthew B.; Medlock, Gregory L.; Kolling, Glynis L.

    2015-01-01

    Genome-scale metabolic network reconstructions and constraint-based analysis are powerful methods that have the potential to make functional predictions about microbial communities. Current use of genome-scale metabolic networks to characterize the metabolic functions of microbial communities includes species compartmentalization, separating species-level and community-level objectives, dynamic analysis, the “enzyme-soup” approach, multi-scale modeling, and others. There are many challenges inherent to the field, including a need for tools that accurately assign high-level omics signals to individual community members, new automated reconstruction methods that rival manual curation, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be proportional advances in the fields of ecology, health science, and microbial community engineering. PMID:26109480

  20. Meta-ecosystem metabolism across fluvial networks

    NASA Astrophysics Data System (ADS)

    Ulseth, A. J.; Singer, G. A.; Battin, T. J.

    2013-12-01

    Freshwater ecosystems store, transform, and export terrestrial carbon and play an important role for regional and global carbon fluxes. Ecosystem metabolism is a measure of how and how much carbon is produced and transformed and - for streams and rivers - is typically estimated at the reach scale (typically tens to hundreds of meters). Due to methodological constraints we so far lack an understanding of metabolism of the fluvial meta-ecosystem continuum, i.e. at the scale of a fluvial network. To address this issue, we measured metabolism in multiple reaches across a 254 km2 catchment in Lower Austria, capturing both temporal dynamics and the spatial scale of the whole fluvial network. Stream metabolism was estimated from diel changes in oxygen and corrected for reaeration; continuously in 15 streams and during a short-term ';snap-shot' campaign across 42 streams. Across the network, the streams varied in catchment size (0.1 to 254 km2) and water chemistry (DOC: 1.0 to 2.7 mg L-1, NO3: 259 to 1277 μg L-1, NH4:<0.1 to 30 μg L-1, and PO4:<0.1 to 20 μg L-1) as a consequence of subcatchment landuse, elevation and geology. Gross primary production (GPP) and ecosystem respiration (ER) were suppressed across the network by high stream discharge events such as snow melt and heavy rains. Larger streams (catchment size > 35 km2) had higher GPP and tended to be more autotrophic than lower order streams. However, streams located above 1000 m elevation had higher GPP than streams of equivalent size found elsewhere in the stream network. During the winter months, all streams across the network were net heterotrophic, with GPP typically <1.0 g 02 m-2 d-1. The degree of autotrophy versus heterotrophy across the network was driven in part by light, which is contingent on location within the stream network. We will discuss scaling GPP and ER from the reach to the network scale in order to elucidate patterns of meta-ecosystem metabolism across fluvial networks.

  1. Phylogenetic sequence of metabolic pathways in Precambrian cellular life

    NASA Technical Reports Server (NTRS)

    Barnabas, J.; Schwartz, R. M.; Dayhoff, M. O.

    1981-01-01

    A sequence of major metabolic events is presented as they may have appeared during prokaryote evolution. This is based on (1) the phylogenetic schema derived from sequences of bacterial ferredoxin, 2Fe-2S ferredoxin, 5S ribosomal RNA, and c-type cytochromes; (2) metabolic settings in which these macromolecules are found; and (3) metabolic capabilities of the prokaryotes that carry these molecules.

  2. On Functional Module Detection in Metabolic Networks

    PubMed Central

    Koch, Ina; Ackermann, Jörg

    2013-01-01

    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. PMID:24958145

  3. On functional module detection in metabolic networks.

    PubMed

    Koch, Ina; Ackermann, Jörg

    2013-08-12

    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.

  4. On functional module detection in metabolic networks.

    PubMed

    Koch, Ina; Ackermann, Jörg

    2013-01-01

    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. PMID:24958145

  5. Kinetic analysis of complex metabolic networks

    SciTech Connect

    Stephanopoulos, G.

    1996-12-31

    A new methodology is presented for the analysis of complex metabolic networks with the goal of metabolite overproduction. The objective is to locate a small number of reaction steps in a network that have maximum impact on network flux amplification and whose rate can also be increased without functional network derangement. This method extends the concepts of Metabolic Control Analysis to groups of reactions and offers the means for calculating group control coefficients as measures of the control exercised by groups of reactions on the overall network fluxes and intracellular metabolite pools. It is further demonstrated that the optimal strategy for the effective increase of network fluxes, while maintaining an uninterrupted supply of intermediate metabolites, is through the coordinated amplification of multiple (as opposed to a single) reaction steps. Satisfying this requirement invokes the concept of the concentration control to coefficient, which emerges as a critical parameter in the identification of feasible enzymatic modifications with maximal impact on the network flux. A case study of aromatic aminoacid production is provided to illustrate these concepts.

  6. The role of actin networks in cellular mechanosensing

    NASA Astrophysics Data System (ADS)

    Azatov, Mikheil

    Physical processes play an important role in many biological phenomena, such as wound healing, organ development, and tumor metastasis. During these processes, cells constantly interact with and adapt to their environment by exerting forces to mechanically probe the features of their surroundings and generating appropriate biochemical responses. The mechanisms underlying how cells sense the physical properties of their environment are not well understood. In this thesis, I present my studies to investigate cellular responses to the stiffness and topography of the environment. In order to sense the physical properties of their environment, cells dynamically reorganize the structure of their actin cytoskeleton, a dynamic network of biopolymers, altering the shape and spatial distribution of protein assemblies. Several observations suggest that proteins that crosslink actin filaments may play an important role in cellular mechanosensitivity. Palladin is an actin-crosslinking protein that is found in the lamellar actin network, stress fibers and focal adhesions, cellular structures that are critical for mechanosensing of the physical environment. By virtue of its close interactions with these structures in the cell, palladin may play an important role in cell mechanics. However, the role of actin crosslinkers in general, and palladin in particular, in cellular force generation and mechanosensing is not well known. I have investigated the role of palladin in regulating the plasticity of the actin cytoskeleton and cellular force generation in response to alterations in substrate stiffness. I have shown that the expression levels of palladin modulate the forces exerted by cells and their ability to sense substrate stiffness. Perturbation experiments also suggest that palladin levels in cells altered myosin motor activity. These results suggest that the actin crosslinkers, such as palladin, and myosin motors coordinate for optimal cell function and to prevent aberrant

  7. Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery☆

    PubMed Central

    Kell, Douglas B.; Goodacre, Royston

    2014-01-01

    Metabolism represents the ‘sharp end’ of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule ‘drug’ transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology. PMID:23892182

  8. Into new territory: improved microbial synthesis through engineering of the essential metabolic network.

    PubMed

    Lynch, Michael D

    2016-04-01

    Advances in synthetic biology and metabolic engineering offer the promise of next generation bioprocesses to produce numerous products including specialty and bulk chemicals and even biofuels sustainably from renewable feedstocks. A primary challenge is the optimization of product flux, within a much larger and complex metabolic network. While simple gene deletion methods can be used in the case of non-essential byproduct pathways, more sophisticated approaches are required when competitive fluxes are essential to host cellular functions. Engineering essential metabolic networks has been traditionally off-limits to metabolic engineers. Newer approaches to be reviewed include the rebalancing or rewiring of the metabolic network by tuning the levels of essential enzymes and the use of dynamic metabolic control strategies to conditionally reduce essential competitive fluxes.

  9. Tools and Models for Integrating Multiple Cellular Networks

    SciTech Connect

    Gerstein, Mark

    2015-11-06

    In this grant, we have systematically investigated the integrated networks, which are responsible for the coordination of activity between metabolic pathways in prokaryotes. We have developed several computational tools to analyze the topology of the integrated networks consisting of metabolic, regulatory, and physical interaction networks. The tools are all open-source, and they are available to download from Github, and can be incorporated in the Knowledgebase. Here, we summarize our work as follow. Understanding the topology of the integrated networks is the first step toward understanding its dynamics and evolution. For Aim 1 of this grant, we have developed a novel algorithm to determine and measure the hierarchical structure of transcriptional regulatory networks [1]. The hierarchy captures the direction of information flow in the network. The algorithm is generally applicable to regulatory networks in prokaryotes, yeast and higher organisms. Integrated datasets are extremely beneficial in understanding the biology of a system in a compact manner due to the conflation of multiple layers of information. Therefore for Aim 2 of this grant, we have developed several tools and carried out analysis for integrating system-wide genomic information. To make use of the structural data, we have developed DynaSIN for protein-protein interactions networks with various dynamical interfaces [2]. We then examined the association between network topology with phenotypic effects such as gene essentiality. In particular, we have organized E. coli and S. cerevisiae transcriptional regulatory networks into hierarchies. We then correlated gene phenotypic effects by tinkering with different layers to elucidate which layers were more tolerant to perturbations [3]. In the context of evolution, we also developed a workflow to guide the comparison between different types of biological networks across various species using the concept of rewiring [4], and Furthermore, we have developed

  10. Using Cellular Communication Networks To Detect Air Pollution.

    PubMed

    David, Noam; Gao, H Oliver

    2016-09-01

    Accurate real time monitoring of atmospheric conditions at ground level is vital for hazard warning, meteorological forecasting, and various environmental applications required for public health and safety. However, conventional monitoring facilities are costly and often insufficient, for example, since they are not representative of the larger space and are not deployed densely enough in the field. There have been numerous scientific works showing the ability of commercial microwave links that comprise the data transmission infrastructure in cellular communication networks to monitor hydrometeors as a potential complementary solution. However, despite the large volume of research carried out in this emerging field during the past decade, no study has shown the ability of the system to provide critical information regarding air quality. Here we reveal the potential for identifying atmospheric conditions prone to air pollution by detecting temperature inversions that trap pollutants at ground level. The technique is based on utilizing standard signal measurements from an existing cellular network during routine operation. PMID:27490182

  11. Using Cellular Communication Networks To Detect Air Pollution.

    PubMed

    David, Noam; Gao, H Oliver

    2016-09-01

    Accurate real time monitoring of atmospheric conditions at ground level is vital for hazard warning, meteorological forecasting, and various environmental applications required for public health and safety. However, conventional monitoring facilities are costly and often insufficient, for example, since they are not representative of the larger space and are not deployed densely enough in the field. There have been numerous scientific works showing the ability of commercial microwave links that comprise the data transmission infrastructure in cellular communication networks to monitor hydrometeors as a potential complementary solution. However, despite the large volume of research carried out in this emerging field during the past decade, no study has shown the ability of the system to provide critical information regarding air quality. Here we reveal the potential for identifying atmospheric conditions prone to air pollution by detecting temperature inversions that trap pollutants at ground level. The technique is based on utilizing standard signal measurements from an existing cellular network during routine operation.

  12. Cellular Automata with network incubation in information technology diffusion

    NASA Astrophysics Data System (ADS)

    Guseo, Renato; Guidolin, Mariangela

    2010-06-01

    Innovation diffusion of network goods determines direct network externalities that depress sales for long periods and delay full benefits. We model this effect through a multiplicative dynamic market potential driven by a latent individual threshold embedded in a special Cellular Automata representation. The corresponding mean field approximation of its aggregate version is a Riccati equation with a closed form solution. This allows the detection of a change-point time separating an incubation period from a subsequent take-off due to a collective threshold (critical mass). Weighted nonlinear least squares are the main inferential methodology. An application is analysed with reference to USA fax machine diffusion.

  13. Growth-Induced Instability in Metabolic Networks

    SciTech Connect

    Goyal, Sidhartha; Wingreen, Ned S.

    2007-03-30

    Product-feedback inhibition is a ubiquitous regulatory scheme for maintaining homeostasis in living cells. Individual metabolic pathways with product-feedback inhibition are stable as long as one pathway step is rate limiting. However, pathways are often coupled both by the use of a common substrate and by stoichiometric utilization of their products for cell growth. We show that such a coupled network with product-feedback inhibition may exhibit limit-cycle oscillations which arise via a Hopf bifurcation. Our results highlight novel evolutionary constraints on the architecture of metabolism.

  14. Network Medicine: From Cellular Networks to the Human Diseasome

    NASA Astrophysics Data System (ADS)

    Barabasi, Albert-Laszlo

    2014-03-01

    Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular network. The tools of network science offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho)phenotypes. Advances in this direction not only enrich our understanding of complex systems, but are also essential to identify new disease genes, to uncover the biological significance of disease-associated mutations identified by genome-wide association studies and full genome sequencing, and to identify drug targets and biomarkers for complex diseases.

  15. Hybrid Spectral Efficient Cellular Network Deployment to Reduce RF Pollution

    NASA Astrophysics Data System (ADS)

    Katiyar, Sumit; K. Jain, R.; K. Agrawal, N.

    2012-09-01

    As the mobile telecommunication systems are growing tremendously all over the world, the numbers of handheld and base stations are also rapidly growing and it became very popular to see these base stations distributed everywhere in the neighborhood and on roof tops which has caused a considerable amount of panic to the public in Palestine concerning wither the radiated electromagnetic fields from these base stations may cause any health effect or hazard. Recently UP High Court in India ordered for removal of BTS towers from residential area, it has created panic among cellular communication network designers too. Green cellular networks could be a solution for the above problem. This paper deals with green cellular networks with the help of multi-layer overlaid hierarchical structure (macro / micro / pico / femto cells). Macrocell for area coverage, micro for pedestrian and a slow moving traffic while pico for indoor use and femto for individual high capacity users. This could be the answer of the problem of energy conservation and enhancement of spectral density also.

  16. A computational analysis of protein interactions in metabolic networks reveals novel enzyme pairs potentially involved in metabolic channeling.

    PubMed

    Huthmacher, Carola; Gille, Christoph; Holzhütter, Hermann-Georg

    2008-06-01

    Protein-protein interactions are operative at almost every level of cell structure and function as, for example, formation of sub-cellular organelles, packaging of chromatin, muscle contraction, signal transduction, and regulation of gene expression. Public databases of reported protein-protein interactions comprise hundreds of thousands interactions, and this number is steadily growing. Elucidating the implications of protein-protein interactions for the regulation of the underlying cellular or extra-cellular reaction network remains a great challenge for computational biochemistry. In this work, we have undertaken a systematic and comprehensive computational analysis of reported enzyme-enzyme interactions in the metabolic networks of the model organisms Escherichia coli and Saccharomyces cerevisiae. We grouped all enzyme pairs according to the topological distance that the catalyzed reactions have in the metabolic network and performed a statistical analysis of reported enzyme-enzyme interactions within these groups. We found a higher frequency of reported enzyme-enzyme interactions within the group of enzymes catalyzing reactions that are adjacent in the network, i.e. sharing at least one metabolite. As some of these interacting enzymes have already been implicated in metabolic channeling our analysis may provide a useful screening for candidates of this phenomenon. To check for a possible regulatory role of interactions between enzymes catalyzing non-neighboring reactions, we determined potentially regulatory enzymes using connectivity in the network and absolute change of Gibbs free energy. Indeed a higher portion of reported interactions pertain to such potentially regulatory enzymes.

  17. THE CELLULAR METABOLISM AND SYSTEMIC TOXICITY OF ARSENIC

    EPA Science Inventory

    Abstract

    Toxic Consequences of the Metabolism of Arsenic. David J. Thomas, Miroslav Styblo, and Shan Lin. (2001). Toxicol. Appl. Pharmacol. 000, xxx-yyy.
    Although it has been known for decades that humans and many other species metabolize inorganic arsenic to methyl ...

  18. CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Noise effect in metabolic networks

    NASA Astrophysics Data System (ADS)

    Li, Zheng-Yan; Xie, Zheng-Wei; Chen, Tong; Ouyang, Qi

    2009-12-01

    Constraint-based models such as flux balance analysis (FBA) are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term 'noise', and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.

  19. Network-based analysis of the sphingolipid metabolism in hypertension

    PubMed Central

    Fenger, Mogens; Linneberg, Allan; Jeppesen, Jørgen

    2015-01-01

    Common diseases like essential hypertension or diabetes mellitus are complex as they are polygenic in nature, such that each genetic variation only has a small influence on the disease. Genes operates in integrated networks providing the blue-print for all biological processes and conditional of the complex genotype determines the state and dynamics of any trait, which may be modified to various extent by non-genetic factors. Thus, diseases are heterogenous ensembles of conditions with a common endpoint. Numerous studies have been performed to define genes of importance for a trait or disease, but only a few genes with small effect have been identified. The major reasons for this modest progress is the unresolved heterogeneity of the regulation of blood pressure and the shortcomings of the prevailing monogenic approach to capture genetic effects in a polygenic condition. Here, a two-step procedure is presented in which physiological heterogeneity is disentangled and genetic effects are analyzed by variance decomposition of genetic interactions and by an information theoretical approach including 162 single nucleotide polymorphisms (SNP) in 84 genes in the sphingolipid metabolism and related networks in blood pressure regulation. As expected, almost no genetic main effects were detected. In contrast, two-gene interactions established the entire sphingolipid metabolic and related genetic network to be highly involved in the regulation of blood pressure. The pattern of interaction clearly revealed that epistasis does not necessarily reflects the topology of the metabolic pathways i.e., the flow of metabolites. Rather, the enzymes and proteins are integrated in complex cellular substructures where communication flows between the components of the networks, which may be composite in structure. The heritabilities for diastolic and systolic blood pressure were estimated to be 0.63 and 0.01, which may in fact be the maximum heritabilities of these traits. This procedure

  20. Network-based analysis of the sphingolipid metabolism in hypertension.

    PubMed

    Fenger, Mogens; Linneberg, Allan; Jeppesen, Jørgen

    2015-01-01

    Common diseases like essential hypertension or diabetes mellitus are complex as they are polygenic in nature, such that each genetic variation only has a small influence on the disease. Genes operates in integrated networks providing the blue-print for all biological processes and conditional of the complex genotype determines the state and dynamics of any trait, which may be modified to various extent by non-genetic factors. Thus, diseases are heterogenous ensembles of conditions with a common endpoint. Numerous studies have been performed to define genes of importance for a trait or disease, but only a few genes with small effect have been identified. The major reasons for this modest progress is the unresolved heterogeneity of the regulation of blood pressure and the shortcomings of the prevailing monogenic approach to capture genetic effects in a polygenic condition. Here, a two-step procedure is presented in which physiological heterogeneity is disentangled and genetic effects are analyzed by variance decomposition of genetic interactions and by an information theoretical approach including 162 single nucleotide polymorphisms (SNP) in 84 genes in the sphingolipid metabolism and related networks in blood pressure regulation. As expected, almost no genetic main effects were detected. In contrast, two-gene interactions established the entire sphingolipid metabolic and related genetic network to be highly involved in the regulation of blood pressure. The pattern of interaction clearly revealed that epistasis does not necessarily reflects the topology of the metabolic pathways i.e., the flow of metabolites. Rather, the enzymes and proteins are integrated in complex cellular substructures where communication flows between the components of the networks, which may be composite in structure. The heritabilities for diastolic and systolic blood pressure were estimated to be 0.63 and 0.01, which may in fact be the maximum heritabilities of these traits. This procedure

  1. Flux Balance Analysis of Cyanobacterial Metabolism: The Metabolic Network of Synechocystis sp. PCC 6803

    PubMed Central

    Knoop, Henning; Gründel, Marianne; Zilliges, Yvonne; Lehmann, Robert; Hoffmann, Sabrina; Lockau, Wolfgang; Steuer, Ralf

    2013-01-01

    Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism. PMID:23843751

  2. Reverse engineering cellular networks with information theoretic methods.

    PubMed

    Villaverde, Alejandro F; Ross, John; Banga, Julio R

    2013-01-01

    Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets.

  3. Global insights into energetic and metabolic networks in Rhodobacter sphaeroides

    PubMed Central

    2013-01-01

    Background Improving our understanding of processes at the core of cellular lifestyles can be aided by combining information from genetic analyses, high-throughput experiments and computational predictions. Results We combined data and predictions derived from phenotypic, physiological, genetic and computational analyses to dissect the metabolic and energetic networks of the facultative photosynthetic bacterium Rhodobacter sphaeroides. We focused our analysis on pathways crucial to the production and recycling of pyridine nucleotides during aerobic respiratory and anaerobic photosynthetic growth in the presence of an organic electron donor. In particular, we assessed the requirement for NADH/NADPH transhydrogenase enzyme, PntAB during respiratory and photosynthetic growth. Using high-throughput phenotype microarrays (PMs), we found that PntAB is essential for photosynthetic growth in the presence of many organic electron donors, particularly those predicted to require its activity to produce NADPH. Utilizing the genome-scale metabolic model iRsp1095, we predicted alternative routes of NADPH synthesis and used gene expression analyses to show that transcripts from a subset of the corresponding genes were conditionally increased in a ΔpntAB mutant. We then used a combination of metabolic flux predictions and mutational analysis to identify flux redistribution patterns utilized in the ΔpntAB mutant to compensate for the loss of this enzyme. Data generated from metabolic and phenotypic analyses of wild type and mutant cells were used to develop iRsp1140, an expanded genome-scale metabolic reconstruction for R. sphaeroides with improved ability to analyze and predict pathways associated with photosynthesis and other metabolic processes. Conclusions These analyses increased our understanding of key aspects of the photosynthetic lifestyle, highlighting the added importance of NADPH production under these conditions. It also led to a significant improvement in the

  4. The reconstruction and analysis of tissue specific human metabolic networks.

    PubMed

    Hao, Tong; Ma, Hong-Wu; Zhao, Xue-Ming; Goryanin, Igor

    2012-02-01

    Human tissues have distinct biological functions. Many proteins/enzymes are known to be expressed only in specific tissues and therefore the metabolic networks in various tissues are different. Though high quality global human metabolic networks and metabolic networks for certain tissues such as liver have already been studied, a systematic study of tissue specific metabolic networks for all main tissues is still missing. In this work, we reconstruct the tissue specific metabolic networks for 15 main tissues in human based on the previously reconstructed Edinburgh Human Metabolic Network (EHMN). The tissue information is firstly obtained for enzymes from Human Protein Reference Database (HPRD) and UniprotKB databases and transfers to reactions through the enzyme-reaction relationships in EHMN. As our knowledge of tissue distribution of proteins is still very limited, we replenish the tissue information of the metabolic network based on network connectivity analysis and thorough examination of the literature. Finally, about 80% of proteins and reactions in EHMN are determined to be in at least one of the 15 tissues. To validate the quality of the tissue specific network, the brain specific metabolic network is taken as an example for functional module analysis and the results reveal that the function of the brain metabolic network is closely related with its function as the centre of the human nervous system. The tissue specific human metabolic networks are available at .

  5. Bioenergetic Impairment in Animal and Cellular Models of Alzheimer's Disease: PARP-1 Inhibition Rescues Metabolic Dysfunctions.

    PubMed

    Martire, Sara; Fuso, Andrea; Mosca, Luciana; Forte, Elena; Correani, Virginia; Fontana, Mario; Scarpa, Sigfrido; Maras, Bruno; d'Erme, Maria

    2016-08-10

    Amyloid-beta peptide accumulation in the brain is one of the main hallmarks of Alzheimer's disease. The amyloid aggregation process is associated with the generation of free radical species responsible for mitochondrial impairment and DNA damage that in turn activates poly(ADP-ribose)polymerase 1 (PARP-1). PARP-1 catalyzes the poly(ADP-ribosylation), a post-translational modification of proteins, cleaving the substrate NAD+ and transferring the ADP-ribose moieties to the enzyme itself or to an acceptor protein to form branched polymers of ADP-ribose. In this paper, we demonstrate that a mitochondrial dysfunction occurs in Alzheimer's transgenic mice TgCRND8, in SH-SY5Y treated with amyloid-beta and in 7PA2 cells. Moreover, PARP-1 activation contributes to the functional energetic decline affecting cytochrome oxidase IV protein levels, oxygen consumption rates, and membrane potential, resulting in cellular bioenergetic deficit. We also observed, for the first time, an increase of pyruvate kinase 2 expression, suggesting a modulation of the glycolytic pathway by PARP-1. PARP-1 inhibitors are able to restore both mitochondrial impairment and pyruvate kinase 2 expression. The overall data here presented indicate a pivotal role for this enzyme in the bioenergetic network of neuronal cells and open new perspectives for investigating molecular mechanisms underlying energy charge decline in Alzheimer's disease. In this scenario, PARP-1 inhibitors might represent a novel therapeutic intervention to rescue cellular energetic metabolism. PMID:27567805

  6. Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models

    PubMed Central

    Hamilton, Joshua J.; Reed, Jennifer L.

    2012-01-01

    Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different

  7. Mechanics of composite actin networks: in vitro and cellular perspectives

    NASA Astrophysics Data System (ADS)

    Upadhyaya, Arpita

    2014-03-01

    Actin filaments and associated actin binding proteins play an essential role in governing the mechanical properties of eukaryotic cells. Even though cells have multiple actin binding proteins (ABPs) that exist simultaneously to maintain the structural and mechanical integrity of the cellular cytoskeleton, how these proteins work together to determine the properties of actin networks is not well understood. The ABP, palladin, is essential for the integrity of cell morphology and movement during development. Palladin coexists with alpha-actinin in stress fibers and focal adhesions and binds to both actin and alpha-actinin. To obtain insight into how mutually interacting actin crosslinking proteins modulate the properties of actin networks, we have characterized the micro-structure and mechanics of actin networks crosslinked with palladin and alpha-actinin. Our studies on composite networks of alpha-actinin/palladin/actin show that palladin and alpha-actinin synergistically determine network viscoelasticity. We have further examined the role of palladin in cellular force generation and mechanosensing. Traction force microscopy revealed that TAFs are sensitive to substrate stiffness as they generate larger forces on substrates of increased stiffness. Contrary to expectations, knocking down palladin increased the forces generated by cells, and also inhibited the ability to sense substrate stiffness for very stiff gels. This was accompanied by significant differences in the actin organization and adhesion dynamics of palladin knock down cells. Perturbation experiments also suggest altered myosin activity in palladin KD cells. Our results suggest that the actin crosslinkers such as palladin and myosin motors coordinate for optimal cell function and to prevent aberrant behavior as in cancer metastasis.

  8. An association of metabolic syndrome constellation with cellular membrane caveolae.

    PubMed

    Zhang, Wei-Zheng

    2014-01-01

    Metabolic syndrome (MetS) is a cluster of metabolic abnormalities that can predispose an individual to a greater risk of developing type-2 diabetes and cardiovascular diseases. The cluster includes abdominal obesity, dyslipidemia, hypertension, and hyperglycemia - all of which are risk factors to public health. While searching for a link among the aforementioned malaises, clues have been focused on the cell membrane domain caveolae, wherein the MetS-associated active molecules are colocalized and interacted with to carry out designated biological activities. Caveola disarray could induce all of those individual metabolic abnormalities to be present in animal models and humans, providing a new target for therapeutic strategy in the management of MetS.

  9. Country-wide rainfall maps from cellular communication networks.

    PubMed

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2013-02-19

    Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal's attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space-time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km(2)), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent. PMID:23382210

  10. Country-wide rainfall maps from cellular communication networks

    PubMed Central

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2013-01-01

    Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal’s attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space–time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km2), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent. PMID:23382210

  11. From Ancient Pathways to Aging Cells-Connecting Metabolism and Cellular Senescence.

    PubMed

    Wiley, Christopher D; Campisi, Judith

    2016-06-14

    Cellular senescence is a complex stress response that permanently arrests the proliferation of cells at risk for oncogenic transformation. However, senescent cells can also drive phenotypes associated with aging. Although the senescence-associated growth arrest prevents the development of cancer, and the metabolism of cancer cells has been studied in depth, the metabolic causes and consequences of cellular senescence were largely unexplored until recently. New findings reveal key roles for several aspects of cellular metabolism in the establishment and control of senescent phenotypes. These discoveries have important implications for both cancer and aging. In this review, we highlight some of the recent links between metabolism and phenotypes that are commonly associated with senescent cells. PMID:27304503

  12. Impaired cellular energy metabolism contributes to bluetongue-virus-induced autophagy.

    PubMed

    Lv, Shuang; Xu, Qingyuan; Sun, Encheng; Zhang, Jikai; Wu, Donglai

    2016-10-01

    Bluetongue virus (BTV) has been found to trigger autophagy to favor its replication, but the underlying mechanisms have not been clarified. Here, we show that cellular energy metabolism is involved in BTV-induced autophagy. Cellular ATP synthesis was impaired by BTV1 infection, causing metabolic stress, which was responsible for activation of autophagy, since the conversion of LC3 and aggregation of GFP-LC3 (autophagy markers) were suppressed when infection-caused energy depletion was reversed via MP (metabolic substrate) treatment. The reduced virus yields with MP further supported this view. Overall, our findings suggest that BTV1-induced disruption of cellular energy metabolism contributes to autophagy, and this provides new insights into BTV-host interactions.

  13. Copper enhances cellular and network excitabilities, and improves temporal processing in the rat hippocampus.

    PubMed

    Maureira, Carlos; Letelier, Juan Carlos; Alvarez, Osvaldo; Delgado, Ricardo; Vergara, Cecilia

    2015-12-01

    Copper, an ion with many important metabolic functions, has also been proposed to have a role as modulator on neuronal function, mostly based on its effects on voltage- and neurotransmitter-gated conductance as well as on neurological symptoms of patients with altered copper homeostasis. Nevertheless, the mechanisms by which copper exerts its neuromodulatory effects have not been clearly established in a functional neuronal network. Using rat hippocampus slices as a neuronal network model, the effects of copper in the range of 10-100 nm were tested on the intrinsic, synaptic and network properties of the CA1 region. Most of the previously described effects of this cation were in the micromolar range of copper concentrations. The current results indicate that copper is a multifaceted neuromodulator, having effects that may be grouped into two categories: (i) activity enhancement, by modulating synaptic communication and action potential (AP) conductances; and (ii) temporal processing and correlation extraction, by improving reliability and depressing inhibition. Specifically it was found that copper hyperpolarizes AP firing threshold, enhances neuronal and network excitability, modifies CA3-CA1 pathway gain, enhances the frequency of spontaneous synaptic events, decreases inhibitory network activity, and improves AP timing reliability. Moreover, copper chelation by bathocuproine decreases spontaneous network spiking activity. These results allow the proposal that copper affects the network activity from cellular to circuit levels on a moment-by-moment basis, and should be considered a crucial functional component of hippocampal neuronal circuitry.

  14. Modeling a Nonlinear Liquid Level System by Cellular Neural Networks

    NASA Astrophysics Data System (ADS)

    Hernandez-Romero, Norberto; Seck-Tuoh-Mora, Juan Carlos; Gonzalez-Hernandez, Manuel; Medina-Marin, Joselito; Flores-Romero, Juan Jose

    This paper presents the analogue simulation of a nonlinear liquid level system composed by two tanks; the system is controlled using the methodology of exact linearization via state feedback by cellular neural networks (CNNs). The relevance of this manuscript is to show how a block diagram representing the analogue modeling and control of a nonlinear dynamical system, can be implemented and regulated by CNNs, whose cells may contain numerical values or arithmetic and control operations. In this way the dynamical system is modeled by a set of local-interacting elements without need of a central supervisor.

  15. GPM ground validation via commercial cellular networks: an exploratory approach

    NASA Astrophysics Data System (ADS)

    Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Brasjen, Noud; Uijlenhoet, Remko

    2016-04-01

    The suitability of commercial microwave link networks for ground validation of GPM (Global Precipitation Measurement) data is evaluated here. Two state-of-the-art rainfall products are compared over the land surface of the Netherlands for a period of 7 months, i.e., rainfall maps from commercial cellular communication networks and Integrated Multi-satellite Retrievals for GPM (IMERG). Commercial microwave link networks are nowadays the core component in telecommunications worldwide. Rainfall rates can be retrieved from measurements of attenuation between transmitting and receiving antennas. If adequately set up, these networks enable rainfall monitoring tens of meters above the ground at high spatiotemporal resolutions (temporal sampling of seconds to tens of minutes, and spatial sampling of hundreds of meters to tens of kilometers). The GPM mission is the successor of TRMM (Tropical Rainfall Measurement Mission). For two years now, IMERG offers rainfall estimates across the globe (180°W - 180°E and 60°N - 60°S) at spatiotemporal resolutions of 0.1° x 0.1° every 30 min. These two data sets are compared against a Dutch gauge-adjusted radar data set, considered to be the ground truth given its accuracy, spatiotemporal resolution and availability. The suitability of microwave link networks in satellite rainfall evaluation is of special interest, given the independent character of this technique, its high spatiotemporal resolutions and availability. These are valuable assets for water management and modeling of floods, landslides, and weather extremes; especially in places where rain gauge networks are scarce or poorly maintained, or where weather radar networks are too expensive to acquire and/or maintain.

  16. Sources of Uncertainty in Rainfall Maps from Cellular Communication Networks

    NASA Astrophysics Data System (ADS)

    Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2015-04-01

    and quantify the sources of uncertainty in such rainfall maps, but also to test the actual and optimal performance of one commercial microwave network from one of the cellular providers in The Netherlands.

  17. Cellular telephone-based wide-area radiation detection network

    DOEpatents

    Craig, William W.; Labov, Simon E.

    2009-06-09

    A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

  18. Global reconstruction of the human metabolic network based on genomic and bibliomic data

    PubMed Central

    Duarte, Natalie C.; Becker, Scott A.; Jamshidi, Neema; Thiele, Ines; Mo, Monica L.; Vo, Thuy D.; Srivas, Rohith; Palsson, Bernhard Ø.

    2007-01-01

    Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype–phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used (i) for the discovery of missing information, (ii) for the formulation of an in silico model, and (iii) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology. PMID:17267599

  19. Network modeling of membrane-based artificial cellular systems

    NASA Astrophysics Data System (ADS)

    Freeman, Eric C.; Philen, Michael K.; Leo, Donald J.

    2013-04-01

    Computational models are derived for predicting the behavior of artificial cellular networks for engineering applications. The systems simulated involve the use of a biomolecular unit cell, a multiphase material that incorporates a lipid bilayer between two hydrophilic compartments. These unit cells may be considered building blocks that enable the fabrication of complex electrochemical networks. These networks can incorporate a variety of stimuli-responsive biomolecules to enable a diverse range of multifunctional behavior. Through the collective properties of these biomolecules, the system demonstrates abilities that recreate natural cellular phenomena such as mechanotransduction, optoelectronic response, and response to chemical gradients. A crucial step to increase the utility of these biomolecular networks is to develop mathematical models of their stimuli-responsive behavior. While models have been constructed deriving from the classical Hodgkin-Huxley model focusing on describing the system as a combination of traditional electrical components (capacitors and resistors), these electrical elements do not sufficiently describe the phenomena seen in experiment as they are not linked to the molecular scale processes. From this realization an advanced model is proposed that links the traditional unit cell parameters such as conductance and capacitance to the molecular structure of the system. Rather than approaching the membrane as an isolated parallel plate capacitor, the model seeks to link the electrical properties to the underlying chemical characteristics. This model is then applied towards experimental cases in order that a more complete picture of the underlying phenomena responsible for the desired sensing mechanisms may be constructed. In this way the stimuli-responsive characteristics may be understood and optimized.

  20. Metabolic Network Discovery by Top-Down and Bottom-Up Approaches and Paths for Reconciliation

    PubMed Central

    Çakır, Tunahan; Khatibipour, Mohammad Jafar

    2014-01-01

    The primary focus in the network-centric analysis of cellular metabolism by systems biology approaches is to identify the active metabolic network for the condition of interest. Two major approaches are available for the discovery of the condition-specific metabolic networks. One approach starts from genome-scale metabolic networks, which cover all possible reactions known to occur in the related organism in a condition-independent manner, and applies methods such as the optimization-based Flux-Balance Analysis to elucidate the active network. The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract information content of the data such that the active network is inferred. These approaches, termed bottom-up and top-down, respectively, are currently employed independently. However, considering that both approaches have the same goal, they can both benefit from each other paving the way for the novel integrative analysis methods of metabolome data- and flux-analysis approaches in the post-genomic era. This study reviews the strengths of constraint-based analysis and network inference methods reported in the metabolic systems biology field; then elaborates on the potential paths to reconcile the two approaches to shed better light on how the metabolism functions. PMID:25520953

  1. Optimizing the bulk modulus of low-density cellular networks.

    PubMed

    Durand, Marc

    2005-07-01

    We present an alternative derivation of upper-bounds for the bulk modulus of both two-dimensional and three-dimensional cellular materials. For two-dimensional materials, we recover exactly the expression of the Hashin-Shtrikman (HS) upper-bound in the low-density limit, while for three-dimensional materials we even improve the HS bound. Furthermore, we establish necessary and sufficient conditions on the cellular structure for maximizing the bulk modulus, for a given solid volume fraction. The conditions are found to be exactly those under which the electrical (or thermal) conductivity of the material reaches its maximal value as well. These results provide a set of straightforward criteria allowing us to address the design of optimized cellular materials, and shed light on recent studies of structures with both maximal bulk modulus and maximal conductivity. Finally, we discuss the specific case of spring networks, and analyze the compatibility of the criteria presented here with the geometrical constraints caused by minimization of surface energy in a real foam.

  2. The Effects of Cholera Toxin on Cellular Energy Metabolism

    PubMed Central

    Snider, Rachel M.; McKenzie, Jennifer R.; Kraft, Lewis; Kozlov, Eugene; Wikswo, John P.; Cliffel, David E.

    2010-01-01

    Multianalyte microphysiometry, a real-time instrument for simultaneous measurement of metabolic analytes in a microfluidic environment, was used to explore the effects of cholera toxin (CTx). Upon exposure of CTx to PC-12 cells, anaerobic respiration was triggered, measured as increases in acid and lactate production and a decrease in the oxygen uptake. We believe the responses observed are due to a CTx-induced activation of adenylate cyclase, increasing cAMP production and resulting in a switch to anaerobic respiration. Inhibitors (H-89, brefeldin A) and stimulators (forskolin) of cAMP were employed to modulate the CTx-induced cAMP responses. The results of this study show the utility of multianalyte microphysiometry to quantitatively determine the dynamic metabolic effects of toxins and affected pathways. PMID:22069603

  3. Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    PubMed Central

    Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

    2013-01-01

    We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

  4. Assessing the weather monitoring capabilities of cellular microwave link networks

    NASA Astrophysics Data System (ADS)

    Fencl, Martin; Vrzba, Miroslav; Rieckermann, Jörg; Bareš, Vojtěch

    2016-04-01

    Using of microwave links for rainfall monitoring was suggested already by (Atlas and Ulbrich, 1977). However, this technique attracted broader attention of scientific community only in the recent decade, with the extensive growth of cellular microwave link (CML) networks, which form the backbone of today's cellular telecommunication infrastructure. Several studies have already shown that CMLs can be conveniently used as weather sensors and have potential to provide near-ground path-integrated observations of rainfall but also humidity or fog. However, although research is still focusing on algorithms to improve the weather sensing capabilities (Fencl et al., 2015), it is not clear how to convince cellular operators to provide the power levels of their network. One step in this direction is to show in which regions or municipalities the networks are sufficiently dense to provide/develop good services. In this contribution we suggest a standardized approach to evaluate CML networks in terms of rainfall observation and to identify suitable regions for CML rainfall monitoring. We estimate precision of single CML based on its sensitivity to rainfall, i.e. as a function of frequency, polarization and path length. Capability of a network to capture rainfall spatial patterns is estimated from the CML coverage and path lengths considering that single CML provides path-integrated rain rates. We also search for suitable predictors for regions where no network topologies are available. We test our approach on several European networks and discuss the results. Our results show that CMLs are very dense in urban areas (> 1 CML/km2), but less in rural areas (< 0.02 CML/km2). We found a strong correlation between a population and CML network density (e.g. R2 = 0.97 in Czech Republic), thus population could be a simple proxy to identify suitable regions for CML weather monitoring. To enable a simple and efficient assessment of the CML monitoring potential for any region worldwide

  5. MicroRNAs Regulate Cellular ATP Levels by Targeting Mitochondrial Energy Metabolism Genes during C2C12 Myoblast Differentiation.

    PubMed

    Siengdee, Puntita; Trakooljul, Nares; Murani, Eduard; Schwerin, Manfred; Wimmers, Klaus; Ponsuksili, Siriluck

    2015-01-01

    In our previous study, we identified an miRNA regulatory network involved in energy metabolism in porcine muscle. To better understand the involvement of miRNAs in cellular ATP production and energy metabolism, here we used C2C12 myoblasts, in which ATP levels increase during differentiation, to identify miRNAs modulating these processes. ATP level, miRNA and mRNA microarray expression profiles during C2C12 differentiation into myotubes were assessed. The results suggest 14 miRNAs (miR-423-3p, miR-17, miR-130b, miR-301a/b, miR-345, miR-15a, miR-16a, miR-128, miR-615, miR-1968, miR-1a/b, and miR-194) as cellular ATP regulators targeting genes involved in mitochondrial energy metabolism (Cox4i2, Cox6a2, Ndufb7, Ndufs4, Ndufs5, and Ndufv1) during C2C12 differentiation. Among these, miR-423-3p showed a high inverse correlation with increasing ATP levels. Besides having implications in promoting cell growth and cell cycle progression, its function in cellular ATP regulation is yet unknown. Therefore, miR-423-3p was selected and validated for the function together with its potential target, Cox6a2. Overexpression of miR-423-3p in C2C12 myogenic differentiation lead to decreased cellular ATP level and decreased expression of Cox6a2 compared to the negative control. These results suggest miR-423-3p as a novel regulator of ATP/energy metabolism by targeting Cox6a2.

  6. Cellular metabolic rate is influenced by life-history traits in tropical and temperate birds.

    PubMed

    Jimenez, Ana Gabriela; Van Brocklyn, James; Wortman, Matthew; Williams, Joseph B

    2014-01-01

    In general, tropical birds have a "slow pace of life," lower rates of whole-animal metabolism and higher survival rates, than temperate species. A fundamental challenge facing physiological ecologists is the understanding of how variation in life-history at the whole-organism level might be linked to cellular function. Because tropical birds have lower rates of whole-animal metabolism, we hypothesized that cells from tropical species would also have lower rates of cellular metabolism than cells from temperate species of similar body size and common phylogenetic history. We cultured primary dermal fibroblasts from 17 tropical and 17 temperate phylogenetically-paired species of birds in a common nutritive and thermal environment and then examined basal, uncoupled, and non-mitochondrial cellular O2 consumption (OCR), proton leak, and anaerobic glycolysis (extracellular acidification rates [ECAR]), using an XF24 Seahorse Analyzer. We found that multiple measures of metabolism in cells from tropical birds were significantly lower than their temperate counterparts. Basal and uncoupled cellular metabolism were 29% and 35% lower in cells from tropical birds, respectively, a decrease closely aligned with differences in whole-animal metabolism between tropical and temperate birds. Proton leak was significantly lower in cells from tropical birds compared with cells from temperate birds. Our results offer compelling evidence that whole-animal metabolism is linked to cellular respiration as a function of an animal's life-history evolution. These findings are consistent with the idea that natural selection has uniquely fashioned cells of long-lived tropical bird species to have lower rates of metabolism than cells from shorter-lived temperate species.

  7. Cellular Metabolic Rate Is Influenced by Life-History Traits in Tropical and Temperate Birds

    PubMed Central

    Jimenez, Ana Gabriela; Van Brocklyn, James; Wortman, Matthew; Williams, Joseph B.

    2014-01-01

    In general, tropical birds have a “slow pace of life,” lower rates of whole-animal metabolism and higher survival rates, than temperate species. A fundamental challenge facing physiological ecologists is the understanding of how variation in life-history at the whole-organism level might be linked to cellular function. Because tropical birds have lower rates of whole-animal metabolism, we hypothesized that cells from tropical species would also have lower rates of cellular metabolism than cells from temperate species of similar body size and common phylogenetic history. We cultured primary dermal fibroblasts from 17 tropical and 17 temperate phylogenetically-paired species of birds in a common nutritive and thermal environment and then examined basal, uncoupled, and non-mitochondrial cellular O2 consumption (OCR), proton leak, and anaerobic glycolysis (extracellular acidification rates [ECAR]), using an XF24 Seahorse Analyzer. We found that multiple measures of metabolism in cells from tropical birds were significantly lower than their temperate counterparts. Basal and uncoupled cellular metabolism were 29% and 35% lower in cells from tropical birds, respectively, a decrease closely aligned with differences in whole-animal metabolism between tropical and temperate birds. Proton leak was significantly lower in cells from tropical birds compared with cells from temperate birds. Our results offer compelling evidence that whole-animal metabolism is linked to cellular respiration as a function of an animal’s life-history evolution. These findings are consistent with the idea that natural selection has uniquely fashioned cells of long-lived tropical bird species to have lower rates of metabolism than cells from shorter-lived temperate species. PMID:24498080

  8. Basic concepts and principles of stoichiometric modeling of metabolic networks.

    PubMed

    Maarleveld, Timo R; Khandelwal, Ruchir A; Olivier, Brett G; Teusink, Bas; Bruggeman, Frank J

    2013-09-01

    Metabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into metabolic plasticity and robustness, and the ability of organisms to cope with different environments. Constraint-based stoichiometric modeling of metabolic networks has become an indispensable tool for such studies. Herein, we review the basic theoretical underpinnings of constraint-based stoichiometric modeling of metabolic networks. Basic concepts, such as stoichiometry, chemical moiety conservation, flux modes, flux balance analysis, and flux solution spaces, are explained with simple, illustrative examples. We emphasize the mathematical definitions and their network topological interpretations.

  9. Alkalizing Reactions Streamline Cellular Metabolism in Acidogenic Microorganisms

    PubMed Central

    Arioli, Stefania; Ragg, Enzio; Scaglioni, Leonardo; Fessas, Dimitrios; Signorelli, Marco; Karp, Matti; Daffonchio, Daniele; De Noni, Ivano; Mulas, Laura; Oggioni, Marco; Guglielmetti, Simone; Mora, Diego

    2010-01-01

    An understanding of the integrated relationships among the principal cellular functions that govern the bioenergetic reactions of an organism is necessary to determine how cells remain viable and optimise their fitness in the environment. Urease is a complex enzyme that catalyzes the hydrolysis of urea to ammonia and carbonic acid. While the induction of urease activity by several microorganisms has been predominantly considered a stress-response that is initiated to generate a nitrogen source in response to a low environmental pH, here we demonstrate a new role of urease in the optimisation of cellular bioenergetics. We show that urea hydrolysis increases the catabolic efficiency of Streptococcus thermophilus, a lactic acid bacterium that is widely used in the industrial manufacture of dairy products. By modulating the intracellular pH and thereby increasing the activity of β-galactosidase, glycolytic enzymes and lactate dehydrogenase, urease increases the overall change in enthalpy generated by the bioenergetic reactions. A cooperative altruistic behaviour of urease-positive microorganisms on the urease-negative microorganisms within the same environment was also observed. The physiological role of a single enzymatic activity demonstrates a novel and unexpected view of the non-transcriptional regulatory mechanisms that govern the bioenergetics of a bacterial cell, highlighting a new role for cytosol-alkalizing biochemical pathways in acidogenic microorganisms. PMID:21152088

  10. Bidirectionality and compartmentation of metabolic fluxes are revealed in the dynamics of isotopomer networks.

    PubMed

    Schryer, David W; Peterson, Pearu; Paalme, Toomas; Vendelin, Marko

    2009-04-01

    Isotope labeling is one of the few methods of revealing the in vivo bidirectionality and compartmentalization of metabolic fluxes within metabolic networks. We argue that a shift from steady state to dynamic isotopomer analysis is required to deal with these cellular complexities and provide a review of dynamic studies of compartmentalized energy fluxes in eukaryotic cells including cardiac muscle, plants, and astrocytes. Knowledge of complex metabolic behaviour on a molecular level is prerequisite for the intelligent design of genetically modified organisms able to realize their potential of revolutionizing food, energy, and pharmaceutical production. We describe techniques to explore the bidirectionality and compartmentalization of metabolic fluxes using information contained in the isotopic transient, and discuss the integration of kinetic models with MFA. The flux parameters of an example metabolic network were optimized to examine the compartmentalization of metabolites and and the bidirectionality of fluxes in the TCA cycle of Saccharomyces uvarum for steady-state respiratory growth. PMID:19468334

  11. Adenylate Kinase and AMP Signaling Networks: Metabolic Monitoring, Signal Communication and Body Energy Sensing

    PubMed Central

    Dzeja, Petras; Terzic, Andre

    2009-01-01

    Adenylate kinase and downstream AMP signaling is an integrated metabolic monitoring system which reads the cellular energy state in order to tune and report signals to metabolic sensors. A network of adenylate kinase isoforms (AK1-AK7) are distributed throughout intracellular compartments, interstitial space and body fluids to regulate energetic and metabolic signaling circuits, securing efficient cell energy economy, signal communication and stress response. The dynamics of adenylate kinase-catalyzed phosphotransfer regulates multiple intracellular and extracellular energy-dependent and nucleotide signaling processes, including excitation-contraction coupling, hormone secretion, cell and ciliary motility, nuclear transport, energetics of cell cycle, DNA synthesis and repair, and developmental programming. Metabolomic analyses indicate that cellular, interstitial and blood AMP levels are potential metabolic signals associated with vital functions including body energy sensing, sleep, hibernation and food intake. Either low or excess AMP signaling has been linked to human disease such as diabetes, obesity and hypertrophic cardiomyopathy. Recent studies indicate that derangements in adenylate kinase-mediated energetic signaling due to mutations in AK1, AK2 or AK7 isoforms are associated with hemolytic anemia, reticular dysgenesis and ciliary dyskinesia. Moreover, hormonal, food and antidiabetic drug actions are frequently coupled to alterations of cellular AMP levels and associated signaling. Thus, by monitoring energy state and generating and distributing AMP metabolic signals adenylate kinase represents a unique hub within the cellular homeostatic network. PMID:19468337

  12. Exploring mitochondrial evolution and metabolism organization principles by comparative analysis of metabolic networks.

    PubMed

    Chang, Xiao; Wang, Zhuo; Hao, Pei; Li, Yuan-Yuan; Li, Yi-Xue

    2010-06-01

    The endosymbiotic theory proposed that mitochondrial genomes are derived from an alpha-proteobacterium-like endosymbiont, which was concluded from sequence analysis. We rebuilt the metabolic networks of mitochondria and 22 relative species, and studied the evolution of mitochondrial metabolism at the level of enzyme content and network topology. Our phylogenetic results based on network alignment and motif identification supported the endosymbiotic theory from the point of view of systems biology for the first time. It was found that the mitochondrial metabolic network were much more compact than the relative species, probably related to the higher efficiency of oxidative phosphorylation of the specialized organelle, and the network is highly clustered around the TCA cycle. Moreover, the mitochondrial metabolic network exhibited high functional specificity to the modules. This work provided insight to the understanding of mitochondria evolution, and the organization principle of mitochondrial metabolic network at the network level.

  13. Integration of metabolomics data into metabolic networks

    PubMed Central

    Töpfer, Nadine; Kleessen, Sabrina; Nikoloski, Zoran

    2015-01-01

    Metabolite levels together with their corresponding metabolic fluxes are integrative outcomes of biochemical transformations and regulatory processes and they can be used to characterize the response of biological systems to genetic and/or environmental changes. However, while changes in transcript or to some extent protein levels can usually be traced back to one or several responsible genes, changes in fluxes and particularly changes in metabolite levels do not follow such rationale and are often the outcome of complex interactions of several components. The increasing quality and coverage of metabolomics technologies have fostered the development of computational approaches for integrating metabolic read-outs with large-scale models to predict the physiological state of a system. Constraint-based approaches, relying on the stoichiometry of the considered reactions, provide a modeling framework amenable to analyses of large-scale systems and to the integration of high-throughput data. Here we review the existing approaches that integrate metabolomics data in variants of constrained-based approaches to refine model reconstructions, to constrain flux predictions in metabolic models, and to relate network structural properties to metabolite levels. Finally, we discuss the challenges and perspectives in the developments of constraint-based modeling approaches driven by metabolomics data. PMID:25741348

  14. Thermodynamic-based computational profiling of cellular regulatory control in hepatocyte metabolism.

    PubMed

    Beard, Daniel A; Qian, Hong

    2005-03-01

    Thermodynamic-based constraints on biochemical fluxes and concentrations are applied in concert with mass balance of fluxes in glycogenesis and glycogenolysis in a model of hepatic cell metabolism. Constraint-based modeling methods that facilitate predictions of reactant concentrations, reaction potentials, and enzyme activities are introduced to identify putative regulatory and control sites in biological networks by computing the minimal control scheme necessary to switch between metabolic modes. Computational predictions of control sites in glycogenic and glycogenolytic operational modes in the hepatocyte network compare favorably with known regulatory mechanisms. The developed hepatic metabolic model is used to computationally analyze the impairment of glucose production in von Gierke's and Hers' diseases, two metabolic diseases impacting glycogen metabolism. The computational methodology introduced here can be generalized to identify downstream targets of agonists, to systematically probe possible drug targets, and to predict the effects of specific inhibitors (or activators) on integrated network function. PMID:15507536

  15. Countrywide rainfall maps from a commercial cellular telecommunication network

    NASA Astrophysics Data System (ADS)

    Overeem, A.; Leijnse, H.; Uijlenhoet, R.

    2012-12-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for hydrological applications, agriculture, meteorology, and climate monitoring. However, the majority of the land surface of the earth lacks accurate rainfall information. Many countries do not have continuously operating weather radars, and have no or few rain gauges. A new development is rainfall estimation from microwave links of commercial cellular telecommunication networks. Such networks cover large parts of the land surface of the earth and have a high density, especially in urban areas. The estimation of rainfall using commercial microwave links could therefore become a valuable source of information. The data produced by microwave links is essentially a by-product of the communication between mobile telephones. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated. Previous studies have shown that average rainfall intensities over the length of a link can be derived from the path-integrated attenuation. A dataset from a commercial microwave link network over the Netherlands is analyzed, containing data from an unprecedented number of links (1500) covering the land surface of the Netherlands (35500 km2). This dataset consists of 24 days with substantial rainfall in June - September 2011. A rainfall retrieval algorithm is presented to derive rainfall intensities from the microwave link data, which have a temporal resolution of 15 min. Rainfall maps (1 km spatial resolution) are generated from these rainfall intensities using Kriging. This algorithm is suited for real-time application, and is calibrated on a subset (12 days) of the dataset. The other 12 days in the dataset are used to validate the algorithm. Both calibration and validation are done using gauge-adjusted radar data

  16. Myosin lever arm directs collective motion on cellular actin network

    PubMed Central

    Hariadi, Rizal F.; Cale, Mario; Sivaramakrishnan, Sivaraj

    2014-01-01

    The molecular motor myosin teams up to drive muscle contraction, membrane traffic, and cell division in biological cells. Myosin function in cells emerges from the interaction of multiple motors tethered to a scaffold, with surrounding actin filaments organized into 3D networks. Despite the importance of myosin function, the influence of intermotor interactions on collective motion remains poorly understood. In this study, we used precisely engineered myosin assemblies to examine emergence in collective myosin movement. We report that tethering multiple myosin VI motors, but not myosin V motors, modifies their movement trajectories on keratocyte actin networks. Single myosin V and VI dimers display similar skewed trajectories, albeit in opposite directions, when traversing the keratocyte actin network. In contrast, tethering myosin VI motors, but not myosin V motors, progressively straightens the trajectories with increasing myosin number. Trajectory shape of multimotor scaffolds positively correlates with the stiffness of the myosin lever arm. Swapping the flexible myosin VI lever arm for the relatively rigid myosin V lever increases trajectory skewness, and vice versa. A simplified model of coupled motor movement demonstrates that the differences in flexural rigidity of the two myosin lever arms is sufficient to account for the differences in observed behavior of groups of myosin V and VI motors. In accordance with this model trajectory, shapes for scaffolds containing both myosin V and VI are dominated by the myosin with a stiffer lever arm. Our findings suggest that structural features unique to each myosin type may confer selective advantages in cellular functions. PMID:24591646

  17. A rigorous framework for multiscale simulation of stochastic cellular networks

    PubMed Central

    Chevalier, Michael W.; El-Samad, Hana

    2009-01-01

    Noise and stochasticity are fundamental to biology and derive from the very nature of biochemical reactions where thermal motion of molecules translates into randomness in the sequence and timing of reactions. This randomness leads to cell-cell variability even in clonal populations. Stochastic biochemical networks are modeled as continuous time discrete state Markov processes whose probability density functions evolve according to a chemical master equation (CME). The CME is not solvable but for the simplest cases, and one has to resort to kinetic Monte Carlo techniques to simulate the stochastic trajectories of the biochemical network under study. A commonly used such algorithm is the stochastic simulation algorithm (SSA). Because it tracks every biochemical reaction that occurs in a given system, the SSA presents computational difficulties especially when there is a vast disparity in the timescales of the reactions or in the number of molecules involved in these reactions. This is common in cellular networks, and many approximation algorithms have evolved to alleviate the computational burdens of the SSA. Here, we present a rigorously derived modified CME framework based on the partition of a biochemically reacting system into restricted and unrestricted reactions. Although this modified CME decomposition is as analytically difficult as the original CME, it can be naturally used to generate a hierarchy of approximations at different levels of accuracy. Most importantly, some previously derived algorithms are demonstrated to be limiting cases of our formulation. We apply our methods to biologically relevant test systems to demonstrate their accuracy and efficiency. PMID:19673546

  18. Myosin lever arm directs collective motion on cellular actin network.

    PubMed

    Hariadi, Rizal F; Cale, Mario; Sivaramakrishnan, Sivaraj

    2014-03-18

    The molecular motor myosin teams up to drive muscle contraction, membrane traffic, and cell division in biological cells. Myosin function in cells emerges from the interaction of multiple motors tethered to a scaffold, with surrounding actin filaments organized into 3D networks. Despite the importance of myosin function, the influence of intermotor interactions on collective motion remains poorly understood. In this study, we used precisely engineered myosin assemblies to examine emergence in collective myosin movement. We report that tethering multiple myosin VI motors, but not myosin V motors, modifies their movement trajectories on keratocyte actin networks. Single myosin V and VI dimers display similar skewed trajectories, albeit in opposite directions, when traversing the keratocyte actin network. In contrast, tethering myosin VI motors, but not myosin V motors, progressively straightens the trajectories with increasing myosin number. Trajectory shape of multimotor scaffolds positively correlates with the stiffness of the myosin lever arm. Swapping the flexible myosin VI lever arm for the relatively rigid myosin V lever increases trajectory skewness, and vice versa. A simplified model of coupled motor movement demonstrates that the differences in flexural rigidity of the two myosin lever arms is sufficient to account for the differences in observed behavior of groups of myosin V and VI motors. In accordance with this model trajectory, shapes for scaffolds containing both myosin V and VI are dominated by the myosin with a stiffer lever arm. Our findings suggest that structural features unique to each myosin type may confer selective advantages in cellular functions.

  19. Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints.

    PubMed

    Chakrabarti, Anirikh; Miskovic, Ljubisa; Soh, Keng Cher; Hatzimanikatis, Vassily

    2013-09-01

    Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.

  20. TFEB controls cellular lipid metabolism through a starvation-induced autoregulatory loop.

    PubMed

    Settembre, Carmine; De Cegli, Rossella; Mansueto, Gelsomina; Saha, Pradip K; Vetrini, Francesco; Visvikis, Orane; Huynh, Tuong; Carissimo, Annamaria; Palmer, Donna; Klisch, Tiemo Jürgen; Wollenberg, Amanda C; Di Bernardo, Diego; Chan, Lawrence; Irazoqui, Javier E; Ballabio, Andrea

    2013-06-01

    The lysosomal-autophagic pathway is activated by starvation and plays an important role in both cellular clearance and lipid catabolism. However, the transcriptional regulation of this pathway in response to metabolic cues is uncharacterized. Here we show that the transcription factor EB (TFEB), a master regulator of lysosomal biogenesis and autophagy, is induced by starvation through an autoregulatory feedback loop and exerts a global transcriptional control on lipid catabolism via Ppargc1α and Ppar1α. Thus, during starvation a transcriptional mechanism links the autophagic pathway to cellular energy metabolism. The conservation of this mechanism in Caenorhabditis elegans suggests a fundamental role for TFEB in the evolution of the adaptive response to food deprivation. Viral delivery of TFEB to the liver prevented weight gain and metabolic syndrome in both diet-induced and genetic mouse models of obesity, suggesting a new therapeutic strategy for disorders of lipid metabolism.

  1. Mitochondrial Mg2+ homeostasis decides cellular energy metabolism and vulnerability to stress

    PubMed Central

    Yamanaka, Ryu; Tabata, Sho; Shindo, Yutaka; Hotta, Kohji; Suzuki, Koji; Soga, Tomoyoshi; Oka, Kotaro

    2016-01-01

    Cellular energy production processes are composed of many Mg2+ dependent enzymatic reactions. In fact, dysregulation of Mg2+ homeostasis is involved in various cellular malfunctions and diseases. Recently, mitochondria, energy-producing organelles, have been known as major intracellular Mg2+ stores. Several biological stimuli alter mitochondrial Mg2+ concentration by intracellular redistribution. However, in living cells, whether mitochondrial Mg2+ alteration affect cellular energy metabolism remains unclear. Mg2+ transporter of mitochondrial inner membrane MRS2 is an essential component of mitochondrial Mg2+ uptake system. Here, we comprehensively analyzed intracellular Mg2+ levels and energy metabolism in Mrs2 knockdown (KD) cells using fluorescence imaging and metabolome analysis. Dysregulation of mitochondrial Mg2+ homeostasis disrupted ATP production via shift of mitochondrial energy metabolism and morphology. Moreover, Mrs2 KD sensitized cellular tolerance against cellular stress. These results indicate regulation of mitochondrial Mg2+ via MRS2 critically decides cellular energy status and cell vulnerability via regulation of mitochondrial Mg2+ level in response to physiological stimuli. PMID:27458051

  2. Mitochondrial Mg(2+) homeostasis decides cellular energy metabolism and vulnerability to stress.

    PubMed

    Yamanaka, Ryu; Tabata, Sho; Shindo, Yutaka; Hotta, Kohji; Suzuki, Koji; Soga, Tomoyoshi; Oka, Kotaro

    2016-01-01

    Cellular energy production processes are composed of many Mg(2+) dependent enzymatic reactions. In fact, dysregulation of Mg(2+) homeostasis is involved in various cellular malfunctions and diseases. Recently, mitochondria, energy-producing organelles, have been known as major intracellular Mg(2+) stores. Several biological stimuli alter mitochondrial Mg(2+) concentration by intracellular redistribution. However, in living cells, whether mitochondrial Mg(2+) alteration affect cellular energy metabolism remains unclear. Mg(2+) transporter of mitochondrial inner membrane MRS2 is an essential component of mitochondrial Mg(2+) uptake system. Here, we comprehensively analyzed intracellular Mg(2+) levels and energy metabolism in Mrs2 knockdown (KD) cells using fluorescence imaging and metabolome analysis. Dysregulation of mitochondrial Mg(2+) homeostasis disrupted ATP production via shift of mitochondrial energy metabolism and morphology. Moreover, Mrs2 KD sensitized cellular tolerance against cellular stress. These results indicate regulation of mitochondrial Mg(2+) via MRS2 critically decides cellular energy status and cell vulnerability via regulation of mitochondrial Mg(2+) level in response to physiological stimuli. PMID:27458051

  3. Steady states and stability in metabolic networks without regulation.

    PubMed

    Ivanov, Oleksandr; van der Schaft, Arjan; Weissing, Franz J

    2016-07-21

    Metabolic networks are often extremely complex. Despite intensive efforts many details of these networks, e.g., exact kinetic rates and parameters of metabolic reactions, are not known, making it difficult to derive their properties. Considerable effort has been made to develop theory about properties of steady states in metabolic networks that are valid for any values of parameters. General results on uniqueness of steady states and their stability have been derived with specific assumptions on reaction kinetics, stoichiometry and network topology. For example, deep results have been obtained under the assumptions of mass-action reaction kinetics, continuous flow stirred tank reactors (CFSTR), concordant reaction networks and others. Nevertheless, a general theory about properties of steady states in metabolic networks is still missing. Here we make a step further in the quest for such a theory. Specifically, we study properties of steady states in metabolic networks with monotonic kinetics in relation to their stoichiometry (simple and general) and the number of metabolites participating in every reaction (single or many). Our approach is based on the investigation of properties of the Jacobian matrix. We show that stoichiometry, network topology, and the number of metabolites that participate in every reaction have a large influence on the number of steady states and their stability in metabolic networks. Specifically, metabolic networks with single-substrate-single-product reactions have disconnected steady states, whereas in metabolic networks with multiple-substrates-multiple-product reactions manifolds of steady states arise. Metabolic networks with simple stoichiometry have either a unique globally asymptotically stable steady state or asymptotically stable manifolds of steady states. In metabolic networks with general stoichiometry the steady states are not always stable and we provide conditions for their stability. In order to demonstrate the biological

  4. Steady states and stability in metabolic networks without regulation.

    PubMed

    Ivanov, Oleksandr; van der Schaft, Arjan; Weissing, Franz J

    2016-07-21

    Metabolic networks are often extremely complex. Despite intensive efforts many details of these networks, e.g., exact kinetic rates and parameters of metabolic reactions, are not known, making it difficult to derive their properties. Considerable effort has been made to develop theory about properties of steady states in metabolic networks that are valid for any values of parameters. General results on uniqueness of steady states and their stability have been derived with specific assumptions on reaction kinetics, stoichiometry and network topology. For example, deep results have been obtained under the assumptions of mass-action reaction kinetics, continuous flow stirred tank reactors (CFSTR), concordant reaction networks and others. Nevertheless, a general theory about properties of steady states in metabolic networks is still missing. Here we make a step further in the quest for such a theory. Specifically, we study properties of steady states in metabolic networks with monotonic kinetics in relation to their stoichiometry (simple and general) and the number of metabolites participating in every reaction (single or many). Our approach is based on the investigation of properties of the Jacobian matrix. We show that stoichiometry, network topology, and the number of metabolites that participate in every reaction have a large influence on the number of steady states and their stability in metabolic networks. Specifically, metabolic networks with single-substrate-single-product reactions have disconnected steady states, whereas in metabolic networks with multiple-substrates-multiple-product reactions manifolds of steady states arise. Metabolic networks with simple stoichiometry have either a unique globally asymptotically stable steady state or asymptotically stable manifolds of steady states. In metabolic networks with general stoichiometry the steady states are not always stable and we provide conditions for their stability. In order to demonstrate the biological

  5. A guide to integrating transcriptional regulatory and metabolic networks using PROM (probabilistic regulation of metabolism).

    PubMed

    Simeonidis, Evangelos; Chandrasekaran, Sriram; Price, Nathan D

    2013-01-01

    The integration of transcriptional regulatory and metabolic networks is a crucial step in the process of predicting metabolic behaviors that emerge from either genetic or environmental changes. Here, we present a guide to PROM (probabilistic regulation of metabolism), an automated method for the construction and simulation of integrated metabolic and transcriptional regulatory networks that enables large-scale phenotypic predictions for a wide range of model organisms.

  6. Mesoscale assembly of chemically modified graphene into complex cellular networks

    NASA Astrophysics Data System (ADS)

    Barg, Suelen; Perez, Felipe Macul; Ni, Na; Do Vale Pereira, Paula; Maher, Robert C.; Garcia-Tuñon, Esther; Eslava, Salvador; Agnoli, Stefano; Mattevi, Cecilia; Saiz, Eduardo

    2014-07-01

    The widespread technological introduction of graphene beyond electronics rests on our ability to assemble this two-dimensional building block into three-dimensional structures for practical devices. To achieve this goal we need fabrication approaches that are able to provide an accurate control of chemistry and architecture from nano to macroscopic levels. Here, we describe a versatile technique to build ultralight (density ≥1 mg cm-3) cellular networks based on the use of soft templates and the controlled segregation of chemically modified graphene to liquid interfaces. These novel structures can be tuned for excellent conductivity; versatile mechanical response (elastic-brittle to elastomeric, reversible deformation, high energy absorption) and organic absorption capabilities (above 600 g per gram of material). The approach can be used to uncover the basic principles that will guide the design of practical devices that by combining unique mechanical and functional performance will generate new technological opportunities.

  7. Mesoscale assembly of chemically modified graphene into complex cellular networks

    PubMed Central

    Barg, Suelen; Perez, Felipe Macul; Ni, Na; do Vale Pereira, Paula; Maher, Robert C.; Garcia-Tuñon, Esther; Eslava, Salvador; Agnoli, Stefano; Mattevi, Cecilia; Saiz, Eduardo

    2014-01-01

    The widespread technological introduction of graphene beyond electronics rests on our ability to assemble this two-dimensional building block into three-dimensional structures for practical devices. To achieve this goal we need fabrication approaches that are able to provide an accurate control of chemistry and architecture from nano to macroscopic levels. Here, we describe a versatile technique to build ultralight (density ≥1 mg cm−3) cellular networks based on the use of soft templates and the controlled segregation of chemically modified graphene to liquid interfaces. These novel structures can be tuned for excellent conductivity; versatile mechanical response (elastic-brittle to elastomeric, reversible deformation, high energy absorption) and organic absorption capabilities (above 600 g per gram of material). The approach can be used to uncover the basic principles that will guide the design of practical devices that by combining unique mechanical and functional performance will generate new technological opportunities. PMID:24999766

  8. AMPK: A cellular metabolic and redox sensor. A minireview

    PubMed Central

    Shirwany, Najeeb A; Zou, Ming-Hui

    2014-01-01

    AMPK is a serine/threonine kinase that is found in all eukaryotes and is ubiquitously expressed in all organ systems. Once activated, AMPK stimulates hepatic fatty acid oxidation and ketogenesis, inhibits cholesterol synthesis, lipogenesis, and triglyceride synthesis, inhibits adipocyte lipolysis and lipogenesis, stimulates skeletal muscle fatty acid oxidation and muscle glucose uptake, and modulates insulin secretion by the pancreas. Thus its importance in many critical cellular processes is well established. For cells it is critical that energy supply and demand are closely matched. AMPK is recognized as a critical integrator of this balance. It is known to be allosterically activated by an increased AMP:ATP ratio. Activation of the kinase switches on catabolic pathways while switching off anabolic ones. It also acts as a redox sensor in endothelial cells where oxidative stress can disturb NO signaling. Abnormal NO signaling leads to disturbed vasodilatory responses. By inhibiting the formation of reactive oxygen species in the endothelium, AMPK can optimize the redox balance in the vasculature. Here, we review the role of AMPK in the cell. PMID:24389195

  9. Country-wide rainfall maps from cellular communication networks

    NASA Astrophysics Data System (ADS)

    Leijnse, Hidde; Overeem, Aart; Uijlenhoet, Remko

    2013-04-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for hydrological applications, agriculture, meteorology, and climate monitoring. However, the majority of the land surface of the earth lacks accurate rainfall information and the number of rain gauges is even severely declining in Europe, South-America, and Africa. This calls for alternative sources of rainfall information. Various studies have shown that microwave links from operational cellular telecommunication networks may be employed for rainfall monitoring. Such networks cover 20% of the land surface of the earth and have a high density, especially in urban areas. The basic principle of rainfall monitoring using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated. Previous studies have shown that average rainfall intensities over the length of a link can be derived from the path-integrated attenuation. Here we show how one cellular telecommunication network can be used to retrieve the space-time dynamics of rainfall for an entire country. A dataset from a commercial microwave link network over the Netherlands is analyzed, containing data from an unprecedented number of links (2400) covering the land surface of the Netherlands (35500 km2). This dataset consists of 24 days with substantial rainfall in June - September 2011. A rainfall retrieval algorithm is presented to derive rainfall intensities from the microwave link data, which have a temporal resolution of 15 min. Rainfall maps (1 km spatial resolution) are generated from these rainfall intensities using Kriging. This algorithm is suited for real-time application, and is calibrated on a subset (12 days) of the dataset. The other 12 days in the dataset are used to validate the algorithm. Both

  10. The Crossroads of Iron with Hypoxia and Cellular Metabolism. Implications in the Pathobiology of Pulmonary Hypertension

    PubMed Central

    Graham, Brian B.; Rouault, Tracey C.; Tuder, Rubin M.

    2014-01-01

    The pathologic hallmark of pulmonary arterial hypertension (PAH) is pulmonary vascular remodeling, characterized by endothelial cell proliferation, smooth muscle hypertrophy, and perivascular inflammation, ultimately contributing to increased pulmonary arterial pressures. Several recent studies have observed that iron deficiency in patients with various forms of PAH is associated with worsened clinical outcome. Iron plays a key role in many cellular processes regulating the response to hypoxia, oxidative stress, cellular proliferation, and cell metabolism. Given the potential importance of iron supplementation in patients with the disease and the broad cellular functions of iron, we review its role in processes that pertain to PAH. PMID:24988529

  11. GIM3E: Condition-specific Models of Cellular Metabolism Developed from Metabolomics and Expression Data

    SciTech Connect

    Schmidt, Brian; Ebrahim, Ali; Metz, Thomas O.; Adkins, Joshua N.; Palsson, Bernard O.; Hyduke, Daniel R.

    2013-11-15

    Motivation: Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been reported. Results: GIMMME (Gene Inactivation Moderated by Metabolism, Metabolomics, and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics, and intracellular metabolomics data. GIMMME establishes metabolite utilization requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions, and also provides calculations of the turnover (production / consumption) flux of metabolites. GIMMME was employed to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. Availability: GIMMME has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/

  12. Correlation between structure and temperature in prokaryotic metabolic networks

    PubMed Central

    Takemoto, Kazuhiro; Nacher, Jose C; Akutsu, Tatsuya

    2007-01-01

    Background In recent years, an extensive characterization of network structures has been made in an effort to elucidate design principles of metabolic networks, providing valuable insights into the functional organization and the evolutionary history of organisms. However, previous analyses have not discussed the effects of environmental factors (i.e., exogenous forces) in shaping network structures. In this work, we investigate the effect of temperature, which is one of the environmental factors that may have contributed to shaping structures of metabolic networks. Results For this, we investigate the correlations between several structural properties characterized by graph metrics like the edge density, the degree exponent, the clustering coefficient, and the subgraph concentration in the metabolic networks of 113 prokaryotes and optimal growth temperature. As a result, we find that these structural properties are correlated with the optimal growth temperature. With increasing temperature, the edge density, the clustering coefficient and the subgraph concentration decrease and the degree exponent becomes large. Conclusion This result implies that the metabolic networks transit with temperature as follows. The density of chemical reactions becomes low, the connectivity of the networks becomes homogeneous such as random networks and both the network modularity, based on the graph-theoretic clustering coefficient, and the frequency of recurring subgraphs decay. In short, metabolic networks undergo a change from heterogeneous and high-modular structures to homogeneous and low-modular structures, such as random networks, with temperature. This finding may suggest that the temperature plays an important role in the design principles of metabolic networks. PMID:17711568

  13. SIRT3 regulates cellular iron metabolism and cancer growth by repressing iron regulatory protein 1.

    PubMed

    Jeong, S M; Lee, J; Finley, L W S; Schmidt, P J; Fleming, M D; Haigis, M C

    2015-04-16

    Iron metabolism is essential for many cellular processes, including oxygen transport, respiration and DNA synthesis, and many cancer cells exhibit dysregulation in iron metabolism. Maintenance of cellular iron homeostasis is regulated by iron regulatory proteins (IRPs), which control the expression of iron-related genes by binding iron-responsive elements (IREs) of target mRNAs. Here, we report that mitochondrial SIRT3 regulates cellular iron metabolism by modulating IRP1 activity. SIRT3 loss increases reactive oxygen species production, leading to elevated IRP1 binding to IREs. As a consequence, IRP1 target genes, such as the transferrin receptor (TfR1), a membrane-associated glycoprotein critical for iron uptake and cell proliferation, are controlled by SIRT3. Importantly, SIRT3 deficiency results in a defect in cellular iron homeostasis. SIRT3 null cells contain high levels of iron and lose iron-dependent TfR1 regulation. Moreover, SIRT3 null mice exhibit higher levels of iron and TfR1 expression in the pancreas. We found that the regulation of iron uptake and TfR1 expression contribute to the tumor-suppressive activity of SIRT3. Indeed, SIRT3 expression is negatively correlated with TfR1 expression in human pancreatic cancers. SIRT3 overexpression decreases TfR1 expression by inhibiting IRP1 and represses proliferation in pancreatic cancer cells. Our data uncover a novel role of SIRT3 in cellular iron metabolism through IRP1 regulation and suggest that SIRT3 functions as a tumor suppressor, in part, by modulating cellular iron metabolism. PMID:24909164

  14. Deciphering transcriptional and metabolic networks associated with lysine metabolism during Arabidopsis seed development.

    PubMed

    Angelovici, Ruthie; Fait, Aaron; Zhu, Xiaohong; Szymanski, Jedrzej; Feldmesser, Ester; Fernie, Alisdair R; Galili, Gad

    2009-12-01

    In order to elucidate transcriptional and metabolic networks associated with lysine (Lys) metabolism, we utilized developing Arabidopsis (Arabidopsis thaliana) seeds as a system in which Lys synthesis could be stimulated developmentally without application of chemicals and coupled this to a T-DNA insertion knockout mutation impaired in Lys catabolism. This seed-specific metabolic perturbation stimulated Lys accumulation starting from the initiation of storage reserve accumulation. Our results revealed that the response of seed metabolism to the inducible alteration of Lys metabolism was relatively minor; however, that which was observable operated in a modular manner. They also demonstrated that Lys metabolism is strongly associated with the operation of the tricarboxylic acid cycle while largely disconnected from other metabolic networks. In contrast, the inducible alteration of Lys metabolism was strongly associated with gene networks, stimulating the expression of hundreds of genes controlling anabolic processes that are associated with plant performance and vigor while suppressing a small number of genes associated with plant stress interactions. The most pronounced effect of the developmentally inducible alteration of Lys metabolism was an induction of expression of a large set of genes encoding ribosomal proteins as well as genes encoding translation initiation and elongation factors, all of which are associated with protein synthesis. With respect to metabolic regulation, the inducible alteration of Lys metabolism was primarily associated with altered expression of genes belonging to networks of amino acids and sugar metabolism. The combined data are discussed within the context of network interactions both between and within metabolic and transcriptional control systems.

  15. Effect of soluble nickel on cellular energy metabolism in A549 cells.

    PubMed

    Chen, Haobin; Costa, Max

    2006-10-01

    Iron is an essential nutrient to most organisms, and is actively involved in oxygen delivery, electron transport, DNA synthesis, and many other biochemical reactions important for cell survival. We previously reported that nickel (Ni) ion exposure decreases cellular iron level and converts cytosolic aconitase (c-aconitase) to iron-regulatory protein-1 in A549 cells (Chen H, Davidson T, Singleton S, Garrick MD, Costa M. Toxicol Appl Pharmacol 206:275-287, 2005). Here, we further investigated the effect of Ni ion exposure on the activity of mitochondrial iron-sulfur (Fe-S) enzymes and cellular energy metabolism. We found that acute Ni ion treatment up to 1 mM exhibits minimal toxicity in A549 cells. Ni ion treatment decreases the activity of several Fe-S enzymes related to cellular energy metabolism, including mitochondrial aconitase (m-aconitase), succinate dehydrogenase (SDH), and NADH:ubiquinone oxidoreductase (complex I). Low doses of Ni ion for 4 weeks resulted in an increased cellular glycolysis and NADH to NAD+ (NADH/NAD+) ratio, although glycolysis was inhibited at higher levels. Collectively, our results show that Ni ions decrease the activity of cellular iron (Fe)-containing enzymes, inhibit oxidative phosphorylation (OxPhos), and increase cellular glycolytic activity. Since increased glycolysis is one of the fundamental alterations of energy metabolism in cancer cells (the Warburg effect), the inhibition of Fe-S enzymes and subsequent changes in cellular energy metabolism caused by Ni ions may play an important role in Ni carcinogenesis. PMID:17018869

  16. Astroglial Metabolic Networks Sustain Hippocampal Synaptic Transmission

    NASA Astrophysics Data System (ADS)

    Rouach, Nathalie; Koulakoff, Annette; Abudara, Veronica; Willecke, Klaus; Giaume, Christian

    2008-12-01

    Astrocytes provide metabolic substrates to neurons in an activity-dependent manner. However, the molecular mechanisms involved in this function, as well as its role in synaptic transmission, remain unclear. Here, we show that the gap-junction subunit proteins connexin 43 and 30 allow intercellular trafficking of glucose and its metabolites through astroglial networks. This trafficking is regulated by glutamatergic synaptic activity mediated by AMPA receptors. In the absence of extracellular glucose, the delivery of glucose or lactate to astrocytes sustains glutamatergic synaptic transmission and epileptiform activity only when they are connected by gap junctions. These results indicate that astroglial gap junctions provide an activity-dependent intercellular pathway for the delivery of energetic metabolites from blood vessels to distal neurons.

  17. Video sequence compression via supervised training on cellular neural networks.

    PubMed

    Rodríguez, L; Zufiria, P J; Berzal, J A

    1997-02-01

    In this paper, a novel approach for video sequence compression using Cellular Neural Networks (CNN's) is presented. CNN's are nets characterized by local interconnections between neurons (usually called cells), and can be modeled as dynamical systems. From among many different types, a CNN model operating in discrete-time (DT-CNN) has been chosen, its parameters being defined so that they are shared among all the cells in the network. The compression process proposed in this work is based on the possibility of replicating a given video sequence as a trajectory generated by the DT-CNN. In order for the CNN to follow a prescribed trajectory, a supervised training algorithm is implemented. Compression is achieved due to the fact that all the information contained in the sequence can be stored into a small number of parameters and initial conditions once training is stopped. Different improvements upon the basic formulation are analyzed and issues such as feasibility and complexity of the compression problem are also addressed. Finally, some examples with real video sequences illustrate the applicability of the method.

  18. Cellular uptake and metabolism of curcuminoids in monocytes/macrophages: regulatory effects on lipid accumulation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    We previously showed that curcumin (CUR) may increase lipid accumulation in cultured THP-1 monocytes/macrophages, but tetrahydrocurcumin (THC), an in vivo metabolite of CUR, had no such effect. In the present study, we have hypothesized that different cellular uptake and/or metabolism of CUR and THC...

  19. Inhibition of HIV by Legalon-SIL is independent of its effect on cellular metabolism

    SciTech Connect

    McClure, Janela; Margineantu, Daciana H.; Sweet, Ian R.; Polyak, Stephen J.

    2014-01-20

    In this report, we further characterized the effects of silibinin (SbN), derived from milk thistle extract, and Legalon-SIL (SIL), a water-soluble derivative of SbN, on T cell metabolism and HIV infection. We assessed the effects of SbN and SIL on peripheral blood mononuclear cells (PBMC) and CEM-T4 cells in terms of cellular growth, ATP content, metabolism, and HIV infection. SIL and SbN caused a rapid and reversible (upon removal) decrease in cellular ATP levels, which was associated with suppression of mitochondrial respiration and glycolysis. SbN, but not SIL inhibited glucose uptake. Exposure of T cells to SIL (but not SbN or metabolic inhibitors) during virus adsorption blocked HIV infection. Thus, both SbN and SIL rapidly perturb T cell metabolism in vitro, which may account for its anti-inflammatory and anti-proliferative effects that arise with prolonged exposure of cells. However, the metabolic effects are not involved in SIL's unique ability to block HIV entry. - Highlights: • Silibinin (SbN) and Legalon-SIL (SIL) are cytoprotective mixtures of natural products. • SbN and SIL reduce T cell oxidative phosphorylation and glycolysis in vitro. • SIL but not SbN blocks entry of multiple HIV isolates into T cells in vitro. • SIL's suppression of HIV appears independent of its effects on T cell metabolism. • Metabolic effects of SIL and SbN may be relevant in inflammatory diseases.

  20. Environmental versatility promotes modularity in genome-scale metabolic networks

    PubMed Central

    2011-01-01

    Background The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Results Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Conclusions Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to

  1. Rainfall maps from cellular communication networks: Assessing uncertainties

    NASA Astrophysics Data System (ADS)

    Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2014-05-01

    Several studies show the potential applicability of commercial cellular communication networks in the retrieval of rainfall fields, sometimes even for an entire country. The key principle of rainfall monitoring using microwave links is based on the attenuation, due to rainfall, of the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link, as a function of time, the path-averaged rainfall intensity can be estimated. This study focuses on the quality of country-wide rainfall maps derived from commercial microwave link data compared to a quality-controlled gauge-adjusted radar rainfall data set, considered as ground-truth. Part of the differences can be attributed to the interpolation methodology, as well as to the much higher spatial resolution (≡38.000 pixels of 0.9 by 0.9 km2) of the radar data compared to the relatively low density of the microwave link network (≡1700 microwave links with an average length of 3.1 km). The magnitude of these factors is assessed by simulating microwave link rainfall depths from the radar rainfall data set. The Ordinary-Kriging (OK) methodology is used to obtain rainfall maps based on the simulated and real microwave link data. This work quantifies what percentage of the errors in link-based rainfall maps can be attributed to the interpolation methodology itself and the limited spatial density of the microwave link network. Moreover, the spatial distribution of the error in rainfall maps is quantified in relation to the spatial density and temporally variable availability of links, which is highly relevant since the microwave link data are non-uniformly distributed in space or time. Finally, the applicability of the OK-methodology is tested over Dutch areas with different spatial densities of commercial microwave links.

  2. Sources of uncertainty in rainfall maps from cellular communication networks

    NASA Astrophysics Data System (ADS)

    Rios Gaona, M. F.; Overeem, A.; Leijnse, H.; Uijlenhoet, R.

    2015-03-01

    ground truth). Thus, we were able to not only identify and quantify the sources of uncertainty in such rainfall maps, but also to test the actual and optimal performance of one commercial microwave network from one of the cellular providers in the Netherlands.

  3. Matching metabolites and reactions in different metabolic networks.

    PubMed

    Qi, Xinjian; Ozsoyoglu, Z Meral; Ozsoyoglu, Gultekin

    2014-10-01

    Comparing and identifying matching metabolites, reactions, and compartments in genome-scale reconstructed metabolic networks can be difficult due to inconsistent naming in different networks. In this paper, we propose metabolite and reaction matching techniques for matching metabolites and reactions in a given metabolic network to metabolites and reactions in another metabolic network. We employ a variety of techniques that include approximate string matching, similarity score functions and multi-step filtering techniques, all enhanced by a set of rules based on the underlying metabolic biochemistry. The proposed techniques are evaluated by an empirical study on four pairs of metabolic networks, and significant accuracy gains are achieved using the proposed metabolite and reaction identification techniques.

  4. TMEM55B is a Novel Regulator of Cellular Cholesterol Metabolism

    PubMed Central

    Medina, Marisa W.; Bauzon, Frederick; Naidoo, Devesh; Theusch, Elizabeth; Stevens, Kristen; Schilde, Jessica; Schubert, Christian; Mangravite, Lara M.; Rudel, Lawrence L.; Temel, Ryan E.; Runz, Heiko; Krauss, Ronald M.

    2014-01-01

    Objective Inter-individual variation in pathways impacting cellular cholesterol metabolism can influence levels of plasma cholesterol, a well-established risk factor for cardiovascular disease. Inherent variation among immortalized lymphoblastoid cell lines (LCLs) from different donors can be leveraged to discover novel genes that modulate cellular cholesterol metabolism. The objective of this study was to identify novel genes that regulate cholesterol metabolism by testing for evidence of correlated gene expression with cellular levels of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) mRNA, a marker for cellular cholesterol homeostasis, in a large panel of LCLs. Approach and Results Expression array profiling was performed on 480 LCLs established from participants of the Cholesterol and Pharmacogenetics statin clinical trial, and transcripts were tested for evidence of correlated expression with HMGCR as a marker of intracellular cholesterol homeostasis. Of these, transmembrane protein 55b (TMEM55B) showed the strongest correlation (r=0.29, p=4.0E-08) of all genes not previously implicated in cholesterol metabolism and was found to be sterol regulated. TMEM55B knock-down in human hepatoma cell lines promoted the decay rate of the low density lipoprotein receptor (LDLR), reduced cell surface LDLR protein, impaired LDL uptake, and reduced intracellular cholesterol. Conclusions Here we report identification of TMEM55B as a novel regulator of cellular cholesterol metabolism through the combination of gene expression profiling and functional studies. The findings highlight the value of an integrated genomic approach for identifying genes that influence cholesterol homeostasis. PMID:25035345

  5. The lysosome as a command-and-control center for cellular metabolism.

    PubMed

    Lim, Chun-Yan; Zoncu, Roberto

    2016-09-12

    Lysosomes are membrane-bound organelles found in every eukaryotic cell. They are widely known as terminal catabolic stations that rid cells of waste products and scavenge metabolic building blocks that sustain essential biosynthetic reactions during starvation. In recent years, this classical view has been dramatically expanded by the discovery of new roles of the lysosome in nutrient sensing, transcriptional regulation, and metabolic homeostasis. These discoveries have elevated the lysosome to a decision-making center involved in the control of cellular growth and survival. Here we review these recently discovered properties of the lysosome, with a focus on how lysosomal signaling pathways respond to external and internal cues and how they ultimately enable metabolic homeostasis and cellular adaptation. PMID:27621362

  6. Perspectives in metabolic engineering: understanding cellular regulation towards the control of metabolic routes.

    PubMed

    Zadran, Sohila; Levine, Raphael D

    2013-01-01

    Metabolic engineering seeks to redirect metabolic pathways through the modification of specific biochemical reactions or the introduction of new ones with the use of recombinant technology. Many of the chemicals synthesized via introduction of product-specific enzymes or the reconstruction of entire metabolic pathways into engineered hosts that can sustain production and can synthesize high yields of the desired product as yields of natural product-derived compounds are frequently low, and chemical processes can be both energy and material expensive; current endeavors have focused on using biologically derived processes as alternatives to chemical synthesis. Such economically favorable manufacturing processes pursue goals related to sustainable development and "green chemistry". Metabolic engineering is a multidisciplinary approach, involving chemical engineering, molecular biology, biochemistry, and analytical chemistry. Recent advances in molecular biology, genome-scale models, theoretical understanding, and kinetic modeling has increased interest in using metabolic engineering to redirect metabolic fluxes for industrial and therapeutic purposes. The use of metabolic engineering has increased the productivity of industrially pertinent small molecules, alcohol-based biofuels, and biodiesel. Here, we highlight developments in the practical and theoretical strategies and technologies available for the metabolic engineering of simple systems and address current limitations.

  7. GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data

    PubMed Central

    Schmidt, Brian J.; Ebrahim, Ali; Metz, Thomas O.; Adkins, Joshua N.; Palsson, Bernhard Ø.; Hyduke, Daniel R.

    2013-01-01

    Motivation: Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed. Results: GIM3E (Gene Inactivation Moderated by Metabolism, Metabolomics and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics and cellular metabolomics data. GIM3E establishes metabolite use requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions and provides calculations of the turnover (production/consumption) flux of metabolites. GIM3E was used to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. Availability: GIM3E has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/ Contacts: brianjamesschmidt@gmail.com or hyduke@usu.edu Supplementary information: Supplementary information is available at Bioinformatics online. PMID:23975765

  8. Environmental Monitoring using Measurements from Cellular Network Infrastructure

    NASA Astrophysics Data System (ADS)

    David, N.; Gao, O. H.

    2015-12-01

    atmospheric phenomena using current and future planned frequencies of cellular network infrastructure will be introduced.

  9. Knowledge-based generalization of metabolic networks: a practical study.

    PubMed

    Zhukova, Anna; Sherman, David J

    2014-04-01

    The complex process of genome-scale metabolic network reconstruction involves semi-automatic reaction inference, analysis, and refinement through curation by human experts. Unfortunately, decisions by experts are hampered by the complexity of the network, which can mask errors in the inferred network. In order to aid an expert in making sense out of the thousands of reactions in the organism's metabolism, we developed a method for knowledge-based generalization that provides a higher-level view of the network, highlighting the particularities and essential structure, while hiding the details. In this study, we show the application of this generalization method to 1,286 metabolic networks of organisms in Path2Models that describe fatty acid metabolism. We compare the generalised networks and show that we successfully highlight the aspects that are important for their curation and comparison. PMID:24712528

  10. Knowledge-based generalization of metabolic networks: a practical study.

    PubMed

    Zhukova, Anna; Sherman, David J

    2014-04-01

    The complex process of genome-scale metabolic network reconstruction involves semi-automatic reaction inference, analysis, and refinement through curation by human experts. Unfortunately, decisions by experts are hampered by the complexity of the network, which can mask errors in the inferred network. In order to aid an expert in making sense out of the thousands of reactions in the organism's metabolism, we developed a method for knowledge-based generalization that provides a higher-level view of the network, highlighting the particularities and essential structure, while hiding the details. In this study, we show the application of this generalization method to 1,286 metabolic networks of organisms in Path2Models that describe fatty acid metabolism. We compare the generalised networks and show that we successfully highlight the aspects that are important for their curation and comparison.

  11. Fluxes through plant metabolic networks: measurements, predictions, insights and challenges.

    PubMed

    Kruger, Nicholas J; Ratcliffe, R George

    2015-01-01

    Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

  12. Path finding methods accounting for stoichiometry in metabolic networks.

    PubMed

    Pey, Jon; Prada, Joaquín; Beasley, John E; Planes, Francisco J

    2011-01-01

    Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks. PMID:21619601

  13. Path finding methods accounting for stoichiometry in metabolic networks

    PubMed Central

    2011-01-01

    Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks. PMID:21619601

  14. The Aryl Hydrocarbon Receptor Relays Metabolic Signals to Promote Cellular Regeneration

    PubMed Central

    2016-01-01

    While sensing the cell environment, the aryl hydrocarbon receptor (AHR) interacts with different pathways involved in cellular homeostasis. This review summarizes evidence suggesting that cellular regeneration in the context of aging and diseases can be modulated by AHR signaling on stem cells. New insights connect orphaned observations into AHR interactions with critical signaling pathways such as WNT to propose a role of this ligand-activated transcription factor in the modulation of cellular regeneration by altering pathways that nurture cellular expansion such as changes in the metabolic efficiency rather than by directly altering cell cycling, proliferation, or cell death. Targeting the AHR to promote regeneration might prove to be a useful strategy to avoid unbalanced disruptions of homeostasis that may promote disease and also provide biological rationale for potential regenerative medicine approaches. PMID:27563312

  15. The Aryl Hydrocarbon Receptor Relays Metabolic Signals to Promote Cellular Regeneration.

    PubMed

    Casado, Fanny L

    2016-01-01

    While sensing the cell environment, the aryl hydrocarbon receptor (AHR) interacts with different pathways involved in cellular homeostasis. This review summarizes evidence suggesting that cellular regeneration in the context of aging and diseases can be modulated by AHR signaling on stem cells. New insights connect orphaned observations into AHR interactions with critical signaling pathways such as WNT to propose a role of this ligand-activated transcription factor in the modulation of cellular regeneration by altering pathways that nurture cellular expansion such as changes in the metabolic efficiency rather than by directly altering cell cycling, proliferation, or cell death. Targeting the AHR to promote regeneration might prove to be a useful strategy to avoid unbalanced disruptions of homeostasis that may promote disease and also provide biological rationale for potential regenerative medicine approaches. PMID:27563312

  16. Metabolic allometric scaling model: combining cellular transportation and heat dissipation constraints.

    PubMed

    Shestopaloff, Yuri K

    2016-08-15

    Living organisms need energy to be 'alive'. Energy is produced by the biochemical processing of nutrients, and the rate of energy production is called the metabolic rate. Metabolism is very important from evolutionary and ecological perspectives, and for organismal development and functioning. It depends on different parameters, of which organism mass is considered to be one of the most important. Simple relationships between the mass of organisms and their metabolic rates were empirically discovered by M. Kleiber in 1932. Such dependence is described by a power function, whose exponent is referred to as the allometric scaling coefficient. With the increase of mass, the metabolic rate usually increases more slowly; if mass increases by two times, the metabolic rate increases less than two times. This fact has far-reaching implications for the organization of life. The fundamental biological and biophysical mechanisms underlying this phenomenon are still not well understood. The present study shows that one such primary mechanism relates to transportation of substances, such as nutrients and waste, at a cellular level. Variations in cell size and associated cellular transportation costs explain the known variance of the allometric exponent. The introduced model also includes heat dissipation constraints. The model agrees with experimental observations and reconciles experimental results across different taxa. It ties metabolic scaling to organismal and environmental characteristics, helps to define perspective directions of future research and allows the prediction of allometric exponents based on characteristics of organisms and the environments they live in.

  17. Metabolic allometric scaling model: combining cellular transportation and heat dissipation constraints.

    PubMed

    Shestopaloff, Yuri K

    2016-08-15

    Living organisms need energy to be 'alive'. Energy is produced by the biochemical processing of nutrients, and the rate of energy production is called the metabolic rate. Metabolism is very important from evolutionary and ecological perspectives, and for organismal development and functioning. It depends on different parameters, of which organism mass is considered to be one of the most important. Simple relationships between the mass of organisms and their metabolic rates were empirically discovered by M. Kleiber in 1932. Such dependence is described by a power function, whose exponent is referred to as the allometric scaling coefficient. With the increase of mass, the metabolic rate usually increases more slowly; if mass increases by two times, the metabolic rate increases less than two times. This fact has far-reaching implications for the organization of life. The fundamental biological and biophysical mechanisms underlying this phenomenon are still not well understood. The present study shows that one such primary mechanism relates to transportation of substances, such as nutrients and waste, at a cellular level. Variations in cell size and associated cellular transportation costs explain the known variance of the allometric exponent. The introduced model also includes heat dissipation constraints. The model agrees with experimental observations and reconciles experimental results across different taxa. It ties metabolic scaling to organismal and environmental characteristics, helps to define perspective directions of future research and allows the prediction of allometric exponents based on characteristics of organisms and the environments they live in. PMID:27284070

  18. A detailed view on sulphur metabolism at the cellular and whole-plant level illustrates challenges in metabolite flux analyses.

    PubMed

    Rennenberg, Heinz; Herschbach, Cornelia

    2014-11-01

    Understanding the dynamics of physiological process in the systems biology era requires approaches at the genome, transcriptome, proteome, and metabolome levels. In this context, metabolite flux experiments have been used in mapping metabolite pathways and analysing metabolic control. In the present review, sulphur metabolism was taken to illustrate current challenges of metabolic flux analyses. At the cellular level, restrictions in metabolite flux analyses originate from incomplete knowledge of the compartmentation network of metabolic pathways. Transport of metabolites through membranes is usually not considered in flux experiments but may be involved in controlling the whole pathway. Hence, steady-state and snapshot readings need to be expanded to time-course studies in combination with compartment-specific metabolite analyses. Because of species-specific differences, differences between tissues, and stress-related responses, the quantitative significance of different sulphur sinks has to be elucidated; this requires the development of methods for whole-sulphur metabolome approaches. Different cell types can contribute to metabolite fluxes to different extents at the tissue and organ level. Cell type-specific analyses are needed to characterize these contributions. Based on such approaches, metabolite flux analyses can be expanded to the whole-plant level by considering long-distance transport and, thus, the interaction of roots and the shoot in metabolite fluxes. However, whole-plant studies need detailed empirical and mathematical modelling that have to be validated by experimental analyses.

  19. Evolution of biomolecular networks: lessons from metabolic and protein interactions.

    PubMed

    Yamada, Takuji; Bork, Peer

    2009-11-01

    Despite only becoming popular at the beginning of this decade, biomolecular networks are now frameworks that facilitate many discoveries in molecular biology. The nodes of these networks are usually proteins (specifically enzymes in metabolic networks), whereas the links (or edges) are their interactions with other molecules. These networks are made up of protein-protein interactions or enzyme-enzyme interactions through shared metabolites in the case of metabolic networks. Evolutionary analysis has revealed that changes in the nodes and links in protein-protein interaction and metabolic networks are subject to different selection pressures owing to distinct topological features. However, many evolutionary constraints can be uncovered only if temporal and spatial aspects are included in the network analysis.

  20. Computational model of cellular metabolic dynamics: effect of insulin on glucose disposal in human skeletal muscle.

    PubMed

    Li, Yanjun; Solomon, Thomas P J; Haus, Jacob M; Saidel, Gerald M; Cabrera, Marco E; Kirwan, John P

    2010-06-01

    Identifying the mechanisms by which insulin regulates glucose metabolism in skeletal muscle is critical to understanding the etiology of insulin resistance and type 2 diabetes. Our knowledge of these mechanisms is limited by the difficulty of obtaining in vivo intracellular data. To quantitatively distinguish significant transport and metabolic mechanisms from limited experimental data, we developed a physiologically based, multiscale mathematical model of cellular metabolic dynamics in skeletal muscle. The model describes mass transport and metabolic processes including distinctive processes of the cytosol and mitochondria. The model simulated skeletal muscle metabolic responses to insulin corresponding to human hyperinsulinemic-euglycemic clamp studies. Insulin-mediated rate of glucose disposal was the primary model input. For model validation, simulations were compared with experimental data: intracellular metabolite concentrations and patterns of glucose disposal. Model variations were simulated to investigate three alternative mechanisms to explain insulin enhancements: Model 1 (M.1), simple mass action; M.2, insulin-mediated activation of key metabolic enzymes (i.e., hexokinase, glycogen synthase, pyruvate dehydrogenase); or M.3, parallel activation by a phenomenological insulin-mediated intracellular signal that modifies reaction rate coefficients. These simulations indicated that models M.1 and M.2 were not sufficient to explain the experimentally measured metabolic responses. However, by application of mechanism M.3, the model predicts metabolite concentration changes and glucose partitioning patterns consistent with experimental data. The reaction rate fluxes quantified by this detailed model of insulin/glucose metabolism provide information that can be used to evaluate the development of type 2 diabetes.

  1. Graph methods for the investigation of metabolic networks in parasitology.

    PubMed

    Cottret, Ludovic; Jourdan, Fabien

    2010-08-01

    Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

  2. The Edinburgh human metabolic network reconstruction and its functional analysis

    PubMed Central

    Ma, Hongwu; Sorokin, Anatoly; Mazein, Alexander; Selkov, Alex; Selkov, Evgeni; Demin, Oleg; Goryanin, Igor

    2007-01-01

    A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts. PMID:17882155

  3. Inferring minimal feasible metabolic networks of Escherichia coli.

    PubMed

    Jiang, Da; Zhou, Shuigeng; Liu, Hui; Chen, Yi-Ping Phoebe

    2010-01-01

    Since the organism contains many redundant reactions, the minimal feasible metabolic network that contains the basic growth function is not the collection of reactions that associate the essential genes. To identify minimal metabolic reaction set is a challenging work in theoretical approach. A new method is presented here to identify the smallest required reaction set of growth-sustaining metabolic networks. The content and number of the minimal reactions for growth are variable in different random processes. Though the different carbon sources also vary the content of the reactions in the minimal metabolic networks, most essential reactions locate in the same metabolic subsystems, such as cofactor and prosthetic group biosynthesis, cell envelope biosynthesis, and membrane lipid metabolism.

  4. Radio Resource Allocation on Complex 4G Wireless Cellular Networks

    NASA Astrophysics Data System (ADS)

    Psannis, Kostas E.

    2015-09-01

    In this article we consider the heuristic algorithm which improves step by step wireless data delivery over LTE cellular networks by using the total transmit power with the constraint on users’ data rates, and the total throughput with the constraints on the total transmit power as well as users’ data rates, which are jointly integrated into a hybrid-layer design framework to perform radio resource allocation for multiple users, and to effectively decide the optimal system parameter such as modulation and coding scheme (MCS) in order to adapt to the varying channel quality. We propose new heuristic algorithm which balances the accessible data rate, the initial data rates of each user allocated by LTE scheduler, the priority indicator which signals delay- throughput- packet loss awareness of the user, and the buffer fullness by achieving maximization of radio resource allocation for multiple users. It is noted that the overall performance is improved with the increase in the number of users, due to multiuser diversity. Experimental results illustrate and validate the accuracy of the proposed methodology.

  5. Hierarchical Interference Mitigation for Massive MIMO Cellular Networks

    NASA Astrophysics Data System (ADS)

    Liu, An; Lau, Vincent

    2014-09-01

    We propose a hierarchical interference mitigation scheme for massive MIMO cellular networks. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and an outer precoder. The inner precoder controls the intra-cell interference and is adaptive to local channel state information (CSI) at each BS (CSIT). The outer precoder controls the inter-cell interference and is adaptive to channel statistics. Such hierarchical precoding structure reduces the number of pilot symbols required for CSI estimation in massive MIMO downlink and is robust to the backhaul latency. We study joint optimization of the outer precoders, the user selection, and the power allocation to maximize a general concave utility which has no closed-form expression. We first apply random matrix theory to obtain an approximated problem with closed-form objective. We show that the solution of the approximated problem is asymptotically optimal with respect to the original problem as the number of antennas per BS grows large. Then using the hidden convexity of the problem, we propose an iterative algorithm to find the optimal solution for the approximated problem. We also obtain a low complexity algorithm with provable convergence. Simulations show that the proposed design has significant gain over various state-of-the-art baselines.

  6. Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction

    NASA Astrophysics Data System (ADS)

    Yeger-Lotem, Esti; Sattath, Shmuel; Kashtan, Nadav; Itzkovitz, Shalev; Milo, Ron; Pinter, Ron Y.; Alon, Uri; Margalit, Hanah

    2004-04-01

    Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

  7. Vacuole membrane contact sites and domains: emerging hubs to coordinate organelle function with cellular metabolism.

    PubMed

    Malia, Pedro Carpio; Ungermann, Christian

    2016-04-15

    Eukaryotic cells rely on a set of membrane-enclosed organelles to perform highly efficient reactions in an optimized environment. Trafficking of molecules via vesicular carriers and membrane contact sites (MCS) allow the coordination between these compartments, though the precise mechanisms are still enigmatic. Among the cellular organelles, the lysosome/vacuole stands out as a central hub, where multiple pathways merge. Importantly, the delivered material is degraded and the monomers are recycled for further usage, which explains its wide variety of roles in controlling cellular metabolism. We will highlight recent advances in the field by focusing on the yeast vacuole as a model system to understand lysosomal function in general.

  8. FCDECOMP: decomposition of metabolic networks based on flux coupling relations.

    PubMed

    Rezvan, Abolfazl; Marashi, Sayed-Amir; Eslahchi, Changiz

    2014-10-01

    A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

  9. When are two multi-layer cellular neural networks the same?

    PubMed

    Ban, Jung-Chao; Chang, Chih-Hung

    2016-07-01

    This paper aims to characterize whether a multi-layer cellular neural network is of deep architecture; namely, when can an n-layer cellular neural network be replaced by an m-layer cellular neural network for mnetwork is revealed.

  10. Inhibition of HIV by Legalon-SIL is independent of its effect on cellular metabolism

    PubMed Central

    McClure, Jan; Margineantu, Daciana H.; Sweet, Ian R.; Polyak, Stephen J.

    2014-01-01

    In this report, we further characterized the effects of silibinin (SbN), derived from milk thistle extract, and Legalon-SIL (SIL), a water-soluble derivative of SbN, on T cell metabolism and HIV infection. We assessed the effects of SbN and SIL on peripheral blood mononuclear cells (PBMC) and CEM-T4 cells in terms of cellular growth, ATP content, metabolism, and HIV infection. SIL and SbN caused a rapid and reversible (upon removal) decrease in cellular ATP levels, which was associated with suppression of mitochondrial respiration and glycolysis. SbN, but not SIL inhibited glucose uptake. Exposure of T cells to SIL (but not SbN or metabolic inhibitors) during virus adsorption blocked HIV infection. Thus, both SbN and SIL rapidly perturb T cell metabolism in vitro, which may account for its anti-inflammatory and anti-proliferative effects that arise with prolonged exposure of cells. However, the metabolic effects are not involved in SIL’s unique ability to block HIV entry. PMID:24418542

  11. Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis

    PubMed Central

    2010-01-01

    Background During infection, Mycobacterium tuberculosis confronts a generally hostile and nutrient-poor in vivo host environment. Existing models and analyses of M. tuberculosis metabolic networks are able to reproduce experimentally measured cellular growth rates and identify genes required for growth in a range of different in vitro media. However, these models, under in vitro conditions, do not provide an adequate description of the metabolic processes required by the pathogen to infect and persist in a host. Results To better account for the metabolic activity of M. tuberculosis in the host environment, we developed a set of procedures to systematically modify an existing in vitro metabolic network by enhancing the agreement between calculated and in vivo-measured gene essentiality data. After our modifications, the new in vivo network contained 663 genes, 838 metabolites, and 1,049 reactions and had a significantly increased sensitivity (0.81) in predicted gene essentiality than the in vitro network (0.31). We verified the modifications generated from the purely computational analysis through a review of the literature and found, for example, that, as the analysis suggested, lipids are used as the main source for carbon metabolism and oxygen must be available for the pathogen under in vivo conditions. Moreover, we used the developed in vivo network to predict the effects of double-gene deletions on M. tuberculosis growth in the host environment, explore metabolic adaptations to life in an acidic environment, highlight the importance of different enzymes in the tricarboxylic acid-cycle under different limiting nutrient conditions, investigate the effects of inhibiting multiple reactions, and look at the importance of both aerobic and anaerobic cellular respiration during infection. Conclusions The network modifications we implemented suggest a distinctive set of metabolic conditions and requirements faced by M. tuberculosis during host infection compared with

  12. Predict drug-protein interaction in cellular networking.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs". To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment. PMID:23889048

  13. Predict drug-protein interaction in cellular networking.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs". To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment.

  14. Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks.

    PubMed

    Ruppin, Eytan; Papin, Jason A; de Figueiredo, Luis F; Schuster, Stefan

    2010-08-01

    With the advent of modern omics technologies, it has become feasible to reconstruct (quasi-) whole-cell metabolic networks and characterize them in more and more detail. Computer simulations of the dynamic behavior of such networks are difficult due to a lack of kinetic data and to computational limitations. In contrast, network analysis based on appropriate constraints such as the steady-state condition (constraint-based analysis) is feasible and allows one to derive conclusions about the system's metabolic capabilities. Here, we review methods for the reconstruction of metabolic networks, modeling techniques such as flux balance analysis and elementary flux modes and current progress in their development and applications. Game-theoretical methods for studying metabolic networks are discussed as well. PMID:20692823

  15. Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks.

    PubMed

    Ruppin, Eytan; Papin, Jason A; de Figueiredo, Luis F; Schuster, Stefan

    2010-08-01

    With the advent of modern omics technologies, it has become feasible to reconstruct (quasi-) whole-cell metabolic networks and characterize them in more and more detail. Computer simulations of the dynamic behavior of such networks are difficult due to a lack of kinetic data and to computational limitations. In contrast, network analysis based on appropriate constraints such as the steady-state condition (constraint-based analysis) is feasible and allows one to derive conclusions about the system's metabolic capabilities. Here, we review methods for the reconstruction of metabolic networks, modeling techniques such as flux balance analysis and elementary flux modes and current progress in their development and applications. Game-theoretical methods for studying metabolic networks are discussed as well.

  16. Molecular Pathways: IDH2 Mutations, Co-opting Cellular Metabolism for Malignant Transformation

    PubMed Central

    Stein, Eytan M.

    2016-01-01

    Mutations in mitochondrial IDH2, one of the three isoforms of IDH, were discovered in patients with gliomas in 2009 and subsequently described in acute myeloid leukemia (AML), angioimmunoblastic T-cell lymphoma (AITL), chondrosarcoma and intrahepatic chloangiocarcinoma. The effects of mutations in IDH2 on cellular metabolism, the epigenetic state of mutated cells, and cellular differentiation have been elucidated in-vitro and in-vivo. Mutations in IDH2 lead to an enzymatic gain of function that catalyzes the conversion of alpha-ketoglutarate to beta-hydroxyglutarate (2-HG). Supra-normal levels of 2-HG lead to hypermethylation of epigenetic targets and a subsequent block in cellular differentiation. AG-221, a small molecule inhibitor of mutant IDH2, is being explored in a phase 1 clinical trial for the treatment of AML, other myeloid malignancies, solid tumors and gliomas. PMID:26553750

  17. Alginate-Iron Speciation and Its Effect on In Vitro Cellular Iron Metabolism

    PubMed Central

    Horniblow, Richard D.; Dowle, Miriam; Iqbal, Tariq H.; Latunde-Dada, Gladys O.; Palmer, Richard E.

    2015-01-01

    Alginates are a class of biopolymers with known iron binding properties which are routinely used in the fabrication of iron-oxide nanoparticles. In addition, alginates have been implicated in influencing human iron absorption. However, the synthesis of iron oxide nanoparticles employs non-physiological pH conditions and whether nanoparticle formation in vivo is responsible for influencing cellular iron metabolism is unclear. Thus the aims of this study were to determine how alginate and iron interact at gastric-comparable pH conditions and how this influences iron metabolism. Employing a range of spectroscopic techniques under physiological conditions alginate-iron complexation was confirmed and, in conjunction with aberration corrected scanning transmission electron microscopy, nanoparticles were observed. The results infer a nucleation-type model of iron binding whereby alginate is templating the condensation of iron-hydroxide complexes to form iron oxide centred nanoparticles. The interaction of alginate and iron at a cellular level was found to decrease cellular iron acquisition by 37% (p < 0.05) and in combination with confocal microscopy the alginate inhibits cellular iron transport through extracellular iron chelation with the resulting complexes not internalised. These results infer alginate as being useful in the chelation of excess iron, especially in the context of inflammatory bowel disease and colorectal cancer where excess unabsorbed luminal iron is thought to be a driver of disease. PMID:26378798

  18. Distinct metabolic network states manifest in the gene expression profiles of pediatric inflammatory bowel disease patients and controls

    NASA Astrophysics Data System (ADS)

    Knecht, Carolin; Fretter, Christoph; Rosenstiel, Philip; Krawczak, Michael; Hütt, Marc-Thorsten

    2016-09-01

    Information on biological networks can greatly facilitate the function-orientated interpretation of high-throughput molecular data. Genome-wide metabolic network models of human cells, in particular, can be employed to contextualize gene expression profiles of patients with the goal of both, a better understanding of individual etiologies and an educated reclassification of (clinically defined) phenotypes. We analyzed publicly available expression profiles of intestinal tissues from treatment-naive pediatric inflammatory bowel disease (IBD) patients and age-matched control individuals, using a reaction-centric metabolic network derived from the Recon2 model. By way of defining a measure of ‘coherence’, we quantified how well individual patterns of expression changes matched the metabolic network. We observed a bimodal distribution of metabolic network coherence in both patients and controls, albeit at notably different mixture probabilities. Multidimensional scaling analysis revealed a bisectional pattern as well that overlapped widely with the metabolic network-based results. Expression differences driving the observed bimodality were related to cellular transport of thiamine and bile acid metabolism, thereby highlighting the crosstalk between metabolism and other vital pathways. We demonstrated how classical data mining and network analysis can jointly identify biologically meaningful patterns in gene expression data.

  19. Distinct metabolic network states manifest in the gene expression profiles of pediatric inflammatory bowel disease patients and controls.

    PubMed

    Knecht, Carolin; Fretter, Christoph; Rosenstiel, Philip; Krawczak, Michael; Hütt, Marc-Thorsten

    2016-09-02

    Information on biological networks can greatly facilitate the function-orientated interpretation of high-throughput molecular data. Genome-wide metabolic network models of human cells, in particular, can be employed to contextualize gene expression profiles of patients with the goal of both, a better understanding of individual etiologies and an educated reclassification of (clinically defined) phenotypes. We analyzed publicly available expression profiles of intestinal tissues from treatment-naive pediatric inflammatory bowel disease (IBD) patients and age-matched control individuals, using a reaction-centric metabolic network derived from the Recon2 model. By way of defining a measure of 'coherence', we quantified how well individual patterns of expression changes matched the metabolic network. We observed a bimodal distribution of metabolic network coherence in both patients and controls, albeit at notably different mixture probabilities. Multidimensional scaling analysis revealed a bisectional pattern as well that overlapped widely with the metabolic network-based results. Expression differences driving the observed bimodality were related to cellular transport of thiamine and bile acid metabolism, thereby highlighting the crosstalk between metabolism and other vital pathways. We demonstrated how classical data mining and network analysis can jointly identify biologically meaningful patterns in gene expression data.

  20. Distinct metabolic network states manifest in the gene expression profiles of pediatric inflammatory bowel disease patients and controls.

    PubMed

    Knecht, Carolin; Fretter, Christoph; Rosenstiel, Philip; Krawczak, Michael; Hütt, Marc-Thorsten

    2016-01-01

    Information on biological networks can greatly facilitate the function-orientated interpretation of high-throughput molecular data. Genome-wide metabolic network models of human cells, in particular, can be employed to contextualize gene expression profiles of patients with the goal of both, a better understanding of individual etiologies and an educated reclassification of (clinically defined) phenotypes. We analyzed publicly available expression profiles of intestinal tissues from treatment-naive pediatric inflammatory bowel disease (IBD) patients and age-matched control individuals, using a reaction-centric metabolic network derived from the Recon2 model. By way of defining a measure of 'coherence', we quantified how well individual patterns of expression changes matched the metabolic network. We observed a bimodal distribution of metabolic network coherence in both patients and controls, albeit at notably different mixture probabilities. Multidimensional scaling analysis revealed a bisectional pattern as well that overlapped widely with the metabolic network-based results. Expression differences driving the observed bimodality were related to cellular transport of thiamine and bile acid metabolism, thereby highlighting the crosstalk between metabolism and other vital pathways. We demonstrated how classical data mining and network analysis can jointly identify biologically meaningful patterns in gene expression data. PMID:27585741

  1. Distinct metabolic network states manifest in the gene expression profiles of pediatric inflammatory bowel disease patients and controls

    PubMed Central

    Knecht, Carolin; Fretter, Christoph; Rosenstiel, Philip; Krawczak, Michael; Hütt, Marc-Thorsten

    2016-01-01

    Information on biological networks can greatly facilitate the function-orientated interpretation of high-throughput molecular data. Genome-wide metabolic network models of human cells, in particular, can be employed to contextualize gene expression profiles of patients with the goal of both, a better understanding of individual etiologies and an educated reclassification of (clinically defined) phenotypes. We analyzed publicly available expression profiles of intestinal tissues from treatment-naive pediatric inflammatory bowel disease (IBD) patients and age-matched control individuals, using a reaction-centric metabolic network derived from the Recon2 model. By way of defining a measure of ‘coherence’, we quantified how well individual patterns of expression changes matched the metabolic network. We observed a bimodal distribution of metabolic network coherence in both patients and controls, albeit at notably different mixture probabilities. Multidimensional scaling analysis revealed a bisectional pattern as well that overlapped widely with the metabolic network-based results. Expression differences driving the observed bimodality were related to cellular transport of thiamine and bile acid metabolism, thereby highlighting the crosstalk between metabolism and other vital pathways. We demonstrated how classical data mining and network analysis can jointly identify biologically meaningful patterns in gene expression data. PMID:27585741

  2. RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations

    PubMed Central

    2012-01-01

    Predicting cellular responses to perturbations is an important task in systems biology. We report a new approach, RELATCH, which uses flux and gene expression data from a reference state to predict metabolic responses in a genetically or environmentally perturbed state. Using the concept of relative optimality, which considers relative flux changes from a reference state, we hypothesize a relative metabolic flux pattern is maintained from one state to another, and that cells adapt to perturbations using metabolic and regulatory reprogramming to preserve this relative flux pattern. This constraint-based approach will have broad utility where predictions of metabolic responses are needed. PMID:23013597

  3. Short- and medium-chain fatty acids in energy metabolism: the cellular perspective.

    PubMed

    Schönfeld, Peter; Wojtczak, Lech

    2016-06-01

    Short- and medium-chain fatty acids (SCFAs and MCFAs), independently of their cellular signaling functions, are important substrates of the energy metabolism and anabolic processes in mammals. SCFAs are mostly generated by colonic bacteria and are predominantly metabolized by enterocytes and liver, whereas MCFAs arise mostly from dietary triglycerides, among them milk and dairy products. A common feature of SCFAs and MCFAs is their carnitine-independent uptake and intramitochondrial activation to acyl-CoA thioesters. Contrary to long-chain fatty acids, the cellular metabolism of SCFAs and MCFAs depends to a lesser extent on fatty acid-binding proteins. SCFAs and MCFAs modulate tissue metabolism of carbohydrates and lipids, as manifested by a mostly inhibitory effect on glycolysis and stimulation of lipogenesis or gluconeogenesis. SCFAs and MCFAs exert no or only weak protonophoric and lytic activities in mitochondria and do not significantly impair the electron transport in the respiratory chain. SCFAs and MCFAs modulate mitochondrial energy production by two mechanisms: they provide reducing equivalents to the respiratory chain and partly decrease efficacy of oxidative ATP synthesis. PMID:27080715

  4. Multiphoton microscopy for skin wound healing study in terms of cellular metabolism and collagen regeneration

    NASA Astrophysics Data System (ADS)

    Deka, Gitanjal; Okano, Kazunori; Wu, Wei-Wen; Kao, Fu-Jen

    2014-02-01

    Multiphoton microscopy was employed to study normal skin wound healing in live rats noninvasively. Wound healing is a process involving series of biochemical events. This study evaluates the regeneration of collagen and change in cellular metabolic activity during wound healing in rats, with second harmonic generation (SHG) and fluorescence lifetime imaging microscopy (FLIM), respectively. In eukaryotic cells ATP is the molecule that holds the energy for cellular functioning. Whereas NADH is an electron donor in the metabolic pathways, required to generate ATP. Fluorescence lifetime of NADH free to protein bound ratio was evaluated to determine the relative metabolic activity. The FLIM data were acquired by a TCSPC system using SPCM software and analyzed by SPCImage software. Additionally, polarization resolved SHG signals were also collected to observe the changes in optical birefringence and hence the anisotropy of regenerated collagens from rat wound biopsy samples. Mat lab programming was used to process the data to construct the anisotropy images. Results indicated that, cells involved in healing had higher metabolic activity during the first week of healing, which decreases gradually and become equivalent to normal skin upon healing completes. A net degradation of collagen during the inflammatory phase and net regeneration starting from day 5 were observed in terms of SHG signal intensity change. Polarization resolved SHG imaging of the wound biopsy sample indicates higher value of anisotropy in proliferative phase, from day 4th to 8th, of wound formation; however the anisotropy decreases upon healing.

  5. Mitochondrial DNA Replication Defects Disturb Cellular dNTP Pools and Remodel One-Carbon Metabolism.

    PubMed

    Nikkanen, Joni; Forsström, Saara; Euro, Liliya; Paetau, Ilse; Kohnz, Rebecca A; Wang, Liya; Chilov, Dmitri; Viinamäki, Jenni; Roivainen, Anne; Marjamäki, Päivi; Liljenbäck, Heidi; Ahola, Sofia; Buzkova, Jana; Terzioglu, Mügen; Khan, Nahid A; Pirnes-Karhu, Sini; Paetau, Anders; Lönnqvist, Tuula; Sajantila, Antti; Isohanni, Pirjo; Tyynismaa, Henna; Nomura, Daniel K; Battersby, Brendan J; Velagapudi, Vidya; Carroll, Christopher J; Suomalainen, Anu

    2016-04-12

    Mitochondrial dysfunction affects cellular energy metabolism, but less is known about the consequences for cytoplasmic biosynthetic reactions. We report that mtDNA replication disorders caused by TWINKLE mutations-mitochondrial myopathy (MM) and infantile onset spinocerebellar ataxia (IOSCA)-remodel cellular dNTP pools in mice. MM muscle shows tissue-specific induction of the mitochondrial folate cycle, purine metabolism, and imbalanced and increased dNTP pools, consistent with progressive mtDNA mutagenesis. IOSCA-TWINKLE is predicted to hydrolyze dNTPs, consistent with low dNTP pools and mtDNA depletion in the disease. MM muscle also modifies the cytoplasmic one-carbon cycle, transsulfuration, and methylation, as well as increases glucose uptake and its utilization for de novo serine and glutathione biosynthesis. Our evidence indicates that the mitochondrial replication machinery communicates with cytoplasmic dNTP pools and that upregulation of glutathione synthesis through glucose-driven de novo serine biosynthesis contributes to the metabolic stress response. These results are important for disorders with primary or secondary mtDNA instability and offer targets for metabolic therapy. PMID:26924217

  6. Proteome evolution and the metabolic origins of translation and cellular life.

    PubMed

    Caetano-Anollés, Derek; Kim, Kyung Mo; Mittenthal, Jay E; Caetano-Anollés, Gustavo

    2011-01-01

    The origin of life has puzzled molecular scientists for over half a century. Yet fundamental questions remain unanswered, including which came first, the metabolic machinery or the encoding nucleic acids. In this study we take a protein-centric view and explore the ancestral origins of proteins. Protein domain structures in proteomes are highly conserved and embody molecular functions and interactions that are needed for cellular and organismal processes. Here we use domain structure to study the evolution of molecular function in the protein world. Timelines describing the age and function of protein domains at fold, fold superfamily, and fold family levels of structural complexity were derived from a structural phylogenomic census in hundreds of fully sequenced genomes. These timelines unfold congruent hourglass patterns in rates of appearance of domain structures and functions, functional diversity, and hierarchical complexity, and revealed a gradual build up of protein repertoires associated with metabolism, translation and DNA, in that order. The most ancient domain architectures were hydrolase enzymes and the first translation domains had catalytic functions for the aminoacylation and the molecular switch-driven transport of RNA. Remarkably, the most ancient domains had metabolic roles, did not interact with RNA, and preceded the gradual build-up of translation. In fact, the first translation domains had also a metabolic origin and were only later followed by specialized translation machinery. Our results explain how the generation of structure in the protein world and the concurrent crystallization of translation and diversified cellular life created further opportunities for proteomic diversification. PMID:21082171

  7. Design mobile satellite system architecture as an integral part of the cellular access digital network

    NASA Technical Reports Server (NTRS)

    Chien, E. S. K.; Marinho, J. A.; Russell, J. E., Sr.

    1988-01-01

    The Cellular Access Digital Network (CADN) is the access vehicle through which cellular technology is brought into the mainstream of the evolving integrated telecommunications network. Beyond the integrated end-to-end digital access and per call network services provisioning of the Integrated Services Digital Network (ISDN), the CADN engenders the added capability of mobility freedom via wireless access. One key element of the CADN network architecture is the standard user to network interface that is independent of RF transmission technology. Since the Mobile Satellite System (MSS) is envisioned to not only complement but also enhance the capabilities of the terrestrial cellular telecommunications network, compatibility and interoperability between terrestrial cellular and mobile satellite systems are vitally important to provide an integrated moving telecommunications network of the future. From a network standpoint, there exist very strong commonalities between the terrestrial cellular system and the mobile satellite system. Therefore, the MSS architecture should be designed as an integral part of the CADN. This paper describes the concept of the CADN, the functional architecture of the MSS, and the user-network interface signaling protocols.

  8. Analysis of blocking rate and bandwidth usage of mobile IPTV services in wireless cellular networks.

    PubMed

    Li, Mingfu

    2014-01-01

    Mobile IPTV services over wireless cellular networks become more and more popular, owing to the significant growth in access bandwidth of wireless cellular networks such as 3G/4G and WiMAX. However, the spectrum resources of wireless cellular networks is rare. How to enhance the spectral efficiency of mobile networks becomes an important issue. Unicast, broadcast, and multicast are the most important transport schemes for offering mobile IPTV services over wireless cellular networks. Therefore, bandwidth usages and blocking rates of unicast, broadcast, and multicast IPTV services were analyzed and compared in this paper. Simulations were also conducted to validate the analytical results. Numerical results demonstrate that the presented analysis is correct, and multicast scheme achieves the best bandwidth usage and blocking rate performance, relative to the other two schemes. PMID:25379521

  9. Analysis of Blocking Rate and Bandwidth Usage of Mobile IPTV Services in Wireless Cellular Networks

    PubMed Central

    Li, Mingfu

    2014-01-01

    Mobile IPTV services over wireless cellular networks become more and more popular, owing to the significant growth in access bandwidth of wireless cellular networks such as 3G/4G and WiMAX. However, the spectrum resources of wireless cellular networks is rare. How to enhance the spectral efficiency of mobile networks becomes an important issue. Unicast, broadcast, and multicast are the most important transport schemes for offering mobile IPTV services over wireless cellular networks. Therefore, bandwidth usages and blocking rates of unicast, broadcast, and multicast IPTV services were analyzed and compared in this paper. Simulations were also conducted to validate the analytical results. Numerical results demonstrate that the presented analysis is correct, and multicast scheme achieves the best bandwidth usage and blocking rate performance, relative to the other two schemes. PMID:25379521

  10. Analysis of blocking rate and bandwidth usage of mobile IPTV services in wireless cellular networks.

    PubMed

    Li, Mingfu

    2014-01-01

    Mobile IPTV services over wireless cellular networks become more and more popular, owing to the significant growth in access bandwidth of wireless cellular networks such as 3G/4G and WiMAX. However, the spectrum resources of wireless cellular networks is rare. How to enhance the spectral efficiency of mobile networks becomes an important issue. Unicast, broadcast, and multicast are the most important transport schemes for offering mobile IPTV services over wireless cellular networks. Therefore, bandwidth usages and blocking rates of unicast, broadcast, and multicast IPTV services were analyzed and compared in this paper. Simulations were also conducted to validate the analytical results. Numerical results demonstrate that the presented analysis is correct, and multicast scheme achieves the best bandwidth usage and blocking rate performance, relative to the other two schemes.

  11. A compendium of inborn errors of metabolism mapped onto the human metabolic network.

    PubMed

    Sahoo, Swagatika; Franzson, Leifur; Jonsson, Jon J; Thiele, Ines

    2012-10-01

    Inborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitine (AC) and fatty acid oxidation (FAO) metabolism. Using literary data, we reconstructed an AC/FAO module consisting of 352 reactions and 139 metabolites. When this module was combined with the human metabolic reconstruction, the synthesis of 39 acylcarnitines and 22 amino acids, which are routinely measured, was captured and 235 distinct IEMs could be mapped. We collected phenotypic and clinical features for each IEM enabling comprehensive classification. We found that carbohydrate, amino acid, and lipid metabolism were most affected by the IEMs, while the brain was the most commonly affected organ. Furthermore, we analyzed the IEMs in the context of metabolic network topology to gain insight into common features between metabolically connected IEMs. While many known examples were identified, we discovered some surprising IEM pairs that shared reactions as well as clinical features but not necessarily causal genes. Moreover, we could also re-confirm that acetyl-CoA acts as a central metabolite. This network based analysis leads to further insight of hot spots in human metabolism with respect to IEMs. The presented comprehensive knowledge base of IEMs will provide a valuable tool in studying metabolic changes involved in inherited metabolic diseases. PMID:22699794

  12. Multiple steady states with distinct cellular metabolism in continuous culture of mammalian cells.

    PubMed

    Europa, A F; Gambhir, A; Fu, P C; Hu, W S

    2000-01-01

    Mammalian cells have the ability to proliferate under different nutrient environments by utilizing different combinations of the nutrients, especially glucose and the amino acids. Under the conditions often used in in vitro cultivation, the cells consume glucose and amino acids in great excess of what is needed for making up biomass and products. They also produce large amounts of metabolites with lactate, ammonia, and some non-essential amino acids such as alanine as the most dominant ones. By controlling glucose and glutamine at low levels, cellular metabolism can be altered and can result in reduced glucose and glutamine consumption as well as in reduced metabolite formation. Using a fed-batch reactor to manipulate glucose at a low level (as compared to a typical batch culture), cell metabolism was altered to a state with substantially reduced lactate production. The culture was then switched to a continuous mode and allowed to reach a steady-state. At this steady-state, the concentrations of cells and antibody were substantially higher than a control culture that was initiated from a batch culture without first altering cellular metabolism. The lactate and other metabolite concentrations were also substantially reduced as compared to the control culture. This newly observed steady-state was achieved at the same dilution rate and feed medium as the control culture. The paths leading to the two steady-states, however, were different. These results demonstrate steady-state multiplicity. At this new steady-state, not only was glucose metabolism altered, but the metabolism of amino acids was altered as well. The amino acid metabolism in the new steady-state was more balanced, and the excretion of non-essential amino acids and ammonia was substantially lower. This approach of reaching a more desirable steady-state with higher concentrations of cells and product opens a new avenue for high-density- and high-productivity-cell culture.

  13. Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity.

    PubMed

    Rolfsson, Óttar; Paglia, Giuseppe; Magnusdóttir, Manuela; Palsson, Bernhard Ø; Thiele, Ines

    2013-01-15

    Metabolic network reconstructions define metabolic information within a target organism and can therefore be used to address incomplete metabolic information. In the present study we used a computational approach to identify human metabolites whose metabolism is incomplete on the basis of their detection in humans but exclusion from the human metabolic network reconstruction RECON 1. Candidate solutions, composed of metabolic reactions capable of explaining the metabolism of these compounds, were then identified computationally from a global biochemical reaction database. Solutions were characterized with respect to how metabolites were incorporated into RECON 1 and their biological relevance. Through detailed case studies we show that biologically plausible non-intuitive hypotheses regarding the metabolism of these compounds can be proposed in a semi-automated manner, in an approach that is similar to de novo network reconstruction. We subsequently experimentally validated one of the proposed hypotheses and report that C9orf103, previously identified as a candidate tumour suppressor gene, encodes a functional human gluconokinase. The results of the present study demonstrate how semi-automatic gap filling can be used to refine and extend metabolic reconstructions, thereby increasing their biological scope. Furthermore, we illustrate how incomplete human metabolic knowledge can be coupled with gene annotation in order to prioritize and confirm gene functions.

  14. Computational model of cellular metabolic dynamics: effect of insulin on glucose disposal in human skeletal muscle

    PubMed Central

    Li, Yanjun; Solomon, Thomas P. J.; Haus, Jacob M.; Saidel, Gerald M.; Cabrera, Marco E.

    2010-01-01

    Identifying the mechanisms by which insulin regulates glucose metabolism in skeletal muscle is critical to understanding the etiology of insulin resistance and type 2 diabetes. Our knowledge of these mechanisms is limited by the difficulty of obtaining in vivo intracellular data. To quantitatively distinguish significant transport and metabolic mechanisms from limited experimental data, we developed a physiologically based, multiscale mathematical model of cellular metabolic dynamics in skeletal muscle. The model describes mass transport and metabolic processes including distinctive processes of the cytosol and mitochondria. The model simulated skeletal muscle metabolic responses to insulin corresponding to human hyperinsulinemic-euglycemic clamp studies. Insulin-mediated rate of glucose disposal was the primary model input. For model validation, simulations were compared with experimental data: intracellular metabolite concentrations and patterns of glucose disposal. Model variations were simulated to investigate three alternative mechanisms to explain insulin enhancements: Model 1 (M.1), simple mass action; M.2, insulin-mediated activation of key metabolic enzymes (i.e., hexokinase, glycogen synthase, pyruvate dehydrogenase); or M.3, parallel activation by a phenomenological insulin-mediated intracellular signal that modifies reaction rate coefficients. These simulations indicated that models M.1 and M.2 were not sufficient to explain the experimentally measured metabolic responses. However, by application of mechanism M.3, the model predicts metabolite concentration changes and glucose partitioning patterns consistent with experimental data. The reaction rate fluxes quantified by this detailed model of insulin/glucose metabolism provide information that can be used to evaluate the development of type 2 diabetes. PMID:20332360

  15. Traffic Driven Analysis of Cellular and WiFi Networks

    ERIC Educational Resources Information Center

    Paul, Utpal Kumar

    2012-01-01

    Since the days Internet traffic proliferated, measurement, monitoring and analysis of network traffic have been critical to not only the basic understanding of large networks, but also to seek improvements in resource management, traffic engineering and security. At the current times traffic in wireless local and wide area networks are facing…

  16. Evolution under fluctuating environments explains observed robustness in metabolic networks.

    PubMed

    Soyer, Orkun S; Pfeiffer, Thomas

    2010-08-26

    A high level of robustness against gene deletion is observed in many organisms. However, it is still not clear which biochemical features underline this robustness and how these are acquired during evolution. One hypothesis, specific to metabolic networks, is that robustness emerges as a byproduct of selection for biomass production in different environments. To test this hypothesis we performed evolutionary simulations of metabolic networks under stable and fluctuating environments. We find that networks evolved under the latter scenario can better tolerate single gene deletion in specific environments. Such robustness is underlined by an increased number of independent fluxes and multifunctional enzymes in the evolved networks. Observed robustness in networks evolved under fluctuating environments was "apparent," in the sense that it decreased significantly as we tested effects of gene deletions under all environments experienced during evolution. Furthermore, when we continued evolution of these networks under a stable environment, we found that any robustness they had acquired was completely lost. These findings provide evidence that evolution under fluctuating environments can account for the observed robustness in metabolic networks. Further, they suggest that organisms living under stable environments should display lower robustness in their metabolic networks, and that robustness should decrease upon switching to more stable environments.

  17. Green pathways: Metabolic network analysis of plant systems.

    PubMed

    Dersch, Lisa Maria; Beckers, Veronique; Wittmann, Christoph

    2016-03-01

    Metabolic engineering of plants with enhanced crop yield and value-added compositional traits is particularly challenging as they probably exhibit the highest metabolic network complexity of all living organisms. Therefore, approaches of plant metabolic network analysis, which can provide systems-level understanding of plant physiology, appear valuable as guidance for plant metabolic engineers. Strongly supported by the sequencing of plant genomes, a number of different experimental and computational methods have emerged in recent years to study plant systems at various levels: from heterotrophic cell cultures to autotrophic entire plants. The present review presents a state-of-the-art toolbox for plant metabolic network analysis. Among the described approaches are different in silico modeling techniques, including flux balance analysis, elementary flux mode analysis and kinetic flux profiling, as well as different variants of experiments with plant systems which use radioactive and stable isotopes to determine in vivo plant metabolic fluxes. The fundamental principles of these techniques, the required data input and the obtained flux information are enriched by technical advices, specific to plants. In addition, pioneering and high-impacting findings of plant metabolic network analysis highlight the potential of the field.

  18. Green pathways: Metabolic network analysis of plant systems.

    PubMed

    Dersch, Lisa Maria; Beckers, Veronique; Wittmann, Christoph

    2016-03-01

    Metabolic engineering of plants with enhanced crop yield and value-added compositional traits is particularly challenging as they probably exhibit the highest metabolic network complexity of all living organisms. Therefore, approaches of plant metabolic network analysis, which can provide systems-level understanding of plant physiology, appear valuable as guidance for plant metabolic engineers. Strongly supported by the sequencing of plant genomes, a number of different experimental and computational methods have emerged in recent years to study plant systems at various levels: from heterotrophic cell cultures to autotrophic entire plants. The present review presents a state-of-the-art toolbox for plant metabolic network analysis. Among the described approaches are different in silico modeling techniques, including flux balance analysis, elementary flux mode analysis and kinetic flux profiling, as well as different variants of experiments with plant systems which use radioactive and stable isotopes to determine in vivo plant metabolic fluxes. The fundamental principles of these techniques, the required data input and the obtained flux information are enriched by technical advices, specific to plants. In addition, pioneering and high-impacting findings of plant metabolic network analysis highlight the potential of the field. PMID:26704307

  19. Metabolic resting-state brain networks in health and disease.

    PubMed

    Spetsieris, Phoebe G; Ko, Ji Hyun; Tang, Chris C; Nazem, Amir; Sako, Wataru; Peng, Shichun; Ma, Yilong; Dhawan, Vijay; Eidelberg, David

    2015-02-24

    The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.

  20. Metabolic networks in motion: 13C-based flux analysis.

    PubMed

    Sauer, Uwe

    2006-01-01

    Many properties of complex networks cannot be understood from monitoring the components--not even when comprehensively monitoring all protein or metabolite concentrations--unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism.

  1. Restricted cooperative games on metabolic networks reveal functionally important reactions.

    PubMed

    Sajitz-Hermstein, Max; Nikoloski, Zoran

    2012-12-01

    Understanding the emerging properties of complex biological systems is in the crux of systems biology studies. Computational methods for elucidating the role of each component in the synergetic interplay can be used to identify targets for genetic and metabolic engineering. In particular, we aim at determining the importance of reactions in a metabolic network with respect to a specific biological function. Therefore, we propose a novel game-theoretic framework which integrates restricted cooperative games with the outcome of flux balance analysis. We define productivity games on metabolic networks and present an analysis of their unrestricted and restricted variants based on the game-theoretic solution concept of the Shapley value. Correspondingly, this concept provides a characterization of the robustness and functional centrality for each enzyme involved in a given metabolic network. Furthermore, the comparison of two different environments - feast and famine - demonstrates the dependence of the results on the imposed flux capacities.

  2. Nuclear ARRB1 induces pseudohypoxia and cellular metabolism reprogramming in prostate cancer.

    PubMed

    Zecchini, Vincent; Madhu, Basetti; Russell, Roslin; Pértega-Gomes, Nelma; Warren, Anne; Gaude, Edoardo; Borlido, Joana; Stark, Rory; Ireland-Zecchini, Heather; Rao, Roheet; Scott, Helen; Boren, Joan; Massie, Charlie; Asim, Mohammad; Brindle, Kevin; Griffiths, John; Frezza, Christian; Neal, David E; Mills, Ian G

    2014-06-17

    Tumour cells sustain their high proliferation rate through metabolic reprogramming, whereby cellular metabolism shifts from oxidative phosphorylation to aerobic glycolysis, even under normal oxygen levels. Hypoxia-inducible factor 1A (HIF1A) is a major regulator of this process, but its activation under normoxic conditions, termed pseudohypoxia, is not well documented. Here, using an integrative approach combining the first genome-wide mapping of chromatin binding for an endocytic adaptor, ARRB1, both in vitro and in vivo with gene expression profiling, we demonstrate that nuclear ARRB1 contributes to this metabolic shift in prostate cancer cells via regulation of HIF1A transcriptional activity under normoxic conditions through regulation of succinate dehydrogenase A (SDHA) and fumarate hydratase (FH) expression. ARRB1-induced pseudohypoxia may facilitate adaptation of cancer cells to growth in the harsh conditions that are frequently encountered within solid tumours. Our study is the first example of an endocytic adaptor protein regulating metabolic pathways. It implicates ARRB1 as a potential tumour promoter in prostate cancer and highlights the importance of metabolic alterations in prostate cancer.

  3. Nuclear ARRB1 induces pseudohypoxia and cellular metabolism reprogramming in prostate cancer

    PubMed Central

    Zecchini, Vincent; Madhu, Basetti; Russell, Roslin; Pértega-Gomes, Nelma; Warren, Anne; Gaude, Edoardo; Borlido, Joana; Stark, Rory; Ireland-Zecchini, Heather; Rao, Roheet; Scott, Helen; Boren, Joan; Massie, Charlie; Asim, Mohammad; Brindle, Kevin; Griffiths, John; Frezza, Christian; Neal, David E; Mills, Ian G

    2014-01-01

    Tumour cells sustain their high proliferation rate through metabolic reprogramming, whereby cellular metabolism shifts from oxidative phosphorylation to aerobic glycolysis, even under normal oxygen levels. Hypoxia-inducible factor 1A (HIF1A) is a major regulator of this process, but its activation under normoxic conditions, termed pseudohypoxia, is not well documented. Here, using an integrative approach combining the first genome-wide mapping of chromatin binding for an endocytic adaptor, ARRB1, both in vitro and in vivo with gene expression profiling, we demonstrate that nuclear ARRB1 contributes to this metabolic shift in prostate cancer cells via regulation of HIF1A transcriptional activity under normoxic conditions through regulation of succinate dehydrogenase A (SDHA) and fumarate hydratase (FH) expression. ARRB1-induced pseudohypoxia may facilitate adaptation of cancer cells to growth in the harsh conditions that are frequently encountered within solid tumours. Our study is the first example of an endocytic adaptor protein regulating metabolic pathways. It implicates ARRB1 as a potential tumour promoter in prostate cancer and highlights the importance of metabolic alterations in prostate cancer. PMID:24837709

  4. [EFFECT OF LIPOPOLYSACCHARIDE ON NEUTRAL LIPID METABOLISM AND CELLULAR ENERGETICS IN FROG URINARY BLADDER EPITHELIAL CELLS].

    PubMed

    Fedorova, E V; Fock, E M; Braylovskaya, I V; Bachteeva, V T; Lavrova, E A; Zabelinskiĭ, S A; Parnova, R G

    2015-09-01

    It was shown previously that colonization of the frog urinary bladder by gram-negative bacteria leads to decreased ability of antidiuretic hormone to reabsorb water from the urinary bladder (Fock et al. J. Exp. Zool., 2013, 319A: 487-494). In the present work performed on epithelial cells isolated from the frog urinary bladder the influence of E. coli lipopolysaccharide (LPS) on neutral lipid metabolism and cellular energetics was studied. It was shown that incubation of cells with LPS led to decrease of fatty acids oxidation and to retention of triacylglycerols (TAG) followed by an increase of the cytoplasmic lipid droplets content and cellular amount of TAG. Fatty acid composition of TAG was not changed under LPS. LPS did not alter mitochondrial membrane potential, however, LPS decreased oxygen consumption rate both in basal and uncoupling conditions. Cellular ATP production was also reduced in the presence of LPS. The data obtained indicate that a decreased ability of antidiuretic hormone to reabsorb water from the urinary bladder induced by bacterial pathogens could be related to inhibition of fatty acids oxidation and impaired energy metabolism. PMID:26672162

  5. Reconstruction of a generic metabolic network model of cancer cells.

    PubMed

    Hadi, Mahdieh; Marashi, Sayed-Amir

    2014-11-01

    A promising strategy for finding new cancer drugs is to use metabolic network models to investigate the essential reactions or genes in cancer cells. In this study, we present a generic constraint-based model of cancer metabolism, which is able to successfully predict the metabolic phenotypes of cancer cells. This model is reconstructed by collecting the available data on tumor suppressor genes. Notably, we show that the activation of oncogene related reactions can be explained by the inactivation of tumor suppressor genes. We show that in a simulated growth medium similar to the body fluids, our model outperforms the previously proposed model of cancer metabolism in predicting expressed genes.

  6. Creatine transporter deficiency leads to increased whole body and cellular metabolism.

    PubMed

    Perna, Marla K; Kokenge, Amanda N; Miles, Keila N; Udobi, Kenea C; Clark, Joseph F; Pyne-Geithman, Gail J; Khuchua, Zaza; Skelton, Matthew R

    2016-08-01

    Creatine (Cr) is a guanidino compound required for rapid replenishment of ATP in cells with a high-energy demand. In humans, mutations in the Cr transporter (CRT;SLC6A8) prevent Cr entry into tissue and result in a significant intellectual impairment, epilepsy, and aphasia. The lack of Cr on both the whole body and cellular metabolism was evaluated in Crt knockout (Crt (-/y) ) mice, a high-fidelity model of human CRT deficiency. Crt (-/y) mice have reduced body mass and, however, show a twofold increase in body fat. There was increased energy expenditure in a home cage environment and during treadmill running in Crt (-/y) mice. Consistent with the increases in the whole-body metabolic function, Crt (-/y) mice show increased cellular metabolism as well. Mitochondrial respiration increased in skeletal muscle fibers and hippocampal lysates from Crt (-/y) mice. In addition, Crt (-/y) mice had increased citrate synthase activity, suggesting a higher number of mitochondria instead of an increase in mitochondrial activity. To determine if the increase in respiration was due to increased mitochondrial numbers, we measured oxygen consumption in an equal number of mitochondria from Crt (+/y) and Crt (-/y) mice. There were no changes in mitochondrial respiration when normalized to mitochondrial number, suggesting that the increase in respiration observed could be to higher mitochondrial content in Crt (-/y) mice.

  7. Creatine transporter deficiency leads to increased whole body and cellular metabolism.

    PubMed

    Perna, Marla K; Kokenge, Amanda N; Miles, Keila N; Udobi, Kenea C; Clark, Joseph F; Pyne-Geithman, Gail J; Khuchua, Zaza; Skelton, Matthew R

    2016-08-01

    Creatine (Cr) is a guanidino compound required for rapid replenishment of ATP in cells with a high-energy demand. In humans, mutations in the Cr transporter (CRT;SLC6A8) prevent Cr entry into tissue and result in a significant intellectual impairment, epilepsy, and aphasia. The lack of Cr on both the whole body and cellular metabolism was evaluated in Crt knockout (Crt (-/y) ) mice, a high-fidelity model of human CRT deficiency. Crt (-/y) mice have reduced body mass and, however, show a twofold increase in body fat. There was increased energy expenditure in a home cage environment and during treadmill running in Crt (-/y) mice. Consistent with the increases in the whole-body metabolic function, Crt (-/y) mice show increased cellular metabolism as well. Mitochondrial respiration increased in skeletal muscle fibers and hippocampal lysates from Crt (-/y) mice. In addition, Crt (-/y) mice had increased citrate synthase activity, suggesting a higher number of mitochondria instead of an increase in mitochondrial activity. To determine if the increase in respiration was due to increased mitochondrial numbers, we measured oxygen consumption in an equal number of mitochondria from Crt (+/y) and Crt (-/y) mice. There were no changes in mitochondrial respiration when normalized to mitochondrial number, suggesting that the increase in respiration observed could be to higher mitochondrial content in Crt (-/y) mice. PMID:27401086

  8. Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii.

    PubMed

    Gargouri, Mahmoud; Park, Jeong-Jin; Holguin, F Omar; Kim, Min-Jeong; Wang, Hongxia; Deshpande, Rahul R; Shachar-Hill, Yair; Hicks, Leslie M; Gang, David R

    2015-08-01

    Microalgae-based biofuels are promising sources of alternative energy, but improvements throughout the production process are required to establish them as economically feasible. One of the most influential improvements would be a significant increase in lipid yields, which could be achieved by altering the regulation of lipid biosynthesis and accumulation. Chlamydomonas reinhardtii accumulates oil (triacylglycerols, TAG) in response to nitrogen (N) deprivation. Although a few important regulatory genes have been identified that are involved in controlling this process, a global understanding of the larger regulatory network has not been developed. In order to uncover this network in this species, a combined omics (transcriptomic, proteomic and metabolomic) analysis was applied to cells grown in a time course experiment after a shift from N-replete to N-depleted conditions. Changes in transcript and protein levels of 414 predicted transcription factors (TFs) and transcriptional regulators (TRs) were monitored relative to other genes. The TF and TR genes were thus classified by two separate measures: up-regulated versus down-regulated and early response versus late response relative to two phases of polar lipid synthesis (before and after TAG biosynthesis initiation). Lipidomic and primary metabolite profiling generated compound accumulation levels that were integrated with the transcript dataset and TF profiling to produce a transcriptional regulatory network. Evaluation of this proposed regulatory network led to the identification of several regulatory hubs that control many aspects of cellular metabolism, from N assimilation and metabolism, to central metabolism, photosynthesis and lipid metabolism.

  9. Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii

    PubMed Central

    Gargouri, Mahmoud; Park, Jeong-Jin; Holguin, F. Omar; Kim, Min-Jeong; Wang, Hongxia; Deshpande, Rahul R.; Shachar-Hill, Yair; Hicks, Leslie M.; Gang, David R.

    2015-01-01

    Microalgae-based biofuels are promising sources of alternative energy, but improvements throughout the production process are required to establish them as economically feasible. One of the most influential improvements would be a significant increase in lipid yields, which could be achieved by altering the regulation of lipid biosynthesis and accumulation. Chlamydomonas reinhardtii accumulates oil (triacylglycerols, TAG) in response to nitrogen (N) deprivation. Although a few important regulatory genes have been identified that are involved in controlling this process, a global understanding of the larger regulatory network has not been developed. In order to uncover this network in this species, a combined omics (transcriptomic, proteomic and metabolomic) analysis was applied to cells grown in a time course experiment after a shift from N-replete to N-depleted conditions. Changes in transcript and protein levels of 414 predicted transcription factors (TFs) and transcriptional regulators (TRs) were monitored relative to other genes. The TF and TR genes were thus classified by two separate measures: up-regulated versus down-regulated and early response versus late response relative to two phases of polar lipid synthesis (before and after TAG biosynthesis initiation). Lipidomic and primary metabolite profiling generated compound accumulation levels that were integrated with the transcript dataset and TF profiling to produce a transcriptional regulatory network. Evaluation of this proposed regulatory network led to the identification of several regulatory hubs that control many aspects of cellular metabolism, from N assimilation and metabolism, to central metabolism, photosynthesis and lipid metabolism. PMID:26022256

  10. A Scalable Algorithm to Explore the Gibbs Energy Landscape of Genome-Scale Metabolic Networks

    PubMed Central

    De Martino, Daniele; Figliuzzi, Matteo

    2012-01-01

    The integration of various types of genomic data into predictive models of biological networks is one of the main challenges currently faced by computational biology. Constraint-based models in particular play a key role in the attempt to obtain a quantitative understanding of cellular metabolism at genome scale. In essence, their goal is to frame the metabolic capabilities of an organism based on minimal assumptions that describe the steady states of the underlying reaction network via suitable stoichiometric constraints, specifically mass balance and energy balance (i.e. thermodynamic feasibility). The implementation of these requirements to generate viable configurations of reaction fluxes and/or to test given flux profiles for thermodynamic feasibility can however prove to be computationally intensive. We propose here a fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state. The method is applied to the problem of reconstructing the Gibbs energy landscape underlying metabolic activity in the human red blood cell, and to that of identifying and removing thermodynamically infeasible reaction cycles in the Escherichia coli metabolic network (iAF1260). In the former case, we produce consistent predictions for chemical potentials (or log-concentrations) of intracellular metabolites; in the latter, we identify a restricted set of loops (23 in total) in the periplasmic and cytoplasmic core as the origin of thermodynamic infeasibility in a large sample () of flux configurations generated randomly and compatibly with the prior information available on reaction reversibility. PMID:22737065

  11. Effects of cellular homeostatic intrinsic plasticity on dynamical and computational properties of biological recurrent neural networks.

    PubMed

    Naudé, Jérémie; Cessac, Bruno; Berry, Hugues; Delord, Bruno

    2013-09-18

    Homeostatic intrinsic plasticity (HIP) is a ubiquitous cellular mechanism regulating neuronal activity, cardinal for the proper functioning of nervous systems. In invertebrates, HIP is critical for orchestrating stereotyped activity patterns. The functional impact of HIP remains more obscure in vertebrate networks, where higher order cognitive processes rely on complex neural dynamics. The hypothesis has emerged that HIP might control the complexity of activity dynamics in recurrent networks, with important computational consequences. However, conflicting results about the causal relationships between cellular HIP, network dynamics, and computational performance have arisen from machine-learning studies. Here, we assess how cellular HIP effects translate into collective dynamics and computational properties in biological recurrent networks. We develop a realistic multiscale model including a generic HIP rule regulating the neuronal threshold with actual molecular signaling pathways kinetics, Dale's principle, sparse connectivity, synaptic balance, and Hebbian synaptic plasticity (SP). Dynamic mean-field analysis and simulations unravel that HIP sets a working point at which inputs are transduced by large derivative ranges of the transfer function. This cellular mechanism ensures increased network dynamics complexity, robust balance with SP at the edge of chaos, and improved input separability. Although critically dependent upon balanced excitatory and inhibitory drives, these effects display striking robustness to changes in network architecture, learning rates, and input features. Thus, the mechanism we unveil might represent a ubiquitous cellular basis for complex dynamics in neural networks. Understanding this robustness is an important challenge to unraveling principles underlying self-organization around criticality in biological recurrent neural networks.

  12. Effects of cellular homeostatic intrinsic plasticity on dynamical and computational properties of biological recurrent neural networks.

    PubMed

    Naudé, Jérémie; Cessac, Bruno; Berry, Hugues; Delord, Bruno

    2013-09-18

    Homeostatic intrinsic plasticity (HIP) is a ubiquitous cellular mechanism regulating neuronal activity, cardinal for the proper functioning of nervous systems. In invertebrates, HIP is critical for orchestrating stereotyped activity patterns. The functional impact of HIP remains more obscure in vertebrate networks, where higher order cognitive processes rely on complex neural dynamics. The hypothesis has emerged that HIP might control the complexity of activity dynamics in recurrent networks, with important computational consequences. However, conflicting results about the causal relationships between cellular HIP, network dynamics, and computational performance have arisen from machine-learning studies. Here, we assess how cellular HIP effects translate into collective dynamics and computational properties in biological recurrent networks. We develop a realistic multiscale model including a generic HIP rule regulating the neuronal threshold with actual molecular signaling pathways kinetics, Dale's principle, sparse connectivity, synaptic balance, and Hebbian synaptic plasticity (SP). Dynamic mean-field analysis and simulations unravel that HIP sets a working point at which inputs are transduced by large derivative ranges of the transfer function. This cellular mechanism ensures increased network dynamics complexity, robust balance with SP at the edge of chaos, and improved input separability. Although critically dependent upon balanced excitatory and inhibitory drives, these effects display striking robustness to changes in network architecture, learning rates, and input features. Thus, the mechanism we unveil might represent a ubiquitous cellular basis for complex dynamics in neural networks. Understanding this robustness is an important challenge to unraveling principles underlying self-organization around criticality in biological recurrent neural networks. PMID:24048833

  13. MicroRNAs and oncogenic transcriptional regulatory networks controlling metabolic reprogramming in cancers.

    PubMed

    Pinweha, Pannapa; Rattanapornsompong, Khanti; Charoensawan, Varodom; Jitrapakdee, Sarawut

    2016-01-01

    Altered cellular metabolism is a fundamental adaptation of cancer during rapid proliferation as a result of growth factor overstimulation. We review different pathways involving metabolic alterations in cancers including aerobic glycolysis, pentose phosphate pathway, de novo fatty acid synthesis, and serine and glycine metabolism. Although oncoproteins, c-MYC, HIF1α and p53 are the major drivers of this metabolic reprogramming, post-transcriptional regulation by microRNAs (miR) also plays an important role in finely adjusting the requirement of the key metabolic enzymes underlying this metabolic reprogramming. We also combine the literature data on the miRNAs that potentially regulate 40 metabolic enzymes responsible for metabolic reprogramming in cancers, with additional miRs from computational prediction. Our analyses show that: (1) a metabolic enzyme is frequently regulated by multiple miRs, (2) confidence scores from prediction algorithms might be useful to help narrow down functional miR-mRNA interaction, which might be worth further experimental validation. By combining known and predicted interactions of oncogenic transcription factors (TFs) (c-MYC, HIF1α and p53), sterol regulatory element binding protein 1 (SREBP1), 40 metabolic enzymes, and regulatory miRs we have established one of the first reference maps for miRs and oncogenic TFs that regulate metabolic reprogramming in cancers. The combined network shows that glycolytic enzymes are linked to miRs via p53, c-MYC, HIF1α, whereas the genes in serine, glycine and one carbon metabolism are regulated via the c-MYC, as well as other regulatory organization that cannot be observed by investigating individual miRs, TFs, and target genes. PMID:27358718

  14. Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli

    PubMed Central

    McCloskey, Douglas; Palsson, Bernhard Ø; Feist, Adam M

    2013-01-01

    The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype–phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large-scale network models with sufficient accuracy. PMID:23632383

  15. On Window Control Algorithm over Wireless Cellular Networks with Large Delay Variation

    NASA Astrophysics Data System (ADS)

    Lee, Ho-Jin; Byun, Hee-Jung; Lim, Jong-Tae

    In addition to high bit error rates, large and sudden variations in delay often occur in wireless cellular networks. The delay can be several times the typical round-trip time, which can cause the spurious timeout. In this letter, we propose a new window control algorithm to improve TCP performance in wireless cellular networks with large delay variation and high bit error rates. Simulation results illustrate that our proposal improves the performance of TCP in terms of fairness and link utilization.

  16. Metabolic Network Constrains Gene Regulation of C4 Photosynthesis: The Case of Maize

    PubMed Central

    Robaina-Estévez, Semidán; Nikoloski, Zoran

    2016-01-01

    Engineering C3 plants to increase their efficiency of carbon fixation as well as of nitrogen and water use simultaneously may be facilitated by understanding the mechanisms that underpin the C4 syndrome. Existing experimental studies have indicated that the emergence of the C4 syndrome requires co-ordination between several levels of cellular organization, from gene regulation to metabolism, across two co-operating cell systems—mesophyll and bundle sheath cells. Yet, determining the extent to which the structure of the C4 plant metabolic network may constrain gene expression remains unclear, although it will provide an important consideration in engineering C4 photosynthesis in C3 plants. Here, we utilize flux coupling analysis with the second-generation maize metabolic models to investigate the correspondence between metabolic network structure and transcriptomic phenotypes along the maize leaf gradient. The examined scenarios with publically available data from independent experiments indicate that the transcriptomic programs of the two cell types are co-ordinated, quantitatively and qualitatively, due to the presence of coupled metabolic reactions in specific metabolic pathways. Taken together, our study demonstrates that precise quantitative coupling will have to be achieved in order to ensure a successfully engineered transition from C3 to C4 crops. PMID:26903529

  17. Current concepts in chronic inflammatory diseases: Interactions between microbes, cellular metabolism, and inflammation.

    PubMed

    Garn, Holger; Bahn, Sabine; Baune, Bernhard T; Binder, Elisabeth B; Bisgaard, Hans; Chatila, Talal A; Chavakis, Triantafyllos; Culmsee, Carsten; Dannlowski, Udo; Gay, Steffen; Gern, James; Haahtela, Tari; Kircher, Tilo; Müller-Ladner, Ulf; Neurath, Markus F; Preissner, Klaus T; Reinhardt, Christoph; Rook, Graham; Russell, Shannon; Schmeck, Bernd; Stappenbeck, Thaddeus; Steinhoff, Ulrich; van Os, Jim; Weiss, Scott; Zemlin, Michael; Renz, Harald

    2016-07-01

    Recent research indicates that chronic inflammatory diseases, including allergies and autoimmune and neuropsychiatric diseases, share common pathways of cellular and molecular dysregulation. It was the aim of the International von-Behring-Röntgen Symposium (October 16-18, 2014, in Marburg, Germany) to discuss recent developments in this field. These include a concept of biodiversity; the contribution of urbanization, lifestyle factors, and nutrition (eg, vitamin D); and new mechanisms of metabolic and immune dysregulation, such as extracellular and intracellular RNAs and cellular and mitochondrial stress. Epigenetic mechanisms contribute further to altered gene expression and therefore to the development of chronic inflammation. These novel findings provide the foundation for further development of preventive and therapeutic strategies. PMID:27373325

  18. Cues for cellular assembly of vascular elastin networks

    NASA Astrophysics Data System (ADS)

    Kothapalli, Chandrasekhar R.

    Elastin, a structural protein distributed in the extracellular matrix of vascular tissues is critical to the maintenance of vascular mechanics, besides regulation of cell-signaling pathways involved in injury response and morphogenesis. Thus, congenital absence or disease-mediated degradation of vascular elastin and its malformation within native vessels due to innately poor elastin synthesis by adult vascular cells compromise vascular homeostasis. Current elastin regenerative strategies using tissue engineering principles are limited by the progressive destabilization of tropoelastin mRNA expression in adult vascular cells and the unavailability of scaffolds that can provide cellular cues necessary to up-regulate elastin synthesis and regenerate faithful mimics of native elastin. Since our earlier studies demonstrated the elastogenic utility of hyaluronan (HA)-based cues, we have currently sought to identify a unique set of culture conditions based on HA fragments (0.756-2000 kDa), growth factors (TGF-beta1, IGF-1) and other biomolecules (Cu2+ ions, LOX), which will together enhance synthesis, crosslinking, maturation and fibrous elastin matrix formation by adult SMCs, under both healthy and inflammatory conditions. It was observed that TGF-beta1 (1 ng/mL) together with HA oligomers (0.2 microg/mL) synergistically suppressed SMC proliferation, enhanced tropoelastin (8-fold) and matrix elastin synthesis (5.5-fold), besides improving matrix yield (4.5-fold), possibly by increasing production and activity of lysyl oxidase (LOX). Though addition of IGF-1 alone did not offer any advantage, HA fragments (20-200 kDa) in the presence of IGF-1 stimulated tropoelastin and soluble elastin synthesis more than 2.2-fold, with HMW HA contributing for ˜5-fold increase in crosslinked matrix elastin synthesis. Similarly, 0.1 M of Cu2+ ions, alone or together with HA fragments stimulated synthesis of tropoelastin (4-fold) and crosslinked matrix elastin (4.5-fold), via increases in

  19. Mammalian Mss51 is a skeletal muscle-specific gene modulating cellular metabolism

    PubMed Central

    Moyer, Adam L.; Wagner, Kathryn R.

    2015-01-01

    Background The transforming growth factor β (TGF-β) signaling pathways modulate skeletal muscle growth, regeneration, and cellular metabolism. Several recent gene expression studies have shown that inhibition of myostatin and TGF-β1 signaling consistently leads to a significant reduction in expression of Mss51, also named Zmynd17. The function of mammalian Mss51 is unknown although a putative homolog in yeast is a mitochondrial translational activator. Objective The objective of this work was to characterize mammalian Mss51. Methods Quantitative RT-PCR and immunoblot of subcellular fractionation were used to determine expression patterns and localization of Mss51. The CRISPR/Cas9 system was used to reduce expression of Mss51 in C2C12 myoblasts and the function of Mss51 was evaluated in assays of proliferation, differentiation and cellular metabolism. Results Mss51 was predominantly expressed in skeletal muscle and in those muscles dominated by fast-twitch fibers. In vitro, its expression was upregulated upon differentiation of C2C12 myoblasts into myotubes. Expression of Mss51 was modulated in response to altered TGF-β family signaling. In human muscle, Mss51 localized to the mitochondria. Its genetic disruption resulted in increased levels of cellular ATP, β-oxidation, glycolysis, and oxidative phosphorylation. Conclusions Mss51 is a novel, skeletal muscle-specific gene and a key target of myostatin and TGF-β1 signaling. Unlike myostatin, TGF-β1 and IGF-1, Mss51 does not regulate myoblast proliferation or differentiation. Rather, Mss51 appears to be one of the effectors of these growth factors on metabolic processes including fatty acid oxidation, glycolysis and oxidative phosphorylation. PMID:26634192

  20. An integrated text mining framework for metabolic interaction network reconstruction.

    PubMed

    Patumcharoenpol, Preecha; Doungpan, Narumol; Meechai, Asawin; Shen, Bairong; Chan, Jonathan H; Vongsangnak, Wanwipa

    2016-01-01

    Text mining (TM) in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals) as well as their relationships. Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions) through metabolic events. Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module-MEE) and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module-MINR). The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score. Evaluation of the MEE module using the constructed Metabolic Entities (ME) corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively. As for the testing of the entity tagger for Gene and Protein (GP) and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis. Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score >70%. Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data) for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme-metabolite interaction, respectively. This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network. This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network. The ME corpus, test corpus, source code, and virtual

  1. Flux analysis in plant metabolic networks: increasing throughput and coverage.

    PubMed

    Junker, Björn H

    2014-04-01

    Quantitative information about metabolic networks has been mainly obtained at the level of metabolite contents, transcript abundance, and enzyme activities. However, the active process of metabolism is represented by the flow of matter through the pathways. These metabolic fluxes can be predicted by Flux Balance Analysis or determined experimentally by (13)C-Metabolic Flux Analysis. These relatively complicated and time-consuming methods have recently seen significant improvements at the level of coverage and throughput. Metabolic models have developed from single cell models into whole-organism dynamic models. Advances in lab automation and data handling have significantly increased the throughput of flux measurements. This review summarizes advances to increase coverage and throughput of metabolic flux analysis in plants.

  2. Computational identification of obligatorily autocatalytic replicators embedded in metabolic networks

    PubMed Central

    Kun, Ádám; Papp, Balázs; Szathmáry, Eörs

    2008-01-01

    Background If chemical A is necessary for the synthesis of more chemical A, then A has the power of replication (such systems are known as autocatalytic systems). We provide the first systems-level analysis searching for small-molecular autocatalytic components in the metabolisms of diverse organisms, including an inferred minimal metabolism. Results We find that intermediary metabolism is invariably autocatalytic for ATP. Furthermore, we provide evidence for the existence of additional, organism-specific autocatalytic metabolites in the forms of coenzymes (NAD+, coenzyme A, tetrahydrofolate, quinones) and sugars. Although the enzymatic reactions of a number of autocatalytic cycles are present in most of the studied organisms, they display obligatorily autocatalytic behavior in a few networks only, hence demonstrating the need for a systems-level approach to identify metabolic replicators embedded in large networks. Conclusion Metabolic replicators are apparently common and potentially both universal and ancestral: without their presence, kick-starting metabolic networks is impossible, even if all enzymes and genes are present in the same cell. Identification of metabolic replicators is also important for attempts to create synthetic cells, as some of these autocatalytic molecules will presumably be needed to be added to the system as, by definition, the system cannot synthesize them without their initial presence. PMID:18331628

  3. Metabolic networks evolve towards states of maximum entropy production

    PubMed Central

    Unrean, Pornkamol; Srienc, Friedrich

    2011-01-01

    A metabolic network can be described by a set of elementary modes or pathways representing discrete metabolic states that support cell function. We have recently shown that in the most likely metabolic state the usage probability of individual elementary modes is distributed according to the Boltzmann distribution law while complying with the principle of maximum entropy production. To demonstrate that a metabolic network evolves towards such state we have carried out adaptive evolution experiments with Thermoanaerobacterium saccharolyticum operating with a reduced metabolic functionality based on a reduced set of elementary modes. In such reduced metabolic network metabolic fluxes can be conveniently computed from the measured metabolite secretion pattern. Over a time span of 300 generations the specific growth rate of the strain continuously increased together with a continuous increase in the rate of entropy production. We show that the rate of entropy production asymptotically approaches the maximum entropy production rate predicted from the state when the usage probability of individual elementary modes is distributed according to the Boltzmann distribution. Therefore, the outcome of evolution of a complex biological system can be predicted in highly quantitative terms using basic statistical mechanical principles. PMID:21903175

  4. Impact of cellular metabolism on the biological effects of benzo[a]pyrene and related hydrocarbons.

    PubMed

    Miller, K P; Ramos, K S

    2001-02-01

    Polycyclic aromatic hydrocarbons are ubiquitous contaminants in the environment. Benzo[a]pyrene (BaP), a prototypical member of this class of chemicals, has been extensively studied for its toxic effects in laboratory animals and human populations. BaP toxicity is often mediated by oxidative metabolism to reactive intermediates that interact with macromolecules leading to alterations in target cell structure and function. More recent evidence suggests that disruption of cellular signaling pathways involved in the regulation of growth and differentiation contribute significantly to the toxicity of BaP and its metabolites. This review summarizes recent advances in our understanding of biological mechanisms of BaP toxicity at the molecular level, and the role of metabolic intermediates in carcinogenesis, atherogenesis, and teratogenesis.

  5. Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

    NASA Astrophysics Data System (ADS)

    Thomas, Alex; Rahmanian, Sorena; Bordbar, Aarash; Palsson, Bernhard Ø.; Jamshidi, Neema

    2014-01-01

    Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.

  6. Participatory sensing as an enabler for self-organisation in future cellular networks

    NASA Astrophysics Data System (ADS)

    Imran, Muhammad Ali; Imran, Ali; Onireti, Oluwakayode

    2013-12-01

    In this short review paper we summarise the emerging challenges in the field of participatory sensing for the self-organisation of the next generation of wireless cellular networks. We identify the potential of participatory sensing in enabling the self-organisation, deployment optimisation and radio resource management of wireless cellular networks. We also highlight how this approach can meet the future goals for the next generation of cellular system in terms of infrastructure sharing, management of multiple radio access techniques, flexible usage of spectrum and efficient management of very small data cells.

  7. Simulating Quantitative Cellular Responses Using Asynchronous Threshold Boolean Network Ensembles

    EPA Science Inventory

    With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate t...

  8. Filament formation by metabolic enzymes is a specific adaptation to an advanced state of cellular starvation

    PubMed Central

    Petrovska, Ivana; Nüske, Elisabeth; Munder, Matthias C; Kulasegaran, Gayathrie; Malinovska, Liliana; Kroschwald, Sonja; Richter, Doris; Fahmy, Karim; Gibson, Kimberley; Verbavatz, Jean-Marc; Alberti, Simon

    2014-01-01

    One of the key questions in biology is how the metabolism of a cell responds to changes in the environment. In budding yeast, starvation causes a drop in intracellular pH, but the functional role of this pH change is not well understood. Here, we show that the enzyme glutamine synthetase (Gln1) forms filaments at low pH and that filament formation leads to enzymatic inactivation. Filament formation by Gln1 is a highly cooperative process, strongly dependent on macromolecular crowding, and involves back-to-back stacking of cylindrical homo-decamers into filaments that associate laterally to form higher order fibrils. Other metabolic enzymes also assemble into filaments at low pH. Hence, we propose that filament formation is a general mechanism to inactivate and store key metabolic enzymes during a state of advanced cellular starvation. These findings have broad implications for understanding the interplay between nutritional stress, the metabolism and the physical organization of a cell. DOI: http://dx.doi.org/10.7554/eLife.02409.001 PMID:24771766

  9. Cellular lead toxicity and metabolism in primary and clonal osteoblastic bone cells

    SciTech Connect

    Long, G.J.; Rosen, J.F.; Pounds, J.G. )

    1990-02-01

    A knowledge of bone lead metabolism is critical for understanding the toxicological importance of bone lead, as a toxicant both to bone cells and to soft tissues of the body, as lead is mobilized from large reservoirs in hard tissues. To further understand the processes that mediate metabolism of lead in bone, it is necessary to determine lead metabolism at the cellular level. Experiments were conducted to determine the intracellular steady-state {sup 210}Pb kinetics in cultures of primary and clonal osteoblastic bone cells. Osteoblastic bone cells obtained by sequential collagenase digestion of mouse calvaria or rat osteosarcoma (ROS 17/2.8) cells were labeled with {sup 210}Pb as 5 microM lead acetate for 20 hr, and kinetic parameters were determined by measuring the efflux of {sup 210}Pb from the cells over a {sup 210}-min period. The intracellular metabolism of {sup 210}Pb was characterized by three kinetic pools of {sup 210}Pb in both cell types. Although the values of these parameters differed between the primary osteoblastic cells and ROS cells, the profile of {sup 210}Pb was remarkably similar in both cell types. Both types exhibited one large, slowly exchanging pool (S3), indicative of mitochondrial lead. These data show that primary osteoblastic bone cells and ROS cells exhibit similar steady-state lead kinetics, and intracellular lead distribution. These data also establish a working model of lead kinetics in osteoblastic bone cells and now permit an integrated view of lead kinetics in bone.

  10. Phylogeny of metabolic networks: a spectral graph theoretical approach.

    PubMed

    Deyasi, Krishanu; Banerjee, Anirban; Deb, Bony

    2015-10-01

    Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of 79 fully sequenced organisms and compared their architectures. We used spectral density of normalized Laplacian matrix for comparing the structure of networks. The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the phylogeny from these distances. In our analysis, we focused on the species that belong to different classes, but appear more related to each other in the phylogeny. We tried to explore whether they have evolved under similar environmental conditions or have similar life histories. With this focus, we have obtained interesting insights into the phylogenetic commonality between different organisms. PMID:26564980

  11. Phylogeny of metabolic networks: a spectral graph theoretical approach.

    PubMed

    Deyasi, Krishanu; Banerjee, Anirban; Deb, Bony

    2015-10-01

    Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of 79 fully sequenced organisms and compared their architectures. We used spectral density of normalized Laplacian matrix for comparing the structure of networks. The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the phylogeny from these distances. In our analysis, we focused on the species that belong to different classes, but appear more related to each other in the phylogeny. We tried to explore whether they have evolved under similar environmental conditions or have similar life histories. With this focus, we have obtained interesting insights into the phylogenetic commonality between different organisms.

  12. Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli.

    PubMed

    Seo, Sang Woo; Kim, Donghyuk; Latif, Haythem; O'Brien, Edward J; Szubin, Richard; Palsson, Bernhard O

    2014-09-15

    The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response to iron availability using genome-wide measurements. Integrative data analysis reveals that a total of 81 genes in 42 transcription units are directly regulated by three different modes of Fur regulation, including apo- and holo-Fur activation and holo-Fur repression. We show that Fur connects iron transport and utilization enzymes with negative-feedback loop pairs for iron homeostasis. In addition, direct involvement of Fur in the regulation of DNA synthesis, energy metabolism and biofilm development is found. These results show how Fur exhibits a comprehensive regulatory role affecting many fundamental cellular processes linked to iron metabolism in order to coordinate the overall response of E. coli to iron availability.

  13. Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli.

    PubMed

    Seo, Sang Woo; Kim, Donghyuk; Latif, Haythem; O'Brien, Edward J; Szubin, Richard; Palsson, Bernhard O

    2014-01-01

    The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response to iron availability using genome-wide measurements. Integrative data analysis reveals that a total of 81 genes in 42 transcription units are directly regulated by three different modes of Fur regulation, including apo- and holo-Fur activation and holo-Fur repression. We show that Fur connects iron transport and utilization enzymes with negative-feedback loop pairs for iron homeostasis. In addition, direct involvement of Fur in the regulation of DNA synthesis, energy metabolism and biofilm development is found. These results show how Fur exhibits a comprehensive regulatory role affecting many fundamental cellular processes linked to iron metabolism in order to coordinate the overall response of E. coli to iron availability. PMID:25222563

  14. Cellular Metabolic Activity and the Oxygen and Hydrogen Stable Isotope Composition of Intracellular Water and Metabolites

    NASA Astrophysics Data System (ADS)

    Kreuzer-Martin, H. W.; Hegg, E. L.

    2008-12-01

    Intracellular water is an important pool of oxygen and hydrogen atoms for biosynthesis. Intracellular water is usually assumed to be isotopically identical to extracellular water, but an unexpected experimental result caused us to question this assumption. Heme O isolated from Escherichia coli cells grown in 95% H218O contained only a fraction of the theoretical value of labeled oxygen at a position where the O atom was known to be derived from water. In fact, fewer than half of the oxygen atoms were labeled. In an effort to explain this surprising result, we developed a method to determine the isotope ratios of intracellular water in cultured cells. The results of our experiments showed that during active growth, up to 70% of the oxygen atoms and 50% of the hydrogen atoms in the intracellular water of E. coli are generated during metabolism and can be isotopically distinct from extracellular water. The fraction of isotopically distinct atoms was substantially less in stationary phase and chilled cells, consistent with our hypothesis that less metabolically-generated water would be present in cells with lower metabolic activity. Our results were consistent with and explained the result of the heme O labeling experiment. Only about 40% of the O atoms on the heme O molecule were labeled because, presumably, only about 40% of the water inside the cells was 18O water that had diffused in from the culture medium. The rest of the intracellular water contained 16O atoms derived from either nutrients or atmospheric oxygen. To test whether we could also detect metabolically-derived hydrogen atoms in cellular constituents, we isolated fatty acids from log-phase and stationary phase E. coli and determined the H isotope ratios of individual fatty acids. The results of these experiments showed that environmental water contributed more H atoms to fatty acids isolated in stationary phase than to the same fatty acids isolated from log-phase cells. Stable isotope analyses of

  15. Metabolic Network Prediction of Drug Side Effects.

    PubMed

    Shaked, Itay; Oberhardt, Matthew A; Atias, Nir; Sharan, Roded; Ruppin, Eytan

    2016-03-23

    Drug side effects levy a massive cost on society through drug failures, morbidity, and mortality cases every year, and their early detection is critically important. Here, we describe the array of model-based phenotype predictors (AMPP), an approach that leverages medical informatics resources and a human genome-scale metabolic model (GSMM) to predict drug side effects. AMPP is substantially predictive (AUC > 0.7) for >70 drug side effects, including very serious ones such as interstitial nephritis and extrapyramidal disorders. We evaluate AMPP's predictive signal through cross-validation, comparison across multiple versions of a side effects database, and co-occurrence analysis of drug side effect associations in scientific abstracts (hypergeometric p value = 2.2e-40). AMPP outperforms a previous biochemical structure-based method in predicting metabolically based side effects (aggregate AUC = 0.65 versus 0.59). Importantly, AMPP enables the identification of key metabolic reactions and biomarkers that are predictive of specific side effects. Taken together, this work lays a foundation for future detection of metabolically grounded side effects during early stages of drug development. PMID:27135366

  16. MaizeCyc: Metabolic networks in maize

    Technology Transfer Automated Retrieval System (TEKTRAN)

    MaizeCyc is a catalog of known and predicted metabolic and transport pathways that enables plant researchers to graphically represent the metabolome of maize (Zea mays), thereby supporting integrated systems-biology analysis. Supported analyses include molecular and genetic/phenotypic profiling (e.g...

  17. Efficient Reconstruction of Predictive Consensus Metabolic Network Models.

    PubMed

    van Heck, Ruben G A; Ganter, Mathias; Martins Dos Santos, Vitor A P; Stelling, Joerg

    2016-08-01

    Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions. PMID:27563720

  18. Efficient Reconstruction of Predictive Consensus Metabolic Network Models

    PubMed Central

    Martins dos Santos, Vitor A. P.; Stelling, Joerg

    2016-01-01

    Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions. PMID:27563720

  19. Embryonic MicroRNA-369 Controls Metabolic Splicing Factors and Urges Cellular Reprograming.

    PubMed

    Konno, Masamitsu; Koseki, Jun; Kawamoto, Koichi; Nishida, Naohiro; Matsui, Hidetoshi; Dewi, Dyah Laksmi; Ozaki, Miyuki; Noguchi, Yuko; Mimori, Koshi; Gotoh, Noriko; Tanuma, Nobuhiro; Shima, Hiroshi; Doki, Yuichiro; Mori, Masaki; Ishii, Hideshi

    2015-01-01

    Noncoding microRNAs inhibit translation and lower the transcript stability of coding mRNA, however miR-369 s, in aberrant silencing genomic regions, stabilizes target proteins under cellular stress. We found that in vitro differentiation of embryonic stem cells led to chromatin methylation of histone H3K4 at the miR-369 region on chromosome 12qF in mice, which is expressed in embryonic cells and is critical for pluripotency. Proteomic analyses revealed that miR-369 stabilized translation of pyruvate kinase (Pkm2) splicing factors such as HNRNPA2B1. Overexpression of miR-369 stimulated Pkm2 splicing and enhanced induction of cellular reprogramming by induced pluripotent stem cell factors, whereas miR-369 knockdown resulted in suppression. Furthermore, immunoprecipitation analysis showed that the Argonaute complex contained the fragile X mental retardation-related protein 1 and HNRNPA2B1 in a miR-369-depedent manner. Our findings demonstrate a unique role of the embryonic miR-369-HNRNPA2B1 axis in controlling metabolic enzyme function, and suggest a novel pathway linking epigenetic, transcriptional, and metabolic control in cell reprogramming. PMID:26176628

  20. Embryonic MicroRNA-369 Controls Metabolic Splicing Factors and Urges Cellular Reprograming

    PubMed Central

    Konno, Masamitsu; Koseki, Jun; Kawamoto, Koichi; Nishida, Naohiro; Matsui, Hidetoshi; Dewi, Dyah Laksmi; Ozaki, Miyuki; Noguchi, Yuko; Mimori, Koshi; Gotoh, Noriko; Tanuma, Nobuhiro; Shima, Hiroshi; Doki, Yuichiro

    2015-01-01

    Noncoding microRNAs inhibit translation and lower the transcript stability of coding mRNA, however miR-369 s, in aberrant silencing genomic regions, stabilizes target proteins under cellular stress. We found that in vitro differentiation of embryonic stem cells led to chromatin methylation of histone H3K4 at the miR-369 region on chromosome 12qF in mice, which is expressed in embryonic cells and is critical for pluripotency. Proteomic analyses revealed that miR-369 stabilized translation of pyruvate kinase (Pkm2) splicing factors such as HNRNPA2B1. Overexpression of miR-369 stimulated Pkm2 splicing and enhanced induction of cellular reprogramming by induced pluripotent stem cell factors, whereas miR-369 knockdown resulted in suppression. Furthermore, immunoprecipitation analysis showed that the Argonaute complex contained the fragile X mental retardation-related protein 1 and HNRNPA2B1 in a miR-369-depedent manner. Our findings demonstrate a unique role of the embryonic miR-369-HNRNPA2B1 axis in controlling metabolic enzyme function, and suggest a novel pathway linking epigenetic, transcriptional, and metabolic control in cell reprogramming. PMID:26176628

  1. Roles of reactive oxygen species and selected antioxidants in regulation of cellular metabolism.

    PubMed

    Stańczyk, Małgorzata; Gromadzińska, Jolanta; Wasowicz, Wojciech

    2005-01-01

    Reactive oxygen species (ROS) are essential for life of aerobic organisms. They are produced in normal cells and formed as a result of exposure to numerous factors, both chemical and physical. In normal cells, oxygen derivatives are neutralized or eliminated owing to the presence of a natural defense mechanism that involves enzymatic antioxidants (glutathione peroxidase, superoxide dismutase, catalase) and water or fat-soluble non-enzymatic antioxidants (vitamins C and E, glutathione, selenium). Under certain conditions, however, ROS production during cellular metabolism also stimulated by external agents may exceed the natural ability of cells to eliminate them from the organism. The disturbed balance leads to the state known as oxidative stress inducing damage of DNA, proteins, and lipids. An inefficient repair mechanism may finally trigger the process of neoplastic transformation or cell death. Reactive oxygen species are also an integral part of signal transduction essential for intercellular communication. The balance between pro- and antioxidative processes determines normal cellular metabolism manifested by genes activation and/or proteins expression in response to exo- and endogenous stimuli. PMID:16052887

  2. Predicting novel pathways in genome-scale metabolic networks.

    PubMed

    Schuster, Stefan; de Figueiredo, Luís F; Kaleta, Christoph

    2010-10-01

    Elementary-modes analysis has become a well-established theoretical tool in metabolic pathway analysis. It allows one to decompose complex metabolic networks into the smallest functional entities, which can be interpreted as biochemical pathways. This analysis has, in medium-size metabolic networks, led to the successful theoretical prediction of hitherto unknown pathways. For illustration, we discuss the example of the phosphoenolpyruvate-glyoxylate cycle in Escherichia coli. Elementary-modes analysis meets with the problem of combinatorial explosion in the number of pathways with increasing system size, which has hampered scaling it up to genome-wide models. We present a novel approach to overcoming this obstacle. That approach is based on elementary flux patterns, which are defined as sets of reactions representing the basic routes through a particular subsystem that are compatible with admissible fluxes in a (possibly) much larger metabolic network. The subsystem can be made up by reactions in which we are interested in, for example, reactions producing a certain metabolite. This allows one to predict novel metabolic pathways in genome-scale networks.

  3. Topological Signatures of Species Interactions in Metabolic Networks

    PubMed Central

    Feldman, Marcus W.

    2009-01-01

    Abstract The topology of metabolic networks can provide insight not only into the metabolic processes that occur within each species, but also into interactions between different species. Here, we introduce a novel pair-wise, topology-based measure of biosynthetic support, reflecting the extent to which the nutritional requirements of one species could be satisfied by the biosynthetic capacity of another. To evaluate the biosynthetic support for a given pair of species, we use a graph-based algorithm to identify the set of exogenously acquired compounds in the metabolic network of the first species, and calculate the fraction of this set that occurs in the metabolic network of the second species. Reconstructing the metabolic network of 569 bacterial species and several eukaryotes, and calculating the biosynthetic support score for all bacterial-eukaryotic pairs, we show that this measure indeed reflects host-parasite interactions and facilitates a successful prediction of such interactions on a large-scale. Integrating this method with phylogenetic analysis and calculating the biosynthetic support of ancestral species in the Firmicutes division (as well as other bacterial divisions) further reveals a large-scale evolutionary trend of biosynthetic capacity loss in parasites. The inference of ecological features from genomic-based data presented here lays the foundations for an exciting “reverse ecology” framework for studying the complex web of interactions characterizing various ecosystems. PMID:19178139

  4. Topological signatures of species interactions in metabolic networks.

    PubMed

    Borenstein, Elhanan; Feldman, Marcus W

    2009-02-01

    The topology of metabolic networks can provide insight not only into the metabolic processes that occur within each species, but also into interactions between different species. Here, we introduce a novel pair-wise, topology-based measure of biosynthetic support, reflecting the extent to which the nutritional requirements of one species could be satisfied by the biosynthetic capacity of another. To evaluate the biosynthetic support for a given pair of species, we use a graph-based algorithm to identify the set of exogenously acquired compounds in the metabolic network of the first species, and calculate the fraction of this set that occurs in the metabolic network of the second species. Reconstructing the metabolic network of 569 bacterial species and several eukaryotes, and calculating the biosynthetic support score for all bacterial-eukaryotic pairs, we show that this measure indeed reflects host-parasite interactions and facilitates a successful prediction of such interactions on a large-scale. Integrating this method with phylogenetic analysis and calculating the biosynthetic support of ancestral species in the Firmicutes division (as well as other bacterial divisions) further reveals a large-scale evolutionary trend of biosynthetic capacity loss in parasites. The inference of ecological features from genomic-based data presented here lays the foundations for an exciting "reverse ecology" framework for studying the complex web of interactions characterizing various ecosystems.

  5. Evolutionary constraint and adaptation in the metabolic network of Drosophila.

    PubMed

    Greenberg, Anthony J; Stockwell, Sarah R; Clark, Andrew G

    2008-12-01

    Organisms must carefully control their metabolism in order to survive. On the other hand, enzymes must adapt in response to evolutionary pressures on the pathways in which they are imbedded. Taking advantage of the newly available whole-genome sequences of 12 Drosophila species, we examined how protein function and metabolic network architecture influence rates of enzyme evolution. We found that despite high overall constraint, there were significant differences in rates of amino acid substitution among functional classes of enzymes. This heterogeneity arises because proteins involved in the metabolism of foreign compounds evolve relatively rapidly, whereas enzymes that act in "core" metabolism exhibit much slower rates of amino acid replacement, suggesting strong selective constraint. Network architecture also influences enzymes' rates of amino acid replacement. In particular, enzymes that share metabolites with many other enzymes are relatively constrained, although apparently not because they are more likely to be essential. Our analyses suggest that this pattern is driven by strong constraint of enzymes acting at branch points in metabolic pathways. We conclude that metabolic network architecture and enzyme function separately affect enzyme evolution rates.

  6. Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Uijlenhoet, Remko; Leijnse, Hidde

    2016-04-01

    Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide rainfall maps can be derived from the signal attenuations of microwave links in such a network. We present a rainfall retrieval algorithm, which is employed to obtain rainfall maps from microwave links in a cellular communication network. We compare these rainfall maps to gauge-adjusted radar rainfall maps. The microwave link data set, as well as the developed code, a package in the open source scripting language "R", are freely available at GitHub (https://github.com/overeem11/RAINLINK). The purpose of this presentation is to promote rainfall mapping utilizing microwave links from cellular communication networks as an alternative or complementary means for continental-scale rainfall monitoring.

  7. Arctigenin preferentially induces tumor cell death under glucose deprivation by inhibiting cellular energy metabolism.

    PubMed

    Gu, Yuan; Qi, Chunting; Sun, Xiaoxiao; Ma, Xiuquan; Zhang, Haohao; Hu, Lihong; Yuan, Junying; Yu, Qiang

    2012-08-15

    Selectively eradicating cancer cells with minimum adverse effects on normal cells is a major challenge in the development of anticancer therapy. We hypothesize that nutrient-limiting conditions frequently encountered by cancer cells in poorly vascularized solid tumors might provide an opportunity for developing selective therapy. In this study, we investigated the function and molecular mechanisms of a natural compound, arctigenin, in regulating tumor cell growth. We demonstrated that arctigenin selectively promoted glucose-starved A549 tumor cells to undergo necrosis by inhibiting mitochondrial respiration. In doing so, arctigenin elevated cellular level of reactive oxygen species (ROS) and blocked cellular energy metabolism in the glucose-starved tumor cells. We also demonstrated that cellular ROS generation was caused by intracellular ATP depletion and played an essential role in the arctigenin-induced tumor cell death under the glucose-limiting condition. Furthermore, we combined arctigenin with the glucose analogue 2-deoxyglucose (2DG) and examined their effects on tumor cell growth. Interestingly, this combination displayed preferential cell-death inducing activity against tumor cells compared to normal cells. Hence, we propose that the combination of arctigenin and 2DG may represent a promising new cancer therapy with minimal normal tissue toxicity.

  8. Autophagy and phagocytosis-like cell cannibalism exert opposing effects on cellular survival during metabolic stress

    PubMed Central

    Poels, J; Spasić, M R; Gistelinck, M; Mutert, J; Schellens, A; Callaerts, P; Norga, K K

    2012-01-01

    Understanding mechanisms controlling neuronal cell death and survival under conditions of altered energy supply (e.g., during stroke) is fundamentally important for the development of therapeutic strategies. The function of autophagy herein is unclear, as both its beneficial and detrimental roles have been described. We previously demonstrated that loss of AMP-activated protein kinase (AMPK), an evolutionarily conserved enzyme that maintains cellular energy balance, leads to activity-dependent degeneration in neuronal tissue. Here, we show that energy depletion in Drosophila AMPK mutants results in increased autophagy that convincingly promotes, rather than rescues, neurodegeneration. The generated excessive autophagic response is accompanied by increased TOR and S6K activity in the absence of an AMPK-mediated negative regulatory feedback loop. Moreover, energy-depleted neurons use a phagocytic-like process as a means to cellular survival at the expense of surrounding cells. Consequently, phagocytosis stimulation by expression of the scavenger receptor Croquemort significantly delays neurodegeneration. This study thus reveals a potentially novel strategy for cellular survival during conditions of extreme energy depletion, resembling xeno-cannibalistic events seen in metastatic tumors. We provide new insights into the roles of autophagy and phagocytosis in the neuronal metabolic stress response and open new avenues into understanding of human disease and development of therapeutic strategies. PMID:22498699

  9. Defective Ca2+ metabolism in Duchenne muscular dystrophy: effects on cellular and viral growth.

    PubMed Central

    Fingerman, E; Campisi, J; Pardee, A B

    1984-01-01

    Normal fibroblasts in medium containing 0.02 mM CaCl2 arrested growth within 24 hr, whereas Duchenne muscular dystrophy fibroblasts continued to grow for 5 days, albeit at 40% of their rate in standard medium (1.8 mM CaCl2). Moreover, Duchenne cells in calcium-deficient medium showed an enhanced rate of protein synthesis (60% over the rate in standard medium), whereas normal cells were unaffected. Previously we described a general assay for detection of mutant cells by using herpes simplex virus I replication as a probe of cellular function. By altering the growth medium, one can elicit changes in viral DNA replication that depend upon cellular differences. Duchenne fibroblasts in calcium-deficient low-serum (0.5%) medium supported viral replication at a rate 7- to 10-fold greater than did normal cells infected under the same conditions. Using this viral assay, we have successfully identified all 10 samples of a blind coded set of Duchenne muscular dystrophy, normal, and heterozygote cells. In addition, differences of a lower magnitude were found between these cell strains as measured by cellular growth or protein synthesis. Therefore, a cell's ability to grow and support viral replication in calcium-deficient medium can be used to readily distinguish Duchenne muscular dystrophy fibroblasts from normal ones. These results suggest that the viral assay could be used as a prenatal diagnostic test. A defect related to calcium metabolism may be fundamental to this disease. PMID:6095311

  10. Accelerated Cellular Uptake and Metabolism of L-Thyroxine during Acute Salmonella typhimurium Sepsis

    PubMed Central

    DeRubertis, Frederick R.; Woeber, Kenneth A.

    1973-01-01

    The effects of acute Salmonella typhimurium sepsis on the kinetics of peripheral L-thyroxine (T4) distribution and metabolism and on serum total and free T4 concentrations were studied in rhesus monkeys inoculated i.v. with either heat-killed or viable organisms. The rate of disappearance of labeled T4 from serum was increased within 8 h after inoculation of monkeys with either heat-killed or viable Salmonella. The effects of the heat-killed organisms were transient and no longer evident by 16 h postinoculation. The monkeys inoculated with the viable Salmonella experienced a 2-3 day febrile, septic illness that was accompanied by an increase in the absolute rate of T4 disposal. In the infected monkeys, serum total T4 and endogenously labeled protein-bound iodine concentrations fell significantly during the period of acute sepsis and then rose during convalescence to values that exceeded the preinoculation values, suggesting that thyroidal secretion of hormone had increased in response to a primary depletion of the peripheral hormonal pool. Total cellular and hepatic uptakes of T4 were enhanced by 4 h after inoculation of monkeys with either heat-killed or viable Salmonella, but the increase in total cellular uptake persisted for 24 h only in the monkeys inoculated with the viable organisms. These alterations in T4 kinetics could neither be correlated with changes in the binding of T4 in plasma nor attributed to an increase in vascular permeability. Moreover, they could not be ascribed to an in vitro product of bacterial growth, suggesting that the presence of the organisms themselves was required. An acceleration of T4 disappearance was also observed during Escherichia coli and Diplococcus pucumoniae bacteremias. Our findings are consistent with a primary increase in the cellular uptake and metabolism of T4 during bacterial sepsis, possibly related to phagocytic cell function in the host. PMID:4629910

  11. The folate metabolic network of Falciparum malaria.

    PubMed

    Salcedo-Sora, J Enrique; Ward, Steve A

    2013-03-01

    The targeting of key enzymes in the folate pathway continues to be an effective chemotherapeutic approach that has earned antifolate drugs a valuable position in the medical pharmacopoeia. The successful therapeutic use of antifolates as antimalarials has been a catalyst for ongoing research into the biochemistry of folate and pterin biosynthesis in malaria parasites. However, our understanding of the parasites folate metabolism remains partial and patchy, especially in relation to the shikimate pathway, the folate cycle, and folate salvage. A sizeable number of potential folate targets remain to be characterised. Recent reports on the parasite specific transport of folate precursors that would normally be present in the human host awaken previous hypotheses on the salvage of folate precursors or by-products. As the parasite progresses through its life-cycle it encounters very contrasting host cell environments that present radically different metabolic milieus and biochemical challenges. It would seem probable that as the parasite encounters differing environments it would need to modify its biochemistry. This would be reflected in the folate homeostasis in Plasmodium. Recent drug screening efforts and insights into folate membrane transport substantiate the argument that folate metabolism may still offer unexplored opportunities for therapeutic attack.

  12. Channel modeling for fifth generation cellular networks and wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Torabi, Amir

    In view of exponential growth in data traffic demand, the wireless communications industry has aimed to increase the capacity of existing networks by 1000 times over the next 20 years. A combination of extreme cell densification, more bandwidth, and higher spectral efficiency is needed to support the data traffic requirements for fifth generation (5G) cellular communications. In this research, the potential improvements achieved by using three major 5G enabling technologies (i.e., small cells, millimeter-wave spectrum, and massive MIMO) in rural and urban environments are investigated. This work develops SPM and KA-based ray models to investigate the impact of geometrical parameters on terrain-based multiuser MIMO channel characteristic. Moreover, a new directional 3D channel model is developed for urban millimeter-wave (mmW) small cells. Path-loss, spatial correlation, coverage distance, and coherence length are studied in urban areas. Exploiting physical optics (PO) and geometric optics (GO) solutions, closed form expressions are derived for spatial correlation. Achievable spatial diversity is evaluated using horizontal and vertical linear arrays as well as planar 2D arrays. In another study, a versatile near-ground field prediction model is proposed to facilitate accurate wireless sensor network (WSN) simulations. Monte Carlo simulations are used to investigate the effects of antenna height, frequency of operation, polarization, and terrain dielectric and roughness properties on WSNs performance.

  13. Quantifying Uncertainties in Rainfall Maps from Cellular Communication Networks

    NASA Astrophysics Data System (ADS)

    Uijlenhoet, R.; Rios Gaona, M. F.; Overeem, A.; Leijnse, H.

    2014-12-01

    The core idea behind rainfall retrievals from commercial microwave link networks is to measure the decrease in power due to attenuation of the electromagnetic signal by raindrops along the link path. Accurate rainfall measurements are of vital importance in hydrological applications, for instance, flash-flood early-warning systems, agriculture, and climate modeling. Hence, such an alternative technique fulfills the need for measurements with higher resolution in time and space, especially in places where standard rain gauge-networks are scarce or poorly maintained. Rainfall estimation via commercial microwave link networks, at country-wide scales, has recently been demonstrated. Despite their potential applicability in rainfall estimation at higher spatiotemporal resolutions, the uncertainties present in link-based rainfall maps are not yet fully comprehended. Now we attempt to quantify the inherent sources of uncertainty present in interpolated maps computed from commercial microwave link rainfall retrievals. In order to disentangle these sources of uncertainty we identified four main sources of error: 1) microwave link measurements, 2) availability of microwave link measurements, 3) spatial distribution of the network, and 4) interpolation methodology. We computed more than 1000 rainfall fields, for The Netherlands, from real and simulated microwave link data. These rainfall fields were compared to quality-controlled gauge-adjusted radar rainfall maps considered as ground-truth. Thus we were able to quantify the contribution of errors in microwave link measurements to the overall uncertainty. The actual performance of the commercial microwave link network is affected by the intermittent availability of the links, not only in time but also in space. We simulated a fully-operational network in time and space, and thus we quantified the role of the availability of microwave link measurements to the overall uncertainty. This research showed that the largest source of

  14. Reverse engineering cellular decisions for hybrid reconfigurable network modeling

    NASA Astrophysics Data System (ADS)

    Blair, Howard A.; Saranak, Jureepan; Foster, Kenneth W.

    2011-06-01

    Cells as microorganisms and within multicellular organisms make robust decisions. Knowing how these complex cells make decisions is essential to explain, predict or mimic their behavior. The discovery of multi-layer multiple feedback loops in the signaling pathways of these modular hybrid systems suggests their decision making is sophisticated. Hybrid systems coordinate and integrate signals of various kinds: discrete on/off signals, continuous sensory signals, and stochastic and continuous fluctuations to regulate chemical concentrations. Such signaling networks can form reconfigurable networks of attractors and repellors giving them an extra level of organization that has resilient decision making built in. Work on generic attractor and repellor networks and on the already identified feedback networks and dynamic reconfigurable regulatory topologies in biological cells suggests that biological systems probably exploit such dynamic capabilities. We present a simple behavior of the swimming unicellular alga Chlamydomonas that involves interdependent discrete and continuous signals in feedback loops. We show how to rigorously verify a hybrid dynamical model of a biological system with respect to a declarative description of a cell's behavior. The hybrid dynamical systems we use are based on a unification of discrete structures and continuous topologies developed in prior work on convergence spaces. They involve variables of discrete and continuous types, in the sense of type theory in mathematical logic. A unification such as afforded by convergence spaces is necessary if one wants to take account of the affect of the structural relationships within each type on the dynamics of the system.

  15. Resource Management in QoS-Aware Wireless Cellular Networks

    ERIC Educational Resources Information Center

    Zhang, Zhi

    2011-01-01

    Emerging broadband wireless networks that support high speed packet data with heterogeneous quality of service (QoS) requirements demand more flexible and efficient use of the scarce spectral resource. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. In this work, we study…

  16. Application of Cellular Automata to Detection of Malicious Network Packets

    ERIC Educational Resources Information Center

    Brown, Robert L.

    2014-01-01

    A problem in computer security is identification of attack signatures in network packets. An attack signature is a pattern of bits that characterizes a particular attack. Because there are many kinds of attacks, there are potentially many attack signatures. Furthermore, attackers may seek to avoid detection by altering the attack mechanism so that…

  17. EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT

    PubMed Central

    Choudhary, Kumari Sonal; Rohatgi, Neha; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

    2016-01-01

    Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend. PMID:27253373

  18. Early Cellular Changes in the Ascending Aorta and Myocardium in a Swine Model of Metabolic Syndrome

    PubMed Central

    Mahmood, Feroze; Owais, Khurram; Bardia, Amit; Khabbaz, Kamal R.; Liu, David; Senthilnathan, Venkatachalam; Lassaletta, Antonio D.; Sellke, Frank; Matyal, Robina

    2016-01-01

    Background Metabolic syndrome is associated with pathological remodeling of the heart and adjacent vessels. The early biochemical and cellular changes underlying the vascular damage are not fully understood. In this study, we sought to establish the nature, extent, and initial timeline of cytochemical derangements underlying reduced ventriculo-arterial compliance in a swine model of metabolic syndrome. Methods Yorkshire swine (n = 8 per group) were fed a normal diet (ND) or a high-cholesterol (HCD) for 12 weeks. Myocardial function and blood flow was assessed before harvesting the heart. Immuno-blotting and immuno-histochemical staining were used to assess the cellular changes in the myocardium, ascending aorta and left anterior descending artery (LAD). Results There was significant increase in body mass index, blood glucose and mean arterial pressures (p = 0.002, p = 0.001 and p = 0.024 respectively) in HCD group. At the cellular level there was significant increase in anti-apoptotic factors p-Akt (p = 0.007 and p = 0.002) and Bcl-xL (p = 0.05 and p = 0.01) in the HCD aorta and myocardium, respectively. Pro-fibrotic markers TGF-β (p = 0.01), pSmad1/5 (p = 0.03) and MMP-9 (p = 0.005) were significantly increased in the HCD aorta. The levels of pro-apoptotic p38MAPK, Apaf-1 and cleaved Caspase3 were significantly increased in aorta of HCD (p = 0.03, p = 0.04 and p = 0.007 respectively). Similar changes in coronary arteries were not observed in either group. Functionally, the high cholesterol diet resulted in significant increase in ventricular end systolic pressure and–dp/dt (p = 0.05 and p = 0.007 respectively) in the HCD group. Conclusion Preclinical metabolic syndrome initiates pro-apoptosis and pro-fibrosis pathways in the heart and ascending aorta, while sparing coronary arteries at this early stage of dietary modification. PMID:26766185

  19. Reconstructed Metabolic Network Models Predict Flux-Level Metabolic Reprogramming in Glioblastoma

    PubMed Central

    Özcan, Emrah; Çakır, Tunahan

    2016-01-01

    Developments in genome scale metabolic modeling techniques and omics technologies have enabled the reconstruction of context-specific metabolic models. In this study, glioblastoma multiforme (GBM), one of the most common and aggressive malignant brain tumors, is investigated by mapping GBM gene expression data on the growth-implemented brain specific genome-scale metabolic network, and GBM-specific models are generated. The models are used to calculate metabolic flux distributions in the tumor cells. Metabolic phenotypes predicted by the GBM-specific metabolic models reconstructed in this work reflect the general metabolic reprogramming of GBM, reported both in in-vitro and in-vivo experiments. The computed flux profiles quantitatively predict that major sources of the acetyl-CoA and oxaloacetic acid pool used in TCA cycle are pyruvate dehydrogenase from glycolysis and anaplerotic flux from glutaminolysis, respectively. Also, our results, in accordance with recent studies, predict a contribution of oxidative phosphorylation to ATP pool via a slightly active TCA cycle in addition to the major contributor aerobic glycolysis. We verified our results by using different computational methods that incorporate transcriptome data with genome-scale models and by using different transcriptome datasets. Correct predictions of flux distributions in glycolysis, glutaminolysis, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM. PMID:27147948

  20. The Anticancer Plant Triterpenoid, Avicin D, Regulates Glucocorticoid Receptor Signaling: Implications for Cellular Metabolism

    PubMed Central

    Haridas, Valsala; Xu, Zhi-Xiang; Kitchen, Doug; Jiang, Anna; Michels, Peter; Gutterman, Jordan U.

    2011-01-01

    Avicins, a family of apoptotic triterpene electrophiles, are known to regulate cellular metabolism and energy homeostasis, by targeting the mitochondria. Having evolved from “ancient hopanoids,” avicins bear a structural resemblance with glucocorticoids (GCs), which are the endogenous regulators of metabolism and energy balance. These structural and functional similarities prompted us to compare the mode of action of avicin D with dexamethasone (Dex), a prototypical GC. Using cold competition assay, we show that Avicin D competes with Dex for binding to the GC receptor (GR), leading to its nuclear translocation. In contrast to Dex, avicin-induced nuclear translocation of GR does not result in transcriptional activation of GC-dependent genes. Instead we observe a decrease in the expression of GC-dependent metabolic proteins such as PEPCK and FASN. However, like Dex, avicin D treatment does induce a transrepressive effect on the pro-inflammatory transcription factor NF-κB. While avicin's ability to inhibit NF-κB and its downstream targets appear to be GR-dependent, its pro-apoptotic effects were independent of GR expression. Using various deletion mutants of GR, we demonstrate the requirement of both the DNA and ligand binding domains of GR in mediating avicin D's transrepressive effects. Modeling of avicin-GR interaction revealed that avicin molecule binds only to the antagonist confirmation of GR. These findings suggest that avicin D has properties of being a selective GR modulator that separates transactivation from transrepression. Since the gene-activating properties of GR are mainly linked to its metabolic effects, and the negative interference with the activity of transcription factors to its anti-inflammatory and immune suppressive effects, the identification of such a dissociated GR ligand could have great potential for therapeutic use. PMID:22132201

  1. The anticancer plant triterpenoid, avicin D, regulates glucocorticoid receptor signaling: implications for cellular metabolism.

    PubMed

    Haridas, Valsala; Xu, Zhi-Xiang; Kitchen, Doug; Jiang, Anna; Michels, Peter; Gutterman, Jordan U

    2011-01-01

    Avicins, a family of apoptotic triterpene electrophiles, are known to regulate cellular metabolism and energy homeostasis, by targeting the mitochondria. Having evolved from "ancient hopanoids," avicins bear a structural resemblance with glucocorticoids (GCs), which are the endogenous regulators of metabolism and energy balance. These structural and functional similarities prompted us to compare the mode of action of avicin D with dexamethasone (Dex), a prototypical GC. Using cold competition assay, we show that Avicin D competes with Dex for binding to the GC receptor (GR), leading to its nuclear translocation. In contrast to Dex, avicin-induced nuclear translocation of GR does not result in transcriptional activation of GC-dependent genes. Instead we observe a decrease in the expression of GC-dependent metabolic proteins such as PEPCK and FASN. However, like Dex, avicin D treatment does induce a transrepressive effect on the pro-inflammatory transcription factor NF-κB. While avicin's ability to inhibit NF-κB and its downstream targets appear to be GR-dependent, its pro-apoptotic effects were independent of GR expression. Using various deletion mutants of GR, we demonstrate the requirement of both the DNA and ligand binding domains of GR in mediating avicin D's transrepressive effects. Modeling of avicin-GR interaction revealed that avicin molecule binds only to the antagonist confirmation of GR. These findings suggest that avicin D has properties of being a selective GR modulator that separates transactivation from transrepression. Since the gene-activating properties of GR are mainly linked to its metabolic effects, and the negative interference with the activity of transcription factors to its anti-inflammatory and immune suppressive effects, the identification of such a dissociated GR ligand could have great potential for therapeutic use.

  2. Cellular network entropy as the energy potential in Waddington's differentiation landscape.

    PubMed

    Banerji, Christopher R S; Miranda-Saavedra, Diego; Severini, Simone; Widschwendter, Martin; Enver, Tariq; Zhou, Joseph X; Teschendorff, Andrew E

    2013-10-24

    Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular sample's position in the global differentiation hierarchy. Here we adopt a systems view and consider the sample's network entropy, a measure of signaling pathway promiscuity, computable from a sample's genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a sample's differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cell's undifferentiated state, defining its elevation in Waddington's landscape.

  3. Logical Modeling and Dynamical Analysis of Cellular Networks

    PubMed Central

    Abou-Jaoudé, Wassim; Traynard, Pauline; Monteiro, Pedro T.; Saez-Rodriguez, Julio; Helikar, Tomáš; Thieffry, Denis; Chaouiya, Claudine

    2016-01-01

    The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle. PMID:27303434

  4. Logical Modeling and Dynamical Analysis of Cellular Networks.

    PubMed

    Abou-Jaoudé, Wassim; Traynard, Pauline; Monteiro, Pedro T; Saez-Rodriguez, Julio; Helikar, Tomáš; Thieffry, Denis; Chaouiya, Claudine

    2016-01-01

    The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.

  5. Dynamical modeling and analysis of large cellular regulatory networks

    NASA Astrophysics Data System (ADS)

    Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.

    2013-06-01

    The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

  6. Blueprint for antimicrobial hit discovery targeting metabolic networks

    PubMed Central

    Shen, Y.; Liu, J.; Estiu, G.; Isin, B.; Ahn, Y-Y.; Lee, D-S.; Barabási, A-L.; Kapatral, V.; Wiest, O.; Oltvai, Z. N.

    2010-01-01

    Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy. PMID:20080587

  7. Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.

    PubMed

    Adadi, Roi; Volkmer, Benjamin; Milo, Ron; Heinemann, Matthias; Shlomi, Tomer

    2012-01-01

    Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate. PMID:22792053

  8. Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.

    PubMed

    Adadi, Roi; Volkmer, Benjamin; Milo, Ron; Heinemann, Matthias; Shlomi, Tomer

    2012-01-01

    Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.

  9. Multi-scale modularity and motif distributional effect in metabolic networks.

    PubMed

    Gao, Shang; Chen, Alan; Rahmani, Ali; Zeng, Jia; Tan, Mehmet; Alhajj, Reda; Rokne, Jon; Demetrick, Douglas; Wei, Xiaohui

    2016-01-01

    Metabolism is a set of fundamental processes that play important roles in a plethora of biological and medical contexts. It is understood that the topological information of reconstructed metabolic networks, such as modular organization, has crucial implications on biological functions. Recent interpretations of modularity in network settings provide a view of multiple network partitions induced by different resolution parameters. Here we ask the question: How do multiple network partitions affect the organization of metabolic networks? Since network motifs are often interpreted as the super families of evolved units, we further investigate their impact under multiple network partitions and investigate how the distribution of network motifs influences the organization of metabolic networks. We studied Homo sapiens, Saccharomyces cerevisiae and Escherichia coli metabolic networks; we analyzed the relationship between different community structures and motif distribution patterns. Further, we quantified the degree to which motifs participate in the modular organization of metabolic networks.

  10. Differential contribution of key metabolic substrates and cellular oxygen in HIF signalling

    SciTech Connect

    Zhdanov, Alexander V.; Waters, Alicia H.C.; Golubeva, Anna V.; Papkovsky, Dmitri B.

    2015-01-01

    Changes in availability and utilisation of O{sub 2} and metabolic substrates are common in ischemia and cancer. We examined effects of substrate deprivation on HIF signalling in PC12 cells exposed to different atmospheric O{sub 2}. Upon 2–4 h moderate hypoxia, HIF-α protein levels were dictated by the availability of glutamine and glucose, essential for deep cell deoxygenation and glycolytic ATP flux. Nuclear accumulation of HIF-1α dramatically decreased upon inhibition of glutaminolysis or glutamine deprivation. Elevation of HIF-2α levels was transcription-independent and associated with the activation of Akt and Erk1/2. Upon 2 h anoxia, HIF-2α levels strongly correlated with cellular ATP, produced exclusively via glycolysis. Without glucose, HIF signalling was suppressed, giving way to other regulators of cell adaptation to energy crisis, e.g. AMPK. Consequently, viability of cells deprived of O{sub 2} and glucose decreased upon inhibition of AMPK with dorsomorphin. The capacity of cells to accumulate HIF-2α decreased after 24 h glucose deprivation. This effect, associated with increased AMPKα phosphorylation, was sensitive to dorsomorphin. In chronically hypoxic cells, glutamine played no major role in HIF-2α accumulation, which became mainly glucose-dependent. Overall, the availability of O{sub 2} and metabolic substrates intricately regulates HIF signalling by affecting cell oxygenation, ATP levels and pathways involved in production of HIF-α. - Highlights: • Gln and Glc regulate HIF levels in hypoxic cells by maintaining low O{sub 2} and high ATP. • HIF-α levels under anoxia correlate with cellular ATP and critically depend on Glc. • Gln and Glc modulate activity of Akt, Erk and AMPK, regulating HIF production. • HIF signalling is differentially inhibited by prolonged Glc and Gln deprivation. • Unlike Glc, Gln plays no major role in HIF signalling in chronically hypoxic cells.

  11. Intrinsic Cellular Properties and Connectivity Density Determine Variable Clustering Patterns in Randomly Connected Inhibitory Neural Networks

    PubMed Central

    Rich, Scott; Booth, Victoria; Zochowski, Michal

    2016-01-01

    The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics

  12. Parameter estimation for metabolic networks with two stage Bregman regularization homotopy inversion algorithm.

    PubMed

    Wang, Hong; Wang, Xi-cheng

    2014-02-21

    Metabolism is a very important cellular process and its malfunction contributes to human disease. Therefore, building dynamic models for metabolic networks with experimental data in order to analyze biological process rationally has attracted a lot of attention. Owing to the technical limitations, some unknown parameters contained in models need to be estimated effectively by means of the computational method. Generally, problems of parameter estimation of nonlinear biological network are known to be ill condition and multimodal. In particular, with the increasing amount and enlarging the scope of parameters, many optimization algorithms often fail to find a global solution. In this paper, two-stage variable factor Bregman regularization homotopy method is proposed. Discrete homotopy is used to identify the possible extreme region and continuous homotopy is executed for the purpose of stability of path tracing in the special region. Meanwhile, Latin hypercube sampling is introduced to get the good initial guess value and a perturbation strategy is developed to jump out of the local optimum. Three metabolic network inverse problems are investigated to demonstrate the effectiveness of the proposed method. PMID:24060619

  13. Pronounced alterations of cellular metabolism and structure due to hyper- or hypo-osmosis.

    PubMed

    Mao, Lei; Hartl, Daniela; Nolden, Tobias; Koppelstätter, Andrea; Klose, Joachim; Himmelbauer, Heinz; Zabel, Claus

    2008-09-01

    Cell volume alteration represents an important factor contributing to the pathology of late-onset diseases. Previously, it was reported that protein biosynthesis and degradation are inversely (trans) regulated during cell volume regulation. Upon cell shrinkage, protein biosynthesis was up-regulated and protein degradation down-regulated. Cell swelling showed opposite regulation. Recent evidence suggests a decrease of protein biodegradation activity in many neurodegenerative diseases and even during aging; both also show prominent cell shrinkage. To clarify the effect of cell volume regulation on the overall protein turnover dynamics, we investigated mouse embryonic stem cells under hyper- and hypotonic osmotic conditions using a 2-D gel based proteomics approach. These conditions cause cell swelling and shrinkage, respectively. Our results demonstrate that the adaption to altered osmotic conditions and therefore cell volume alterations affects a broad spectrum of cellular pathways, including stress response, cytoskeleton remodeling and importantly, cellular metabolism and protein degradation. Interestingly, protein synthesis and degradation appears to be cis-regulated (same direction) on a global level. Our findings also support the hypothesis that protein alterations due to osmotic stress contribute to the pathology of neurodegenerative diseases due to a 60% expression overlap with proteins found altered in Alzheimer's, Huntington's, or Parkinson's disease. Eighteen percent of the proteins altered are even shared with all three disorders. PMID:18646788

  14. Mitochondria: the cellular hub of the dynamic coordinated network.

    PubMed

    Yin, Fei; Cadenas, Enrique

    2015-04-20

    Mitochondria are the powerhouses of the eukaryotic cell. After billions of years of evolution, mitochondria have adaptively integrated into the symbiont. Such integration is not only evidenced by the consolidation of genetic information, that is, the transfer of most mitochondrial genes into the nucleus, but also manifested by the functional recombination by which mitochondria participate seamlessly in various cellular processes. In the past decade, the field of mitochondria biology has been focused on the dynamic and interactive features of these semiautonomous organelles. Aspects of a complex multilayer quality control system coordinating mitochondrial function and environmental changes are being uncovered and refined. This Forum summarizes the recent progress of these critical topics, with a focus on the dynamic quality control of mitochondrial reticulum, including their biogenesis, dynamic remodeling, and degradation, as well as the homeostasis of the mitochondrial proteome. These diverse but interconnected mechanisms are found to be critical in the maintenance of a functional, efficient, and responsive mitochondrial population and could therefore become therapeutic targets in numerous mitochondrion-implicated disorders.

  15. Proportional Fair Scheduling for Multicast Services in Wireless Cellular Networks

    NASA Astrophysics Data System (ADS)

    Koh, Chung Ha; Kim, Young Yong

    Recently, there has been extensive research on resource allocation schemes for multicast services that would satisfy the requirements of multimedia traffic. Although several schemes have been proposed to improve the performance of individual multicast groups, it is not easy to achieve both throughput efficiency and user fairness. In this study, we propose a new multicast scheduling scheme for achieving proportional fair (PF) allocation in wireless cellular systems. The basic idea of PF is to schedule the user whose corresponding instantaneous channel quality is the highest relative to the average channel condition over a given time scale. We first extend the PF metric to the extent that the scheduler can reflect the user's varying channel gain, and fairness, not only in the unicast case, but also in multicast transmissions. A multicast PF scheme maximizes the summation of the logarithmic average rate of all multicasting users. Thus, it improves the fairness to mobile users when compared to max-rate allocation, because the logarithmic rate gives more weight to lower rate users, while achieving high throughput. Moreover, the proposed scheme is less complex than max-rate allocation.

  16. Mechanisms in photodynamic therapy: part two—cellular signaling, cell metabolism and modes of cell death

    PubMed Central

    Castano, Ana P.; Demidova, Tatiana N.; Hamblin, Michael R.

    2013-01-01

    Summary Photodynamic therapy (PDT) has been known for over a hundred years, but is only now becoming widely used. Originally developed as a tumor therapy, some of its most successful applications are for non-malignant disease. In the second of a series of three reviews, we will discuss the mechanisms that operate in PDT on a cellular level. In Part I [Castano AP, Demidova TN, Hamblin MR. Mechanism in photodynamic therapy: part one—photosensitizers, photochemistry and cellular localization. Photodiagn Photodyn Ther 2004;1:279–93] it was shown that one of the most important factors governing the outcome of PDT, is how the photosensitizer (PS) interacts with cells in the target tissue or tumor, and the key aspect of this interaction is the subcellular localization of the PS. PS can localize in mitochondria, lysosomes, endoplasmic reticulum, Golgi apparatus and plasma membranes. An explosion of investigation and explorations in the field of cell biology have elucidated many of the pathways that mammalian cells undergo when PS are delivered in tissue culture and subsequently illuminated. There is an acute stress response leading to changes in calcium and lipid metabolism and production of cytokines and stress proteins. Enzymes particularly, protein kinases, are activated and transcription factors are expressed. Many of the cellular responses are centered on mitochondria. These effects frequently lead to induction of apoptosis either by the mitochondrial pathway involving caspases and release of cytochrome c, or by pathways involving ceramide or death receptors. However, under certain circumstances cells subjected to PDT die by necrosis. Although there have been many reports of DNA damage caused by PDT, this is not thought to be an important cell-death pathway. This mechanistic research is expected to lead to optimization of PDT as a tumor treatment, and to rational selection of combination therapies that include PDT as a component. PMID:25048553

  17. The cellular metabolism of lead and calcium: A kinetic analysis in cultured osteoclastic bone cells

    SciTech Connect

    Rosen, J.F.; Pounds, J.G.

    1986-01-01

    Characterization of lead metabolism in bone is necessary to understand the role of skeletal lead, an endogenous source of this toxic metal, in the expression of adverse effects of lead intoxication in concert with external sources. Moreover, it has been postulated that an early manifestation of lead toxicity common to diverse cell types may be pertubations in intracellular calcium homeostasis. Furthermore, it has been suggested that calciotropic hormones may express their actions on bone cells by modulating the messenger functions of intracellular Ca. Before these postulates can be more fully tested, the homeostasis of intracellular Pb and Ca must be understood more clearly. We have designed experiments to characterize the steady-state kinetic distribution and behavior of /sup 210/Pb and /sup 45/Ca in osteoclastic bone cells and to identify biological structures or functions associated with the kinetic pools. Cultures were labeled with /sup 210/Pb or /sup 45/Ca and the kinetic parameters were obtained by analysis of isotope washout curves. Cellular metabolism was defined by three kinetic pools of intracellular Pb and Ca. The largest and slowest exchanging pool was characterized as a mitochondrial compartment for both metals. These data indicate that Pb and Ca share similar intracellular pathways in osteoclastic bone cells and that both metals are readily exchangeable in this in vitro system. The results of other experiments have revealed that lead, at relatively low medium concentrations, produces a marked increase in all three intracellular Ca compartments. 18 refs., 2 figs.

  18. Monitoring intra-cellular lipid metabolism in macrophages by Raman- and CARS-microscopy

    NASA Astrophysics Data System (ADS)

    Matthäus, Christian; Bergner, Gero; Krafft, Christoph; Dietzek, Benjamin; Lorkowski, Stefan; Popp, Jürgen

    2010-04-01

    Monocyte-derived macrophages play a key role in lipid metabolism in vessel wall tissues. Macrophages can take up lipids by various mechanisms. As phagocytes, macrophages are important for the decomposition of lipid plaques within arterial walls that contribute to arteriosclerosis. Of special interest are uptake dynamics and intra-cellular fate of different individual types of lipids as, for example, fatty acids, triglycerides or free and esterified cholesterol. Here we utilize Raman microscopy to image the metabolism of such lipids and follow subsequent storage or degradation patterns. The combination of optical microscopy with Raman spectroscopy allows visualization at the diffraction limit of the employed laser light and biochemical characterization through the associated spectral information. Relatively long measuring times, due to the weakness of Raman scattering can be overcome by non-linear effects such as coherent anti-Stokes Raman scattering (CARS). With this contribution we introduce first results to monitor the incorporation of lipid components into individual cells employing Raman and CARS microscopy.

  19. Differential contribution of key metabolic substrates and cellular oxygen in HIF signalling.

    PubMed

    Zhdanov, Alexander V; Waters, Alicia H C; Golubeva, Anna V; Papkovsky, Dmitri B

    2015-01-01

    Changes in availability and utilisation of O2 and metabolic substrates are common in ischemia and cancer. We examined effects of substrate deprivation on HIF signalling in PC12 cells exposed to different atmospheric O2. Upon 2-4h moderate hypoxia, HIF-α protein levels were dictated by the availability of glutamine and glucose, essential for deep cell deoxygenation and glycolytic ATP flux. Nuclear accumulation of HIF-1α dramatically decreased upon inhibition of glutaminolysis or glutamine deprivation. Elevation of HIF-2α levels was transcription-independent and associated with the activation of Akt and Erk1/2. Upon 2h anoxia, HIF-2α levels strongly correlated with cellular ATP, produced exclusively via glycolysis. Without glucose, HIF signalling was suppressed, giving way to other regulators of cell adaptation to energy crisis, e.g. AMPK. Consequently, viability of cells deprived of O2 and glucose decreased upon inhibition of AMPK with dorsomorphin. The capacity of cells to accumulate HIF-2α decreased after 24h glucose deprivation. This effect, associated with increased AMPKα phosphorylation, was sensitive to dorsomorphin. In chronically hypoxic cells, glutamine played no major role in HIF-2α accumulation, which became mainly glucose-dependent. Overall, the availability of O2 and metabolic substrates intricately regulates HIF signalling by affecting cell oxygenation, ATP levels and pathways involved in production of HIF-α. PMID:25447307

  20. Parameter estimation in tree graph metabolic networks

    PubMed Central

    Stigter, Hans; Gomez Roldan, Maria Victoria; van Eeuwijk, Fred; Hall, Robert D.; Groenenboom, Marian; Molenaar, Jaap J.

    2016-01-01

    We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis–Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

  1. Parameter estimation in tree graph metabolic networks.

    PubMed

    Astola, Laura; Stigter, Hans; Gomez Roldan, Maria Victoria; van Eeuwijk, Fred; Hall, Robert D; Groenenboom, Marian; Molenaar, Jaap J

    2016-01-01

    We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

  2. Parameter estimation in tree graph metabolic networks.

    PubMed

    Astola, Laura; Stigter, Hans; Gomez Roldan, Maria Victoria; van Eeuwijk, Fred; Hall, Robert D; Groenenboom, Marian; Molenaar, Jaap J

    2016-01-01

    We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings. PMID:27688960

  3. Parameter estimation in tree graph metabolic networks

    PubMed Central

    Stigter, Hans; Gomez Roldan, Maria Victoria; van Eeuwijk, Fred; Hall, Robert D.; Groenenboom, Marian; Molenaar, Jaap J.

    2016-01-01

    We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis–Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings. PMID:27688960

  4. Out of fuzzy chemistry: from prebiotic chemistry to metabolic networks.

    PubMed

    Peretó, Juli

    2012-08-21

    The origin of life on Earth was a chemical affair. So how did primitive biochemical systems originate from geochemical and cosmochemical processes on the young planet? Contemporary research into the origins of life subscribes to the Darwinian principle of material causes operating in an evolutionary context, as advocated by A. I. Oparin and J. B. S. Haldane in the 1920s. In its simplest form (e.g., a bacterial cell) extant biological complexity relies on the functional integration of metabolic networks and replicative genomes inside a lipid boundary. Different research programmes have explored the prebiotic plausibility of each of these autocatalytic subsystems and combinations thereof: self-maintained networks of small molecules, template chemistry, and self-reproductive vesicles. This tutorial review focuses on the debates surrounding the origin of metabolism and offers a brief overview of current studies on the evolution of metabolic networks. I suggest that a leitmotif in the origin and evolution of metabolism is the role played by catalysers' substrate ambiguity and multifunctionality.

  5. Out of fuzzy chemistry: from prebiotic chemistry to metabolic networks.

    PubMed

    Peretó, Juli

    2012-08-21

    The origin of life on Earth was a chemical affair. So how did primitive biochemical systems originate from geochemical and cosmochemical processes on the young planet? Contemporary research into the origins of life subscribes to the Darwinian principle of material causes operating in an evolutionary context, as advocated by A. I. Oparin and J. B. S. Haldane in the 1920s. In its simplest form (e.g., a bacterial cell) extant biological complexity relies on the functional integration of metabolic networks and replicative genomes inside a lipid boundary. Different research programmes have explored the prebiotic plausibility of each of these autocatalytic subsystems and combinations thereof: self-maintained networks of small molecules, template chemistry, and self-reproductive vesicles. This tutorial review focuses on the debates surrounding the origin of metabolism and offers a brief overview of current studies on the evolution of metabolic networks. I suggest that a leitmotif in the origin and evolution of metabolism is the role played by catalysers' substrate ambiguity and multifunctionality. PMID:22508108

  6. Migration-induced variation of fatty acid transporters and cellular metabolic intensity in passerine birds.

    PubMed

    Zhang, Yufeng; King, Marisa O; Harmon, Erin; Eyster, Kathleen; Swanson, David L

    2015-10-01

    Because lipids are the main fuel supporting avian endurance activity, lipid transport and oxidation capacities may increase during migration. We measured enzyme activities, mRNA expression and protein levels in pectoralis and heart for several key steps of lipid transport and catabolism pathways to investigate whether these pathways were upregulated during migration. We used yellow-rumped (Setophaga coronata) and yellow (S. petechia) warblers and warbling vireos (Vireo gilvus) as study species because they all show migration-induced increases in organismal metabolic capacities. For yellow-rumped warblers, β-hydroxyacyl CoA-dehydrogenase (HOAD) activities and fatty acid transporter mRNA and/or protein levels were higher during spring than fall in pectoralis and heart, except that fatty acid translocase (FAT/CD36) protein levels showed the opposite pattern in heart. Lipid transporter protein levels, but not mRNA expression, in pectoralis and heart of warbling vireos were higher either during spring or fall than summer, but this was not true for HOAD activities. For yellow warblers, pectoralis, but not heart, protein levels of lipid transporters were upregulated during migration relative to summer, but this pattern was not evident for mRNA expression or HOAD activity. Finally, muscle and heart citrate synthase and carnitine palmitoyl transferase activities showed little seasonal variation for any species. These data suggest that pectoralis and heart lipid transport and catabolism capacities are often, but not universally, important correlates of elevated organismal metabolic capacity during migration. In contrast, migration-induced variation in cellular metabolic intensity and mitochondrial membrane transport are apparently not common correlates of the migratory phenotype in passerines.

  7. Migration-induced variation of fatty acid transporters and cellular metabolic intensity in passerine birds.

    PubMed

    Zhang, Yufeng; King, Marisa O; Harmon, Erin; Eyster, Kathleen; Swanson, David L

    2015-10-01

    Because lipids are the main fuel supporting avian endurance activity, lipid transport and oxidation capacities may increase during migration. We measured enzyme activities, mRNA expression and protein levels in pectoralis and heart for several key steps of lipid transport and catabolism pathways to investigate whether these pathways were upregulated during migration. We used yellow-rumped (Setophaga coronata) and yellow (S. petechia) warblers and warbling vireos (Vireo gilvus) as study species because they all show migration-induced increases in organismal metabolic capacities. For yellow-rumped warblers, β-hydroxyacyl CoA-dehydrogenase (HOAD) activities and fatty acid transporter mRNA and/or protein levels were higher during spring than fall in pectoralis and heart, except that fatty acid translocase (FAT/CD36) protein levels showed the opposite pattern in heart. Lipid transporter protein levels, but not mRNA expression, in pectoralis and heart of warbling vireos were higher either during spring or fall than summer, but this was not true for HOAD activities. For yellow warblers, pectoralis, but not heart, protein levels of lipid transporters were upregulated during migration relative to summer, but this pattern was not evident for mRNA expression or HOAD activity. Finally, muscle and heart citrate synthase and carnitine palmitoyl transferase activities showed little seasonal variation for any species. These data suggest that pectoralis and heart lipid transport and catabolism capacities are often, but not universally, important correlates of elevated organismal metabolic capacity during migration. In contrast, migration-induced variation in cellular metabolic intensity and mitochondrial membrane transport are apparently not common correlates of the migratory phenotype in passerines. PMID:26194862

  8. Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks

    PubMed Central

    Chokkathukalam, Achuthanunni; Kim, Dong-Hyun; Barrett, Michael P; Breitling, Rainer; Creek, Darren J

    2014-01-01

    The rapid emergence of metabolomics has enabled system-wide measurements of metabolites in various organisms. However, advances in the mechanistic understanding of metabolic networks remain limited, as most metabolomics studies cannot routinely provide accurate metabolite identification, absolute quantification and flux measurement. Stable isotope labeling offers opportunities to overcome these limitations. Here we describe some current approaches to stable isotope-labeled metabolomics and provide examples of the significant impact that these studies have had on our understanding of cellular metabolism. Furthermore, we discuss recently developed software solutions for the analysis of stable isotope-labeled metabolomics data and propose the bioinformatics solutions that will pave the way for the broader application and optimal interpretation of system-scale labeling studies in metabolomics. PMID:24568354

  9. The Nrf2 regulatory network provides an interface between redox and intermediary metabolism.

    PubMed

    Hayes, John D; Dinkova-Kostova, Albena T

    2014-04-01

    Nuclear factor-erythroid 2 p45-related factor 2 (Nrf2, also called Nfe2l2) is a transcription factor that regulates the cellular redox status. Nrf2 is controlled through a complex transcriptional/epigenetic and post-translational network that ensures its activity increases during redox perturbation, inflammation, growth factor stimulation and nutrient/energy fluxes, thereby enabling the factor to orchestrate adaptive responses to diverse forms of stress. Besides mediating stress-stimulated induction of antioxidant and detoxification genes, Nrf2 contributes to adaptation by upregulating the repair and degradation of damaged macromolecules, and by modulating intermediary metabolism. In the latter case, Nrf2 inhibits lipogenesis, supports β-oxidation of fatty acids, facilitates flux through the pentose phosphate pathway, and increases NADPH regeneration and purine biosynthesis; these observations suggest Nrf2 directs metabolic reprogramming during stress.

  10. Quantitative Mass Spectrometry Reveals Plasticity of Metabolic Networks in Mycobacterium smegmatis *

    PubMed Central

    Chopra, Tarun; Hamelin, Romain; Armand, Florence; Chiappe, Diego; Moniatte, Marc; McKinney, John D.

    2014-01-01

    Mycobacterium tuberculosis has a remarkable ability to persist within the human host as a clinically inapparent or chronically active infection. Fatty acids are thought to be an important carbon source used by the bacteria during long term infection. Catabolism of fatty acids requires reprogramming of metabolic networks, and enzymes central to this reprogramming have been targeted for drug discovery. Mycobacterium smegmatis, a nonpathogenic relative of M. tuberculosis, is often used as a model system because of the similarity of basic cellular processes in these two species. Here, we take a quantitative proteomics-based approach to achieve a global view of how the M. smegmatis metabolic network adjusts to utilization of fatty acids as a carbon source. Two-dimensional liquid chromatography and mass spectrometry of isotopically labeled proteins identified a total of 3,067 proteins with high confidence. This number corresponds to 44% of the predicted M. smegmatis proteome and includes most of the predicted metabolic enzymes. Compared with glucose-grown cells, 162 proteins showed differential abundance in acetate- or propionate-grown cells. Among these, acetate-grown cells showed a higher abundance of proteins that could constitute a functional glycerate pathway. Gene inactivation experiments confirmed that both the glyoxylate shunt and the glycerate pathway are operational in M. smegmatis. In addition to proteins with annotated functions, we demonstrate carbon source-dependent differential abundance of proteins that have not been functionally characterized. These proteins might play as-yet-unidentified roles in mycobacterial carbon metabolism. This study reveals several novel features of carbon assimilation in M. smegmatis, which suggests significant functional plasticity of metabolic networks in this organism. PMID:24997995

  11. Quantitative mass spectrometry reveals plasticity of metabolic networks in Mycobacterium smegmatis.

    PubMed

    Chopra, Tarun; Hamelin, Romain; Armand, Florence; Chiappe, Diego; Moniatte, Marc; McKinney, John D

    2014-11-01

    Mycobacterium tuberculosis has a remarkable ability to persist within the human host as a clinically inapparent or chronically active infection. Fatty acids are thought to be an important carbon source used by the bacteria during long term infection. Catabolism of fatty acids requires reprogramming of metabolic networks, and enzymes central to this reprogramming have been targeted for drug discovery. Mycobacterium smegmatis, a nonpathogenic relative of M. tuberculosis, is often used as a model system because of the similarity of basic cellular processes in these two species. Here, we take a quantitative proteomics-based approach to achieve a global view of how the M. smegmatis metabolic network adjusts to utilization of fatty acids as a carbon source. Two-dimensional liquid chromatography and mass spectrometry of isotopically labeled proteins identified a total of 3,067 proteins with high confidence. This number corresponds to 44% of the predicted M. smegmatis proteome and includes most of the predicted metabolic enzymes. Compared with glucose-grown cells, 162 proteins showed differential abundance in acetate- or propionate-grown cells. Among these, acetate-grown cells showed a higher abundance of proteins that could constitute a functional glycerate pathway. Gene inactivation experiments confirmed that both the glyoxylate shunt and the glycerate pathway are operational in M. smegmatis. In addition to proteins with annotated functions, we demonstrate carbon source-dependent differential abundance of proteins that have not been functionally characterized. These proteins might play as-yet-unidentified roles in mycobacterial carbon metabolism. This study reveals several novel features of carbon assimilation in M. smegmatis, which suggests significant functional plasticity of metabolic networks in this organism.

  12. A Mathematical Model to study the Dynamics of Epithelial Cellular Networks

    PubMed Central

    Abate, Alessandro; Vincent, Stéphane; Dobbe, Roel; Silletti, Alberto; Master, Neal; Axelrod, Jeffrey D.; Tomlin, Claire J.

    2013-01-01

    Epithelia are sheets of connected cells that are essential across the animal kingdom. Experimental observations suggest that the dynamical behavior of many single-layered epithelial tissues has strong analogies with that of specific mechanical systems, namely large networks consisting of point masses connected through spring-damper elements and undergoing the influence of active and dissipating forces. Based on this analogy, this work develops a modeling framework to enable the study of the mechanical properties and of the dynamic behavior of large epithelial cellular networks. The model is built first by creating a network topology that is extracted from the actual cellular geometry as obtained from experiments, then by associating a mechanical structure and dynamics to the network via spring-damper elements. This scalable approach enables running simulations of large network dynamics: the derived modeling framework in particular is predisposed to be tailored to study general dynamics (for example, morphogenesis) of various classes of single-layered epithelial cellular networks. In this contribution we test the model on a case study of the dorsal epithelium of the Drosophila melanogaster embryo during early dorsal closure (and, less conspicuously, germband retraction). PMID:23221083

  13. Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors

    PubMed Central

    PETERSON, CHRISTINE; VANNUCCI, MARINA; KARAKAS, CEMAL; CHOI, WILLIAM; MA, LIHUA; MALETIĆ-SAVATIĆ, MIRJANA

    2014-01-01

    Metabolic processes are essential for cellular function and survival. We are interested in inferring a metabolic network in activated microglia, a major neuroimmune cell in the brain responsible for the neuroinflammation associated with neurological diseases, based on a set of quantified metabolites. To achieve this, we apply the Bayesian adaptive graphical lasso with informative priors that incorporate known relationships between covariates. To encourage sparsity, the Bayesian graphical lasso places double exponential priors on the off-diagonal entries of the precision matrix. The Bayesian adaptive graphical lasso allows each double exponential prior to have a unique shrinkage parameter. These shrinkage parameters share a common gamma hyperprior. We extend this model to create an informative prior structure by formulating tailored hyperpriors on the shrinkage parameters. By choosing parameter values for each hyperprior that shift probability mass toward zero for nodes that are close together in a reference network, we encourage edges between covariates with known relationships. This approach can improve the reliability of network inference when the sample size is small relative to the number of parameters to be estimated. When applied to the data on activated microglia, the inferred network includes both known relationships and associations of potential interest for further investigation. PMID:24533172

  14. Beta Cell Formation in vivo Through Cellular Networking, Integration and Processing (CNIP) in Wild Type Adult Mice.

    PubMed

    Doiron, Bruno; Hu, Wenchao; DeFronzo, Ralph A

    2016-01-01

    Insulin replacement therapy is essential in type 1 diabetic individuals and is required in ~40- 50% of type 2 diabetics during their lifetime. Prior attempts at beta cell regeneration have relied upon pancreatic injury to induce beta cell proliferation, dedifferentiation and activation of the embryonic pathway, or stem cell replacement. We report an alternative method to transform adult non-stem (somatic) cells into pancreatic beta cells. The Cellular Networking, Integration and Processing (CNIP) approach targets cellular mechanisms involved in pancreatic function in the organ's adult state and utilizes a synergistic mechanism that integrates three important levels of cellular regulation to induce beta cell formation: (i) glucose metabolism, (ii) membrane receptor function, and (iii) gene transcription. The aim of the present study was to induce pancreatic beta cell formation in vivo in adult animals without stem cells and without dedifferentiating cells to recapitulate the embryonic pathway as previously published (1-3). Our results employing CNIP demonstrate that: (i) insulin secreting cells can be generated in adult pancreatic tissue in vivo and circumvent the problem of generating endocrine (glucagon and somatostatin) cells that exert deleterious effects on glucose homeostasis, and (ii) longterm normalization of glucose tolerance and insulin secretion can be achieved in a wild type diabetic mouse model. The CNIP cocktail has the potential to be used as a preventative or therapeutic treatment or cure for both type 1 and type 2 diabetes. PMID:26696016

  15. MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks.

    PubMed

    Vitkin, Edward; Shlomi, Tomer

    2012-01-01

    Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for constraint-based modeling analysis and specifically for metabolic engineering. The reconstructed network models are supplied via standard SBML files. PMID:23194418

  16. Exponential synchronization for fuzzy cellular neural networks with time-varying delays and nonlinear impulsive effects.

    PubMed

    Pu, Hao; Liu, Yanmin; Jiang, Haijun; Hu, Cheng

    2015-08-01

    In this paper, the globally exponential synchronization of delayed fuzzy cellular neural networks with nonlinear impulsive effects are concerned. By utilizing inequality techniques and Lyapunov functional method, some sufficient conditions on the exponential synchronization are obtained based on [Formula: see text]-norm. Finally, a simulation example is given to illustrate the effectiveness of the theoretical results.

  17. Metabolomics integrated elementary flux mode analysis in large metabolic networks.

    PubMed

    Gerstl, Matthias P; Ruckerbauer, David E; Mattanovich, Diethard; Jungreuthmayer, Christian; Zanghellini, Jürgen

    2015-03-10

    Elementary flux modes (EFMs) are non-decomposable steady-state pathways in metabolic networks. They characterize phenotypes, quantify robustness or identify engineering targets. An EFM analysis (EFMA) is currently restricted to medium-scale models, as the number of EFMs explodes with the network's size. However, many topologically feasible EFMs are biologically irrelevant. We present thermodynamic EFMA (tEFMA), which calculates only the small(er) subset of thermodynamically feasible EFMs. We integrate network embedded thermodynamics into EFMA and show that we can use the metabolome to identify and remove thermodynamically infeasible EFMs during an EFMA without losing biologically relevant EFMs. Calculating only the thermodynamically feasible EFMs strongly reduces memory consumption and program runtime, allowing the analysis of larger networks. We apply tEFMA to study the central carbon metabolism of E. coli and find that up to 80% of its EFMs are thermodynamically infeasible. Moreover, we identify glutamate dehydrogenase as a bottleneck, when E. coli is grown on glucose and explain its inactivity as a consequence of network embedded thermodynamics. We implemented tEFMA as a Java package which is available for download at https://github.com/mpgerstl/tEFMA.

  18. The evolution of metabolic networks of E. coli

    PubMed Central

    2011-01-01

    Background Despite the availability of numerous complete genome sequences from E. coli strains, published genome-scale metabolic models exist only for two commensal E. coli strains. These models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for E. coli strains could enhance these efforts. Results We used the genomic information from 16 E. coli strains to generate an E. coli pangenome metabolic network by evaluating their collective 76,990 ORFs. Each of these ORFs was assigned to one of 17,647 ortholog groups including ORFs associated with reactions in the most recent metabolic model for E. coli K-12. For orthologous groups that contain an ORF already represented in the MG1655 model, the gene to protein to reaction associations represented in this model could then be easily propagated to other E. coli strain models. All remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model (iEco1712_pan). The pangenome model included reactions from a metabolic model update for E. coli K-12 MG1655 (iEco1339_MG1655) and enabled development of five additional strain-specific genome-scale metabolic models. These additional models include a second K-12 strain (iEco1335_W3110) and four pathogenic strains (two enterohemorrhagic E. coli O157:H7 and two uropathogens). When compared to the E. coli K-12 models, the metabolic models for the enterohemorrhagic (iEco1344_EDL933 and iEco1345_Sakai) and uropathogenic strains (iEco1288_CFT073 and iEco1301_UTI89) contained numerous lineage-specific gene and reaction differences. All six E. coli models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with 0.2% (w/v) glucose. An ancestral genome

  19. Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm

    PubMed Central

    Megchelenbrink, Wout; Rossell, Sergio; Huynen, Martijn A.

    2015-01-01

    Motivation Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA), which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental “omics” data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more “flexible” metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions. Results Here, we propose Maximum Metabolic Flexibility (MMF) a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i) indeed, most of the measured fluxes agree with a high adaptability of the network, ii) this result can be used to further reduce the space of feasible solutions iii) this reduced space improves the quantitative predictions

  20. Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling

    PubMed Central

    Ferrara, Christine T.; Wang, Ping; Neto, Elias Chaibub; Stevens, Robert D.; Bain, James R.; Wenner, Brett R.; Ilkayeva, Olga R.; Keller, Mark P.; Blasiole, Daniel A.; Kendziorski, Christina; Yandell, Brian S.; Newgard, Christopher B.; Attie, Alan D.

    2008-01-01

    Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes. PMID:18369453

  1. Sensitivity of chemical reaction networks: a structural approach. 1. Examples and the carbon metabolic network.

    PubMed

    Mochizuki, Atsushi; Fiedler, Bernold

    2015-02-21

    In biological cells, chemical reaction pathways lead to complex network systems like metabolic networks. One experimental approach to the dynamics of such systems examines their "sensitivity": each enzyme mediating a reaction in the system is increased/decreased or knocked out separately, and the responses in the concentrations of chemicals or their fluxes are observed. In this study, we present a mathematical method, named structural sensitivity analysis, to determine the sensitivity of reaction systems from information on the network alone. We investigate how the sensitivity responses of chemicals in a reaction network depend on the structure of the network, and on the position of the perturbed reaction in the network. We establish and prove some general rules which relate the sensitivity response to the structure of the underlying network. We describe a hierarchical pattern in the flux response which is governed by branchings in the network. We apply our method to several hypothetical and real life chemical reaction networks, including the metabolic network of the Escherichia coli TCA cycle.

  2. Cooperative Game-Based Energy Efficiency Management over Ultra-Dense Wireless Cellular Networks.

    PubMed

    Li, Ming; Chen, Pengpeng; Gao, Shouwan

    2016-09-13

    Ultra-dense wireless cellular networks have been envisioned as a promising technique for handling the explosive increase of wireless traffic volume. With the extensive deployment of small cells in wireless cellular networks, the network spectral efficiency (SE) is improved with the use of limited frequency. However, the mutual inter-tier and intra-tier interference between or among small cells and macro cells becomes serious. On the other hand, more chances for potential cooperation among different cells are introduced. Energy efficiency (EE) has become one of the most important problems for future wireless networks. This paper proposes a cooperative bargaining game-based method for comprehensive EE management in an ultra-dense wireless cellular network, which highlights the complicated interference influence on energy-saving challenges and the power-coordination process among small cells and macro cells. Especially, a unified EE utility with the consideration of the interference mitigation is proposed to jointly address the SE, the deployment efficiency (DE), and the EE. In particular, closed-form power-coordination solutions for the optimal EE are derived to show the convergence property of the algorithm. Moreover, a simplified algorithm is presented to reduce the complexity of the signaling overhead, which is significant for ultra-dense small cells. Finally, numerical simulations are provided to illustrate the efficiency of the proposed cooperative bargaining game-based and simplified schemes.

  3. Cooperative Game-Based Energy Efficiency Management over Ultra-Dense Wireless Cellular Networks.

    PubMed

    Li, Ming; Chen, Pengpeng; Gao, Shouwan

    2016-01-01

    Ultra-dense wireless cellular networks have been envisioned as a promising technique for handling the explosive increase of wireless traffic volume. With the extensive deployment of small cells in wireless cellular networks, the network spectral efficiency (SE) is improved with the use of limited frequency. However, the mutual inter-tier and intra-tier interference between or among small cells and macro cells becomes serious. On the other hand, more chances for potential cooperation among different cells are introduced. Energy efficiency (EE) has become one of the most important problems for future wireless networks. This paper proposes a cooperative bargaining game-based method for comprehensive EE management in an ultra-dense wireless cellular network, which highlights the complicated interference influence on energy-saving challenges and the power-coordination process among small cells and macro cells. Especially, a unified EE utility with the consideration of the interference mitigation is proposed to jointly address the SE, the deployment efficiency (DE), and the EE. In particular, closed-form power-coordination solutions for the optimal EE are derived to show the convergence property of the algorithm. Moreover, a simplified algorithm is presented to reduce the complexity of the signaling overhead, which is significant for ultra-dense small cells. Finally, numerical simulations are provided to illustrate the efficiency of the proposed cooperative bargaining game-based and simplified schemes. PMID:27649170

  4. Identification of driving network of cellular differentiation from single sample time course gene expression data

    NASA Astrophysics Data System (ADS)

    Chen, Ye; Wolanyk, Nathaniel; Ilker, Tunc; Gao, Shouguo; Wang, Xujing

    Methods developed based on bifurcation theory have demonstrated their potential in driving network identification for complex human diseases, including the work by Chen, et al. Recently bifurcation theory has been successfully applied to model cellular differentiation. However, there one often faces a technical challenge in driving network prediction: time course cellular differentiation study often only contains one sample at each time point, while driving network prediction typically require multiple samples at each time point to infer the variation and interaction structures of candidate genes for the driving network. In this study, we investigate several methods to identify both the critical time point and the driving network through examination of how each time point affects the autocorrelation and phase locking. We apply these methods to a high-throughput sequencing (RNA-Seq) dataset of 42 subsets of thymocytes and mature peripheral T cells at multiple time points during their differentiation (GSE48138 from GEO). We compare the predicted driving genes with known transcription regulators of cellular differentiation. We will discuss the advantages and limitations of our proposed methods, as well as potential further improvements of our methods.

  5. Cooperative Game-Based Energy Efficiency Management over Ultra-Dense Wireless Cellular Networks

    PubMed Central

    Li, Ming; Chen, Pengpeng; Gao, Shouwan

    2016-01-01

    Ultra-dense wireless cellular networks have been envisioned as a promising technique for handling the explosive increase of wireless traffic volume. With the extensive deployment of small cells in wireless cellular networks, the network spectral efficiency (SE) is improved with the use of limited frequency. However, the mutual inter-tier and intra-tier interference between or among small cells and macro cells becomes serious. On the other hand, more chances for potential cooperation among different cells are introduced. Energy efficiency (EE) has become one of the most important problems for future wireless networks. This paper proposes a cooperative bargaining game-based method for comprehensive EE management in an ultra-dense wireless cellular network, which highlights the complicated interference influence on energy-saving challenges and the power-coordination process among small cells and macro cells. Especially, a unified EE utility with the consideration of the interference mitigation is proposed to jointly address the SE, the deployment efficiency (DE), and the EE. In particular, closed-form power-coordination solutions for the optimal EE are derived to show the convergence property of the algorithm. Moreover, a simplified algorithm is presented to reduce the complexity of the signaling overhead, which is significant for ultra-dense small cells. Finally, numerical simulations are provided to illustrate the efficiency of the proposed cooperative bargaining game-based and simplified schemes. PMID:27649170

  6. Metabolic PathFinding: inferring relevant pathways in biochemical networks.

    PubMed

    Croes, Didier; Couche, Fabian; Wodak, Shoshana J; van Helden, Jacques

    2005-07-01

    Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H2O, NADP and H+). In this study, we present a web server implementing two simple approaches, which circumvent this problem, thereby improving the relevance of the inferred pathways. In the simplest approach, the shortest path is computed, while filtering out the selection of highly connected compounds. In the second approach, the shortest path is computed on the weighted metabolic graph where each compound is assigned a weight equal to its connectivity in the network. This approach significantly increases the accuracy of the inferred pathways, enabling the correct inference of relatively long pathways (e.g. with as many as eight intermediate reactions). Available options include the calculation of the k-shortest paths between two specified seed nodes (either compounds or reactions). Multiple requests can be submitted in a queue. Results are returned by email, in textual as well as graphical formats (available in http://www.scmbb.ulb.ac.be/pathfinding/).

  7. Second Law of Thermodynamics Applied to Metabolic Networks

    NASA Technical Reports Server (NTRS)

    Nigam, R.; Liang, S.

    2003-01-01

    We present a simple algorithm based on linear programming, that combines Kirchoff's flux and potential laws and applies them to metabolic networks to predict thermodynamically feasible reaction fluxes. These law's represent mass conservation and energy feasibility that are widely used in electrical circuit analysis. Formulating the Kirchoff's potential law around a reaction loop in terms of the null space of the stoichiometric matrix leads to a simple representation of the law of entropy that can be readily incorporated into the traditional flux balance analysis without resorting to non-linear optimization. Our technique is new as it can easily check the fluxes got by applying flux balance analysis for thermodynamic feasibility and modify them if they are infeasible so that they satisfy the law of entropy. We illustrate our method by applying it to the network dealing with the central metabolism of Escherichia coli. Due to its simplicity this algorithm will be useful in studying large scale complex metabolic networks in the cell of different organisms.

  8. Dengue Virus NS1 Protein Modulates Cellular Energy Metabolism by Increasing Glyceraldehyde-3-Phosphate Dehydrogenase Activity

    PubMed Central

    Allonso, Diego; Andrade, Iamara S.; Conde, Jonas N.; Coelho, Diego R.; Rocha, Daniele C. P.; da Silva, Manuela L.; Ventura, Gustavo T.

    2015-01-01

    ABSTRACT Dengue is one of the main public health concerns worldwide. Recent estimates indicate that over 390 million people are infected annually with the dengue virus (DENV), resulting in thousands of deaths. Among the DENV nonstructural proteins, the NS1 protein is the only one whose function during replication is still unknown. NS1 is a 46- to 55-kDa glycoprotein commonly found as both a membrane-associated homodimer and a soluble hexameric barrel-shaped lipoprotein. Despite its role in the pathogenic process, NS1 is essential for proper RNA accumulation and virus production. In the present study, we identified that glyceraldehyde-3-phosphate dehydrogenase (GAPDH) interacts with intracellular NS1. Molecular docking revealed that this interaction occurs through the hydrophobic protrusion of NS1 and the hydrophobic residues located at the opposite side of the catalytic site. Moreover, addition of purified recombinant NS1 enhanced the glycolytic activity of GAPDH in vitro. Interestingly, we observed that DENV infection promoted the relocalization of GAPDH to the perinuclear region, where NS1 is commonly found. Both DENV infection and expression of NS1 itself resulted in increased GAPDH activity. Our findings indicate that the NS1 protein acts to increase glycolytic flux and, consequently, energy production, which is consistent with the recent finding that DENV induces and requires glycolysis for proper replication. This is the first report to propose that NS1 is an important modulator of cellular energy metabolism. The data presented here provide new insights that may be useful for further drug design and the development of alternative antiviral therapies against DENV. IMPORTANCE Dengue represents a serious public health problem worldwide and is caused by infection with dengue virus (DENV). Estimates indicate that half of the global population is at risk of infection, with almost 400 million cases occurring per year. The NS1 glycoprotein is found in both the

  9. The Electrophysiological MEMS Device with Micro Channel Array for Cellular Network Analysis

    NASA Astrophysics Data System (ADS)

    Tonomura, Wataru; Kurashima, Toshiaki; Takayama, Yuzo; Moriguchi, Hiroyuki; Jimbo, Yasuhiko; Konishi, Satoshi

    This paper describes a new type of MCA (Micro Channel Array) for simultaneous multipoint measurement of cellular network. Presented MCA employing the measurement principles of the patch-clamp technique is designed for advanced neural network analysis which has been studied by co-authors using 64ch MEA (Micro Electrode Arrays) system. First of all, sucking and clamping of cells through channels of developed MCA is expected to improve electrophysiological signal detections. Electrophysiological sensing electrodes integrated around individual channels of MCA by using MEMS (Micro Electro Mechanical System) technologies are electrically isolated for simultaneous multipoint measurement. In this study, we tested the developed MCA using the non-cultured rat's cerebral cortical slice and the hippocampal neurons. We could measure the spontaneous action potential of the slice simultaneously at multiple points and culture the neurons on developed MCA. Herein, we describe the experimental results together with the design and fabrication of the electrophysiological MEMS device with MCA for cellular network analysis.

  10. A self-learning call admission control scheme for CDMA cellular networks.

    PubMed

    Liu, Derong; Zhang, Yi; Zhang, Huaguang

    2005-09-01

    In the present paper, a call admission control scheme that can learn from the network environment and user behavior is developed for code division multiple access (CDMA) cellular networks that handle both voice and data services. The idea is built upon a novel learning control architecture with only a single module instead of two or three modules in adaptive critic designs (ACDs). The use of adaptive critic approach for call admission control in wireless cellular networks is new. The call admission controller can perform learning in real-time as well as in offline environments and the controller improves its performance as it gains more experience. Another important contribution in the present work is the choice of utility function for the present self-learning control approach which makes the present learning process much more efficient than existing learning control methods. The performance of our algorithm will be shown through computer simulation and compared with existing algorithms. PMID:16252828

  11. Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2016-06-01

    Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide (≈ 35 500 km2) 15 min rainfall maps can be derived from the signal attenuations of approximately 2400 microwave links in such a network. Here we give a detailed description of the employed rainfall retrieval algorithm. Moreover, the documented, modular, and user-friendly code (a package in the scripting language "R") is made available, including a 2-day data set of approximately 2600 commercial microwave links from the Netherlands. The purpose of this paper is to promote rainfall mapping utilising microwave links from cellular communication networks as an alternative or complementary means for continental-scale rainfall monitoring.

  12. Current Understanding of the Formation and Adaptation of Metabolic Systems Based on Network Theory

    PubMed Central

    Takemoto, Kazuhiro

    2012-01-01

    Formation and adaptation of metabolic networks has been a long-standing question in biology. With recent developments in biotechnology and bioinformatics, the understanding of metabolism is progressively becoming clearer from a network perspective. This review introduces the comprehensive metabolic world that has been revealed by a wide range of data analyses and theoretical studies; in particular, it illustrates the role of evolutionary events, such as gene duplication and horizontal gene transfer, and environmental factors, such as nutrient availability and growth conditions, in evolution of the metabolic network. Furthermore, the mathematical models for the formation and adaptation of metabolic networks have also been described, according to the current understanding from a perspective of metabolic networks. These recent findings are helpful in not only understanding the formation of metabolic networks and their adaptation, but also metabolic engineering. PMID:24957641

  13. Ocean warming alters cellular metabolism and induces mortality in fish early life stages: A proteomic approach.

    PubMed

    Madeira, D; Araújo, J E; Vitorino, R; Capelo, J L; Vinagre, C; Diniz, M S

    2016-07-01

    Climate change has pervasive effects on marine ecosystems, altering biodiversity patterns, abundance and distribution of species, biological interactions, phenology, and organisms' physiology, performance and fitness. Fish early life stages have narrow thermal windows and are thus more vulnerable to further changes in water temperature. The aim of this study was to address the sensitivity and underlying molecular changes of larvae of a key fisheries species, the sea bream Sparus aurata, towards ocean warming. Larvae were exposed to three temperatures: 18°C (control), 24°C (warm) and 30°C (heat wave) for seven days. At the end of the assay, i) survival curves were plotted for each temperature treatment and ii) entire larvae were collected for proteomic analysis via 2D gel electrophoresis, image analysis and mass spectrometry. Survival decreased with increasing temperature, with no larvae surviving at 30°C. Therefore, proteomic analysis was only carried out for 18°C and 24°C. Larvae up-regulated protein folding and degradation, cytoskeletal re-organization, transcriptional regulation and the growth hormone while mostly down-regulating cargo transporting and porphyrin metabolism upon exposure to heat stress. No changes were detected in proteins related to energetic metabolism suggesting that larval fish may not have the energetic plasticity needed to sustain cellular protection in the long-term. These results indicate that despite proteome modulation, S. aurata larvae do not seem able to fully acclimate to higher temperatures as shown by the low survival rates. Consequently, elevated temperatures seem to have bottleneck effects during fish early life stages, and future ocean warming can potentially compromise recruitment's success of key fisheries species. PMID:27062348

  14. Flexible Sheet-Type Sensor for Noninvasive Measurement of Cellular Oxygen Metabolism on a Culture Dish

    PubMed Central

    Akagi, Takanori; Shiono, Hirofumi; Ichiki, Takanori

    2015-01-01

    A novel flexible sensor was developed for the noninvasive oxygen metabolism measurement of cultivated cells and tissues. This device is composed of a transparent double-layered polymer sheet of ethylene-vinyl alcohol (EVOH) and poly(dimethylsiloxane) (PDMS) having an array of microhole structures of 90 μm diameter and 50 μm depth on its surface. All the microhole structures were equipped with a 1-μm-thick optical chemical sensing layer of platinum porphyrin-fluoropolymer on their bottom. The three-dimensional microstructures of the sensor were fabricated by a newly developed simple and low-cost production method named self-aligned hot embossing. The device was designed to be attached slightly above the cells cultivated on a dish to form a temporarily closed microspace over the target cells during measurement. Since the change in oxygen concentration is relatively fast in the microcompartmentalized culture medium, a rapid evaluation of the oxygen consumption rate is possible by measuring the phosphorescence lifetime of the platinum porphyrin-fluoropolymer. The combined use of the device and an automated optical measurement system enabled the high-throughput sensing of cellular oxygen consumption (100 points/min). We monitored the oxygen metabolism of the human breast cancer cell line MCF7 on a Petri dish and evaluated the oxygen consumption rate to be 0.72 ± 0.12 fmol/min/cell. Furthermore, to demonstrate the utility of the developed sensing system, we demonstrated the mapping of the oxygen consumption rate of rat brain slices and succeeded in visualizing a clear difference among the layer structures of the hippocampus, i.e., the cornu ammonis (CA1 and CA3) and dentate gyrus (DG). PMID:26624889

  15. Cross‐talk between circadian clocks, sleep‐wake cycles, and metabolic networks: Dispelling the darkness

    PubMed Central

    Ray, Sandipan

    2016-01-01

    Integration of knowledge concerning circadian rhythms, metabolic networks, and sleep‐wake cycles is imperative for unraveling the mysteries of biological cycles and their underlying mechanisms. During the last decade, enormous progress in circadian biology research has provided a plethora of new insights into the molecular architecture of circadian clocks. However, the recent identification of autonomous redox oscillations in cells has expanded our view of the clockwork beyond conventional transcription/translation feedback loop models, which have been dominant since the first circadian period mutants were identified in fruit fly. Consequently, non‐transcriptional timekeeping mechanisms have been proposed, and the antioxidant peroxiredoxin proteins have been identified as conserved markers for 24‐hour rhythms. Here, we review recent advances in our understanding of interdependencies amongst circadian rhythms, sleep homeostasis, redox cycles, and other cellular metabolic networks. We speculate that systems‐level investigations implementing integrated multi‐omics approaches could provide novel mechanistic insights into the connectivity between daily cycles and metabolic systems. PMID:26866932

  16. Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks.

    PubMed

    Krumholz, Elias W; Libourel, Igor G L

    2015-07-31

    Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable.

  17. Cellular automata with object-oriented features for parallel molecular network modeling.

    PubMed

    Zhu, Hao; Wu, Yinghui; Huang, Sui; Sun, Yan; Dhar, Pawan

    2005-06-01

    Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented. PMID:16117022

  18. Hierarchical decomposition of metabolic networks using k-modules.

    PubMed

    Reimers, Arne C

    2015-12-01

    The optimal solutions obtained by flux balance analysis (FBA) are typically not unique. Flux modules have recently been shown to be a very useful tool to simplify and decompose the space of FBA-optimal solutions. Since yield-maximization is sometimes not the primary objective encountered in vivo, we are also interested in understanding the space of sub-optimal solutions. Unfortunately, the flux modules are too restrictive and not suited for this task. We present a generalization, called k-module, which compensates the limited applicability of flux modules to the space of sub-optimal solutions. Intuitively, a k-module is a sub-network with low connectivity to the rest of the network. Recursive application of k-modules yields a hierarchical decomposition of the metabolic network, which is also known as branch decomposition in matroid theory. In particular, decompositions computed by existing methods, like the null-space-based approach, introduced by Poolman et al. [(2007) J. Theor. Biol. 249: , 691-705] can be interpreted as branch decompositions. With k-modules we can now compare alternative decompositions of metabolic networks to the classical sub-systems of glycolysis, tricarboxylic acid (TCA) cycle, etc. They can be used to speed up algorithmic problems [theoretically shown for elementary flux modes (EFM) enumeration] and have the potential to present computational solutions in a more intuitive way independently from the classical sub-systems.

  19. Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks

    PubMed Central

    2013-01-01

    A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models. Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input–output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous. We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to

  20. Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks.

    PubMed

    Samaga, Regina; Klamt, Steffen

    2013-01-01

    A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models.Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input-output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous.We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to logical

  1. Alteration of heme metabolism in a cellular model of Diamond-Blackfan anemia.

    PubMed

    Mercurio, Sonia; Aspesi, Anna; Silengo, Lorenzo; Altruda, Fiorella; Dianzani, Irma; Chiabrando, Deborah

    2016-04-01

    Diamond-Blackfan anemia (DBA) is a congenital pure red cell aplasia often associated with skeletal malformations. Mutations in ribosomal protein coding genes, mainly in RPS19, account for the majority of DBA cases. The molecular mechanisms underlying DBA pathogenesis are still not completely understood. Alternative spliced isoforms of FLVCR1 (feline leukemia virus subgroup C receptor 1) transcript coding for non-functional proteins have been reported in some DBA patients. Consistently, a phenotype very close to DBA has been described in animal models of FLVCR1 deficiency. FLVCR1 gene codes for two proteins: the plasma membrane heme exporter FLVCR1a and the mitochondrial heme exporter FLVCR1b. The coordinated expression of both FLVCR1 isoforms regulates an intracellular heme pool, necessary for proper expansion and differentiation of erythroid precursors. Here, we investigate the role of FLVCR1 isoforms in a cellular model of DBA. RPS19-downregulated TF1 cells show reduced FLVCR1a and FLVCR1b mRNA levels associated with heme overload. The downregulation of FLVCR1 isoforms affects cell cycle progression and apoptosis in differentiating K562 cells, a phenotype similar to DBA. Taken together, these data suggest that alteration of heme metabolism could play a role in the pathogenesis of DBA.

  2. Neurophysiological, metabolic and cellular compartments that drive neurovascular coupling and neuroimaging signals

    PubMed Central

    Moreno, Andrea; Jego, Pierrick; de la Cruz, Feliberto; Canals, Santiago

    2013-01-01

    Complete understanding of the mechanisms that coordinate work and energy supply of the brain, the so called neurovascular coupling, is fundamental to interpreting brain energetics and their influence on neuronal coding strategies, but also to interpreting signals obtained from brain imaging techniques such as functional magnetic resonance imaging. Interactions between neuronal activity and cerebral blood flow regulation are largely compartmentalized. First, there exists a functional compartmentalization in which glutamatergic peri-synaptic activity and its electrophysiological events occur in close proximity to vascular responses. Second, the metabolic processes that fuel peri-synaptic activity are partially segregated between glycolytic and oxidative compartments. Finally, there is cellular segregation between astrocytic and neuronal compartments, which has potentially important implications on neurovascular coupling. Experimental data is progressively showing a tight interaction between the products of energy consumption and neurotransmission-driven signaling molecules that regulate blood flow. Here, we review some of these issues in light of recent findings with special attention to the neuron-glia interplay on the generation of neuroimaging signals. PMID:23543907

  3. Neurophysiological, metabolic and cellular compartments that drive neurovascular coupling and neuroimaging signals.

    PubMed

    Moreno, Andrea; Jego, Pierrick; de la Cruz, Feliberto; Canals, Santiago

    2013-01-01

    Complete understanding of the mechanisms that coordinate work and energy supply of the brain, the so called neurovascular coupling, is fundamental to interpreting brain energetics and their influence on neuronal coding strategies, but also to interpreting signals obtained from brain imaging techniques such as functional magnetic resonance imaging. Interactions between neuronal activity and cerebral blood flow regulation are largely compartmentalized. First, there exists a functional compartmentalization in which glutamatergic peri-synaptic activity and its electrophysiological events occur in close proximity to vascular responses. Second, the metabolic processes that fuel peri-synaptic activity are partially segregated between glycolytic and oxidative compartments. Finally, there is cellular segregation between astrocytic and neuronal compartments, which has potentially important implications on neurovascular coupling. Experimental data is progressively showing a tight interaction between the products of energy consumption and neurotransmission-driven signaling molecules that regulate blood flow. Here, we review some of these issues in light of recent findings with special attention to the neuron-glia interplay on the generation of neuroimaging signals. PMID:23543907

  4. Redox metabolism in Trypanosoma cruzi. Biochemical characterization of dithiol glutaredoxin dependent cellular pathways.

    PubMed

    Márquez, Vanina E; Arias, Diego G; Chiribao, Maria L; Faral-Tello, Paula; Robello, Carlos; Iglesias, Alberto A; Guerrero, Sergio A

    2014-11-01

    In Trypanosoma cruzi, the modification of thiols by glutathionylation-deglutathionylation and its potential relation to protective, regulatory or signaling functions have been scarcely explored. Herein we characterize a dithiolic glutaredoxin (TcrGrx), a redox protein with deglutathionylating activity, having potential functionality to control intracellular homeostasis of protein and non-protein thiols. The catalytic mechanism followed by TcrGrx was found dependent on thiol concentration. Results suggest that TcrGrx operates as a dithiolic or a monothiolic Grx, depending on GSH concentration. TcrGrx functionality to mediate reduction of protein and non-protein disulfides was studied. TcrGrx showed a preference for glutathionylated substrates respect to protein disulfides. From in vivo assays involving TcrGrx overexpressing parasites, we observed the contribution of the protein to increase the general resistance against oxidative damage and intracellular replication of the amastigote stage. Also, studies performed with epimastigotes overexpressing TcrGrx strongly suggest the involvement of the protein in a cellular pathway connecting an apoptotic stimulus and apoptotic-like cell death. Novel information is presented about the participation of this glutaredoxin not only in redox metabolism but also in redox signaling pathways in T. cruzi. The influence of TcrGrx in several parasite physiological processes suggests novel insights about the protein involvement in redox signaling.

  5. An Organic Anion Transporter 1 (OAT1)-centered Metabolic Network.

    PubMed

    Liu, Henry C; Jamshidi, Neema; Chen, Yuchen; Eraly, Satish A; Cho, Sai Yee; Bhatnagar, Vibha; Wu, Wei; Bush, Kevin T; Abagyan, Ruben; Palsson, Bernhard O; Nigam, Sanjay K

    2016-09-01

    There has been a recent interest in the broader physiological importance of multispecific "drug" transporters of the SLC and ABC transporter families. Here, a novel multi-tiered systems biology approach was used to predict metabolites and signaling molecules potentially affected by the in vivo deletion of organic anion transporter 1 (Oat1, Slc22a6, originally NKT), a major kidney-expressed drug transporter. Validation of some predictions in wet-lab assays, together with re-evaluation of existing transport and knock-out metabolomics data, generated an experimentally validated, confidence ranked set of OAT1-interacting endogenous compounds enabling construction of an "OAT1-centered metabolic interaction network." Pathway and enrichment analysis indicated an important role for OAT1 in metabolism involving: the TCA cycle, tryptophan and other amino acids, fatty acids, prostaglandins, cyclic nucleotides, odorants, polyamines, and vitamins. The partly validated reconstructed network is also consistent with a major role for OAT1 in modulating metabolic and signaling pathways involving uric acid, gut microbiome products, and so-called uremic toxins accumulating in chronic kidney disease. Together, the findings are compatible with the hypothesized role of drug transporters in remote inter-organ and inter-organismal communication: The Remote Sensing and Signaling Hypothesis (Nigam, S. K. (2015) Nat. Rev. Drug Disc. 14, 29). The fact that OAT1 can affect many systemic biological pathways suggests that drug-metabolite interactions need to be considered beyond simple competition for the drug transporter itself and may explain aspects of drug-induced metabolic syndrome. Our approach should provide novel mechanistic insights into the role of OAT1 and other drug transporters implicated in metabolic diseases like gout, diabetes, and chronic kidney disease. PMID:27440044

  6. Multi-objective optimization of enzyme manipulations in metabolic networks considering resilience effects

    PubMed Central

    2011-01-01

    Background Improving the synthesis rate of desired metabolites in metabolic systems is one of the main tasks in metabolic engineering. In the last decade, metabolic engineering approaches based on the mathematical optimization have been used extensively for the analysis and manipulation of metabolic networks. Experimental evidence shows that mutants reflect resilience phenomena against gene alterations. Although researchers have published many studies on the design of metabolic systems based on kinetic models and optimization strategies, almost no studies discuss the multi-objective optimization problem for enzyme manipulations in metabolic networks considering resilience phenomenon. Results This study proposes a generalized fuzzy multi-objective optimization approach to formulate the enzyme intervention problem for metabolic networks considering resilience phenomena and cell viability. This approach is a general framework that can be applied to any metabolic networks to investigate the influence of resilience phenomena on gene intervention strategies and maximum target synthesis rates. This study evaluates the performance of the proposed approach by applying it to two metabolic systems: S. cerevisiae and E. coli. Results show that the maximum synthesis rates of target products by genetic interventions are always over-estimated in metabolic networks that do not consider the resilience effects. Conclusions Considering the resilience phenomena in metabolic networks can improve the predictions of gene intervention and maximum synthesis rates in metabolic engineering. The proposed generalized fuzzy multi-objective optimization approach has the potential to be a good and practical framework in the design of metabolic networks. PMID:21929795

  7. Alterations in metabolic pathways and networks in Alzheimer's disease.

    PubMed

    Kaddurah-Daouk, R; Zhu, H; Sharma, S; Bogdanov, M; Rozen, S G; Matson, W; Oki, N O; Motsinger-Reif, A A; Churchill, E; Lei, Z; Appleby, D; Kling, M A; Trojanowski, J Q; Doraiswamy, P M; Arnold, S E

    2013-04-09

    The pathogenic mechanisms of Alzheimer's disease (AD) remain largely unknown and clinical trials have not demonstrated significant benefit. Biochemical characterization of AD and its prodromal phase may provide new diagnostic and therapeutic insights. We used targeted metabolomics platform to profile cerebrospinal fluid (CSF) from AD (n=40), mild cognitive impairment (MCI, n=36) and control (n=38) subjects; univariate and multivariate analyses to define between-group differences; and partial least square-discriminant analysis models to classify diagnostic groups using CSF metabolomic profiles. A partial correlation network was built to link metabolic markers, protein markers and disease severity. AD subjects had elevated methionine (MET), 5-hydroxyindoleacetic acid (5-HIAA), vanillylmandelic acid, xanthosine and glutathione versus controls. MCI subjects had elevated 5-HIAA, MET, hypoxanthine and other metabolites versus controls. Metabolite ratios revealed changes within tryptophan, MET and purine pathways. Initial pathway analyses identified steps in several pathways that appear altered in AD and MCI. A partial correlation network showed total tau most directly related to norepinephrine and purine pathways; amyloid-β (Ab42) was related directly to an unidentified metabolite and indirectly to 5-HIAA and MET. These findings indicate that MCI and AD are associated with an overlapping pattern of perturbations in tryptophan, tyrosine, MET and purine pathways, and suggest that profound biochemical alterations are linked to abnormal Ab42 and tau metabolism. Metabolomics provides powerful tools to map interlinked biochemical pathway perturbations and study AD as a disease of network failure.

  8. Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions

    PubMed Central

    Burgard, Anthony P.; Nikolaev, Evgeni V.; Schilling, Christophe H.; Maranas, Costas D.

    2004-01-01

    In this paper, we introduce the Flux Coupling Finder (FCF) framework for elucidating the topological and flux connectivity features of genome-scale metabolic networks. The framework is demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. The analysis allows one to determine whether any two metabolic fluxes, v1 and v2, are (1) directionally coupled, if a non-zero flux for v1 implies a non-zero flux for v2 but not necessarily the reverse; (2) partially coupled, if a non-zero flux for v1 implies a non-zero, though variable, flux for v2 and vice versa; or (3) fully coupled, if a non-zero flux for v1 implies not only a non-zero but also a fixed flux for v2 and vice versa. Flux coupling analysis also enables the global identification of blocked reactions, which are all reactions incapable of carrying flux under a certain condition; equivalent knockouts, defined as the set of all possible reactions whose deletion forces the flux through a particular reaction to zero; and sets of affected reactions denoting all reactions whose fluxes are forced to zero if a particular reaction is deleted. The FCF approach thus provides a novel and versatile tool for aiding metabolic reconstructions and guiding genetic manipulations. PMID:14718379

  9. Application of GA in optimization of pore network models generated by multi-cellular growth algorithms

    NASA Astrophysics Data System (ADS)

    Jamshidi, Saeid; Boozarjomehry, Ramin Bozorgmehry; Pishvaie, Mahmoud Reza

    2009-10-01

    In pore network modeling, the void space of a rock sample is represented at the microscopic scale by a network of pores connected by throats. Construction of a reasonable representation of the geometry and topology of the pore space will lead to a reliable prediction of the properties of porous media. Recently, the theory of multi-cellular growth (or L-systems) has been used as a flexible tool for generation of pore network models which do not require any special information such as 2D SEM or 3D pore space images. In general, the networks generated by this method are irregular pore network models which are inherently closer to the complicated nature of the porous media rather than regular lattice networks. In this approach, the construction process is controlled only by the production rules that govern the development process of the network. In this study, genetic algorithm has been used to obtain the optimum values of the uncertain parameters of these production rules to build an appropriate irregular lattice network capable of the prediction of both static and hydraulic information of the target porous medium.

  10. A new cellular nonlinear network emulation on FPGA for EEG signal processing in epilepsy

    NASA Astrophysics Data System (ADS)

    Müller, Jens; Müller, Jan; Tetzlaff, Ronald

    2011-05-01

    For processing of EEG signals, we propose a new architecture for the hardware emulation of discrete-time Cellular Nonlinear Networks (DT-CNN). Our results show the importance of a high computational accuracy in EEG signal prediction that cannot be achieved with existing analogue VLSI circuits. The refined architecture of the processing elements and its resource schedule, the cellular network structure with local couplings, the FPGA-based embedded system containing the DT-CNN, and the data flow in the entire system will be discussed in detail. The proposed DT-CNN design has been implemented and tested on an Xilinx FPGA development platform. The embedded co-processor with a multi-threading kernel is utilised for control and pre-processing tasks and data exchange to the host via Ethernet. The performance of the implemented DT-CNN has been determined for a popular example and compared to that of a conventional computer.

  11. Application of neural networks to channel assignment for cellular CDMA networks with multiple services and mobile base stations

    NASA Astrophysics Data System (ADS)

    Hortos, William S.

    1996-03-01

    The use of artificial neural networks to the channel assignment problem for cellular code- division multiple access (CDMA) telecommunications systems is considered. CDMA takes advantage of voice activity and spatial isolation because its capacity is only interference limited, unlike time-division multiple access (TDMA) and frequency-division multiple access (FDMA) where capacities are bandwidth limited. Any reduction in interference in CDMA translates linearly into increased capacity. FDMA and TDMA use a frequency reuse pattern as a method to increase capacity, while CDMA reuses the same frequency for all cells and gains a reuse efficiency by means of orthogonal codes. The latter method can improve system capacity by factors of four to six over digital TDMA or FDMA. Cellular carriers are planning to provide multiple communication services using CDMA in the next generation cellular system infrastructure. The approach of this study is the use of neural network methods for automatic and local network control, based on traffic behavior in specific cell cites and demand history. The goal is to address certain problems associated with the management of mobile and personal communication services in a cellular radio communications environment. In planning a cellular radio network, the operator assigns channels to the radio cells so that the probability of the processed carrier-to-interference ratio, CII, exceeding a predefined value is sufficiently low. The RF propagation, determined from the topography and infrastructure in the operating area, is used in conjunction with the densities of expected communications traffic to formulate interference constraints. These constraints state which radio cells may use the same code (channel) or adjacent channels at a time. The traffic loading and the number of service grades can also be used to calculate the number of required channels (codes) for each cell. The general assignment problem is the task of assigning the required number

  12. Measuring information flow in cellular networks by the systems biology method through microarray data.

    PubMed

    Chen, Bor-Sen; Li, Cheng-Wei

    2015-01-01

    In general, it is very difficult to measure the information flow in a cellular network directly. In this study, based on an information flow model and microarray data, we measured the information flow in cellular networks indirectly by using a systems biology method. First, we used a recursive least square parameter estimation algorithm to identify the system parameters of coupling signal transduction pathways and the cellular gene regulatory network (GRN). Then, based on the identified parameters and systems theory, we estimated the signal transductivities of the coupling signal transduction pathways from the extracellular signals to each downstream protein and the information transductivities of the GRN between transcription factors in response to environmental events. According to the proposed method, the information flow, which is characterized by signal transductivity in coupling signaling pathways and information transductivity in the GRN, can be estimated by microarray temporal data or microarray sample data. It can also be estimated by other high-throughput data such as next-generation sequencing or proteomic data. Finally, the information flows of the signal transduction pathways and the GRN in leukemia cancer cells and non-leukemia normal cells were also measured to analyze the systematic dysfunction in this cancer from microarray sample data. The results show that the signal transductivities of signal transduction pathways change substantially from normal cells to leukemia cancer cells.

  13. Proteomic analysis of Nrf2 deficient transgenic mice reveals cellular defence and lipid metabolism as primary Nrf2-dependent pathways in the liver.

    PubMed

    Kitteringham, Neil R; Abdullah, Azman; Walsh, Joanne; Randle, Laura; Jenkins, Rosalind E; Sison, Rowena; Goldring, Christopher E P; Powell, Helen; Sanderson, Christopher; Williams, Samantha; Higgins, Larry; Yamamoto, Masayuki; Hayes, John; Park, B Kevin

    2010-06-16

    The transcription factor Nrf2 regulates expression of multiple cellular defence proteins through the antioxidant response element (ARE). Nrf2-deficient mice (Nrf2(-/-)) are highly susceptible to xenobiotic-mediated toxicity, but the precise molecular basis of enhanced toxicity is unknown. Oligonucleotide array studies suggest that a wide range of gene products is altered constitutively, however no equivalent proteomics analyses have been conducted. To define the range of Nrf2-regulated proteins at the constitutive level, protein expression profiling of livers from Nrf2(-/-) and wild type mice was conducted using both stable isotope labelling (iTRAQ) and gel electrophoresis methods. To establish a robust reproducible list of Nrf2-dependent proteins, three independent groups of mice were analysed. Correlative network analysis (MetaCore) identified two predominant groups of Nrf2-regulated proteins. As expected, one group comprised proteins involved in phase II drug metabolism, which were down-regulated in the absence of Nrf2. Surprisingly, the most profound changes were observed amongst proteins involved in the synthesis and metabolism of fatty acids and other lipids. Importantly, we show here for the first time, that the enzyme ATP-citrate lyase, responsible for acetyl-CoA production, is negatively regulated by Nrf2. This latter finding suggests that Nrf2 is a major regulator of cellular lipid disposition in the liver.

  14. A New Cellular Architecture for Information Retrieval from Sensor Networks through Embedded Service and Security Protocols.

    PubMed

    Shahzad, Aamir; Landry, René; Lee, Malrey; Xiong, Naixue; Lee, Jongho; Lee, Changhoon

    2016-06-14

    Substantial changes have occurred in the Information Technology (IT) sectors and with these changes, the demand for remote access to field sensor information has increased. This allows visualization, monitoring, and control through various electronic devices, such as laptops, tablets, i-Pads, PCs, and cellular phones. The smart phone is considered as a more reliable, faster and efficient device to access and monitor industrial systems and their corresponding information interfaces anywhere and anytime. This study describes the deployment of a protocol whereby industrial system information can be securely accessed by cellular phones via a Supervisory Control And Data Acquisition (SCADA) server. To achieve the study goals, proprietary protocol interconnectivity with non-proprietary protocols and the usage of interconnectivity services are considered in detail. They support the visualization of the SCADA system information, and the related operations through smart phones. The intelligent sensors are configured and designated to process real information via cellular phones by employing information exchange services between the proprietary protocol and non-proprietary protocols. SCADA cellular access raises the issue of security flaws. For these challenges, a cryptography-based security method is considered and deployed, and it could be considered as a part of a proprietary protocol. Subsequently, transmission flows from the smart phones through a cellular network.

  15. A New Cellular Architecture for Information Retrieval from Sensor Networks through Embedded Service and Security Protocols

    PubMed Central

    Shahzad, Aamir; Landry, René; Lee, Malrey; Xiong, Naixue; Lee, Jongho; Lee, Changhoon

    2016-01-01

    Substantial changes have occurred in the Information Technology (IT) sectors and with these changes, the demand for remote access to field sensor information has increased. This allows visualization, monitoring, and control through various electronic devices, such as laptops, tablets, i-Pads, PCs, and cellular phones. The smart phone is considered as a more reliable, faster and efficient device to access and monitor industrial systems and their corresponding information interfaces anywhere and anytime. This study describes the deployment of a protocol whereby industrial system information can be securely accessed by cellular phones via a Supervisory Control And Data Acquisition (SCADA) server. To achieve the study goals, proprietary protocol interconnectivity with non-proprietary protocols and the usage of interconnectivity services are considered in detail. They support the visualization of the SCADA system information, and the related operations through smart phones. The intelligent sensors are configured and designated to process real information via cellular phones by employing information exchange services between the proprietary protocol and non-proprietary protocols. SCADA cellular access raises the issue of security flaws. For these challenges, a cryptography-based security method is considered and deployed, and it could be considered as a part of a proprietary protocol. Subsequently, transmission flows from the smart phones through a cellular network. PMID:27314351

  16. A New Cellular Architecture for Information Retrieval from Sensor Networks through Embedded Service and Security Protocols.

    PubMed

    Shahzad, Aamir; Landry, René; Lee, Malrey; Xiong, Naixue; Lee, Jongho; Lee, Changhoon

    2016-01-01

    Substantial changes have occurred in the Information Technology (IT) sectors and with these changes, the demand for remote access to field sensor information has increased. This allows visualization, monitoring, and control through various electronic devices, such as laptops, tablets, i-Pads, PCs, and cellular phones. The smart phone is considered as a more reliable, faster and efficient device to access and monitor industrial systems and their corresponding information interfaces anywhere and anytime. This study describes the deployment of a protocol whereby industrial system information can be securely accessed by cellular phones via a Supervisory Control And Data Acquisition (SCADA) server. To achieve the study goals, proprietary protocol interconnectivity with non-proprietary protocols and the usage of interconnectivity services are considered in detail. They support the visualization of the SCADA system information, and the related operations through smart phones. The intelligent sensors are configured and designated to process real information via cellular phones by employing information exchange services between the proprietary protocol and non-proprietary protocols. SCADA cellular access raises the issue of security flaws. For these challenges, a cryptography-based security method is considered and deployed, and it could be considered as a part of a proprietary protocol. Subsequently, transmission flows from the smart phones through a cellular network. PMID:27314351

  17. An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities.

    PubMed

    Matsubara, Takashi; Torikai, Hiroyuki

    2016-04-01

    Modeling and implementation approaches for the reproduction of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of high nonlinearity, the traditional modeling and implementation approaches encounter difficulties in terms of generalization ability (i.e., performance when reproducing an unknown data set) and computational resources (i.e., computation time and circuit elements). To overcome these difficulties, asynchronous cellular automaton-based neuron (ACAN) models, which are described as special kinds of cellular automata that can be implemented as small asynchronous sequential logic circuits have been proposed. This paper presents a novel type of such ACAN and a theoretical analysis of its excitability. This paper also presents a novel network of such neurons, which can mimic input-output relationships of biological and nonlinear ordinary differential equation model neural networks. Numerical analyses confirm that the presented network has a higher generalization ability than other major modeling and implementation approaches. In addition, Field-Programmable Gate Array-implementations confirm that the presented network requires lower computational resources.

  18. On Algebraic Properties of Extreme Pathways in Metabolic Networks

    PubMed Central

    Jevremovic, Dimitrije; Trinh, Cong T.; Srienc, Friedrich

    2010-01-01

    Abstract We give a concise development of some of the major algebraic properties of extreme pathways (pathways that cannot be the result of combining other pathways) of metabolic networks, contrasting them to those of elementary flux modes (pathways involving a minimal set of reactions). In particular, we show that an extreme pathway can be recognized by a rank test as simple as the existing rank test for elementary flux modes, without computing all the modes. We make the observation that, unlike elementary flux modes, the property of being an extreme pathway depends on the presence or absence of reactions beyond those involved in the pathway itself. Hence, the property of being an extreme pathway is not a local property. As a consequence, we find that the set of all elementary flux modes for a network includes all the elementary flux modes for all its subnetworks, but that this property does not hold for the set of all extreme pathways. PMID:20170399

  19. On algebraic properties of extreme pathways in metabolic networks.

    PubMed

    Jevremovic, Dimitrije; Trinh, Cong T; Srienc, Friedrich; Boley, Daniel

    2010-02-01

    We give a concise development of some of the major algebraic properties of extreme pathways (pathways that cannot be the result of combining other pathways) of metabolic networks, contrasting them to those of elementary flux modes (pathways involving a minimal set of reactions). In particular, we show that an extreme pathway can be recognized by a rank test as simple as the existing rank test for elementary flux modes, without computing all the modes. We make the observation that, unlike elementary flux modes, the property of being an extreme pathway depends on the presence or absence of reactions beyond those involved in the pathway itself. Hence, the property of being an extreme pathway is not a local property. As a consequence, we find that the set of all elementary flux modes for a network includes all the elementary flux modes for all its subnetworks, but that this property does not hold for the set of all extreme pathways.

  20. Effect of GO-Fe3O4 and rotating magnetic field on cellular metabolic activity of mammalian cells.

    PubMed

    Urbas, Karolina; Jedrzejczak-Silicka, Magdalena; Rakoczy, Rafal; Zaborski, Daniel; Mijowska, Ewa

    2016-04-01

    The effect of hybrid material-graphene flakes with Fe3O4 nanospheres (GO-Fe3O4), graphene oxide (GO) and magnetite nanospheres (Fe3O4) in rotating magnetic field on mammalian cells metabolism has been studied. Several reports shown that exposure to magnetic field may have influence on cellular membrane permeability. Thus, the aim of presented study was to determine the cellular response of L929 fibroblast cells to nanomaterials and rotating magnetic field for 8-h exposure experiment. The GO had tendency to adsorb proteins, thus cell metabolism was decreased and the effect of that mechanism was enhanced by impact of nanospheres and rotating magnetic field. The highest reduction of cellular metabolism was recorded for WST-1 and NR assays at concentration 100 µg/mL of all tested nanomaterials and magnetic induction value 10.06 mT. The lactate dehydrogenase leakage assay has shown significant changes in membrane permeability. Further studies need to be carried out to precisely determine the mechanism of that process. PMID:26809700

  1. Motion Adaptive Vertical Handoff in Cellular/WLAN Heterogeneous Wireless Network

    PubMed Central

    Ma, Lin; Xu, Yubin; Fu, Yunhai

    2014-01-01

    In heterogeneous wireless network, vertical handoff plays an important role for guaranteeing quality of service and overall performance of network. Conventional vertical handoff trigger schemes are mostly developed from horizontal handoff in homogeneous cellular network. Basically, they can be summarized as hysteresis-based and dwelling-timer-based algorithms, which are reliable on avoiding unnecessary handoff caused by the terminals dwelling at the edge of WLAN coverage. However, the coverage of WLAN is much smaller compared with cellular network, while the motion types of terminals can be various in a typical outdoor scenario. As a result, traditional algorithms are less effective in avoiding unnecessary handoff triggered by vehicle-borne terminals with various speeds. Besides that, hysteresis and dwelling-timer thresholds usually need to be modified to satisfy different channel environments. For solving this problem, a vertical handoff algorithm based on Q-learning is proposed in this paper. Q-learning can provide the decider with self-adaptive ability for handling the terminals' handoff requests with different motion types and channel conditions. Meanwhile, Neural Fuzzy Inference System (NFIS) is embedded to retain a continuous perception of the state space. Simulation results verify that the proposed algorithm can achieve lower unnecessary handoff probability compared with the other two conventional algorithms. PMID:24741347

  2. Phase transitions in pancreatic islet cellular networks and implications for type-1 diabetes

    NASA Astrophysics Data System (ADS)

    Stamper, I. J.; Jackson, Elais; Wang, Xujing

    2014-01-01

    In many aspects the onset of a chronic disease resembles a phase transition in a complex dynamic system: Quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. In this study we examine a special case, the onset of type-1 diabetes (T1D), a disease that results from loss of the insulin-producing pancreatic islet β cells. Within each islet, the β cells are electrically coupled to each other via gap-junctional channels. This intercellular coupling enables the β cells to synchronize their insulin release, thereby generating the multiscale temporal rhythms in blood insulin that are critical to maintaining blood glucose homeostasis. Using percolation theory we show how normal islet function is intrinsically linked to network connectivity. In particular, the critical amount of β-cell death at which the islet cellular network loses site percolation is consistent with laboratory and clinical observations of the threshold loss of β cells that causes islet functional failure. In addition, numerical simulations confirm that the islet cellular network needs to be percolated for β cells to synchronize. Furthermore, the interplay between site percolation and bond strength predicts the existence of a transient phase of islet functional recovery after onset of T1D and introduction of treatment, potentially explaining the honeymoon phenomenon. Based on these results, we hypothesize that the onset of T1D may be the result of a phase transition of the islet β-cell network.

  3. Co-evolution of Hormone Metabolism and Signaling Networks Expands Plant Adaptive Plasticity.

    PubMed

    Weng, Jing-Ke; Ye, Mingli; Li, Bin; Noel, Joseph P

    2016-08-11

    Classically, hormones elicit specific cellular responses by activating dedicated receptors. Nevertheless, the biosynthesis and turnover of many of these hormone molecules also produce chemically related metabolites. These molecules may also possess hormonal activities; therefore, one or more may contribute to the adaptive plasticity of signaling outcomes in host organisms. Here, we show that a catabolite of the plant hormone abscisic acid (ABA), namely phaseic acid (PA), likely emerged in seed plants as a signaling molecule that fine-tunes plant physiology, environmental adaptation, and development. This trait was facilitated by both the emergence-selection of a PA reductase that modulates PA concentrations and by the functional diversification of the ABA receptor family to perceive and respond to PA. Our results suggest that PA serves as a hormone in seed plants through activation of a subset of ABA receptors. This study demonstrates that the co-evolution of hormone metabolism and signaling networks can expand organismal resilience.

  4. Co-evolution of Hormone Metabolism and Signaling Networks Expands Plant Adaptive Plasticity.

    PubMed

    Weng, Jing-Ke; Ye, Mingli; Li, Bin; Noel, Joseph P

    2016-08-11

    Classically, hormones elicit specific cellular responses by activating dedicated receptors. Nevertheless, the biosynthesis and turnover of many of these hormone molecules also produce chemically related metabolites. These molecules may also possess hormonal activities; therefore, one or more may contribute to the adaptive plasticity of signaling outcomes in host organisms. Here, we show that a catabolite of the plant hormone abscisic acid (ABA), namely phaseic acid (PA), likely emerged in seed plants as a signaling molecule that fine-tunes plant physiology, environmental adaptation, and development. This trait was facilitated by both the emergence-selection of a PA reductase that modulates PA concentrations and by the functional diversification of the ABA receptor family to perceive and respond to PA. Our results suggest that PA serves as a hormone in seed plants through activation of a subset of ABA receptors. This study demonstrates that the co-evolution of hormone metabolism and signaling networks can expand organismal resilience. PMID:27518563

  5. The translational machinery is an optimized molecular network that affects cellular homoeostasis and disease.

    PubMed

    Kazana, Eleanna; von der Haar, Tobias

    2014-02-01

    Translation involves interactions between mRNAs, ribosomes, tRNAs and a host of translation factors. Emerging evidence on the eukaryotic translational machinery indicates that these factors are organized in a highly optimized network, in which the levels of the different factors are finely matched to each other. This optimal factor network is essential for producing proteomes that result in optimal fitness, and perturbations to the optimal network that significantly affect translational activity therefore result in non-optimal proteomes, fitness losses and disease. On the other hand, experimental evidence indicates that translation and cell growth are relatively robust to perturbations, and viability can be maintained even upon significant damage to individual translation factors. How the eukaryotic translational machinery is optimized, and how it can maintain optimization in the face of changing internal parameters, are open questions relevant to the interaction between translation and cellular disease states.

  6. Issues for the integration of satellite and terrestrial cellular networks for mobile communications

    NASA Astrophysics Data System (ADS)

    Delre, Enrico; Mistretta, Ignazio; Dellipriscoli, Francesco; Settimo, Franco

    1991-09-01

    Satellite and terrestrial cellular systems naturally complement each other for land mobile communications, even though present systems have been developed independently. The main advantages of the integrated system are a faster wide area coverage, a better management of overloading traffic conditions, an extension to geographical areas not covered by the terrestrial network and, in perspective, the provision of only one integrated system for all mobile communications (land, aeronautical, and maritime). To achieve these goals, as far as possible the same protocols of the terrestrial network should be used also for the satellite network. Discussed here are the main issues arising from the requirements of the main integrated system. Some results are illustrated, and possible future improvements due to technical solutions are presented.

  7. Optimal design of sewer networks using cellular automata-based hybrid methods: Discrete and continuous approaches

    NASA Astrophysics Data System (ADS)

    Afshar, M. H.; Rohani, M.

    2012-01-01

    In this article, cellular automata based hybrid methods are proposed for the optimal design of sewer networks and their performance is compared with some of the common heuristic search methods. The problem of optimal design of sewer networks is first decomposed into two sub-optimization problems which are solved iteratively in a two stage manner. In the first stage, the pipe diameters of the network are assumed fixed and the nodal cover depths of the network are determined by solving a nonlinear sub-optimization problem. A cellular automata (CA) method is used for the solution of the optimization problem with the network nodes considered as the cells and their cover depths as the cell states. In the second stage, the nodal cover depths calculated from the first stage are fixed and the pipe diameters are calculated by solving a second nonlinear sub-optimization problem. Once again a CA method is used to solve the optimization problem of the second stage with the pipes considered as the CA cells and their corresponding diameters as the cell states. Two different updating rules are derived and used for the CA of the second stage depending on the treatment of the pipe diameters. In the continuous approach, the pipe diameters are considered as continuous variables and the corresponding updating rule is derived mathematically from the original objective function of the problem. In the discrete approach, however, an adhoc updating rule is derived and used taking into account the discrete nature of the pipe diameters. The proposed methods are used to optimally solve two sewer network problems and the results are presented and compared with those obtained by other methods. The results show that the proposed CA based hybrid methods are more efficient and effective than the most powerful search methods considered in this work.

  8. Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

    PubMed

    Wallach, Thomas; Schellenberg, Katja; Maier, Bert; Kalathur, Ravi Kiran Reddy; Porras, Pablo; Wanker, Erich E; Futschik, Matthias E; Kramer, Achim

    2013-03-01

    Essentially all biological processes depend on protein-protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner. PMID:23555304

  9. Inactivation of Metabolic Genes Causes Short- and Long-Range dys-Regulation in Escherichia coli Metabolic Network

    PubMed Central

    Barupal, Dinesh Kumar; Lee, Sang Jun; Karoly, Edward D.; Adhya, Sankar

    2013-01-01

    The metabolic network in E. coli can be severely affected by the inactivation of metabolic genes that are required to catabolize a nutrient (D-galactose). We hypothesized that the resulting accumulation of small molecules can yield local as well as systemic effects on the metabolic network. Analysis of metabolomics data in wild-type and D-galactose non-utilizing mutants, galT, galU and galE, reveal the large metabolic differences between the wild-type and the mutants when the strains were grown in D-galactose. Network mapping suggested that the enzymatic defects affected the metabolic modules located both at short- and long-ranges from the D-galactose metabolic module. These modules suggested alterations in glutathione, energy, nucleotide and lipid metabolism and disturbed carbon to nitrogen ratio in mutant strains. The altered modules are required for normal cell growth for the wild-type strain, explaining why the cell growth is inhibited in the mutants in the presence of D-galactose. Identification of these distance-based dys-regulations would enhance the systems level understanding of metabolic networks of microorganisms having importance in biomedical and biotechnological research. PMID:24363806

  10. Hands-on metabolism analysis of complex biochemical networks using elementary flux modes.

    PubMed

    Schäuble, Sascha; Schuster, Stefan; Kaleta, Christoph

    2011-01-01

    The aim of this chapter is to discuss the basic principles and reasoning behind elementary flux mode analysis (EFM analysis)--an important tool for the analysis of metabolic networks. We begin with a short introduction into metabolic pathway analysis and subsequently outline in detail fundamentals of EFM analysis by way of a small example network. We discuss issues arising in the reconstruction of metabolic networks required for EFM analysis and how they can be circumvented. Subsequently, we analyze a more elaborate example network representing photosynthate metabolism. Finally, we give an overview of applications of EFM analysis in biotechnology and other fields and discuss issues arising when applying methods from metabolic pathway analysis to genome-scale metabolic networks.

  11. In Absence of the Cellular Prion Protein, Alterations in Copper Metabolism and Copper-Dependent Oxidase Activity Affect Iron Distribution

    PubMed Central

    Gasperini, Lisa; Meneghetti, Elisa; Legname, Giuseppe; Benetti, Federico

    2016-01-01

    Essential elements as copper and iron modulate a wide range of physiological functions. Their metabolism is strictly regulated by cellular pathways, since dysregulation of metal homeostasis is responsible for many detrimental effects. Neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease and prion diseases are characterized by alterations of metal ions. These neurodegenerative maladies involve proteins that bind metals and mediate their metabolism through not well-defined mechanisms. Prion protein, for instance, interacts with divalent cations via multiple metal-binding sites and it modulates several metal-dependent physiological functions, such as S-nitrosylation of NMDA receptors. In this work we focused on the effect of prion protein absence on copper and iron metabolism during development and adulthood. In particular, we investigated copper and iron functional values in serum and several organs such as liver, spleen, total brain and isolated hippocampus. Our results show that iron content is diminished in prion protein-null mouse serum, while it accumulates in liver and spleen. Our data suggest that these alterations can be due to impairments in copper-dependent cerulopalsmin activity which is known to affect iron mobilization. In prion protein-null mouse total brain and hippocampus, metal ion content shows a fluctuating trend, suggesting the presence of homeostatic compensatory mechanisms. However, copper and iron functional values are likely altered also in these two organs, as indicated by the modulation of metal-binding protein expression levels. Altogether, these results reveal that the absence of the cellular prion protein impairs copper metabolism and copper-dependent oxidase activity, with ensuing alteration of iron mobilization from cellular storage compartments. PMID:27729845

  12. Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform

    NASA Astrophysics Data System (ADS)

    Karabiber, Fethullah; Vecchio, Pietro; Grassi, Giuseppe

    2011-12-01

    The Bio-inspired (Bi-i) Cellular Vision System is a computing platform consisting of sensing, array sensing-processing, and digital signal processing. The platform is based on the Cellular Neural/Nonlinear Network (CNN) paradigm. This article presents the implementation of a novel CNN-based segmentation algorithm onto the Bi-i system. Each part of the algorithm, along with the corresponding implementation on the hardware platform, is carefully described through the article. The experimental results, carried out for Foreman and Car-phone video sequences, highlight the feasibility of the approach, which provides a frame rate of about 26 frames/s. Comparisons with existing CNN-based methods show that the conceived approach is more accurate, thus representing a good trade-off between real-time requirements and accuracy.

  13. Cellular network formation of hydrophobic alkanethiol capped gold nanoparticles on mica surface mediated by water islands.

    PubMed

    John, Neena S; Raina, Gargi; Sharma, Ashutosh; Kulkarni, Giridhar U

    2010-09-01

    Dendritic and cellular networks of nanoparticles are known to form commonly either by random diffusion-limited aggregation or by solvent evaporation dynamics. Using alkanethiol capped gold nanoparticles deposited on mica imaged under ambient and controlled water vapor conditions by atomic force microscope and in situ scanning electron microscope, respectively, we show a third mechanism in action. The cellular network consisting of open and closed polygons is formed by the nucleation and lateral growth of adsorbed water islands, the contact lines of which push the randomly distributed hydrophobic nanoparticles along the growth directions, eventually leading to the polygonal structure formation as the boundaries of the growing islands meet. Such nanoparticle displacement has been possible due to the weakly adhering nature of the hydrophilic substrate, mica. These results demonstrate an important but hitherto neglected effect of adsorbed water in the structure formation on hydrophilic substrates and provide a facile tool for the fabrication of nanoparticle networks without specific particle or substrate modifications and without a tight control on particle deposition conditions during the solvent evaporation. PMID:20831330

  14. Cellular Signaling Networks Function as Generalized Wiener-Kolmogorov Filters to Suppress Noise

    NASA Astrophysics Data System (ADS)

    Hinczewski, Michael; Thirumalai, D.

    2014-10-01

    Cellular signaling involves the transmission of environmental information through cascades of stochastic biochemical reactions, inevitably introducing noise that compromises signal fidelity. Each stage of the cascade often takes the form of a kinase-phosphatase push-pull network, a basic unit of signaling pathways whose malfunction is linked with a host of cancers. We show that this ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov optimal noise filter. Using concepts from umbral calculus, we generalize the linear Wiener-Kolmogorov theory, originally introduced in the context of communication and control engineering, to take nonlinear signal transduction and discrete molecule populations into account. This allows us to derive rigorous constraints for efficient noise reduction in this biochemical system. Our mathematical formalism yields bounds on filter performance in cases important to cellular function—such as ultrasensitive response to stimuli. We highlight features of the system relevant for optimizing filter efficiency, encoded in a single, measurable, dimensionless parameter. Our theory, which describes noise control in a large class of signal transduction networks, is also useful both for the design of synthetic biochemical signaling pathways and the manipulation of pathways through experimental probes such as oscillatory input.

  15. Integration of metabolic and gene regulatory networks modulates the C. elegans dietary response.

    PubMed

    Watson, Emma; MacNeil, Lesley T; Arda, H Efsun; Zhu, Lihua Julie; Walhout, Albertha J M

    2013-03-28

    Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network composed of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several transcription factors (TFs) that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus.

  16. Three Years of Country-Wide Rainfall Maps from Cellular Communication Networks

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Rios Gaona, Manuel Felipe; Leijnse, Hidde; Uijlenhoet, Remko

    2015-04-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for hydrological applications, agriculture, meteorology, and climate monitoring. However, the majority of the land surface of the earth lacks accurate rainfall information and the number of rain gauges is even severely declining in Europe, South-America, and Africa. This calls for alternative sources of rainfall information. Various studies have shown that microwave links from operational cellular communication networks may be used for rainfall monitoring. Such networks cover 20% of the land surface of the earth and have a high density, especially in urban areas. The basic principle of rainfall estimation using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated, which can be converted to average rainfall intensities over the length of a link. This is particularly interesting for those countries where few surface rainfall observations are available. A data set from a commercial microwave link network over the Netherlands is analyzed. The data set runs from January 2011 - January 2014 and consists of roughly 2,000 links covering the land surface of the Netherlands (35,500 square kilometers). From this 3-year data set country-wide rainfall maps are retrieved, which are compared to a gauge-adjusted radar data set. The ability of cellular communication networks to estimate rainfall is studied for different temporal and spatial scales (including the catchment scale). To summarize, the results further confirm the potential of these networks for rainfall monitoring for hydrological applications.

  17. Three Years of Country-Wide Rainfall Maps from Cellular Communication Networks

    NASA Astrophysics Data System (ADS)

    Uijlenhoet, R.; Overeem, A.; Leijnse, H.; Rios Gaona, M. F.

    2014-12-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for hydrological applications, agriculture, meteorology, and climate monitoring. However, the majority of the land surface of the earth lacks accurate rainfall information and the number of rain gauges is even severely declining in Europe, South-America, and Africa. This calls for alternative sources of rainfall information. Various studies have shown that microwave links from operational cellular communication networks may be used for rainfall monitoring. Such networks cover 20% of the land surface of the earth and have a high density, especially in urban areas. The basic principle of rainfall estimation using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated, which can be converted to average rainfall intensities over the length of a link. This is particularly interesting for those countries where few surface rainfall observations are available. A data set from a commercial microwave link network over the Netherlands is analyzed. The data set runs from January 2011 - January 2014 and consists of roughly 2000 links covering the land surface of the Netherlands (35,500 square kilometers). From this 3-year data set country-wide rainfall maps are retrieved, which are compared to a gauge-adjusted radar data set. The ability of cellular communication networks to estimate rainfall is studied for different temporal and spatial scales, as well as for several air temperature classes. Case studies are presented to investigate the performance of the algorithm during snow and sleet and to show the influence of dew formation on the antennas on the received signal levels. To summarize, the results further confirm the potential of these networks for rainfall monitoring.

  18. An Arabidopsis WDR protein coordinates cellular networks involved in light, stress response and hormone signals.

    PubMed

    Chuang, Huey-Wen; Feng, Ji-Huan; Feng, Yung-Lin; Wei, Miam-Ju

    2015-12-01

    The WD-40 repeat (WDR) protein acts as a scaffold for protein interactions in various cellular events. An Arabidopsis WDR protein exhibited sequence similarity with human WDR26, a scaffolding protein implicated in H2O2-induced cell death in neural cells. The AtWDR26 transcript was induced by auxin, abscisic acid (ABA), ethylene (ET), osmostic stress and salinity. The expression of AtWDR26 was regulated by light, and seed germination of the AtWDR26 overexpression (OE) and seedling growth of the T-DNA knock-out (KO) exhibited altered sensitivity to light. Root growth of the OE seedlings increased tolerance to ZnSO4 and NaCl stresses and were hypersensitive to inhibition of osmotic stress. Seedlings of OE and KO altered sensitivities to multiple hormones. Transcriptome analysis of the transgenic plants overexpressing AtWDR26 showed that genes involved in the chloroplast-related metabolism constituted the largest group of the up-regulated genes. AtWDR26 overexpression up-regulated a large number of genes related to defense cellular events including biotic and abiotic stress response. Furthermore, several members of genes functioning in the regulation of Zn homeostasis, and hormone synthesis and perception of auxin and JA were strongly up-regulated in the transgenic plants. Our data provide physiological and transcriptional evidence for AtWDR26 role in hormone, light and abiotic stress cellular events.

  19. Metabolic impact assessment for heterologous protein production in Streptomyces lividans based on genome-scale metabolic network modeling.

    PubMed

    Lule, Ivan; D'Huys, Pieter-Jan; Van Mellaert, Lieve; Anné, Jozef; Bernaerts, Kristel; Van Impe, Jan

    2013-11-01

    The metabolic impact exerted on a microorganism due to heterologous protein production is still poorly understood in Streptomyces lividans. In this present paper, based on exometabolomic data, a proposed genome-scale metabolic network model is used to assess this metabolic impact in S. lividans. Constraint-based modeling results obtained in this work revealed that the metabolic impact due to heterologous protein production is widely distributed in the genome of S. lividans, causing both slow substrate assimilation and a shift in active pathways. Exchange fluxes that are critical for model performance have been identified for metabolites of mouse tumor necrosis factor, histidine, valine and lysine, as well as biomass. Our results unravel the interaction of heterologous protein production with intracellular metabolism of S. lividans, thus, a possible basis for further studies in relieving the metabolic burden via metabolic or bioprocess engineering.

  20. Perspectives for a better understanding of the metabolic integration of photorespiration within a complex plant primary metabolism network.

    PubMed

    Hodges, Michael; Dellero, Younès; Keech, Olivier; Betti, Marco; Raghavendra, Agepati S; Sage, Rowan; Zhu, Xin-Guang; Allen, Doug K; Weber, Andreas P M

    2016-05-01

    Photorespiration is an essential high flux metabolic pathway that is found in all oxygen-producing photosynthetic organisms. It is often viewed as a closed metabolic repair pathway that serves to detoxify 2-phosphoglycolic acid and to recycle carbon to fuel the Calvin-Benson cycle. However, this view is too simplistic since the photorespiratory cycle is known to interact with several primary metabolic pathways, including photosynthesis, nitrate assimilation, amino acid metabolism, C1 metabolism and the Krebs (TCA) cycle. Here we will review recent advances in photorespiration research and discuss future priorities to better understand (i) the metabolic integration of the photorespiratory cycle within the complex network of plant primary metabolism and (ii) the importance of photorespiration in response to abiotic and biotic stresses. PMID:27053720

  1. Expression of transferrin receptors on mitogen-stimulated human peripheral blood lymphocytes: relation to cellular activation and related metabolic events.

    PubMed Central

    Galbraith, R M; Galbraith, G M

    1981-01-01

    Mitogen-activated normal human peripheral blood lymphocytes bind transferrin to specific membrane receptors. In this study, lymphocytes stimulated with phytohaemagglutinin for 0-66 hr were examined to determine the relation of this phenomenon to cellular activation and related metabolic events. Transferrin receptors were first detected at 20-24 hr. This event was consistently preceded by RNA and protein turnover which commenced during the first 6 hr of culture, whereas initiation of DNA synthesis was detected concurrently with the appearance of receptors or slightly later (24-30 hr). Exposure of cells to inhibitors of RNA and protein synthesis early during culture (at 0 or 24 hr) prevented the expression of transferrin receptors, but also caused generalized metabolic failure, and abrogated cellular activation. In contrast, later addition of these agents at 48 hr did not interfere significantly with the process of activation, but did suppress the terminal increase in receptor-bearing cells observed during the final 18 hr in control cultures lacking inhibitor. After deliberate thermal stripping of receptors from activated cells, the reappearance of membrance binding sites which normally occurred within 30 min, was also blocked by cycloheximide, puromycin and actinomycin D. However, similar inhibition of DNA which was induced by hydroxyurea had much less effect upon both the initial appearance of receptors and their reappearance after ligand-induced depletion. These results demonstrate that the appearance of transferrin receptors upon human lymphocytes is dependent upon cellular activation and requires synthesis of protein and RNA. PMID:6172372

  2. Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response.

    PubMed

    Nikiforov, M P; Reukov, V V; Thompson, G L; Vertegel, A A; Guo, S; Kalinin, S V; Jesse, S

    2009-10-01

    Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

  3. New color image encryption algorithm based on compound chaos mapping and hyperchaotic cellular neural network

    NASA Astrophysics Data System (ADS)

    Li, Jinqing; Bai, Fengming; Di, Xiaoqiang

    2013-01-01

    We propose an image encryption/decryption algorithm based on chaotic control parameter and hyperchaotic system with the composite permutation-diffusion structure. Compound chaos mapping is used to generate control parameters in the permutation stage. The high correlation between pixels is shuffled. In the diffusion stage, compound chaos mapping of different initial condition and control parameter generates the diffusion parameters, which are applied to hyperchaotic cellular neural networks. The diffusion key stream is obtained by this process and implements the pixels' diffusion. Compared with the existing methods, both simulation and statistical analysis of our proposed algorithm show that the algorithm has a good performance against attacks and meets the corresponding security level.

  4. Functional Recognition Imaging Using Artificial Neural Networks: Applications to Rapid Cellular Identification by Broadband Electromechanical Response

    PubMed Central

    Nikiforov, M.P.; Reukov, V.V.; Thompson, G.L.; Vertegel, A.A.; Guo, S.; Jesse, S.; Kalinin, S.V.

    2010-01-01

    Functional recognition imaging in Scanning Probe Microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses to identify the target behavior, reminiscent of associative thinking in the human brain and obviating the need for analytical models. As an example of recognition imaging, we demonstrate rapid identification of cellular organisms using difference in electromechanical activity in a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method. PMID:19752493

  5. A flexible state-space approach for the modeling of metabolic networks II: advanced interrogation of hybridoma metabolism.

    PubMed

    Baughman, Adam C; Sharfstein, Susan T; Martin, Lealon L

    2011-03-01

    Having previously introduced the mathematical framework of topological metabolic analysis (TMA) - a novel optimization-based technique for modeling metabolic networks of arbitrary size and complexity - we demonstrate how TMA facilitates unique methods of metabolic interrogation. With the aid of several hybridoma metabolic investigations as case-studies (Bonarius et al., 1995, 1996, 2001), we first establish that the TMA framework identifies biologically important aspects of the metabolic network under investigation. We also show that the use of a structured weighting approach within our objective provides a substantial modeling benefit over an unstructured, uniform, weighting approach. We then illustrate the strength of TAM as an advanced interrogation technique, first by using TMA to prove the existence of (and to quantitatively describe) multiple topologically distinct configurations of a metabolic network that each optimally model a given set of experimental observations. We further show that such alternate topologies are indistinguishable using existing stoichiometric modeling techniques, and we explain the biological significance of the topological variables appearing within our model. By leveraging the manner in which TMA implements metabolite inputs and outputs, we also show that metabolites whose possible metabolic fates are inadequately described by a given network reconstruction can be quickly identified. Lastly, we show how the use of the TMA aggregate objective function (AOF) permits the identification of modeling solutions that can simultaneously consider experimental observations, underlying biological motivations, or even purely engineering- or design-based goals.

  6. Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality

    DOE PAGESBeta

    Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; Overall, Christopher C.; Hill, Eric A.; Beliaev, Alex S.

    2015-03-27

    Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termedmore » as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.« less

  7. Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality

    SciTech Connect

    Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; Overall, Christopher C.; Hill, Eric A.; Beliaev, Alex S.

    2015-03-27

    Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.

  8. Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

    PubMed Central

    Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; Overall, Christopher C.; Hill, Eric A.; Beliaev, Alexander S.

    2015-01-01

    Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as “topologically important.” Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as “functionally important” genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles. PMID:25826650

  9. Evolving Transport Networks With Cellular Automata Models Inspired by Slime Mould.

    PubMed

    Tsompanas, Michail-Antisthenis I; Sirakoulis, Georgios Ch; Adamatzky, Andrew I

    2015-09-01

    Man-made transport networks and their design are closely related to the shortest path problem and considered amongst the most debated problems of computational intelligence. Apart from using conventional or bio-inspired computer algorithms, many researchers tried to solve this kind of problem using biological computing substrates, gas-discharge solvers, prototypes of a mobile droplet, and hot ice computers. In this aspect, another example of biological computer is the plasmodium of acellular slime mould Physarum polycephalum (P. polycephalum), which is a large single cell visible by an unaided eye and has been proven as a reliable living substrate for implementing biological computing devices for computational geometry, graph-theoretical problems, and optimization and imitation of transport networks. Although P. polycephalum is easy to experiment with, computing devices built with the living slime mould are extremely slow; it takes slime mould days to execute a computation. Consequently, mapping key computing mechanisms of the slime mould onto silicon would allow us to produce efficient bio-inspired computing devices to tackle with hard to solve computational intelligence problems like the aforementioned. Toward this direction, a cellular automaton (CA)-based, Physarum-inspired, network designing model is proposed. This novel CA-based model is inspired by the propagating strategy, the formation of tubular networks, and the computing abilities of the plasmodium of P. polycephalum. The results delivered by the CA model demonstrate a good match with several previously published results of experimental laboratory studies on imitation of man-made transport networks with P. polycephalum. Consequently, the proposed CA model can be used as a virtual, easy-to-access, and biomimicking laboratory emulator that will economize large time periods needed for biological experiments while producing networks almost identical to the tubular networks of the real-slime mould.

  10. A Bayesian Approach to the Evolution of Metabolic Networks on a Phylogeny

    PubMed Central

    Mithani, Aziz; Preston, Gail M.; Hein, Jotun

    2010-01-01

    The availability of genomes of many closely related bacteria with diverse metabolic capabilities offers the possibility of tracing metabolic evolution on a phylogeny relating the genomes to understand the evolutionary processes and constraints that affect the evolution of metabolic networks. Using simple (independent loss/gain of reactions) or complex (incorporating dependencies among reactions) stochastic models of metabolic evolution, it is possible to study how metabolic networks evolve over time. Here, we describe a model that takes the reaction neighborhood into account when modeling metabolic evolution. The model also allows estimation of the strength of the neighborhood effect during the course of evolution. We present Gibbs samplers for sampling networks at the internal node of a phylogeny and for estimating the parameters of evolution over a phylogeny without exploring the whole search space by iteratively sampling from the conditional distributions of the internal networks and parameters. The samplers are used to estimate the parameters of evolution of metabolic networks of bacteria in the genus Pseudomonas and to infer the metabolic networks of the ancestral pseudomonads. The results suggest that pathway maps that are conserved across the Pseudomonas phylogeny have a stronger neighborhood structure than those which have a variable distribution of reactions across the phylogeny, and that some Pseudomonas lineages are going through genome reduction resulting in the loss of a number of reactions from their metabolic networks. PMID:20700467

  11. Cellular telephone-based radiation sensor and wide-area detection network

    DOEpatents

    Craig, William W.; Labov, Simon E.

    2006-12-12

    A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

  12. Stability analysis of switched cellular neural networks: A mode-dependent average dwell time approach.

    PubMed

    Huang, Chuangxia; Cao, Jie; Cao, Jinde

    2016-10-01

    This paper addresses the exponential stability of switched cellular neural networks by using the mode-dependent average dwell time (MDADT) approach. This method is quite different from the traditional average dwell time (ADT) method in permitting each subsystem to have its own average dwell time. Detailed investigations have been carried out for two cases. One is that all subsystems are stable and the other is that stable subsystems coexist with unstable subsystems. By employing Lyapunov functionals, linear matrix inequalities (LMIs), Jessen-type inequality, Wirtinger-based inequality, reciprocally convex approach, we derived some novel and less conservative conditions on exponential stability of the networks. Comparing to ADT, the proposed MDADT show that the minimal dwell time of each subsystem is smaller and the switched system stabilizes faster. The obtained results extend and improve some existing ones. Moreover, the validness and effectiveness of these results are demonstrated through numerical simulations.

  13. Attractivity analysis of memristor-based cellular neural networks with time-varying delays.

    PubMed

    Guo, Zhenyuan; Wang, Jun; Yan, Zheng

    2014-04-01

    This paper presents new theoretical results on the invariance and attractivity of memristor-based cellular neural networks (MCNNs) with time-varying delays. First, sufficient conditions to assure the boundedness and global attractivity of the networks are derived. Using state-space decomposition and some analytic techniques, it is shown that the number of equilibria located in the saturation regions of the piecewise-linear activation functions of an n-neuron MCNN with time-varying delays increases significantly from 2(n) to 2(2n2)+n) (2(2n2) times) compared with that without a memristor. In addition, sufficient conditions for the invariance and local or global attractivity of equilibria or attractive sets in any designated region are derived. Finally, two illustrative examples are given to elaborate the characteristics of the results in detail.

  14. Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification

    PubMed Central

    2016-01-01

    For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases like cancers or neurodegenerative disorders. In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address these limitations. This survey provides an overview of these recent developments and compares pathway- and network-based specimen classification approaches in terms of their utility for improving model robustness, accuracy and biological interpretability. Different routes to translate omics-based multifactorial biomarker models into clinical diagnostic tests are discussed, and a previous study is presented as example. PMID:26141830

  15. Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification.

    PubMed

    Glaab, Enrico

    2016-05-01

    For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases like cancers or neurodegenerative disorders. In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address these limitations. This survey provides an overview of these recent developments and compares pathway- and network-based specimen classification approaches in terms of their utility for improving model robustness, accuracy and biological interpretability. Different routes to translate omics-based multifactorial biomarker models into clinical diagnostic tests are discussed, and a previous study is presented as example.

  16. Microbial diversity and metabolic networks in acid mine drainage habitats

    PubMed Central

    Méndez-García, Celia; Peláez, Ana I.; Mesa, Victoria; Sánchez, Jesús; Golyshina, Olga V.; Ferrer, Manuel

    2015-01-01

    Acid mine drainage (AMD) emplacements are low-complexity natural systems. Low-pH conditions appear to be the main factor underlying the limited diversity of the microbial populations thriving in these environments, although temperature, ionic composition, total organic carbon, and dissolved oxygen are also considered to significantly influence their microbial life. This natural reduction in diversity driven by extreme conditions was reflected in several studies on the microbial populations inhabiting the various micro-environments present in such ecosystems. Early studies based on the physiology of the autochthonous microbiota and the growing success of omics-based methodologies have enabled a better understanding of microbial ecology and function in low-pH mine outflows; however, complementary omics-derived data should be included to completely describe their microbial ecology. Furthermore, recent updates on the distribution of eukaryotes and archaea recovered through sterile filtering (herein referred to as filterable fraction) in these environments demand their inclusion in the microbial characterization of AMD systems. In this review, we present a complete overview of the bacterial, archaeal (including filterable fraction), and eukaryotic diversity in these ecosystems, and include a thorough depiction of the metabolism and element cycling in AMD habitats. We also review different metabolic network structures at the organismal level, which is necessary to disentangle the role of each member of the AMD communities described thus far. PMID:26074887

  17. MetaNetX/MNXref--reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks.

    PubMed

    Moretti, Sébastien; Martin, Olivier; Van Du Tran, T; Bridge, Alan; Morgat, Anne; Pagni, Marco

    2016-01-01

    MetaNetX is a repository of genome-scale metabolic networks (GSMNs) and biochemical pathways from a number of major resources imported into a common namespace of chemical compounds, reactions, cellular compartments--namely MNXref--and proteins. The MetaNetX.org website (http://www.metanetx.org/) provides access to these integrated data as well as a variety of tools that allow users to import their own GSMNs, map them to the MNXref reconciliation, and manipulate, compare, analyze, simulate (using flux balance analysis) and export the resulting GSMNs. MNXref and MetaNetX are regularly updated and freely available.

  18. MetaNetX/MNXref – reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks

    PubMed Central

    Moretti, Sébastien; Martin, Olivier; Van Du Tran, T.; Bridge, Alan; Morgat, Anne; Pagni, Marco

    2016-01-01

    MetaNetX is a repository of genome-scale metabolic networks (GSMNs) and biochemical pathways from a number of major resources imported into a common namespace of chemical compounds, reactions, cellular compartments—namely MNXref—and proteins. The MetaNetX.org website (http://www.metanetx.org/) provides access to these integrated data as well as a variety of tools that allow users to import their own GSMNs, map them to the MNXref reconciliation, and manipulate, compare, analyze, simulate (using flux balance analysis) and export the resulting GSMNs. MNXref and MetaNetX are regularly updated and freely available. PMID:26527720

  19. Long term country-wide rainfall monitoring employing cellular communication networks

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2013-04-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for hydrological applications, agriculture, meteorology, and climate monitoring. However, the majority of the land surface of the earth lacks accurate rainfall information and the number of rain gauges is even severely declining in Europe, South-America, and Africa. This calls for alternative sources of rainfall information. Various studies have shown that microwave links from operational cellular telecommunication networks may be employed for rainfall monitoring. Such networks cover 20% of the land surface of the earth and have a high density, especially in urban areas. The basic principle of rainfall monitoring using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated. Previous studies have shown that average rainfall intensities over the length of a link can be derived from the path-integrated attenuation. This is particularly interesting for those countries where few surface rainfall observations are available. Here we present preliminary results of long term country-wide rainfall monitoring employing cellular communication networks. A dataset from a commercial microwave link network over the Netherlands is analyzed, containing data from an unprecedented number of links (~ 2000) covering the land surface of the Netherlands (35500 square kilometres). This dataset spans from January 2011 through October 2012. Daily rainfall maps (1 km spatial resolution) are derived from the microwave link data and compared to maps from a gauge-adjusted radar dataset. The performance of the rainfall retrieval algorithm will be investigated, particularly a possible seasonal dependence.

  20. A state of the art of metabolic networks of unicellular microalgae and cyanobacteria for biofuel production.

    PubMed

    Baroukh, Caroline; Muñoz-Tamayo, Rafael; Steyer, Jean-Philippe; Bernard, Olivier

    2015-07-01

    The most promising and yet challenging application of microalgae and cyanobacteria is the production of renewable energy: biodiesel from microalgae triacylglycerols and bioethanol from cyanobacteria carbohydrates. A thorough understanding of microalgal and cyanobacterial metabolism is necessary to master and optimize biofuel production yields. To this end, systems biology and metabolic modeling have proven to be very efficient tools if supported by an accurate knowledge of the metabolic network. However, unlike heterotrophic microorganisms that utilize the same substrate for energy and as carbon source, microalgae and cyanobacteria require light for energy and inorganic carbon (CO2 or bicarbonate) as carbon source. This double specificity, together with the complex mechanisms of light capture, makes the representation of metabolic network nonstandard. Here, we review the existing metabolic networks of photoautotrophic microalgae and cyanobacteria. We highlight how these networks have been useful for gaining insight on photoautotrophic metabolism.

  1. Cellular oxidative damage is more sensitive to biosynthetic rate than to metabolic rate: A test of the theoretical model on hornworms (Manduca sexta larvae).

    PubMed

    Amunugama, Kaushalya; Jiao, Lihong; Olbricht, Gayla R; Walker, Chance; Huang, Yue-Wern; Nam, Paul K; Hou, Chen

    2016-09-01

    We develop a theoretical model from an energetic viewpoint for unraveling the entangled effects of metabolic and biosynthetic rates on oxidative cellular damage accumulation during animal's growth, and test the model by experiments in hornworms. The theoretical consideration suggests that most of the cellular damages caused by the oxidative metabolism can be repaired by the efficient maintenance mechanisms, if the energy required by repair is unlimited. However, during growth a considerable amount of energy is allocated to the biosynthesis, which entails tradeoffs with the requirements of repair. Thus, the model predicts that cellular damage is more influenced by the biosynthetic rate than the metabolic rate. To test the prediction, we induced broad variations in metabolic and biosynthetic rates in hornworms, and assayed the lipid peroxidation and protein carbonyl. We found that the increase in the cellular damage was mainly caused by the increase in biosynthetic rate, and the variations in metabolic rate had negligible effect. The oxidative stress hypothesis of aging suggests that high metabolism leads to high cellular damage and short lifespan. However, some empirical studies showed that varying biosynthetic rate, rather than metabolic rate, changes animal's lifespan. The conflicts between the empirical evidence and the hypothesis are reconciled by this study. PMID:27296440

  2. Cellular oxidative damage is more sensitive to biosynthetic rate than to metabolic rate: A test of the theoretical model on hornworms (Manduca sexta larvae).

    PubMed

    Amunugama, Kaushalya; Jiao, Lihong; Olbricht, Gayla R; Walker, Chance; Huang, Yue-Wern; Nam, Paul K; Hou, Chen

    2016-09-01

    We develop a theoretical model from an energetic viewpoint for unraveling the entangled effects of metabolic and biosynthetic rates on oxidative cellular damage accumulation during animal's growth, and test the model by experiments in hornworms. The theoretical consideration suggests that most of the cellular damages caused by the oxidative metabolism can be repaired by the efficient maintenance mechanisms, if the energy required by repair is unlimited. However, during growth a considerable amount of energy is allocated to the biosynthesis, which entails tradeoffs with the requirements of repair. Thus, the model predicts that cellular damage is more influenced by the biosynthetic rate than the metabolic rate. To test the prediction, we induced broad variations in metabolic and biosynthetic rates in hornworms, and assayed the lipid peroxidation and protein carbonyl. We found that the increase in the cellular damage was mainly caused by the increase in biosynthetic rate, and the variations in metabolic rate had negligible effect. The oxidative stress hypothesis of aging suggests that high metabolism leads to high cellular damage and short lifespan. However, some empirical studies showed that varying biosynthetic rate, rather than metabolic rate, changes animal's lifespan. The conflicts between the empirical evidence and the hypothesis are reconciled by this study.

  3. NMR analysis of a stress response metabolic signaling network.

    PubMed

    Zhang, Bo; Halouska, Steven; Schiaffo, Charles E; Sadykov, Marat R; Somerville, Greg A; Powers, Robert

    2011-08-01

    We previously hypothesized that Staphylococcus epidermidis senses a diverse set of environmental and nutritional factors associated with biofilm formation through a modulation in the activity of the tricarboxylic acid (TCA) cycle. Herein, we report our further investigation of the impact of additional environmental stress factors on TCA cycle activity and provide a detailed description of our NMR methodology. S. epidermidis wild-type strain 1457 was treated with stressors that are associated with biofilm formation, a sublethal dose of tetracycline, 5% NaCl, 2% glucose, and autoinducer-2 (AI-2). As controls and to integrate our current data with our previous study, 4% ethanol stress and iron-limitation were also used. Consistent with our prior observations, the effect of many environmental stress factors on the S. epidermidis metabolome was essentially identical to the effect of TCA cycle inactivation in the aconitase mutant strain 1457-acnA::tetM. A detailed quantitative analysis of metabolite concentration changes using 2D (1)H-(13)C HSQC and (1)H-(1)H TOCSY spectra identified a network of 37 metabolites uniformly affected by the stressors and TCA cycle inactivation. We postulate that the TCA cycle acts as the central pathway in a metabolic signaling network.

  4. Text mining for metabolic pathways, signaling cascades, and protein networks.

    PubMed

    Hoffmann, Robert; Krallinger, Martin; Andres, Eduardo; Tamames, Javier; Blaschke, Christian; Valencia, Alfonso

    2005-05-10

    The complexity of the information stored in databases and publications on metabolic and signaling pathways, the high throughput of experimental data, and the growing number of publications make it imperative to provide systems to help the researcher navigate through these interrelated information resources. Text-mining methods have started to play a key role in the creation and maintenance of links between the information stored in biological databases and its original sources in the literature. These links will be extremely useful for database updating and curation, especially if a number of technical problems can be solved satisfactorily, including the identification of protein and gene names (entities in general) and the characterization of their types of interactions. The first generation of openly accessible text-mining systems, such as iHOP (Information Hyperlinked over Proteins), provides additional functions to facilitate the reconstruction of protein interaction networks, combine database and text information, and support the scientist in the formulation of novel hypotheses. The next challenge is the generation of comprehensive information regarding the general function of signaling pathways and protein interaction networks. PMID:15886388

  5. NMR analysis of a stress response metabolic signaling network.

    PubMed

    Zhang, Bo; Halouska, Steven; Schiaffo, Charles E; Sadykov, Marat R; Somerville, Greg A; Powers, Robert

    2011-08-01

    We previously hypothesized that Staphylococcus epidermidis senses a diverse set of environmental and nutritional factors associated with biofilm formation through a modulation in the activity of the tricarboxylic acid (TCA) cycle. Herein, we report our further investigation of the impact of additional environmental stress factors on TCA cycle activity and provide a detailed description of our NMR methodology. S. epidermidis wild-type strain 1457 was treated with stressors that are associated with biofilm formation, a sublethal dose of tetracycline, 5% NaCl, 2% glucose, and autoinducer-2 (AI-2). As controls and to integrate our current data with our previous study, 4% ethanol stress and iron-limitation were also used. Consistent with our prior observations, the effect of many environmental stress factors on the S. epidermidis metabolome was essentially identical to the effect of TCA cycle inactivation in the aconitase mutant strain 1457-acnA::tetM. A detailed quantitative analysis of metabolite concentration changes using 2D (1)H-(13)C HSQC and (1)H-(1)H TOCSY spectra identified a network of 37 metabolites uniformly affected by the stressors and TCA cycle inactivation. We postulate that the TCA cycle acts as the central pathway in a metabolic signaling network. PMID:21692534

  6. Automated refinement and inference of analytical models for metabolic networks

    PubMed Central

    Schmidt, Michael D; Vallabhajosyula, Ravishankar R; Jenkins, Jerry W; Hood, Jonathan E; Soni, Abhishek S; Wikswo, John P; Lipson, Hod

    2013-01-01

    The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model – suggesting nonlinear terms and structural modifications – or even constructing a new model that agrees with the system’s time-series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillations. This approach corrected mistakes reliably in both approximated and overspecified models. The method performed well to high levels of noise for most states, could identify the correct model de novo, and make better predictions than ordinary parametric regression and neural network models. We identified an invariant quantity in the model, which accurately derived kinetics and the numerical sensitivity coefficients of the system. Finally, we compared the system to dynamic flux estimation and discussed the scaling and application of this methodology to automated experiment design and control in biological systems in real-time. PMID:21832805

  7. Interferon-γ regulates cellular metabolism and mRNA translation to potentiate macrophage activation.

    PubMed

    Su, Xiaodi; Yu, Yingpu; Zhong, Yi; Giannopoulou, Eugenia G; Hu, Xiaoyu; Liu, Hui; Cross, Justin R; Rätsch, Gunnar; Rice, Charles M; Ivashkiv, Lionel B

    2015-08-01

    Interferon-γ (IFN-γ) primes macrophages for enhanced microbial killing and inflammatory activation by Toll-like receptors (TLRs), but little is known about the regulation of cell metabolism or mRNA translation during this priming. We found that IFN-γ regulated the metabolism and mRNA translation of human macrophages by targeting the kinases mTORC1 and MNK, both of which converge on the selective regulator of translation initiation eIF4E. Physiological downregulation of mTORC1 by IFN-γ was associated with autophagy and translational suppression of repressors of inflammation such as HES1. Genome-wide ribosome profiling in TLR2-stimulated macrophages showed that IFN-γ selectively modulated the macrophage translatome to promote inflammation, further reprogram metabolic pathways and modulate protein synthesis. These results show that IFN-γ-mediated metabolic reprogramming and translational regulation are key components of classical inflammatory macrophage activation.

  8. Decentralized Dynamic Sub-Carrier Assignment for OFDMA-Based Adhoc and Cellular Networks

    NASA Astrophysics Data System (ADS)

    Nguyen, Van-Duc; Haas, Harald; Kyamakya, Kyandoghere; Chedjou, Jean-Chamerlain; Nguyen, Tien-Hoa; Yoon, Seokho; Choo, Hyunseung

    In this paper, a novel decentralised dynamic sub-carrier assignment (DSA) algorithm for orthogonal frequency division multiple access (OFDMA)-based adhoc and cellular networks operating in time division duplexing (TDD) mode is proposed to solve the hidden and exposed node problem in media access control (MAC). This method reduces the co-channel interference (CCI), and thus increases the overall throughput of the network. Reduced CCI and increased throughput can be achieved, if time and frequency selectivity of the multi-path fading channel and the channel reciprocity offered by the TDD are fully exploited. The time and frequency selectivity of the channel are usually the main problem in mobile communication. However, in the context of channel assignment for OFDMA-based networks in TDD mode, the time and frequency selectivity of the channel are the key to reduce the interference. In the proposed channel assignment mechanism, several clusters of sub-carriers are assigned for data transmission between a transmitter and a receiver only if the corresponding channels of those sub-carriers linking this transmitter to potential victim receivers are deeply faded. In addition, the proposed algorithm works in a fully decentralised fashion and, therefore, it is able to effectively support ad hoc and multihop communication as well as network self-organisation. Numerical results show that the throughput obtained by the proposed approach for a given quality of service is higher than those of the conventional methods in any precondition of adhoc geographic scenario.

  9. Effect of crystals and fibrous network polymer additives on cellular morphology of microcellular foams

    NASA Astrophysics Data System (ADS)

    Miyamoto, Ryoma; Utano, Tatsumi; Yasuhara, Shunya; Ishihara, Shota; Ohshima, Masahiro

    2015-05-01

    In this study, the core-back foam injection molding was used for preparing microcelluar polypropylene (PP) foam with either a 1,3:2,4 bis-O-(4-methylbenzylidene)-D-sorbitol gelling agent (Gel-all MD) or a fibros network polymer additive (Metablen 3000). Both agent and addiive could effectively control the celluar morphology in foams but somehow different ways. In course of cooling the polymer with Gel-all MD in the mold caity, the agent enhanced the crystal nucleation and resulted in the large number of small crystals. The crystals acted as effective bubble nucleation agent in foaming process. Thus, the agent reduced the cell size and increased the cell density, drastically. Furthermore, the small crystals provided an inhomogenuity to the expanding cell wall and produced the high open cell content with nano-scale fibril structure. Gell-all as well as Metablene 3000 formed a gel-like fibrous network in melt. The network increased the elongational viscosity and tended to prevent the cell wall from breaking up. The foaming temperature window was widened by the presence of the network. Especially, the temperature window where the macro-fibrous structure was formed was expanded to the higher temperature. The effects of crystal nucleating agent and PTFE on crystals' size and number, viscoelsticity, rheological propreties of PP and cellular morphology were compared and thorougly investigated.

  10. Controllability in cancer metabolic networks according to drug targets as driver nodes.

    PubMed

    Asgari, Yazdan; Salehzadeh-Yazdi, Ali; Schreiber, Falk; Masoudi-Nejad, Ali

    2013-01-01

    Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.

  11. Controllability in Cancer Metabolic Networks According to Drug Targets as Driver Nodes

    PubMed Central

    Asgari, Yazdan; Salehzadeh-Yazdi, Ali; Schreiber, Falk; Masoudi-Nejad, Ali

    2013-01-01

    Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine. PMID:24282504

  12. Rearrangements of the transcriptional regulatory networks of metabolic pathways in fungi.

    PubMed

    Lavoie, Hugo; Hogues, Hervé; Whiteway, Malcolm

    2009-12-01

    Growing evidence suggests that transcriptional regulatory networks in many organisms are highly flexible. Here, we discuss the evolution of transcriptional regulatory networks governing the metabolic machinery of sequenced ascomycetes. In particular, recent work has shown that transcriptional rewiring is common in regulons controlling processes such as production of ribosome components and metabolism of carbohydrates and lipids. We note that dramatic rearrangements of the transcriptional regulatory components of metabolic functions have occurred among ascomycetes species.

  13. The yeast galactose network as a quantitative model for cellular memory

    PubMed Central

    Stockwell, Sarah R.; Landry, Christian R.; Rifkin, Scott A.

    2014-01-01

    Recent experiments have revealed surprising behavior in the yeast galactose (GAL) pathway, one of the preeminent systems for studying gene regulation. Under certain circumstances, yeast cells display memory of their prior nutrient environments. We distinguish two kinds of cellular memory discovered by quantitative investigations of the GAL network and present a conceptual framework for interpreting new experiments and current ideas on GAL memory. Reinduction memory occurs when cells respond transcriptionally to one environment, shut down the response during several generations in a second environment, then respond faster and with less cell-to-cell variation when returned to the first environment. Persistent memory describes a long-term, arguably stable response in which cells adopt a bimodal or unimodal distribution of induction levels depending on their preceding environment. Deep knowledge of how the yeast GAL pathway responds to different sugar environments has enabled rapid progress in uncovering the mechanisms behind GAL memory, which include cytoplasmic inheritance of inducer proteins and positive feedback loops among regulatory genes. This network of genes, long used to study gene regulation, is now emerging as a model system for cellular memory. PMID:25328105

  14. ComPPI: a cellular compartment-specific database for protein–protein interaction network analysis

    PubMed Central

    Veres, Daniel V.; Gyurkó, Dávid M.; Thaler, Benedek; Szalay, Kristóf Z.; Fazekas, Dávid; Korcsmáros, Tamás; Csermely, Peter

    2015-01-01

    Here we present ComPPI, a cellular compartment-specific database of proteins and their interactions enabling an extensive, compartmentalized protein–protein interaction network analysis (URL: http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter biologically unlikely interactions, where the two interacting proteins have no common subcellular localizations and to predict novel properties, such as compartment-specific biological functions. ComPPI is an integrated database covering four species (S. cerevisiae, C. elegans, D. melanogaster and H. sapiens). The compilation of nine protein–protein interaction and eight subcellular localization data sets had four curation steps including a manually built, comprehensive hierarchical structure of >1600 subcellular localizations. ComPPI provides confidence scores for protein subcellular localizations and protein–protein interactions. ComPPI has user-friendly search options for individual proteins giving their subcellular localization, their interactions and the likelihood of their interactions considering the subcellular localization of their interacting partners. Download options of search results, whole-proteomes, organelle-specific interactomes and subcellular localization data are available on its website. Due to its novel features, ComPPI is useful for the analysis of experimental results in biochemistry and molecular biology, as well as for proteome-wide studies in bioinformatics and network science helping cellular biology, medicine and drug design. PMID:25348397

  15. Condition monitoring of 3G cellular networks through competitive neural models.

    PubMed

    Barreto, Guilherme A; Mota, João C M; Souza, Luis G M; Frota, Rewbenio A; Aguayo, Leonardo

    2005-09-01

    We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behavior is detected, local NPs are used in a component-wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile-based confidence intervals computed over the global and local NPs. We compared the performance of four competitive algorithms [winner-take-all (WTA), frequency-sensitive competitive learning (FSCL), self-organizing map (SOM), and neural-gas algorithm (NGA)] and the results suggest that the joint use of global and local NPs is more efficient and more robust than current single-threshold methods.

  16. Path planning on cellular nonlinear network using active wave computing technique

    NASA Astrophysics Data System (ADS)

    Yeniçeri, Ramazan; Yalçın, Müstak E.

    2009-05-01

    This paper introduces a simple algorithm to solve robot path finding problem using active wave computing techniques. A two-dimensional Cellular Neural/Nonlinear Network (CNN), consist of relaxation oscillators, has been used to generate active waves and to process the visual information. The network, which has been implemented on a Field Programmable Gate Array (FPGA) chip, has the feature of being programmed, controlled and observed by a host computer. The arena of the robot is modelled as the medium of the active waves on the network. Active waves are employed to cover the whole medium with their own dynamics, by starting from an initial point. The proposed algorithm is achieved by observing the motion of the wave-front of the active waves. Host program first loads the arena model onto the active wave generator network and command to start the generation. Then periodically pulls the network image from the generator hardware to analyze evolution of the active waves. When the algorithm is completed, vectorial data image is generated. The path from any of the pixel on this image to the active wave generating pixel is drawn by the vectors on this image. The robot arena may be a complicated labyrinth or may have a simple geometry. But, the arena surface always must be flat. Our Autowave Generator CNN implementation which is settled on the Xilinx University Program Virtex-II Pro Development System is operated by a MATLAB program running on the host computer. As the active wave generator hardware has 16, 384 neurons, an arena with 128 × 128 pixels can be modeled and solved by the algorithm. The system also has a monitor and network image is depicted on the monitor simultaneously.

  17. Metabolic and cellular profile of wether goats: protein fractions and lactate dehydrogenase isoenzymes- reference values.

    PubMed

    Blackwell, J G; Libby, D W

    1982-06-01

    Blood serum concentrations of electrolytes; mineral, serum protein, and biochemical components; serum lactate dehydrogenase (LDH) activity; and blood cellular components were determined in 8 wether goats over a 59-day feeding period. The blood was collected on 6 sampling days, the blood cellular components were analyzed on collection days. The biochemical components were analyzed from frozen samples approximately 3 weeks after collection. The serum protein fractions and LDH isoenzymes were separated electrophoretically from frozen samples. The purpose was to determine the variability and changes in these values, using sample-day intervals over the feeding period. The blood serum concentrations of many of the biochemical and blood cellular components that were measured or calculated varied considerably, showing fluctuations over some portion of the period and ending with an erratic peak at period's end. The WBC count, neutrophil, and band neutrophil values were sharply decreased on sampling days 24 and 35, followed by fluctuations over the last half of the period.

  18. Cellular metabolic, stress, and histological response on exposure to acute toxicity of endosulfan in tilapia (Oreochromis mossambicus).

    PubMed

    Kumar, Neeraj; Sharma, Rupam; Tripathi, Gayatri; Kumar, Kundan; Dalvi, Rishikesh S; Krishna, Gopal

    2016-01-01

    Endosulfan is one of the most hazardous organochlorines pesticides responsible for environmental pollution, as it is very persistent and shows bio-magnification. This study evaluated the impact of acute endosulfan toxicity on metabolic enzymes, lysozyme activities, heat shock protein (Hsp) 70 expression, and histopathology in Tilapia (Oreochromis mossambicus). Among the indicators that were induced in dose dependent manner were the enzymes of amino acid metabolism (serum alanine aminotransferase and aspartate aminotransferase), carbohydrate metabolism (serum lactate dehydrogenase), pentose phosphate pathway (Glucose-6-phosphate dehydrogenase) as well as lysozyme and Hsp70 in liver and gill, while liver and gill Isocitrate dehydrogenase (TCA cycle enzyme) and marker of general energetics (Total adenosine triphosphatase) were inhibited. Histopathological alterations in gill were clubbing of secondary gill lamellae, marked hyperplasia, complete loss of secondary lamellae and atrophy of primary gill filaments. Whereas in liver, swollen hepatocyte, and degeneration with loss of cellular boundaries were distinctly noticed. Overall results clearly demonstrated the unbalanced metabolism and damage of the vital organs like liver and gill in Tilapia due to acute endosulfan exposure.

  19. Country-wide rainfall maps from a commercial cellular telephone network

    NASA Astrophysics Data System (ADS)

    Overeem, A.; Leijnse, H.; Uijlenhoet, R.

    2012-04-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for many applications, for instance, as input for hydrological models. Weather radars often provide data with sufficient spatial and temporal resolution, but usually need adjustment. In general, only few rain gauge measurements are available to adjust the radar data in real-time, for example, each hour. Physically based methods, such as a VPR correction, can be valuable and hold a promise. However, they are not always performed in real-time yet and can be difficult to implement. The estimation of rainfall using microwave links from commercial cellular telephone networks is a new and potentially valuable source of information. Such networks cover large parts of the land surface of the earth and have a high density. The data produced by the microwave links in such networks is essentially a by-product of the communication between mobile telephones. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated. Previous studies have shown that average rainfall intensities over the length of a link can be derived from the path-integrated attenuation. A recent study of us shows that urban rainfall can be estimated from commercial microwave link data for the Rotterdam region, a densely-populated delta city in the Netherlands. A data set from a commercial microwave link network over the Netherlands is analyzed, containing approximately 1500 links covering the land surface of the Netherlands (35500 km2). This data set consists of several days with extreme rainfall in June, July and August 2011. A methodology is presented to derive rainfall intensities and daily rainfall depths from the microwave link data, which have a temporal resolution of 15 min. The magnitude and dynamics of these rainfall intensities

  20. Involvement of cellular metabolism in age-related LTP modifications in rat hippocampal slices.

    PubMed

    Drulis-Fajdasz, Dominika; Wójtowicz, Tomasz; Wawrzyniak, Marcin; Wlodarczyk, Jakub; Mozrzymas, Jerzy W; Rakus, Dariusz

    2015-06-10

    Recent studies emphasized crucial role of astrocytic glycogen metabolism in regulation of synaptic transmission and plasticity in young animals. However, the interplay between age-related synaptic plasticity impairments and changes in energetic metabolism remains obscure. To address this issue, we investigated, in hippocampal slices of young (one month) and aged rats (20-22-months), the impact of glycogen degradation inhibition on LTP, mRNA expression for glycogen metabolism enzymes and morphology of dendritic spines. We show that, whereas in young hippocampi, inhibition of glycogen phosphorolysis disrupts the late phase of LTP in the Schaffer collateral-CA1 pathway, in aged rats, blockade of glycogen phosphorylase tends to enhance it. Gene expression for key energy metabolism enzymes, such as glycogen synthase and phosphorylase and glutamine synthetase showed marked differences between young and aged groups and changes in expression of these enzymes preceded plasticity phenomena. Interestingly, in the aged group, a prominent expression of these enzymes was found also in neurons. Concluding, we show that LTP in the considered pathway is differentially modulated by metabolic processes in young and aging animals, indicating a novel venue of studies aiming at preventing cognitive decline during aging. PMID:26101857

  1. A cellular and metabolic assessment of the thermal stress responses in the endemic gastropod Benedictia limnaeoides ongurensis from Lake Baikal.

    PubMed

    Axenov-Gribanov, Denis V; Bedulina, Daria S; Shatilina, Zhanna M; Lubyaga, Yulia A; Vereshchagina, Kseniya P; Timofeyev, Maxim A

    2014-01-01

    Our objective was to determine if the Lake Baikal endemic gastropod Benedictia limnaeoides ongurensis, which inhabits in stable cold waters expresses a thermal stress response. We hypothesized that the evolution of this species in the stable cold waters of Lake Baikal resulted in a reduction of its thermal stress-response mechanisms at the biochemical and cellular levels. Contrary to our hypothesis, our results show that exposure to a thermal challenge activates the cellular and biochemical mechanisms of thermal resistance, such as heat shock proteins and antioxidative enzymes, and alters energetic metabolism in B. limnaeoides ongurensis. Thermal stress caused the elevation of heat shock protein 70 and the products of anaerobic glycolysis together with the depletion of glucose and phosphagens in the studied species. Thus, a temperature increase activates the complex biochemical system of stress response and alters the energetic metabolism in this endemic Baikal gastropod. It is concluded that the deepwater Lake Baikal endemic gastropod B. limnaeoides ongurensis retains the ability to activate well-developed biochemical stress-response mechanisms when exposed to a thermal challenge. PMID:24076104

  2. Function and expression of a novel rat salt-tolerant protein: evidence of a role in cellular sodium metabolism.

    PubMed

    Tsuji, E; Tsuji, Y; Sasaguri, M; Arakawa, K

    1998-09-01

    Higher dietary salt intake in humans is associated with higher BP, but the BP response to NaCl, so-called salt sensitivity, is heterogeneous among individuals. It has been postulated that modifications in cellular cation metabolism may be related to salt sensitivity in mammalian hypertension. The authors have isolated a novel rat complementary DNA, called salt-tolerant protein (STP), that can functionally complement Saccharomyces cervisiae HAL1, which improves salt tolerance by modulating the cation transport system. On high-salt (8% NaCl) diets, both Dahl salt-sensitive and salt-resistant rats displayed an elevated BP and increased STP mRNA expression. Immunohistochemistry using an anti-rat STP antibody demonstrated the presence of STP immunoreactivity in the proximal tubules. In cells that transiently expressed STP, the intracellular [Na+]/[K+] ratio was higher than that in control cells. STP contains predicted coiled-coil and Src homology 3 domains, and shows a partially high degree of nucleotide identity to human thyroid-hormone receptor interacting protein. These results suggest that STP may play an important role in salt sensitivity through cellular sodium metabolism by mediating signal transduction and a hormone-dependent transcription mechanism. PMID:9727364

  3. Identification of Circular RNAs from the Parental Genes Involved in Multiple Aspects of Cellular Metabolism in Barley.

    PubMed

    Darbani, Behrooz; Noeparvar, Shahin; Borg, Søren

    2016-01-01

    RNA circularization made by head-to-tail back-splicing events is involved in the regulation of gene expression from transcriptional to post-translational levels. By exploiting RNA-Seq data and down-stream analysis, we shed light on the importance of circular RNAs in plants. The results introduce circular RNAs as novel interactors in the regulation of gene expression in plants and imply the comprehensiveness of this regulatory pathway by identifying circular RNAs for a diverse set of genes. These genes are involved in several aspects of cellular metabolism as hormonal signaling, intracellular protein sorting, carbohydrate metabolism and cell-wall biogenesis, respiration, amino acid biosynthesis, transcription and translation, and protein ubiquitination. Additionally, these parental loci of circular RNAs, from both nuclear and mitochondrial genomes, encode for different transcript classes including protein coding transcripts, microRNA, rRNA, and long non-coding/microprotein coding RNAs. The results shed light on the mitochondrial exonic circular RNAs and imply the importance of circular RNAs for regulation of mitochondrial genes. Importantly, we introduce circular RNAs in barley and elucidate their cellular-level alterations across tissues and in response to micronutrients iron and zinc. In further support of circular RNAs' functional roles in plants, we report several cases where fluctuations of circRNAs do not correlate with the levels of their parental-loci encoded linear transcripts. PMID:27375638

  4. Identification of Circular RNAs from the Parental Genes Involved in Multiple Aspects of Cellular Metabolism in Barley

    PubMed Central

    Darbani, Behrooz; Noeparvar, Shahin; Borg, Søren

    2016-01-01

    RNA circularization made by head-to-tail back-splicing events is involved in the regulation of gene expression from transcriptional to post-translational levels. By exploiting RNA-Seq data and down-stream analysis, we shed light on the importance of circular RNAs in plants. The results introduce circular RNAs as novel interactors in the regulation of gene expression in plants and imply the comprehensiveness of this regulatory pathway by identifying circular RNAs for a diverse set of genes. These genes are involved in several aspects of cellular metabolism as hormonal signaling, intracellular protein sorting, carbohydrate metabolism and cell-wall biogenesis, respiration, amino acid biosynthesis, transcription and translation, and protein ubiquitination. Additionally, these parental loci of circular RNAs, from both nuclear and mitochondrial genomes, encode for different transcript classes including protein coding transcripts, microRNA, rRNA, and long non-coding/microprotein coding RNAs. The results shed light on the mitochondrial exonic circular RNAs and imply the importance of circular RNAs for regulation of mitochondrial genes. Importantly, we introduce circular RNAs in barley and elucidate their cellular-level alterations across tissues and in response to micronutrients iron and zinc. In further support of circular RNAs' functional roles in plants, we report several cases where fluctuations of circRNAs do not correlate with the levels of their parental-loci encoded linear transcripts. PMID:27375638

  5. Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

    PubMed Central

    Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie

    2016-01-01

    Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine. PMID:27774993

  6. Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular

    PubMed Central

    Okasaka, Shozo; Weiler, Richard J.; Keusgen, Wilhelm; Pudeyev, Andrey; Maltsev, Alexander; Karls, Ingolf; Sakaguchi, Kei

    2016-01-01

    The fifth-generation mobile networks (5G) will not only enhance mobile broadband services, but also enable connectivity for a massive number of Internet-of-Things devices, such as wireless sensors, meters or actuators. Thus, 5G is expected to achieve a 1000-fold or more increase in capacity over 4G. The use of the millimeter-wave (mmWave) spectrum is a key enabler to allowing 5G to achieve such enhancement in capacity. To fully utilize the mmWave spectrum, 5G is expected to adopt a heterogeneous network (HetNet) architecture, wherein mmWave small cells are overlaid onto a conventional macro-cellular network. In the mmWave-integrated HetNet, splitting of the control plane (CP) and user plane (UP) will allow continuous connectivity and increase the capacity of the mmWave small cells. mmWave communication can be used not only for access linking, but also for wireless backhaul linking, which will facilitate the installation of mmWave small cells. In this study, a proof-of-concept (PoC) was conducted to demonstrate the practicality of a prototype mmWave-integrated HetNet, using mmWave technologies for both backhaul and access. PMID:27571074

  7. Network, cellular, and molecular mechanisms underlying long-term memory formation.

    PubMed

    Carasatorre, Mariana; Ramírez-Amaya, Víctor

    2013-01-01

    The neural network stores information through activity-dependent synaptic plasticity that occurs in populations of neurons. Persistent forms of synaptic plasticity may account for long-term memory storage, and the most salient forms are the changes in the structure of synapses. The theory proposes that encoding should use a sparse code and evidence suggests that this can be achieved through offline reactivation or by sparse initial recruitment of the network units. This idea implies that in some cases the neurons that underwent structural synaptic plasticity might be a subpopulation of those originally recruited; However, it is not yet clear whether all the neurons recruited during acquisition are the ones that underwent persistent forms of synaptic plasticity and responsible for memory retrieval. To determine which neural units underlie long-term memory storage, we need to characterize which are the persistent forms of synaptic plasticity occurring in these neural ensembles and the best hints so far are the molecular signals underlying structural modifications of the synapses. Structural synaptic plasticity can be achieved by the activity of various signal transduction pathways, including the NMDA-CaMKII and ACh-MAPK. These pathways converge with the Rho family of GTPases and the consequent ERK 1/2 activation, which regulates multiple cellular functions such as protein translation, protein trafficking, and gene transcription. The most detailed explanation may come from models that allow us to determine the contribution of each piece of this fascinating puzzle that is the neuron and the neural network.

  8. Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular.

    PubMed

    Okasaka, Shozo; Weiler, Richard J; Keusgen, Wilhelm; Pudeyev, Andrey; Maltsev, Alexander; Karls, Ingolf; Sakaguchi, Kei

    2016-01-01

    The fifth-generation mobile networks (5G) will not only enhance mobile broadband services, but also enable connectivity for a massive number of Internet-of-Things devices, such as wireless sensors, meters or actuators. Thus, 5G is expected to achieve a 1000-fold or more increase in capacity over 4G. The use of the millimeter-wave (mmWave) spectrum is a key enabler to allowing 5G to achieve such enhancement in capacity. To fully utilize the mmWave spectrum, 5G is expected to adopt a heterogeneous network (HetNet) architecture, wherein mmWave small cells are overlaid onto a conventional macro-cellular network. In the mmWave-integrated HetNet, splitting of the control plane (CP) and user plane (UP) will allow continuous connectivity and increase the capacity of the mmWave small cells. mmWave communication can be used not only for access linking, but also for wireless backhaul linking, which will facilitate the installation of mmWave small cells. In this study, a proof-of-concept (PoC) was conducted to demonstrate the practicality of a prototype mmWave-integrated HetNet, using mmWave technologies for both backhaul and access.

  9. A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways

    PubMed Central

    Taipale, Mikko; Tucker, George; Peng, Jian; Krykbaeva, Irina; Lin, Zhen-Yuan; Larsen, Brett; Choi, Hyungwon; Berger, Bonnie; Gingras, Anne-Claude; Lindquist, Susan

    2014-01-01

    Chaperones are abundant cellular proteins that promote the folding and function of their substrate proteins (clients). In vivo, chaperones also associate with a large and diverse set of co-factors (co-chaperones) that regulate their specificity and function. However, how these co-chaperones regulate protein folding and whether they have chaperone-independent biological functions is largely unknown. We have combined mass spectrometry and quantitative high-throughput LUMIER assays to systematically characterize the chaperone/co-chaperone/client interaction network in human cells. We uncover hundreds of novel chaperone clients, delineate their participation in specific co-chaperone complexes, and establish a surprisingly distinct network of protein/protein interactions for co-chaperones. As a salient example of the power of such analysis, we establish that NUDC family co-chaperones specifically associate with structurally related but evolutionarily distinct β-propeller folds. We provide a framework for deciphering the proteostasis network, its regulation in development and disease, and expand the use of chaperones as sensors for drug/target engagement. PMID:25036637

  10. Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular.

    PubMed

    Okasaka, Shozo; Weiler, Richard J; Keusgen, Wilhelm; Pudeyev, Andrey; Maltsev, Alexander; Karls, Ingolf; Sakaguchi, Kei

    2016-01-01

    The fifth-generation mobile networks (5G) will not only enhance mobile broadband services, but also enable connectivity for a massive number of Internet-of-Things devices, such as wireless sensors, meters or actuators. Thus, 5G is expected to achieve a 1000-fold or more increase in capacity over 4G. The use of the millimeter-wave (mmWave) spectrum is a key enabler to allowing 5G to achieve such enhancement in capacity. To fully utilize the mmWave spectrum, 5G is expected to adopt a heterogeneous network (HetNet) architecture, wherein mmWave small cells are overlaid onto a conventional macro-cellular network. In the mmWave-integrated HetNet, splitting of the control plane (CP) and user plane (UP) will allow continuous connectivity and increase the capacity of the mmWave small cells. mmWave communication can be used not only for access linking, but also for wireless backhaul linking, which will facilitate the installation of mmWave small cells. In this study, a proof-of-concept (PoC) was conducted to demonstrate the practicality of a prototype mmWave-integrated HetNet, using mmWave technologies for both backhaul and access. PMID:27571074

  11. Network, cellular, and molecular mechanisms underlying long-term memory formation.

    PubMed

    Carasatorre, Mariana; Ramírez-Amaya, Víctor

    2013-01-01

    The neural network stores information through activity-dependent synaptic plasticity that occurs in populations of neurons. Persistent forms of synaptic plasticity may account for long-term memory storage, and the most salient forms are the changes in the structure of synapses. The theory proposes that encoding should use a sparse code and evidence suggests that this can be achieved through offline reactivation or by sparse initial recruitment of the network units. This idea implies that in some cases the neurons that underwent structural synaptic plasticity might be a subpopulation of those originally recruited; However, it is not yet clear whether all the neurons recruited during acquisition are the ones that underwent persistent forms of synaptic plasticity and responsible for memory retrieval. To determine which neural units underlie long-term memory storage, we need to characterize which are the persistent forms of synaptic plasticity occurring in these neural ensembles and the best hints so far are the molecular signals underlying structural modifications of the synapses. Structural synaptic plasticity can be achieved by the activity of various signal transduction pathways, including the NMDA-CaMKII and ACh-MAPK. These pathways converge with the Rho family of GTPases and the consequent ERK 1/2 activation, which regulates multiple cellular functions such as protein translation, protein trafficking, and gene transcription. The most detailed explanation may come from models that allow us to determine the contribution of each piece of this fascinating puzzle that is the neuron and the neural network. PMID:22976275

  12. Firing patterns in a random network cellular automata model of the brain

    NASA Astrophysics Data System (ADS)

    Acedo, L.; Lamprianidou, E.; Moraño, J.-A.; Villanueva-Oller, J.; Villanueva, R.-J.

    2015-10-01

    One of the main challenges in the simulation of even reduced areas of the brain is the presence of a large number of neurons and a large number of connections among them. Even from a theoretical point of view, the behaviour of dynamical models of complex networks with high connectivity is unknown, precisely because the cost of computation is still unaffordable and it will likely be in the near future. In this paper we discuss the simulation of a cellular automata network model of the brain including up to one million sites with a maximum average of three hundred connections per neuron. This level of connectivity was achieved thanks to a distributed computing environment based on the BOINC (Berkeley Open Infrastructure for Network Computing) platform. Moreover, in this work we consider the interplay among excitatory neurons (which induce the excitation of their neighbours) and inhibitory neurons (which prevent resting neurons from firing and induce firing neurons to pass to the refractory state). Our objective is to classify the normal (noisy but asymptotically constant patterns) and the abnormal (high oscillations with spindle-like behaviour) patterns of activity in the model brain and their stability and parameter ranges in order to determine the role of excitatory and inhibitory compensatory effects in healthy and diseased individuals.

  13. Design of pathway-level bioprocess monitoring and control strategies supported by metabolic networks.

    PubMed

    Isidro, Inês A; Ferreira, Ana R; Clemente, João J; Cunha, António E; Dias, João M L; Oliveira, Rui

    2013-01-01

    In this chapter we explore the basic tools for the design of bioprocess monitoring, optimization, and control algorithms that incorporate a priori knowledge of metabolic networks