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Sample records for cellular metabolic networks

  1. 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.

  2. 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.

  3. Caenorhabditis elegans metabolic gene regulatory networks govern the cellular economy.

    PubMed

    Watson, Emma; Walhout, Albertha J M

    2014-10-01

    Diet greatly impacts metabolism in health and disease. In response to the presence or absence of specific nutrients, metabolic gene regulatory networks sense the metabolic state of the cell and regulate metabolic flux accordingly, for instance by the transcriptional control of metabolic enzymes. Here, we discuss recent insights regarding metazoan metabolic regulatory networks using the nematode Caenorhabditis elegans as a model, including the modular organization of metabolic gene regulatory networks, the prominent impact of diet on the transcriptome and metabolome, specialized roles of nuclear hormone receptors (NHRs) in responding to dietary conditions, regulation of metabolic genes and metabolic regulators by miRNAs, and feedback between metabolic genes and their regulators.

  4. A metabolic-transcriptional network links sleep and cellular energetics in the brain.

    PubMed

    Wisor, Jonathan P

    2012-01-01

    This review proposes a mechanistic link between cellular metabolic status, transcriptional regulatory changes and sleep. Sleep loss is associated with changes in cellular metabolic status in the brain. Metabolic sensors responsive to cellular metabolic status regulate the circadian clock transcriptional network. Modifications of the transcriptional activity of circadian clock genes affect sleep/wake state changes. Changes in sleep state reverse sleep loss-induced changes in cellular metabolic status. It is thus proposed that the regulation of circadian clock genes by cellular metabolic sensors is a critical intermediate step in the link between cellular metabolic status and sleep. Studies of this regulatory relationship may offer insights into the function of sleep at the cellular level.

  5. 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

  6. Peroxisome Metabolism and Cellular Aging

    PubMed Central

    Titorenko, Vladimir I.; Terlecky, Stanley R.

    2010-01-01

    The essential role of peroxisomes in fatty acid oxidation, anaplerotic metabolism, and hydrogen peroxide turnover is well established. Recent findings suggest these and other related biochemical processes governed by the organelle may also play a critical role in regulating cellular aging. The goal of this review is to summarize and integrate into a model, the evidence that peroxisome metabolism actually helps define the replicative and chronological age of a eukaryotic cell. In this model, peroxisomal reactive oxygen species (ROS) are seen as altering organelle biogenesis and function, and eliciting changes in the dynamic communication networks that exist between peroxisomes and other cellular compartments. At low levels, peroxisomal ROS activate an anti-aging program in the cell; at concentrations beyond a specific threshold, a pro-aging course is triggered. PMID:21083858

  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. Metabolic Networks

    NASA Astrophysics Data System (ADS)

    Palumbo, Maria Concetta; Farina, Lorenzo; Colosimo, Alfredo; Giuliani, Alessandro

    The use of the term `network' is more and more widespread in all fields of biology. It evokes a systemic approach to biological problems able to overcome the evident limitations of the strict reductionism of the past twenty years. The expectations produced by taking into considerations not only the single elements but even the intermingled `web' of links connecting different parts of biological entities, are huge. Nevertheless, we believe that the lack of consciousness that networks, beside their biological `likelihood', are modeling tools and not real entities, could be detrimental to the exploitation of the full potential of this paradigm. Like any modeling tool the network paradigm has a range of application going from situations in which it is particularly fit to situations in which its application can be largely misleading. In this chapter we deal with an aspect of biological entities that is particularly fit for the network approach: the intermediate metabolism. This fit derives both from the existence of a privileged formalization in which the relative role of nodes (metabolites) and arches (enzymes) is immediately suggested by the system architecture. Here we will discuss some applications of both graph theory based analysis and multidimensional statistics method to metabolic network studies with the emphasis on the derivation of biologically meaningful information.

  9. Mathematical Modeling of Cellular Metabolism.

    PubMed

    Berndt, Nikolaus; Holzhütter, Hermann-Georg

    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.

  10. 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

  11. Robustness of metabolic networks

    NASA Astrophysics Data System (ADS)

    Jeong, Hawoong

    2009-03-01

    We investigated the robustness of cellular metabolism by simulating the system-level computational models, and also performed the corresponding experiments to validate our predictions. We address the cellular robustness from the ``metabolite''-framework by using the novel concept of ``flux-sum,'' which is the sum of all incoming or outgoing fluxes (they are the same under the pseudo-steady state assumption). By estimating the changes of the flux-sum under various genetic and environmental perturbations, we were able to clearly decipher the metabolic robustness; the flux-sum around an essential metabolite does not change much under various perturbations. We also identified the list of the metabolites essential to cell survival, and then ``acclimator'' metabolites that can control the cell growth were discovered. Furthermore, this concept of ``metabolite essentiality'' should be useful in developing new metabolic engineering strategies for improved production of various bioproducts and designing new drugs that can fight against multi-antibiotic resistant superbacteria by knocking-down the enzyme activities around an essential metabolite. Finally, we combined a regulatory network with the metabolic network to investigate its effect on dynamic properties of cellular metabolism.

  12. 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

  13. Interconnectivity of human cellular metabolism and disease prevalence

    NASA Astrophysics Data System (ADS)

    Lee, Deok-Sun

    2010-12-01

    Fluctuations of metabolic reaction fluxes may cause abnormal concentrations of toxic or essential metabolites, possibly leading to metabolic diseases. The mutual binding of enzymatic proteins and ones involving common metabolites enforces distinct coupled reactions, by which local perturbations may spread through the cellular network. Such network effects at the molecular interaction level in human cellular metabolism can reappear in the patterns of disease occurrence. Here we construct the enzyme-reaction network and the metabolite-reaction network, capturing the flux coupling of metabolic reactions caused by the interacting enzymes and the shared metabolites, respectively. Diseases potentially caused by the failure of individual metabolic reactions can be identified by using the known disease-gene association, which allows us to derive the probability of an inactivated reaction causing diseases from the disease records at the population level. We find that the greater the number of proteins that catalyze a reaction, the higher the mean prevalence of its associated diseases. Moreover, the number of connected reactions and the mean size of the avalanches in the networks constructed are also shown to be positively correlated with the disease prevalence. These findings illuminate the impact of the cellular network topology on disease development, suggesting that the global organization of the molecular interaction network should be understood to assist in disease diagnosis, treatment, and drug discovery.

  14. 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.

  15. 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

  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. Metabolic networks: beyond the graph.

    PubMed

    Bernal, Andrés; Daza, Edgar

    2011-06-01

    Drugs are devised to enter into the metabolism of an organism in order to produce a desired effect. From the chemical point of view, cellular metabolism is constituted by a complex network of reactions transforming metabolites one in each other. Knowledge on the structure of this network could help to develop novel methods for drug design, and to comprehend the root of known unexpected side effects. Many large-scale studies on the structure of metabolic networks have been developed following models based on different kinds of graphs as the fundamental image of the reaction network. Graphs models, however, comport wrong assumptions regarding the structure of reaction networks that may lead into wrong conclusions if they are not taken into account. In this article we critically review some graph-theoretical approaches to the analysis of centrality, vulnerability and modularity of metabolic networks, analyzing their limitations in estimating these key network properties, consider some proposals explicit or implicitly based on directed hypergraphs regarding their ability to overcome these issues, and review some recent implementation improvements that make the application of these models in increasingly large networks a viable option.

  18. 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

  19. Immunometabolism: Cellular Metabolism Turns Immune Regulator*

    PubMed Central

    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

  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.

  1. Control of fluxes in metabolic networks

    PubMed Central

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

    2016-01-01

    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. 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.

  3. Primitive control of cellular metabolism

    NASA Technical Reports Server (NTRS)

    Mitz, M. A.

    1974-01-01

    It is pointed out that control substances must have existed from the earliest times in the evolution of life and that the same control mechanisms must exist today. The investigation reported is concerned with the concept that carbon dioxide is a primitive regulator of cell function. The effects of carbon dioxide on cellular materials are examined, taking into account questions of solubilization, dissociation, changes of charge, stabilization, structural changes, wettability, the exclusion of other gases, the activation of compounds, changes in plasticity, and changes in membrane permeability.

  4. Regulation of cellular iron metabolism

    PubMed Central

    Wang, Jian; Pantopoulos, Kostas

    2011-01-01

    Iron is an essential but potentially hazardous biometal. Mammalian cells require sufficient amounts of iron to satisfy metabolic needs or to accomplish specialized functions. Iron is delivered to tissues by circulating transferrin, a transporter that captures iron released into the plasma mainly from intestinal enterocytes or reticuloendothelial macrophages. The binding of iron-laden transferrin to the cell-surface transferrin receptor 1 results in endocytosis and uptake of the metal cargo. Internalized iron is transported to mitochondria for the synthesis of haem or iron–sulfur clusters, which are integral parts of several metalloproteins, and excess iron is stored and detoxified in cytosolic ferritin. Iron metabolism is controlled at different levels and by diverse mechanisms. The present review summarizes basic concepts of iron transport, use and storage and focuses on the IRE (iron-responsive element)/IRP (iron-regulatory protein) system, a well known post-transcriptional regulatory circuit that not only maintains iron homoeostasis in various cell types, but also contributes to systemic iron balance. PMID:21348856

  5. 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.

  6. 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

  7. 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

  8. 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

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

    PubMed Central

    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.

    2015-01-01

    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. PMID:26153245

  10. 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.

  11. Complement-mediated regulation of metabolism and basic cellular processes

    PubMed Central

    Hess, Christoph; Kemper, Claudia

    2016-01-01

    Complement is well appreciated as critical arm of innate immunity. It is required for the removal of invading pathogens and functions by direct pathogen destruction and 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 processes of the cell, such as survival, proliferation, and autophagy. Novel 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

  12. 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.

  13. Radiogenic metabolism: an alternative cellular energy source.

    PubMed

    Benford, M S

    2001-01-01

    The concept of 'healing energy' is commonly used in complementary and alternative medicine; however, efforts to define this concept using contemporary scientific theory, and measure it using modern scientific methods, have been limited to date. Recent experimental testing by Benford et al. observed a uniform, substantial, and consistent decrease in gamma radiation during alternative healing sessions, thus supporting a new energy-balance paradigm hypothesizing ionizing radiation as an alternative cellular energy source. This hypothesis extends the known elements of radiogenic metabolism to potentially explain a number of presumably biopositive energy-related phenomena, including fasting and radiation hormesis, as well as to demystify unexplained anomalies such as idiopathic thermogenesis, halos and auras, and incorruptibility of human corpses.

  14. Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks

    PubMed Central

    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

  15. 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

  16. Microbial regulatory and metabolic networks.

    PubMed

    Cho, Byung-Kwan; Charusanti, Pep; Herrgård, Markus J; Palsson, Bernhard O

    2007-08-01

    Reconstruction of transcriptional regulatory and metabolic networks is the foundation of large-scale microbial systems and synthetic biology. An enormous amount of information including the annotated genomic sequences and the genomic locations of DNA-binding regulatory proteins can be used to define metabolic and regulatory networks in cells. In particular, advances in experimental methods to map regulatory networks in microbial cells have allowed reliable data-driven reconstruction of these networks. Recent work on metabolic engineering and experimental evolution of microbes highlights the key role of global regulatory networks in controlling specific metabolic processes and the need to consider the integrated function of multiple types of networks for both scientific and engineering purposes.

  17. Complexity, dynamic cellular network, and tumorigenesis.

    PubMed

    Waliszewski, P

    1997-01-01

    A holistic approach to tumorigenesis is proposed. The main element of the model is the existence of dynamic cellular network. This network comprises a molecular and an energetistic structure of a cell connected through the multidirectional flow of information. The interactions within dynamic cellular network are complex, stochastic, nonlinear, and also involve quantum effects. From this non-reductionist perspective, neither tumorigenesis can be limited to the genetic aspect, nor the initial event must be of molecular nature, nor mutations and epigenetic factors are mutually exclusive, nor a link between cause and effect can be established. Due to complexity, an unstable stationary state of dynamic cellular network rather than a group of unrelated genes determines the phenotype of normal and transformed cells. This implies relativity of tumor suppressor genes and oncogenes. A bifurcation point is defined as an unstable state of dynamic cellular network leading to the other phenotype-stationary state. In particular, the bifurcation point may be determined by a change of expression of a single gene. Then, the gene is called bifurcation point gene. The unstable stationary state facilitates the chaotic dynamics. This may result in a fractal dimension of both normal and tumor tissues. The co-existence of chaotic dynamics and complexity is the essence of cellular processes and shapes differentiation, morphogenesis, and tumorigenesis. In consequence, tumorigenesis is a complex, unpredictable process driven by the interplay between self-organisation and selection.

  18. 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

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

    PubMed

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

    2015-08-01

    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.

  20. Inferring cellular networks using probabilistic graphical models.

    PubMed

    Friedman, Nir

    2004-02-06

    High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets. These models provide a concise representation of complex cellular networks by composing simpler submodels. Procedures based on well-understood principles for inferring such models from data facilitate a model-based methodology for analysis and discovery. This methodology and its capabilities are illustrated by several recent applications to gene expression data.

  1. 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

  2. Redox biocatalysis and metabolism: molecular mechanisms and metabolic network analysis.

    PubMed

    Blank, Lars M; Ebert, Birgitta E; Buehler, Katja; Bühler, Bruno

    2010-08-01

    Whole-cell biocatalysis utilizes native or recombinant enzymes produced by cellular metabolism to perform synthetically interesting reactions. Besides hydrolases, oxidoreductases represent the most applied enzyme class in industry. Oxidoreductases are attributed a high future potential, especially for applications in the chemical and pharmaceutical industries, as they enable highly interesting chemistry (e.g., the selective oxyfunctionalization of unactivated C-H bonds). Redox reactions are characterized by electron transfer steps that often depend on redox cofactors as additional substrates. Their regeneration typically is accomplished via the metabolism of whole-cell catalysts. Traditionally, studies towards productive redox biocatalysis focused on the biocatalytic enzyme, its activity, selectivity, and specificity, and several successful examples of such processes are running commercially. However, redox cofactor regeneration by host metabolism was hardly considered for the optimization of biocatalytic rate, yield, and/or titer. This article reviews molecular mechanisms of oxidoreductases with synthetic potential and the host redox metabolism that fuels biocatalytic reactions with redox equivalents. The tools discussed in this review for investigating redox metabolism provide the basis for studies aiming at a deeper understanding of the interplay between synthetically active enzymes and metabolic networks. The ultimate goal of rational whole-cell biocatalyst engineering and use for fine chemical production is discussed.

  3. 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.

  4. 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

  5. Network motifs modulate druggability of cellular targets

    PubMed Central

    Wu, Fan; Ma, Cong; Tan, Cheemeng

    2016-01-01

    Druggability refers to the capacity of a cellular target to be modulated by a small-molecule drug. To date, druggability is mainly studied by focusing on direct binding interactions between a drug and its target. However, druggability is impacted by cellular networks connected to a drug target. Here, we use computational approaches to reveal basic principles of network motifs that modulate druggability. Through quantitative analysis, we find that inhibiting self-positive feedback loop is a more robust and effective treatment strategy than inhibiting other regulations, and adding direct regulations to a drug-target generally reduces its druggability. The findings are explained through analytical solution of the motifs. Furthermore, we find that a consensus topology of highly druggable motifs consists of a negative feedback loop without any positive feedback loops, and consensus motifs with low druggability have multiple positive direct regulations and positive feedback loops. Based on the discovered principles, we predict potential genetic targets in Escherichia coli that have either high or low druggability based on their network context. Our work establishes the foundation toward identifying and predicting druggable targets based on their network topology. PMID:27824147

  6. MOLECULAR PROCESSES IN CELLULAR ARSENIC METABOLISM

    EPA Science Inventory

    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 conceptu...

  7. Dynamic metabolic flux analysis--tools for probing transient states of metabolic networks.

    PubMed

    Antoniewicz, Maciek R

    2013-12-01

    Computational approaches for analyzing dynamic states of metabolic networks provide a practical framework for design, control, and optimization of biotechnological processes. In recent years, two promising modeling approaches have emerged for characterizing transients in cellular metabolism, dynamic metabolic flux analysis (DMFA), and dynamic flux balance analysis (DFBA). Both approaches combine metabolic network analysis based on pseudo steady-state (PSS) assumption for intracellular metabolism with dynamic models for extracellular environment. One strategy to capture dynamics is by combining network analysis with a kinetic model. Predictive models are thus established that can be used to optimize bioprocessing conditions and identify useful genetic manipulations. Alternatively, by combining network analysis with methods for analyzing extracellular time-series data, transients in intracellular metabolic fluxes can be determined and applied for process monitoring and control.

  8. A cellular perspective on brain energy metabolism and functional imaging.

    PubMed

    Magistretti, Pierre J; Allaman, Igor

    2015-05-20

    The energy demands of the brain are high: they account for at least 20% of the body's energy consumption. Evolutionary studies indicate that the emergence of higher cognitive functions in humans is associated with an increased glucose utilization and expression of energy metabolism genes. Functional brain imaging techniques such as fMRI and PET, which are widely used in human neuroscience studies, detect signals that monitor energy delivery and use in register with neuronal activity. Recent technological advances in metabolic studies with cellular resolution have afforded decisive insights into the understanding of the cellular and molecular bases of the coupling between neuronal activity and energy metabolism and point at a key role of neuron-astrocyte metabolic interactions. This article reviews some of the most salient features emerging from recent studies and aims at providing an integration of brain energy metabolism across resolution scales.

  9. Heterogeneous Force Chains in Cellularized Biopolymer Network

    NASA Astrophysics Data System (ADS)

    Liang, Long; Jones, Christopher Allen Rucksack; Sun, Bo; Jiao, Yang

    Biopolymer Networks play an important role in coordinating and regulating collective cellular dynamics via a number of signaling pathways. Here, we investigate the mechanical response of a model biopolymer network due to the active contraction of embedded cells. Specifically, a graph (bond-node) model derived from confocal microscopy data is used to represent the network microstructure, and cell contraction is modeled by applying correlated displacements at specific nodes, representing the focal adhesion sites. A force-based stochastic relaxation method is employed to obtain force-balanced network under cell contraction. We find that the majority of the forces are carried by a small number of heterogeneous force chains emerged from the contracting cells. The force chains consist of fiber segments that either possess a high degree of alignment before cell contraction or are aligned due to the reorientation induced by cell contraction. Large fluctuations of the forces along different force chains are observed. Importantly, the decay of the forces along the force chains is significantly slower than the decay of radially averaged forces in the system, suggesting that the fibreous nature of biopolymer network structure could support long-range mechanical signaling between cells.

  10. 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.

  11. 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

  12. 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…

  13. Mitochondrial dysfunction and cellular metabolic deficiency in Alzheimer's disease.

    PubMed

    Gu, Xue-Mei; Huang, Han-Chang; Jiang, Zhao-Feng

    2012-10-01

    Alzheimer's disease (AD) is an age-related neurodegenerative disorder. The pathology of AD includes amyloid-β (Aβ) deposits in neuritic plaques and neurofibrillary tangles composed of hyperphosphorylated tau, as well as neuronal loss in specific brain regions. Increasing epidemiological and functional neuroimaging evidence indicates that global and regional disruptions in brain metabolism are involved in the pathogenesis of this disease. Aβ precursor protein is cleaved to produce both extracellular and intracellular Aβ, accumulation of which might interfere with the homeostasis of cellular metabolism. Mitochondria are highly dynamic organelles that not only supply the main energy to the cell but also regulate apoptosis. Mitochondrial dysfunction might contribute to Aβ neurotoxicity. In this review, we summarize the pathways of Aβ generation and its potential neurotoxic effects on cellular metabolism and mitochondrial dysfunction.

  14. Structural correlations in bacterial metabolic networks

    PubMed Central

    2011-01-01

    Background Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution. Results We consider the 'union-network' of 134 bacterial metabolisms, and also the union of two smaller subsets of closely related species. Each reaction-node is tagged with the number of organisms it belongs to, which we denote organism degree (OD), a key concept in our study. Network analysis shows that common reactions are found at the centre of the network and that the average OD decreases as we move to the periphery. Nodes of the same OD are also more likely to be connected to each other compared to a random OD relabelling based on their occurrence in the real data. This trend persists up to a distance of around five reactions. A simple growth model of metabolic networks is used to investigate the biochemical constraints put on metabolic-network evolution. Despite this seemingly drastic simplification, a 'union-network' of a collection of unrelated model networks, free of any selective pressure, still exhibit similar structural features as their bacterial counterpart. Conclusions The OD distribution quantifies topological properties of the evolutionary history of bacterial metabolic networks, and lends additional support to the importance of horizontal gene transfer during bacterial metabolic evolution where new reactions are attached at the periphery of the network. The neutral model of metabolic network growth can reproduce the main features of real networks, but we observe that the real networks contain a smaller common core, while they are more similar at the periphery of the network. This suggests

  15. Contaminant effect on cellular metabolic differential pressure curves.

    PubMed

    Milani, Marziale; Ballerini, Monica; Ferraro, L; Zabeo, M; Barberis, M; Cannone, M; Faraone, V

    2004-01-01

    The possibility of a pressure monitoring system by differential pressure sensors to detect contaminant effects on cellular cultures metabolic activity is discussed using Saccharomyces cerevisiae, lymphocyte, and AHH1 cell cultures. Metabolic (aerobic and anaerobic) processes in cells are accompanied by CO(2) production that induces changes in pressure values when cells are cultured in sealed vessels. These values are subsequently converted in voltage units and plotted pressure dynamics versus time. This procedure leads to a standard curve, typical of the cellular line, which characterizes cellular metabolism when all parameters are controlled, such as temperature and nutrients. Different phases appear in the S. cerevisiae differential pressure curve: an initial growth up to a maximum, followed by a decrement that leads to a typical "depression" (pressure values inside the test-tubes are lower than the initial one) after about 35 h from the beginning. The S. cerevisiae differential pressure curve is successfully used to test the effects of chemical (Amuchina, trieline) and physical (UV radiation, blue light, magnetic fields) contaminants. The same technique is applied to lymphocytes and AHH1 cultures to investigate the effects generated by a 72-h exposure to a 50-Hz, 60-microT electromagnetic field. Lymphocyte samples, cultured in a PHA medium, grow less than control ones, but exhibit a greater metabolic activity: changes in the exposure system configuration influence neither sample growth differences nor metabolic response variations between control and irradiated samples, while all the other irradiation parameters remain constant. Control and irradiated lymphocyte samples, without PHA in culture medium, show the same behavior both during irradiation and metabolic test. AHH1 control and irradiated samples show no difference both in growth percentage during irradiation and in metabolic activity. Different cell cultures respond to the same stimulus in different

  16. Cascading failure and robustness in metabolic networks.

    PubMed

    Smart, Ashley G; Amaral, Luis A N; Ottino, Julio M

    2008-09-09

    We investigate the relationship between structure and robustness in the metabolic networks of Escherichia coli, Methanosarcina barkeri, Staphylococcus aureus, and Saccharomyces cerevisiae, using a cascading failure model based on a topological flux balance criterion. We find that, compared to appropriate null models, the metabolic networks are exceptionally robust. Furthermore, by decomposing each network into rigid clusters and branched metabolites, we demonstrate that the enhanced robustness is related to the organization of branched metabolites, as rigid cluster formations in the metabolic networks appear to be consistent with null model behavior. Finally, we show that cascading in the metabolic networks can be described as a percolation process.

  17. Supporting performance and configuration management of GTE cellular networks

    SciTech Connect

    Tan, Ming; Lafond, C.; Jakobson, G.; Young, G.

    1996-12-31

    GTE Laboratories, in cooperation with GTE Mobilnet, has developed and deployed PERFFEX (PERFormance Expert), an intelligent system for performance and configuration management of cellular networks. PERFEX assists cellular network performance and radio engineers in the analysis of large volumes of cellular network performance and configuration data. It helps them locate and determine the probable causes of performance problems, and provides intelligent suggestions about how to correct them. The system combines an expert cellular network performance tuning capability with a map-based graphical user interface, data visualization programs, and a set of special cellular engineering tools. PERFEX is in daily use at more than 25 GTE Mobile Switching Centers. Since the first deployment of the system in late 1993, PERFEX has become a major GTE cellular network performance optimization tool.

  18. Cellular Metabolic and Autophagic Pathways: Traffic Control by Redox Signaling

    PubMed Central

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

    2013-01-01

    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. PMID:23702245

  19. 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.

  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. [Construction and structural analysis of integrated cellular network of Corynebacterium glutamicum].

    PubMed

    Jiang, Jinguo; Song, Lifu; Zheng, Ping; Jia, Shiru; Sun, Jibin

    2012-05-01

    Corynebacterium glutamicum is one of the most important traditional industrial microorganisms and receiving more and more attention towards a novel cellular factory due to the recently rapid development in genomics and genetic operation toolboxes for Corynebacterium. However, compared to other model organisms such as Escherichia coli, there were few studies on its metabolic regulation, especially a genome-scale integrated cellular network model currently missing for Corynebacterium, which hindered the systematic study of Corynebacterium glutamicum and large-scale rational design and optimization for strains. Here, by gathering relevant information from a number of public databases, we successfully constructed an integrated cellular network, which was composed of 1384 reactions, 1276 metabolites, 88 transcriptional factors and 999 pairs of transcriptional regulatory relationships. The transcriptional regulatory sub-network could be arranged into five layers and the metabolic sub-network presented a clear bow-tie structure. We proposed a new method to extract complex metabolic and regulatory sub-network for product-orientated study taking lysine biosynthesis as an example. The metabolic and regulatory sub-network extracted by our method was more close to the real functional network than the simplex biochemical pathways. The results would be greatly helpful for understanding the high-yielding biomechanism for amino acids and the re-design of the industrial strains.

  2. Heterogeneous force network in 3D cellularized collagen networks.

    PubMed

    Liang, Long; Jones, Christopher; Chen, Shaohua; Sun, Bo; Jiao, Yang

    2016-10-25

    Collagen networks play an important role in coordinating and regulating collective cellular dynamics via a number of signaling pathways. Here, we investigate the transmission of forces generated by contractile cells in 3D collagen-I networks. Specifically, the graph (bond-node) representations of collagen networks with collagen concentrations of 1, 2 and 4 mg ml(-1) are derived from confocal microscopy data and used to model the network microstructure. Cell contraction is modeled by applying correlated displacements at specific nodes of the network, representing the focal adhesion sites. A nonlinear elastic model is employed to characterize the mechanical behavior of individual fiber bundles including strain hardening during stretching and buckling under compression. A force-based relaxation method is employed to obtain equilibrium network configurations under cell contraction. We find that for all collagen concentrations, the majority of the forces are carried by a small number of heterogeneous force chains emitted from the contracting cells, which is qualitatively consistent with our experimental observations. The force chains consist of fiber segments that either possess a high degree of alignment before cell contraction or are aligned due to fiber reorientation induced by cell contraction. The decay of the forces along the force chains is significantly slower than the decay of radially averaged forces in the system, suggesting that the fibreous nature of biopolymer network structure can support long-range force transmission. The force chains emerge even at very small cell contractions, and the number of force chains increases with increasing cell contraction. At large cell contractions, the fibers close to the cell surface are in the nonlinear regime, and the nonlinear region is localized in a small neighborhood of the cell. In addition, the number of force chains increases with increasing collagen concentration, due to the larger number of focal adhesion sites

  3. Heterogeneous force network in 3D cellularized collagen networks

    NASA Astrophysics Data System (ADS)

    Liang, Long; Jones, Christopher; Chen, Shaohua; Sun, Bo; Jiao, Yang

    2016-12-01

    Collagen networks play an important role in coordinating and regulating collective cellular dynamics via a number of signaling pathways. Here, we investigate the transmission of forces generated by contractile cells in 3D collagen-I networks. Specifically, the graph (bond-node) representations of collagen networks with collagen concentrations of 1, 2 and 4 mg ml-1 are derived from confocal microscopy data and used to model the network microstructure. Cell contraction is modeled by applying correlated displacements at specific nodes of the network, representing the focal adhesion sites. A nonlinear elastic model is employed to characterize the mechanical behavior of individual fiber bundles including strain hardening during stretching and buckling under compression. A force-based relaxation method is employed to obtain equilibrium network configurations under cell contraction. We find that for all collagen concentrations, the majority of the forces are carried by a small number of heterogeneous force chains emitted from the contracting cells, which is qualitatively consistent with our experimental observations. The force chains consist of fiber segments that either possess a high degree of alignment before cell contraction or are aligned due to fiber reorientation induced by cell contraction. The decay of the forces along the force chains is significantly slower than the decay of radially averaged forces in the system, suggesting that the fibreous nature of biopolymer network structure can support long-range force transmission. The force chains emerge even at very small cell contractions, and the number of force chains increases with increasing cell contraction. At large cell contractions, the fibers close to the cell surface are in the nonlinear regime, and the nonlinear region is localized in a small neighborhood of the cell. In addition, the number of force chains increases with increasing collagen concentration, due to the larger number of focal adhesion sites

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

    PubMed

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

    2015-09-10

    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.

  8. Metabolic and cellular organization in evolutionarily diverse microalgae as related to biofuels production.

    PubMed

    Hildebrand, Mark; Abbriano, Raffaela M; Polle, Juergen E W; Traller, Jesse C; Trentacoste, Emily M; Smith, Sarah R; Davis, Aubrey K

    2013-06-01

    Microalgae are among the most diverse organisms on the planet, and as a result of symbioses and evolutionary selection, the configuration of core metabolic networks is highly varied across distinct algal classes. The differences in photosynthesis, carbon fixation and processing, carbon storage, and the compartmentation of cellular and metabolic processes are substantial and likely to transcend into the efficiency of various steps involved in biofuel molecule production. By highlighting these differences, we hope to provide a framework for comparative analyses to determine the efficiency of the different arrangements or processes. This sets the stage for optimization on the based on information derived from evolutionary selection to diverse algal classes and to synthetic systems.

  9. Modeling Rice Metabolism: From Elucidating Environmental Effects on Cellular Phenotype to Guiding Crop Improvement

    PubMed Central

    Lakshmanan, Meiyappan; Cheung, C. Y. Maurice; Mohanty, Bijayalaxmi; Lee, Dong-Yup

    2016-01-01

    Crop productivity is severely limited by various biotic and abiotic stresses. Thus, it is highly needed to understand the underlying mechanisms of environmental stress response and tolerance in plants, which could be addressed by systems biology approach. To this end, high-throughput omics profiling and in silico modeling can be considered to explore the environmental effects on phenotypic states and metabolic behaviors of rice crops at the systems level. Especially, the advent of constraint-based metabolic reconstruction and analysis paves a way to characterize the plant cellular physiology under various stresses by combining the mathematical network models with multi-omics data. Rice metabolic networks have been reconstructed since 2013 and currently six such networks are available, where five are at genome-scale. Since their publication, these models have been utilized to systematically elucidate the rice abiotic stress responses and identify agronomic traits for crop improvement. In this review, we summarize the current status of the existing rice metabolic networks and models with their applications. Furthermore, we also highlight future directions of rice modeling studies, particularly stressing how these models can be used to contextualize the affluent multi-omics data that are readily available in the public domain. Overall, we envisage a number of studies in the future, exploiting the available metabolic models to enhance the yield and quality of rice and other food crops. PMID:27965696

  10. 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.

  11. 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.

  12. Advanced stoichiometric analysis of metabolic networks of mammalian systems.

    PubMed

    Orman, Mehmet A; Berthiaume, Francois; Androulakis, Ioannis P; Ierapetritou, Marianthi G

    2011-01-01

    Metabolic engineering tools have been widely applied to living organisms to gain a comprehensive understanding about cellular networks and to improve cellular properties. Metabolic flux analysis (MFA), flux balance analysis (FBA), and metabolic pathway analysis (MPA) are among the most popular tools in stoichiometric network analysis. Although application of these tools into well-known microbial systems is extensive in the literature, various barriers prevent them from being utilized in mammalian cells. Limited experimental data, complex regulatory mechanisms, and the requirement of more complex nutrient media are some major obstacles in mammalian cell systems. However, mammalian cells have been used to produce therapeutic proteins, to characterize disease states or related abnormal metabolic conditions, and to analyze the toxicological effects of some medicinally important drugs. Therefore, there is a growing need for extending metabolic engineering principles to mammalian cells in order to understand their underlying metabolic functions. In this review article, advanced metabolic engineering tools developed for stoichiometric analysis including MFA, FBA, and MPA are described. Applications of these tools in mammalian cells are discussed in detail, and the challenges and opportunities are highlighted.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. Mathematical optimization applications in metabolic networks.

    PubMed

    Zomorrodi, Ali R; Suthers, Patrick F; Ranganathan, Sridhar; Maranas, Costas D

    2012-11-01

    Genome-scale metabolic models are increasingly becoming available for a variety of microorganisms. This has spurred the development of a wide array of computational tools, and in particular, mathematical optimization approaches, to assist in fundamental metabolic network analyses and redesign efforts. This review highlights a number of optimization-based frameworks developed towards addressing challenges in the analysis and engineering of metabolic networks. In particular, three major types of studies are covered here including exploring model predictions, correction and improvement of models of metabolism, and redesign of metabolic networks for the targeted overproduction of a desired compound. Overall, the methods reviewed in this paper highlight the diversity of queries, breadth of questions and complexity of redesigns that are amenable to mathematical optimization strategies.

  19. 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.

  20. Energy efficiency analysis of relay-assisted cellular networks

    NASA Astrophysics Data System (ADS)

    Yu, Huan; Li, Yunzhou; Kountouris, Marios; Xu, Xibin; Wang, Jing

    2014-12-01

    To meet the demand for higher throughput, improved coverage and enhanced reliability, future wireless cellular networks face significant technical challenges. One promising solution is to place relay stations between transmitters and receivers in the cellular network. Meanwhile, as energy consumption reduction has been an important concern for the wireless industry, energy-efficient communications is of prime interest for future networks. In this paper, we study whether and how relays can improve the energy efficiency of cellular networks. Specifically, the energy efficiency of relay-assisted cellular networks is analyzed using tools of stochastic geometry. We first derive the coverage probability for the macro base station (MBS) to user (UE), the MBS to relay station (RS), and the RS to UE links, and then we model the power consumption at the MBS and RS. Based on the analytical model and expressions, the energy efficiency of relay-assisted cellular networks is then evaluated and is shown to be strictly quasi-concave on the transmit power for MBS to UE link or the RS to UE link. Numerical results show that the energy efficiency first improves while it hits a ceiling as the MBS density increases.

  1. [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.

  2. Genotype networks in metabolic reaction spaces

    PubMed Central

    2010-01-01

    Background A metabolic genotype comprises all chemical reactions an organism can catalyze via enzymes encoded in its genome. A genotype is viable in a given environment if it is capable of producing all biomass components the organism needs to survive and reproduce. Previous work has focused on the properties of individual genotypes while little is known about how genome-scale metabolic networks with a given function can vary in their reaction content. Results We here characterize spaces of such genotypes. Specifically, we study metabolic genotypes whose phenotype is viability in minimal chemical environments that differ in their sole carbon sources. We show that regardless of the number of reactions in a metabolic genotype, the genotypes of a given phenotype typically form vast, connected, and unstructured sets -- genotype networks -- that nearly span the whole of genotype space. The robustness of metabolic phenotypes to random reaction removal in such spaces has a narrow distribution with a high mean. Different carbon sources differ in the number of metabolic genotypes in their genotype network; this number decreases as a genotype is required to be viable on increasing numbers of carbon sources, but much less than if metabolic reactions were used independently across different chemical environments. Conclusions Our work shows that phenotype-preserving genotype networks have generic organizational properties and that these properties are insensitive to the number of reactions in metabolic genotypes. PMID:20302636

  3. Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction

    PubMed Central

    Heavner, Benjamin D.; Price, Nathan D.

    2015-01-01

    We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype. PMID:26566239

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

    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.

  5. 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

  6. 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.

  7. Computational approaches to the topology, stability and dynamics of metabolic networks.

    PubMed

    Steuer, Ralf

    2007-01-01

    Cellular metabolism is characterized by an intricate network of interactions between biochemical fluxes, metabolic compounds and regulatory interactions. To investigate and eventually understand the emergent global behavior arising from such networks of interaction is not possible by intuitive reasoning alone. This contribution seeks to describe recent computational approaches that aim to asses the topological and functional properties of metabolic networks. In particular, based on a recently proposed method, it is shown that it is possible to acquire a quantitative picture of the possible dynamics of metabolic systems, without assuming detailed knowledge of the underlying enzyme-kinetic rate equations and parameters. Rather, the method builds upon a statistical exploration of the comprehensive parameter space to evaluate the dynamic capabilities of a metabolic system, thus providing a first step towards the transition from topology to function of metabolic pathways. Utilizing this approach, the role of feedback mechanisms in the maintenance of stability is discussed using minimal models of cellular pathways.

  8. 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.

  9. An entropic characterization of protein interaction networks and cellular robustness.

    PubMed

    Manke, Thomas; Demetrius, Lloyd; Vingron, Martin

    2006-12-22

    The structure of molecular networks is believed to determine important aspects of their cellular function, such as the organismal resilience against random perturbations. Ultimately, however, cellular behaviour is determined by the dynamical processes, which are constrained by network topology. The present work is based on a fundamental relation from dynamical systems theory, which states that the macroscopic resilience of a steady state is correlated with the uncertainty in the underlying microscopic processes, a property that can be measured by entropy. Here, we use recent network data from large-scale protein interaction screens to characterize the diversity of possible pathways in terms of network entropy. This measure has its origin in statistical mechanics and amounts to a global characterization of both structural and dynamical resilience in terms of microscopic elements. We demonstrate how this approach can be used to rank network elements according to their contribution to network entropy and also investigate how this suggested ranking reflects on the functional data provided by gene knockouts and RNAi experiments in yeast and Caenorhabditis elegans. Our analysis shows that knockouts of proteins with large contribution to network entropy are preferentially lethal. This observation is robust with respect to several possible errors and biases in the experimental data. It underscores the significance of entropy as a fundamental invariant of the dynamical system, and as a measure of structural and dynamical properties of networks. Our analytical approach goes beyond the phenomenological studies of cellular robustness based on local network observables, such as connectivity. One of its principal achievements is to provide a rationale to study proxies of cellular resilience and rank proteins according to their importance within the global network context.

  10. Chemical approaches to study metabolic networks.

    PubMed

    Medina-Cleghorn, Daniel; Nomura, Daniel K

    2013-03-01

    One of the more provocative realizations that have come out of the genome sequencing projects is that organisms possess a large number of uncharacterized or poorly characterized enzymes. This finding belies the commonly held notion that our knowledge of cell metabolism is nearly complete, underscoring the vast landscape of unannotated metabolic and signaling networks that operate under normal physiological conditions, let alone in disease states where metabolic networks may be rewired, dysregulated, or altered to drive disease progression. Consequently, the functional annotation of enzymatic pathways represents a grand challenge for researchers in the post-genomic era. This review will highlight the chemical technologies that have been successfully used to characterize metabolism, and put forth some of the challenges we face as we expand our map of metabolic pathways.

  11. Widrow-cellular neural network and optoelectronic implementation

    NASA Astrophysics Data System (ADS)

    Bal, Abdullah

    A new type of optoelectronic cellular neural network has been developed by providing the capability of coefficients adjusment of cellular neural network (CNN) using Widrow based perceptron learning algorithm. The new supervised cellular neural network is called Widrow-CNN. Despite the unsupervised CNN, the proposed learning algorithm allows to use the Widrow-CNN for various image processing applications easily. Also, the capability of CNN for image processing and feature extraction has been improved using basic joint transform correlation architecture. This hardware application presents high speed processing capability compared to digital applications. The optoelectronic Widrow-CNN has been tested for classic CNN feature extraction problems. It yields the best results even in case of hard feature extraction problems such as diagonal line detection and vertical line determination.

  12. Cellular and network mechanisms of electrographic seizures

    PubMed Central

    Bazhenov, Maxim; Timofeev, Igor; Fröhlich, Flavio; Sejnowski, Terrence J.

    2008-01-01

    Epileptic seizures constitute a complex multiscale phenomenon that is characterized by synchronized hyperexcitation of neurons in neuronal networks. Recent progress in understanding pathological seizure dynamics provides crucial insights into underlying mechanisms and possible new avenues for the development of novel treatment modalities. Here we review some recent work that combines in vivo experiments and computational modeling to unravel the pathophysiology of seizures of cortical origin. We particularly focus on how activity-dependent changes in extracellular potassium concentration affects the intrinsic dynamics of neurons involved in cortical seizures characterized by spike/wave complexes and fast runs. PMID:19190736

  13. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks.

    PubMed

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-22

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  14. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    NASA Astrophysics Data System (ADS)

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  15. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    PubMed Central

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-01-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability. PMID:27874024

  16. TRANSWESD: inferring cellular networks with transitive reduction

    PubMed Central

    Klamt, Steffen; Flassig, Robert J.; Sundmacher, Kai

    2010-01-01

    Motivation: Distinguishing direct from indirect influences is a central issue in reverse engineering of biological networks because it facilitates detection and removal of false positive edges. Transitive reduction is one approach for eliminating edges reflecting indirect effects but its use in reconstructing cyclic interaction graphs with true redundant structures is problematic. Results: We present TRANSWESD, an elaborated variant of TRANSitive reduction for WEighted Signed Digraphs that overcomes conceptual problems of existing versions. Major changes and improvements concern: (i) new statistical approaches for generating high-quality perturbation graphs from systematic perturbation experiments; (ii) the use of edge weights (association strengths) for recognizing true redundant structures; (iii) causal interpretation of cycles; (iv) relaxed definition of transitive reduction; and (v) approximation algorithms for large networks. Using standardized benchmark tests, we demonstrate that our method outperforms existing variants of transitive reduction and is, despite its conceptual simplicity, highly competitive with other reverse engineering methods. Contact: klamt@mpi-magdeburg.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20605927

  17. Antiviral and cellular metabolism interactions between Dexelvucitabine and lamivudine.

    PubMed

    Hernandez-Santiago, Brenda I; Mathew, Judy S; Rapp, Kim L; Grier, Jason P; Schinazi, Raymond F

    2007-06-01

    Studies on cellular drug interactions with antiretroviral agents prior to clinical trials are critical to detect possible drug interactions. Herein, we demonstrated that two 2'-deoxycytidine antiretroviral agents, dexelvucitabine (known as beta-d-2',3'-didehydro-2',3'-dideoxy-5-fluorocytidine, DFC, d-d4FC, or RVT) and lamivudine (3TC), combined in primary human peripheral blood mononuclear (PBM) cells infected with human immunodeficiency virus 1 strain LAI (HIV-1(LAI)), resulted in additive-to-synergistic effects. The cellular metabolism of DFC and 3TC was studied in human T-cell lymphoma (CEM) and in primary human PBM cells to determine whether this combination caused any reduction in active nucleoside triphosphate (NTP) levels, which could decrease with their antiviral potency. Competition studies were conducted by coincubation of either radiolabeled DFC with different concentrations of 3TC or radiolabeled 3TC with different concentrations of DFC. Coincubation of radiolabeled 3TC with DFC at concentrations up to 33.3 microM did not cause any marked reduction in 3TC-triphosphate (TP) or any 3TC metabolites. However, a reduction in the level of DFC metabolites was noted at high concentrations of 3TC with radiolabeled DFC. DFC-TP levels in CEM and primary human PBM cells decreased by 88% and 94%, respectively, when high concentrations of 3TC (33.3 and 100 microM) were added, which may influence the effectiveness of DFC-5'-TP on the HIV-1 polymerase. The NTP levels remained well above the median (50%) inhibitory concentration for HIV-1 reverse transcriptase. These results suggest that both beta-d- and beta-l-2'-deoxycytidine analogs, DFC and 3TC, respectively, substrates of 2'-deoxycytidine kinase, could be used in a combined therapeutic modality. However, it may be necessary to decrease the dose of 3TC for this combination to prove effective.

  18. Inference and Prediction of Metabolic Network Fluxes

    PubMed Central

    Nikoloski, Zoran; Perez-Storey, Richard; Sweetlove, Lee J.

    2015-01-01

    In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping. PMID:26392262

  19. 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

  20. 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.

  1. The community structure of human cellular signaling network.

    PubMed

    Diao, Yuanbo; Li, Menglong; Feng, Zinan; Yin, Jiajian; Pan, Yi

    2007-08-21

    Living cell is highly responsive to specific chemicals in its environment, such as hormones and molecules in food or aromas. The reason is ascribed to the existence of widespread and diverse signal transduction pathways, between which crosstalks usually exist, thus constitute a complex signaling network. Evidently, knowledge of topology characteristic of this network could contribute a lot to the understanding of diverse cellular behaviors and life phenomena thus come into being. In this presentation, signal transduction data is extracted from KEGG to construct a cellular signaling network of Homo sapiens, which has 931 nodes and 6798 links in total. Computing the degree distribution, we find it is not a random network, but a scale-free network following a power-law of P(K) approximately K(-gamma), with gamma approximately equal to 2.2. Among three graph partition algorithms, the Guimera's simulated annealing method is chosen to study the details of topology structure and other properties of this cellular signaling network, as it shows the best performance. To reveal the underlying biological implications, further investigation is conducted on ad hoc community and sketch map of individual community is drawn accordingly. The involved experiment data can be found in the supplementary material.

  2. Metabolic regulation of cellular plasticity in the pancreas

    PubMed Central

    Ninov, Nikolay; Hesselson, Daniel; Gut, Philipp; Zhou, Amy; Fidelin, Kevin; Stainier, Didier Y.R.

    2013-01-01

    SUMMARY Obese individuals exhibit an increase in pancreatic β-cell mass; conversely, scarce nutrition during pregnancy has been linked to β-cell insufficiency in the offspring (reviewed in [1, 2]). These phenomena are thought to be mediated mainly through effects on β-cell proliferation, since a nutrient sensitive β-cell progenitor population in the pancreas has not been identified. Here, we employed the FUCCI (Fluorescent Ubiquitination-based Cell Cycle Indicator) system to investigate β-cell replication in real-time, and found that high nutrient concentrations induce rapid β-cell proliferation. Importantly, we found that high nutrient concentrations also stimulate β-cell differentiation from progenitors in the intrapancreatic duct (IPD). Using a new zebrafish line where β-cells are constitutively ablated, we further show that β-cell loss and high nutrient intake synergistically activate these progenitors. At the cellular level, this activation process causes ductal cell reorganization as it stimulates their proliferation and differentiation. Notably, we link the nutrient-dependent activation of these progenitors to a down-regulation of Notch signaling specifically within the IPD. Furthermore, we show that the nutrient sensor mechanistic Target Of Rapamycin (mTOR) is required for endocrine differentiation from the IPD under physiological conditions as well as in the diabetic state. This study thus reveals critical insights into how cells modulate their plasticity in response to metabolic cues and identifies nutrient sensitive progenitors in the mature pancreas. PMID:23791726

  3. 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.

  4. 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.

  5. Cellular metabolic rates from primary dermal fibroblast cells isolated from birds of different body masses.

    PubMed

    Jimenez, Ana Gabriela; Williams, Joseph B

    2014-10-01

    The rate of metabolism is the speed at which organisms use energy, an integration of energy transformations within the body; it governs biological processes that influence rates of growth and reproduction. Progress at understanding functional linkages between whole organism metabolic rate and underlying mechanisms that influence its magnitude has been slow despite the central role this issue plays in evolutionary and physiological ecology. Previous studies that have attempted to relate how cellular processes translate into whole-organism physiology have done so over a range of body masses of subjects. However, the data still remains controversial when observing metabolic rates at the cellular level. To bridge the gap between these ideas, we examined cellular metabolic rate of primary dermal fibroblasts isolated from 49 species of birds representing a 32,000-fold range in body masses to test the hypothesis that metabolic rate of cultured cells scales with body size. We used a Seahorse XF-96 Extracellular flux analyzer to measure cellular respiration in fibroblasts. Additionally, we measured fibroblast size and mitochondrial content. We found no significant correlation between cellular metabolic rate, cell size, or mitochondrial content and body mass. Additionally, there was a significant relationship between cellular basal metabolic rate and proton leak in these cells. We conclude that metabolic rate of cells isolated in culture does not scale with body mass, but cellular metabolic rate is correlated to growth rate in birds.

  6. From molecules to cellular networks: past and future outlook.

    PubMed

    Fang, Xiaona; Wang, Jin

    2017-02-16

    Cellular networks have been the focus of studies in modern systems biology. They are crucial in understanding cell functions and related diseases. We review some past progress in both the theory and experiments, and we also provide several future perspectives for the field.

  7. From molecules to cellular networks: past and future outlook

    NASA Astrophysics Data System (ADS)

    Fang, Xiaona; Wang, Jin

    2017-02-01

    Cellular networks have been the focus of studies in modern systems biology. They are crucial in understanding cell functions and related diseases. We review some past progress in both the theory and experiments, and we also provide several future perspectives for the field.

  8. Tumor cell metabolism: the marriage of molecular genetics and proteomics with cellular intermediary metabolism; proceed with caution!

    PubMed

    Costello, Leslie C; Franklin, Renty B

    2006-11-07

    Metabolic transformations of malignant cells are essential to the development and progression of all cancers. The understanding of the pathogenesis and progression of cancer requires the establishment of the altered genetic/metabolic factors that are essential to the development, growth, and proliferation of the malignant cells. Recognition of this important relationship has resulted in a resurgence of interest in the intermediary metabolism of tumor cells. The role of molecular genetics and proteomics and the application of molecular technology in assessing altered cellular metabolism has become a major area of biomedical research. The contemporary generation of biomedical scientists is exceptionally well trained in all areas of molecular biology and molecular technology, which are now important tools to be applied to the regulation of cellular intermediary metabolism. Simultaneously, the didactic and methodological training associated with the principles and operation of metabolic pathways, enzymology, cellular enzyme activity, and associated biochemical implications has been diminished and often eliminated from the pre- and post-doctoral programs. Interpretations and conclusions of alterations in cellular enzyme activity and associated metabolic pathways based on genetic/proteomic changes can and will result in misrepresentation of important metabolic implications in malignancy and other diseases. It is essential that the genetic/proteomic studies be coupled to biochemical/metabolic cellular events to satisfy the axiom: "genetic transformations and proteomic alterations will have little relevancy to disease processes if the genetic/proteomic alterations are not manifested in altered and impaired cellular and metabolic function". The appropriate marriage of molecular genetics/proteomics with the regulation of cellular intermediary metabolism will provide new revelations and understanding of malignancy that could not be achieved in earlier generations.

  9. 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.

  10. From prebiotic chemistry to cellular metabolism--the chemical evolution of metabolism before Darwinian natural selection.

    PubMed

    Meléndez-Hevia, Enrique; Montero-Gómez, Nancy; Montero, Francisco

    2008-06-07

    It is generally assumed that the complex map of metabolism is a result of natural selection working at the molecular level. However, natural selection can only work on entities that have three basic features: information, metabolism and membrane. Metabolism must include the capability of producing all cellular structures, as well as energy (ATP), from external sources; information must be established on a material that allows its perpetuity, in order to safeguard the goals achieved; and membranes must be able to preserve the internal material, determining a selective exchange with external material in order to ensure that both metabolism and information can be individualized. It is not difficult to understand that protocellular entities that boast these three qualities can evolve through natural selection. The problem is rather to explain the origin of such features under conditions where natural selection could not work. In the present work we propose that these protocells could be built by chemical evolution, starting from the prebiotic primordial soup, by means of chemical selection. This consists of selective increases of the rates of certain specific reactions because of the kinetic or thermodynamic features of the process, such as stoichiometric catalysis or autocatalysis, cooperativity and others, thereby promoting their prevalence among the whole set of chemical possibilities. Our results show that all chemical processes necessary for yielding the basic materials that natural selection needs to work may be achieved through chemical selection, thus suggesting a way for life to begin.

  11. A competitive layer model for cellular neural networks.

    PubMed

    Zhou, Wei; Zurada, Jacek M

    2012-09-01

    This paper discusses a Competitive Layer Model (CLM) for a class of recurrent Cellular Neural Networks (CNNs) from continuous-time type to discrete-time type. The objective of the CLM is to partition a set of input features into salient groups. The complete convergence of such networks in continuous-time type has been discussed first. We give a necessary condition, and a necessary and sufficient condition, which allow the CLM performance existence in our networks. We also discuss the properties of such networks of discrete-time type, and propose a novel CLM iteration method. Such method shows similar performance and storage allocation but faster convergence compared with the previous CLM iteration method (Wersing, Steil, & Ritter, 2001a). Especially for a large scale network with many features and layers, it can significantly reduce the computing time. Examples and simulation results are used to illustrate the developed theory, the comparison between two CLM iteration methods, and the application in image segmentation.

  12. Metabolic network alignment in large scale by network compression.

    PubMed

    Ay, Ferhat; Dang, Michael; Kahveci, Tamer

    2012-03-21

    Metabolic network alignment is a system scale comparative analysis that discovers important similarities and differences across different metabolisms and organisms. Although the problem of aligning metabolic networks has been considered in the past, the computational complexity of the existing solutions has so far limited their use to moderately sized networks. In this paper, we address the problem of aligning two metabolic networks, particularly when both of them are too large to be dealt with using existing methods. We develop a generic framework that can significantly improve the scale of the networks that can be aligned in practical time. Our framework has three major phases, namely the compression phase, the alignment phase and the refinement phase. For the first phase, we develop an algorithm which transforms the given networks to a compressed domain where they are summarized using fewer nodes, termed supernodes, and interactions. In the second phase, we carry out the alignment in the compressed domain using an existing network alignment method as our base algorithm. This alignment results in supernode mappings in the compressed domain, each of which are smaller instances of network alignment problem. In the third phase, we solve each of the instances using the base alignment algorithm to refine the alignment results. We provide a user defined parameter to control the number of compression levels which generally determines the tradeoff between the quality of the alignment versus how fast the algorithm runs. Our experiments on the networks from KEGG pathway database demonstrate that the compression method we propose reduces the sizes of metabolic networks by almost half at each compression level which provides an expected speedup of more than an order of magnitude. We also observe that the alignments obtained by only one level of compression capture the original alignment results with high accuracy. Together, these suggest that our framework results in

  13. 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...

  14. 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

  15. Dissecting Germ Cell Metabolism through Network Modeling.

    PubMed

    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.

  16. 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.

  17. 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.

  18. 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

  19. Mass conservation and inference of metabolic networks from high-throughput mass spectrometry data.

    PubMed

    Bandaru, Pradeep; Bansal, Mukesh; Nemenman, Ilya

    2011-02-01

    We present a step towards the metabolome-wide computational inference of cellular metabolic reaction networks from metabolic profiling data, such as mass spectrometry. The reconstruction is based on identification of irreducible statistical interactions among the metabolite activities using the ARACNE reverse-engineering algorithm and on constraining possible metabolic transformations to satisfy the conservation of mass. The resulting algorithms are validated on synthetic data from an abridged computational model of Escherichia coli metabolism. Precision rates upwards of 50% are routinely observed for identification of full metabolic reactions, and recalls upwards of 20% are also seen.

  20. 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

  1. How biochemical constraints of cellular growth shape evolutionary adaptations in metabolism.

    PubMed

    Berkhout, Jan; Bosdriesz, Evert; Nikerel, Emrah; Molenaar, Douwe; de Ridder, Dick; Teusink, Bas; Bruggeman, Frank J

    2013-06-01

    Evolutionary adaptations in metabolic networks are fundamental to evolution of microbial growth. Studies on unneeded-protein synthesis indicate reductions in fitness upon nonfunctional protein synthesis, showing that cell growth is limited by constraints acting on cellular protein content. Here, we present a theory for optimal metabolic enzyme activity when cells are selected for maximal growth rate given such growth-limiting biochemical constraints. We show how optimal enzyme levels can be understood to result from an enzyme benefit minus cost optimization. The constraints we consider originate from different biochemical aspects of microbial growth, such as competition for limiting amounts of ribosomes or RNA polymerases, or limitations in available energy. Enzyme benefit is related to its kinetics and its importance for fitness, while enzyme cost expresses to what extent resource consumption reduces fitness through constraint-induced reductions of other enzyme levels. A metabolic fitness landscape is introduced to define the fitness potential of an enzyme. This concept is related to the selection coefficient of the enzyme and can be expressed in terms of its fitness benefit and cost.

  2. Cellular-based modeling of oscillatory dynamics in brain networks.

    PubMed

    Skinner, Frances K

    2012-08-01

    Oscillatory, population activities have long been known to occur in our brains during different behavioral states. We know that many different cell types exist and that they contribute in distinct ways to the generation of these activities. I review recent papers that involve cellular-based models of brain networks, most of which include theta, gamma and sharp wave-ripple activities. To help organize the modeling work, I present it from a perspective of three different types of cellular-based modeling: 'Generic', 'Biophysical' and 'Linking'. Cellular-based modeling is taken to encompass the four features of experiment, model development, theory/analyses, and model usage/computation. The three modeling types are shown to include these features and interactions in different ways.

  3. 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.

  4. Protein Kinase CK2: Intricate Relationships within Regulatory Cellular Networks.

    PubMed

    Nuñez de Villavicencio-Diaz, Teresa; Rabalski, Adam J; Litchfield, David W

    2017-03-05

    Protein kinase CK2 is a small family of protein kinases that has been implicated in an expanding array of biological processes. While it is widely accepted that CK2 is a regulatory participant in a multitude of fundamental cellular processes, CK2 is often considered to be a constitutively active enzyme which raises questions about how it can be a regulatory participant in intricately controlled cellular processes. To resolve this apparent paradox, we have performed a systematic analysis of the published literature using text mining as well as mining of proteomic databases together with computational assembly of networks that involve CK2. These analyses reinforce the notion that CK2 is involved in a broad variety of biological processes and also reveal an extensive interplay between CK2 phosphorylation and other post-translational modifications. The interplay between CK2 and other post-translational modifications suggests that CK2 does have intricate roles in orchestrating cellular events. In this respect, phosphorylation of specific substrates by CK2 could be regulated by other post-translational modifications and CK2 could also have roles in modulating other post-translational modifications. Collectively, these observations suggest that the actions of CK2 are precisely coordinated with other constituents of regulatory cellular networks.

  5. Protein Kinase CK2: Intricate Relationships within Regulatory Cellular Networks

    PubMed Central

    Nuñez de Villavicencio-Diaz, Teresa; Rabalski, Adam J.; Litchfield, David W.

    2017-01-01

    Protein kinase CK2 is a small family of protein kinases that has been implicated in an expanding array of biological processes. While it is widely accepted that CK2 is a regulatory participant in a multitude of fundamental cellular processes, CK2 is often considered to be a constitutively active enzyme which raises questions about how it can be a regulatory participant in intricately controlled cellular processes. To resolve this apparent paradox, we have performed a systematic analysis of the published literature using text mining as well as mining of proteomic databases together with computational assembly of networks that involve CK2. These analyses reinforce the notion that CK2 is involved in a broad variety of biological processes and also reveal an extensive interplay between CK2 phosphorylation and other post-translational modifications. The interplay between CK2 and other post-translational modifications suggests that CK2 does have intricate roles in orchestrating cellular events. In this respect, phosphorylation of specific substrates by CK2 could be regulated by other post-translational modifications and CK2 could also have roles in modulating other post-translational modifications. Collectively, these observations suggest that the actions of CK2 are precisely coordinated with other constituents of regulatory cellular networks. PMID:28273877

  6. Digital implementation of shunting-inhibitory cellular neural network

    NASA Astrophysics Data System (ADS)

    Hammadou, Tarik; Bouzerdoum, Abdesselam; Bermak, Amine

    2000-05-01

    Shunting inhibition is a model of early visual processing which can provide contrast and edge enhancement, and dynamic range compression. An architecture of digital Shunting Inhibitory Cellular Neural Network for real time image processing is presented. The proposed architecture is intended to be used in a complete vision system for edge detection and image enhancement. The present hardware architecture, is modeled and simulated in VHDL. Simulation results show the functional validity of the proposed architecture.

  7. 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.

  8. Ecological network analysis of China's societal metabolism.

    PubMed

    Zhang, Yan; Liu, Hong; Li, Yating; Yang, Zhifeng; Li, Shengsheng; Yang, Naijin

    2012-01-01

    Uncontrolled socioeconomic development has strong negative effects on the ecological environment, including pollution and the depletion and waste of natural resources. These serious consequences result from the high flows of materials and energy through a socioeconomic system produced by exchanges between the system and its surroundings, causing the disturbance of metabolic processes. In this paper, we developed an ecological network model for a societal system, and used China in 2006 as a case study to illustrate application of the model. We analyzed China's basic metabolic processes and used ecological network analysis to study the network relationships within the system. Basic components comprised the internal environment, five sectors (agriculture, exploitation, manufacturing, domestic, and recycling), and the external environment. We defined 21 pairs of ecological relationships in China's societal metabolic system (excluding self-mutualism within a component). Using utility and throughflow analysis, we found that exploitation, mutualism, and competition relationships accounted for 76.2, 14.3, and 9.5% of the total relationships, respectively. In our trophic level analysis, the components were divided into producers, consumers, and decomposers according to their positions in the system. Our analyses revealed ways to optimize the system's structure and adjust its functions, thereby promoting healthier socioeconomic development, and suggested ways to apply ecological network analysis in future socioeconomic research.

  9. ATR inhibition rewires cellular signaling networks induced by replication stress.

    PubMed

    Wagner, Sebastian A; Oehler, Hannah; Voigt, Andrea; Dalic, Denis; Freiwald, Anja; Serve, Hubert; Beli, Petra

    2016-02-01

    The slowing down or stalling of replication forks is commonly known as replication stress and arises from multiple causes such as DNA lesions, nucleotide depletion, RNA-DNA hybrids, and oncogene activation. The ataxia telangiectasia and Rad3-related kinase (ATR) plays an essential role in the cellular response to replication stress and inhibition of ATR has emerged as therapeutic strategy for the treatment of cancers that exhibit high levels of replication stress. However, the cellular signaling induced by replication stress and the substrate spectrum of ATR has not been systematically investigated. In this study, we employed quantitative MS-based proteomics to define the cellular signaling after nucleotide depletion-induced replication stress and replication fork collapse following ATR inhibition. We demonstrate that replication stress results in increased phosphorylation of a subset of proteins, many of which are involved in RNA splicing and transcription and have previously not been associated with the cellular replication stress response. Furthermore, our data reveal the ATR-dependent phosphorylation following replication stress and discover novel putative ATR target sites on MCM6, TOPBP1, RAD51AP1, and PSMD4. We establish that ATR inhibition rewires cellular signaling networks induced by replication stress and leads to the activation of the ATM-driven double-strand break repair signaling.

  10. 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.

  11. 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

    2008-06-07

    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.

  12. 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 ...

  13. 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.

  14. Reverse Engineering Cellular Networks with Information Theoretic Methods

    PubMed Central

    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. PMID:24709703

  15. 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 .

  16. 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

  17. 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.

  18. Study and Simulation of Traffic Behavior in Cellular Network

    NASA Astrophysics Data System (ADS)

    Madhup, D. K.; Shrestha, C. L.; Sharma, R. K.

    2007-07-01

    Cellular radio systems accommodate a large number of users with a limited radio spectrum. The concept of trunking allows a large number of users to share the relatively small number of channels in a cell by providing access to each user, on demand, from a pool of available channels. Traffic engineering deals with provisioning of communication circuits in a given area for a number of subscribers with a required grade of service. Traffic in any cell depends upon the number of users, the average request rate and average call duration. Certain number of channels is required for the required GOS. To design an optimum capacity cellular system, traffic behavior on that system is important. The number of channel required can be estimated by using Erlang formula and Erlang table. Erlang table is not always useful to calculate the probability of blocking in various complex scenarios such as channel borrowing strategies. When the total number of channel available in a given cell are divided to serve partly for newly generated calls and partly for handover calls, and if they use dynamic channel assignment strategies like channel borrowing, then the probability of blocking can't be calculated from Erlang table. Simulation model of the behavior help us to determine the blocking and the channel utilization while using various channel assignment strategies. The title "Study and Simulation of Traffic Behavior in Cellular Network" entail the study of the blocking probability of traffic in cellular network for static channel assignment strategies and dynamic channel borrowing strategies through MATLAB programming language and graphic user interface (GUI). The result shows that the dynamic scheme can perform better than static maximizing the overall utilization of the circuits and minimizing the overall blocking.

  19. Cutting the Wires: Modularization of Cellular Networks for Experimental Design

    PubMed Central

    Lang, Moritz; Summers, Sean; Stelling, Jörg

    2014-01-01

    Understanding naturally evolved cellular networks requires the consecutive identification and revision of the interactions between relevant molecular species. In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data. However, increased consistency with experimental data alone is not sufficient to show the existence of biomolecular interactions, because the interplay of different potential extensions might lead to overall similar dynamics. Here, we present a graph-based modularization approach to facilitate the design of experiments targeted at independently validating the existence of several potential network extensions. Our method is based on selecting the outputs to measure during an experiment, such that each potential network extension becomes virtually insulated from all others during data analysis. Each output defines a module that only depends on one hypothetical network extension, and all other outputs act as virtual inputs to achieve insulation. Given appropriate experimental time-series measurements of the outputs, our modules can be analyzed, simulated, and compared to the experimental data separately. Our approach exemplifies the close relationship between structural systems identification and modularization, an interplay that promises development of related approaches in the future. PMID:24411264

  20. 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

  1. On the identifiability of metabolic network models.

    PubMed

    Berthoumieux, Sara; Brilli, Matteo; Kahn, Daniel; de Jong, Hidde; Cinquemani, Eugenio

    2013-12-01

    A major problem for the identification of metabolic network models is parameter identifiability, that is, the possibility to unambiguously infer the parameter values from the data. Identifiability problems may be due to the structure of the model, in particular implicit dependencies between the parameters, or to limitations in the quantity and quality of the available data. We address the detection and resolution of identifiability problems for a class of pseudo-linear models of metabolism, so-called linlog models. Linlog models have the advantage that parameter estimation reduces to linear or orthogonal regression, which facilitates the analysis of identifiability. We develop precise definitions of structural and practical identifiability, and clarify the fundamental relations between these concepts. In addition, we use singular value decomposition to detect identifiability problems and reduce the model to an identifiable approximation by a principal component analysis approach. The criterion is adapted to real data, which are frequently scarce, incomplete, and noisy. The test of the criterion on a model with simulated data shows that it is capable of correctly identifying the principal components of the data vector. The application to a state-of-the-art dataset on central carbon metabolism in Escherichia coli yields the surprising result that only 4 out of 31 reactions, and 37 out of 100 parameters, are identifiable. This underlines the practical importance of identifiability analysis and model reduction in the modeling of large-scale metabolic networks. Although our approach has been developed in the context of linlog models, it carries over to other pseudo-linear models, such as generalized mass-action (power-law) models. Moreover, it provides useful hints for the identifiability analysis of more general classes of nonlinear models of metabolism.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Perturbation biology: inferring signaling networks in cellular systems.

    PubMed

    Molinelli, Evan J; Korkut, Anil; Wang, Weiqing; 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.

  7. 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

  8. Multi-casting approach for vascular networks in cellularized hydrogels.

    PubMed

    Justin, Alexander W; Brooks, Roger A; Markaki, Athina E

    2016-12-01

    Vascularization is essential for living tissue and remains a major challenge in the field of tissue engineering. A lack of a perfusable channel network within a large and densely populated tissue engineered construct leads to necrotic core formation, preventing fabrication of functional tissues and organs. We report a new method for producing a hierarchical, three-dimensional (3D) and perfusable vasculature in a large, cellularized fibrin hydrogel. Bifurcating channels, varying in size from 1 mm to 200-250 µm, are formed using a novel process in which we convert a 3D printed thermoplastic material into a gelatin network template, by way of an intermediate alginate hydrogel. This enables a CAD-based model design, which is highly customizable, reproducible, and which can yield highly complex architectures, to be made into a removable material, which can be used in cellular environments. Our approach yields constructs with a uniform and high density of cells in the bulk, made from bioactive collagen and fibrin hydrogels. Using standard cell staining and immuno-histochemistry techniques, we showed good cell seeding and the presence of tight junctions between channel endothelial cells, and high cell viability and cell spreading in the bulk hydrogel.

  9. Multi-casting approach for vascular networks in cellularized hydrogels

    PubMed Central

    Justin, Alexander W.; Brooks, Roger A.

    2016-01-01

    Vascularization is essential for living tissue and remains a major challenge in the field of tissue engineering. A lack of a perfusable channel network within a large and densely populated tissue engineered construct leads to necrotic core formation, preventing fabrication of functional tissues and organs. We report a new method for producing a hierarchical, three-dimensional (3D) and perfusable vasculature in a large, cellularized fibrin hydrogel. Bifurcating channels, varying in size from 1 mm to 200–250 µm, are formed using a novel process in which we convert a 3D printed thermoplastic material into a gelatin network template, by way of an intermediate alginate hydrogel. This enables a CAD-based model design, which is highly customizable, reproducible, and which can yield highly complex architectures, to be made into a removable material, which can be used in cellular environments. Our approach yields constructs with a uniform and high density of cells in the bulk, made from bioactive collagen and fibrin hydrogels. Using standard cell staining and immuno-histochemistry techniques, we showed good cell seeding and the presence of tight junctions between channel endothelial cells, and high cell viability and cell spreading in the bulk hydrogel. PMID:27928031

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

    PubMed Central

    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. PMID:26010876

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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.

  18. 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

  19. Metabolic network modularity in archaea depends on growth conditions.

    PubMed

    Takemoto, Kazuhiro; Borjigin, Suritalatu

    2011-01-01

    Network modularity is an important structural feature in metabolic networks. A previous study suggested that the variability in natural habitat promotes metabolic network modularity in bacteria. However, since many factors influence the structure of the metabolic network, this phenomenon might be limited and there may be other explanations for the change in metabolic network modularity. Therefore, we focus on archaea because they belong to another domain of prokaryotes and show variability in growth conditions (e.g., trophic requirement and optimal growth temperature), but not in habitats because of their specialized growth conditions (e.g., high growth temperature). The relationship between biological features and metabolic network modularity is examined in detail. We first show the absence of a relationship between network modularity and habitat variability in archaea, as archaeal habitats are more limited than bacterial habitats. Although this finding implies the need for further studies regarding the differences in network modularity, it does not contradict previous work. Further investigations reveal alternative explanations. Specifically, growth conditions, trophic requirement, and optimal growth temperature, in particular, affect metabolic network modularity. We have discussed the mechanisms for the growth condition-dependant changes in network modularity. Our findings suggest different explanations for the changes in network modularity and provide new insights into adaptation and evolution in metabolic networks, despite several limitations of data analysis.

  20. Within-Winter Flexibility in Muscle Masses, Myostatin, and Cellular Aerobic Metabolic Intensity in Passerine Birds.

    PubMed

    Swanson, David L; King, Marisa O; Culver, William; Zhang, Yufeng

    Metabolic rates of passerine birds are flexible traits that vary both seasonally and among and within winters. Seasonal variation in summit metabolic rates (Msum = maximum thermoregulatory metabolism) in birds is consistently correlated with changes in pectoralis muscle and heart masses and sometimes with variation in cellular aerobic metabolic intensity, so these traits might also be associated with shorter-term, within-winter variation in metabolic rates. To determine whether these mechanisms are associated with within-winter variation in Msum, we examined the effects of short-term (ST; 0-7 d), medium-term (MT; 14-30 d), and long-term (LT; 30-yr means) temperature variables on pectoralis muscle and heart masses, pectoralis expression of the muscle-growth inhibitor myostatin and its metalloproteinase activators TLL-1 and TLL-2, and pectoralis and heart citrate synthase (CS; an indicator of cellular aerobic metabolic intensity) activities for two temperate-zone resident passerines, house sparrows (Passer domesticus) and dark-eyed juncos (Junco hyemalis). For both species, pectoralis mass residuals were positively correlated with ST temperature variables, suggesting that cold temperatures resulted in increased turnover of pectoralis muscle, but heart mass showed little within-winter variation for either species. Pectoralis mRNA and protein expression of myostatin and the TLLs were only weakly correlated with ST and MT temperature variables, which is largely consistent with trends in muscle masses for both species. Pectoralis and heart CS activities showed weak and variable trends with ST temperature variables in both species, suggesting only minor effects of temperature variation on cellular aerobic metabolic intensity. Thus, neither muscle or heart masses, regulation by the myostatin system, nor cellular aerobic metabolic intensity varied consistently with winter temperature, suggesting that other factors regulate within-winter metabolic variation in these birds.

  1. 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.

  2. Cellular Neural Network for Real Time Image Processing

    SciTech Connect

    Vagliasindi, G.; Arena, P.; Fortuna, L.; Mazzitelli, G.; Murari, A.

    2008-03-12

    Since their introduction in 1988, Cellular Nonlinear Networks (CNNs) have found a key role as image processing instruments. Thanks to their structure they are able of processing individual pixels in a parallel way providing fast image processing capabilities that has been applied to a wide range of field among which nuclear fusion. In the last years, indeed, visible and infrared video cameras have become more and more important in tokamak fusion experiments for the twofold aim of understanding the physics and monitoring the safety of the operation. Examining the output of these cameras in real-time can provide significant information for plasma control and safety of the machines. The potentiality of CNNs can be exploited to this aim. To demonstrate the feasibility of the approach, CNN image processing has been applied to several tasks both at the Frascati Tokamak Upgrade (FTU) and the Joint European Torus (JET)

  3. Cellular Neural Network for Real Time Image Processing

    NASA Astrophysics Data System (ADS)

    Vagliasindi, G.; Arena, P.; Fortuna, L.; Mazzitelli, G.; Murari, A.

    2008-03-01

    Since their introduction in 1988, Cellular Nonlinear Networks (CNNs) have found a key role as image processing instruments. Thanks to their structure they are able of processing individual pixels in a parallel way providing fast image processing capabilities that has been applied to a wide range of field among which nuclear fusion. In the last years, indeed, visible and infrared video cameras have become more and more important in tokamak fusion experiments for the twofold aim of understanding the physics and monitoring the safety of the operation. Examining the output of these cameras in real-time can provide significant information for plasma control and safety of the machines. The potentiality of CNNs can be exploited to this aim. To demonstrate the feasibility of the approach, CNN image processing has been applied to several tasks both at the Frascati Tokamak Upgrade (FTU) and the Joint European Torus (JET).

  4. Separation of Bouguer anomaly map using cellular neural network

    NASA Astrophysics Data System (ADS)

    Albora, A. Muhittin; Ucan, Osman N.; Ozmen, Atilla; Ozkan, Tulay

    2001-02-01

    In this paper, a modern image-processing technique, the Cellular Neural Network (CNN) has been firstly applied to Bouguer anomaly map of synthetic examples and then to data from the Sivas-Divrigi Akdag region. CNN is an analog parallel computing paradigm defined in space and characterized by the locality of connections between processing neurons. The behaviour of the CNN is defined by two template matrices and a template vector. We have optimised the weight coefficients of these templates using the Recurrent Perceptron Learning Algorithm (RPLA). After testing CNN performance on synthetic examples, the CNN approach has been applied to the Bouguer anomaly of Sivas-Divrigi Akdag region and the results match drilling logs done by Mineral Research and Exploration (MTA).

  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. 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/

  7. Cellular computational networks--a scalable architecture for learning the dynamics of large networked systems.

    PubMed

    Luitel, Bipul; Venayagamoorthy, Ganesh Kumar

    2014-02-01

    Neural networks for implementing large networked systems such as smart electric power grids consist of multiple inputs and outputs. Many outputs lead to a greater number of parameters to be adapted. Each additional variable increases the dimensionality of the problem and hence learning becomes a challenge. Cellular computational networks (CCNs) are a class of sparsely connected dynamic recurrent networks (DRNs). By proper selection of a set of input elements for each output variable in a given application, a DRN can be modified into a CCN which significantly reduces the complexity of the neural network and allows use of simple training methods for independent learning in each cell thus making it scalable. This article demonstrates this concept of developing a CCN using dimensionality reduction in a DRN for scalability and better performance. The concept has been analytically explained and empirically verified through application.

  8. 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.

  9. SIRT4 has tumor suppressive activity and regulates the cellular metabolic response to DNA damage by inhibiting mitochondrial glutamine metabolism

    PubMed Central

    Jeong, Seung Min; Xiao, Cuiying; Finley, Lydia W.S; Lahusen, Tyler; Souza, Amanda L.; Pierce, Kerry; Li, Ying-Hua; Wang, Xiaoxu; Laurent, Gaëlle; German, Natalie J.; Xu, Xiaoling; Li, Cuiling; Wang, Rui-Hong; Lee, Jaewon; Csibi, Alfredo; Cerione, Richard; Blenis, John; Clish, Clary B.; Kimmelman, Alec; Deng, Chu-Xia; Haigis, Marcia C.

    2013-01-01

    SUMMARY DNA damage elicits a cellular signaling response that initiates cell cycle arrest and DNA repair. Here we find that DNA damage triggers a critical block in glutamine metabolism, which is required for proper DNA damage responses. This block requires the mitochondrial SIRT4, which is induced by numerous genotoxic agents and represses the metabolism of glutamine into TCA cycle. SIRT4 loss leads to both increased glutamine-dependent proliferation and stress-induced genomic instability, resulting in tumorigenic phenotypes. Moreover, SIRT4 knockout mice spontaneously develop lung tumors. Our data uncover SIRT4 as an important component of the DNA damage response pathway that orchestrates a metabolic block in glutamine metabolism, cell cycle arrest and tumor suppression. PMID:23562301

  10. 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.

  11. 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.

  12. 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...

  13. 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.

  14. 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.

  15. Coordinated remodeling of cellular metabolism during iron deficiency through targeted mRNA degradation.

    PubMed

    Puig, Sergi; Askeland, Eric; Thiele, Dennis J

    2005-01-14

    Iron (Fe) is an essential micronutrient for virtually all organisms and serves as a cofactor for a wide variety of vital cellular processes. Although Fe deficiency is the primary nutritional disorder in the world, cellular responses to Fe deprivation are poorly understood. We have discovered a posttranscriptional regulatory process controlled by Fe deficiency, which coordinately drives widespread metabolic reprogramming. We demonstrate that, in response to Fe deficiency, the Saccharomyces cerevisiae Cth2 protein specifically downregulates mRNAs encoding proteins that participate in many Fe-dependent processes. mRNA turnover requires the binding of Cth2, an RNA binding protein conserved in plants and mammals, to specific AU-rich elements in the 3' untranslated region of mRNAs targeted for degradation. These studies elucidate coordinated global metabolic reprogramming in response to Fe deficiency and identify a mechanism for achieving this by targeting specific mRNA molecules for degradation, thereby facilitating the utilization of limited cellular Fe levels.

  16. Metabolic changes during cellular senescence investigated by proton NMR-spectroscopy.

    PubMed

    Gey, Claudia; Seeger, Karsten

    2013-03-01

    Cellular senescence is of growing interest due to its role in tumour suppression and its contribution to organismic ageing. This cellular state can be reached by replicative loss of telomeres or certain stresses in cell culture and is characterized by the termination of cell division; however, the cells remain metabolically active. To identify metabolites that are characteristic for senescent cells, extracts of human embryonic lung fibroblast (WI-38 cell line) have been investigated with NMR spectroscopy. Three different types of senescence have been characterized: replicative senescence, DNA damage-induced senescence (etoposide treatment) and oncogene-induced senescence (hyperactive RAF kinase). The metabolite pattern allows (I) discrimination of senescent and control cells and (II) discrimination of the three senescence types. Senescent cells show an increased ratio of glycerophosphocholine to phosphocholine independent from the type of senescence. The increase in glycerophosphocholine implicates a key role of phospholipid metabolism in cellular senescence. The observed changes in the choline metabolism are diametrically opposite to the well-known changes in choline metabolism of tumour cells. As tumours responding to chemotherapeutic agents show a "glycerophosphocholine-to-phosphocholine switch" i.e. an increase in glycerophosphocholine, our metabolic data suggests that these malignant cells enter a senescent state emphasizing the role of senescence in tumour suppression.

  17. Serine and SAM Responsive Complex SESAME Regulates Histone Modification Crosstalk by Sensing Cellular Metabolism.

    PubMed

    Li, Shanshan; Swanson, Selene K; Gogol, Madelaine; Florens, Laurence; Washburn, Michael P; Workman, Jerry L; Suganuma, Tamaki

    2015-11-05

    Pyruvate kinase M2 (PKM2) is a key enzyme for glycolysis and catalyzes the conversion of phosphoenolpyruvate (PEP) to pyruvate, which supplies cellular energy. PKM2 also phosphorylates histone H3 threonine 11 (H3T11); however, it is largely unknown how PKM2 links cellular metabolism to chromatin regulation. Here, we show that the yeast PKM2 homolog, Pyk1, is a part of a novel protein complex named SESAME (Serine-responsive SAM-containing Metabolic Enzyme complex), which contains serine metabolic enzymes, SAM (S-adenosylmethionine) synthetases, and an acetyl-CoA synthetase. SESAME interacts with the Set1 H3K4 methyltransferase complex, which requires SAM synthesized from SESAME, and recruits SESAME to target genes, resulting in phosphorylation of H3T11. SESAME regulates the crosstalk between H3K4 methylation and H3T11 phosphorylation by sensing glycolysis and glucose-derived serine metabolism. This leads to auto-regulation of PYK1 expression. Thus, our study provides insights into the mechanism of regulating gene expression, responding to cellular metabolism via chromatin modifications.

  18. Compartmentalization and molecular traffic in secondary metabolism: a new understanding of established cellular processes

    PubMed Central

    Roze, Ludmila V.; Chanda, Anindya; Linz, John E.

    2010-01-01

    Great progress has been made in understanding the regulation of expression of genes involved in secondary metabolism. Less is known about the mechanisms that govern the spatial distribution of the enzymes, cofactors, and substrates that mediate catalysis of secondary metabolites within the cell. Filamentous fungi in the genus Aspergillus synthesize an array of secondary metabolites and provide useful systems to analyze the mechanisms that mediate the temporal and spatial regulation of secondary metabolism in eukaryotes. For example, aflatoxin biosynthesis in A. parasiticus has been studied intensively because this mycotoxin is highly toxic, mutagenic, and carcinogenic in humans and animals. Using aflatoxin synthesis to illustrate key concepts, this review focuses on the mechanisms by which sub-cellular compartmentalization and intra-cellular molecular traffic contribute to the initiation and completion of secondary metabolism within the cell. We discuss the recent discovery of aflatoxisomes, specialized trafficking vesicles that participate in the compartmentalization of aflatoxin synthesis and export of the toxin to the cell exterior; this work provides a new and clearer understanding of how cells integrate secondary metabolism into basic cellular metabolism via the intracellular trafficking machinery. PMID:20519149

  19. 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.

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

    PubMed Central

    2016-01-01

    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

  1. 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.

  2. The Influence of Cold Temperature on Cellular Excitability of Hippocampal Networks

    PubMed Central

    Vara, Hugo; Caires, Rebeca; Ballesta, Juan J.; Belmonte, Carlos; Viana, Felix

    2012-01-01

    The hippocampus plays an important role in short term memory, learning and spatial navigation. A characteristic feature of the hippocampal region is its expression of different electrical population rhythms and activities during different brain states. Physiological fluctuations in brain temperature affect the activity patterns in hippocampus, but the underlying cellular mechanisms are poorly understood. In this work, we investigated the thermal modulation of hippocampal activity at the cellular network level. Primary cell cultures of mouse E17 hippocampus displayed robust network activation upon light cooling of the extracellular solution from baseline physiological temperatures. The activity generated was dependent on action potential firing and excitatory glutamatergic synaptic transmission. Involvement of thermosensitive channels from the transient receptor potential (TRP) family in network activation by temperature changes was ruled out, whereas pharmacological and immunochemical experiments strongly pointed towards the involvement of temperature-sensitive two-pore-domain potassium channels (K2P), TREK/TRAAK family. In hippocampal slices we could show an increase in evoked and spontaneous synaptic activity produced by mild cooling in the physiological range that was prevented by chloroform, a K2P channel opener. We propose that cold-induced closure of background TREK/TRAAK family channels increases the excitability of some hippocampal neurons, acting as a temperature-sensitive gate of network activation. Our findings in the hippocampus open the possibility that small temperature variations in the brain in vivo, associated with metabolism or blood flow oscillations, act as a switch mechanism of neuronal activity and determination of firing patterns through regulation of thermosensitive background potassium channel activity. PMID:23300680

  3. A role for vaccinia virus protein C16 in reprogramming cellular energy metabolism.

    PubMed

    Mazzon, Michela; Castro, Cecilia; Roberts, Lee D; Griffin, Julian L; Smith, Geoffrey L

    2015-02-01

    Vaccinia virus (VACV) is a large DNA virus that replicates in the cytoplasm and encodes about 200 proteins of which approximately 50 % may be non-essential for viral replication. These proteins enable VACV to suppress transcription and translation of cellular genes, to inhibit the innate immune response, to exploit microtubule- and actin-based transport for virus entry and spread, and to subvert cellular metabolism for the benefit of the virus. VACV strain WR protein C16 induces stabilization of the hypoxia-inducible transcription factor (HIF)-1α by binding to the cellular oxygen sensor prolylhydroxylase domain-containing protein (PHD)2. Stabilization of HIF-1α is induced by several virus groups, but the purpose and consequences are unclear. Here, (1)H-NMR spectroscopy and liquid chromatography-mass spectrometry are used to investigate the metabolic alterations during VACV infection in HeLa and 2FTGH cells. The role of C16 in such alterations was examined by comparing infection to WT VACV (strain WR) and a derivative virus lacking gene C16L (vΔC16). Compared with uninfected cells, VACV infection caused increased nucleotide and glutamine metabolism. In addition, there were increased concentrations of glutamine derivatives in cells infected with WT VACV compared with vΔC16. This indicates that C16 contributes to enhanced glutamine metabolism and this may help preserve tricarboxylic acid cycle activity. These data show that VACV infection reprogrammes cellular energy metabolism towards increased synthesis of the metabolic precursors utilized during viral replication, and that C16 contributes to this anabolic reprogramming of the cell, probably via the stabilization of HIF-1α.

  4. A role for vaccinia virus protein C16 in reprogramming cellular energy metabolism

    PubMed Central

    Mazzon, Michela; Castro, Cecilia; Roberts, Lee D.; Griffin, Julian L.

    2015-01-01

    Vaccinia virus (VACV) is a large DNA virus that replicates in the cytoplasm and encodes about 200 proteins of which approximately 50 % may be non-essential for viral replication. These proteins enable VACV to suppress transcription and translation of cellular genes, to inhibit the innate immune response, to exploit microtubule- and actin-based transport for virus entry and spread, and to subvert cellular metabolism for the benefit of the virus. VACV strain WR protein C16 induces stabilization of the hypoxia-inducible transcription factor (HIF)-1α by binding to the cellular oxygen sensor prolylhydroxylase domain-containing protein (PHD)2. Stabilization of HIF-1α is induced by several virus groups, but the purpose and consequences are unclear. Here, 1H-NMR spectroscopy and liquid chromatography-mass spectrometry are used to investigate the metabolic alterations during VACV infection in HeLa and 2FTGH cells. The role of C16 in such alterations was examined by comparing infection to WT VACV (strain WR) and a derivative virus lacking gene C16L (vΔC16). Compared with uninfected cells, VACV infection caused increased nucleotide and glutamine metabolism. In addition, there were increased concentrations of glutamine derivatives in cells infected with WT VACV compared with vΔC16. This indicates that C16 contributes to enhanced glutamine metabolism and this may help preserve tricarboxylic acid cycle activity. These data show that VACV infection reprogrammes cellular energy metabolism towards increased synthesis of the metabolic precursors utilized during viral replication, and that C16 contributes to this anabolic reprogramming of the cell, probably via the stabilization of HIF-1α. PMID:25351724

  5. Systems analysis of plant functional, transcriptional, physical interaction, and metabolic networks.

    PubMed

    Bassel, George W; Gaudinier, Allison; Brady, Siobhan M; Hennig, Lars; Rhee, Seung Y; De Smet, Ive

    2012-10-01

    Physiological responses, developmental programs, and cellular functions rely on complex networks of interactions at different levels and scales. Systems biology brings together high-throughput biochemical, genetic, and molecular approaches to generate omics data that can be analyzed and used in mathematical and computational models toward uncovering these networks on a global scale. Various approaches, including transcriptomics, proteomics, interactomics, and metabolomics, have been employed to obtain these data on the cellular, tissue, organ, and whole-plant level. We summarize progress on gene regulatory, cofunction, protein interaction, and metabolic networks. We also illustrate the main approaches that have been used to obtain these networks, with specific examples from Arabidopsis thaliana, and describe the pros and cons of each approach.

  6. 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

  7. Molecular and cellular regulation of hypothalamic melanocortin neurons controlling food intake and energy metabolism.

    PubMed

    Koch, M; Horvath, T L

    2014-07-01

    The brain receives and integrates environmental and metabolic information, transforms these signals into adequate neuronal circuit activities, and generates physiological behaviors to promote energy homeostasis. The responsible neuronal circuitries show lifetime plasticity and guaranty metabolic health and survival. However, this highly evolved organization has become challenged nowadays by chronic overload with nutrients and reduced physical activity, which results in an ever-increasing number of obese individuals worldwide. Research within the last two decades has aimed to decipher the responsible molecular and cellular mechanisms for regulation of the hypothalamic melanocortin neurons, which have a key role in the control of food intake and energy metabolism. This review maps the central connections of the melanocortin system and highlights its global position and divergent character in physiological and pathological metabolic events. Moreover, recently uncovered molecular and cellular processes in hypothalamic neurons and glial cells that drive plastic morphological and physiological changes in these cells, and account for regulation of food intake and energy metabolism, are brought into focus. Finally, potential functional interactions between metabolic disorders and psychiatric diseases are discussed.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Systems biology approaches to enzyme kinetics: analyzing network models of drug metabolism.

    PubMed

    Finn, Nnenna A; Kemp, Melissa L

    2014-01-01

    Intracellular drug metabolism involves transport, bioactivation, conjugation, and other biochemical steps. The dynamics of these steps are each dependent on a number of other cellular factors that can ultimately lead to unexpected behavior. In this review, we discuss the confounding processes and coupled reactions within bioactivation networks that require a systems-level perspective in order to fully understand the time-varying behavior. When converting known in vitro characteristics of drug-enzyme interactions into descriptions of cellular systems, features such as substrate availability, cell-to-cell variability, and intracellular redox state deserve special focus. An example of doxorubicin bioactivation is used for discussing points of consideration when constructing and analyzing network models of drug metabolism.

  13. Microbial Community Metabolic Modeling: A Community Data‐Driven Network Reconstruction

    PubMed Central

    Henry, Christopher S.; Bernstein, Hans C.; Weisenhorn, Pamela; Taylor, Ronald C.; Lee, Joon‐Yong; Zucker, Jeremy

    2016-01-01

    Metabolic network modeling of microbial communities provides an in‐depth understanding of community‐wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high‐quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community‐level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph–heterotroph consortium that was used to provide data needed for a community‐level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339–2345, 2016. © 2016 The Authors. Journal of Cellular Physiology Published by Wiley Periodicals, Inc. PMID:27186840

  14. 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.

  15. 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.

  16. Metabolic pathways variability and sequence/networks comparisons

    PubMed Central

    Tun, Kyaw; Dhar, Pawan K; Palumbo, Maria Concetta; Giuliani, Alessandro

    2006-01-01

    Background In this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption. Results We demonstrate both the ability of the proposed method to build reliable biological classification of a set of microrganisms and the strong correlation between the metabolic network wiringand involved enzymes sequence space. Conclusion The method represents a valuable tool for the investigation of genotype/phenotype correlationsallowing for a direct comparison of different species as for their metabolic machinery. In addition the detection of enzymes whose sequence space is maximally correlated with the metabolicnetwork space gives an indication of the most crucial (on an evolutionary viewpoint) steps of the metabolic process. PMID:16420696

  17. Reverse engineering of metabolic networks, a critical assessment.

    PubMed

    Hendrickx, Diana M; Hendriks, Margriet M W B; Eilers, Paul H C; Smilde, Age K; Hoefsloot, Huub C J

    2011-02-01

    Inferring metabolic networks from metabolite concentration data is a central topic in systems biology. Mathematical techniques to extract information about the network from data have been proposed in the literature. This paper presents a critical assessment of the feasibility of reverse engineering of metabolic networks, illustrated with a selection of methods. Appropriate data are simulated to study the performance of four representative methods. An overview of sampling and measurement methods currently in use for generating time-resolved metabolomics data is given and contrasted with the needs of the discussed reverse engineering methods. The results of this assessment show that if full inference of a real-world metabolic network is the goal there is a large discrepancy between the requirements of reverse engineering of metabolic networks and contemporary measurement practice. Recommendations for improved time-resolved experimental designs are given.

  18. 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.

  19. 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.

  20. Cross-talk between circadian clocks, sleep-wake cycles, and metabolic networks: Dispelling the darkness.

    PubMed

    Ray, Sandipan; Reddy, Akhilesh B

    2016-04-01

    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.

  1. Cellular Metabolism in Genetic Transformation of Pneumococci: Requirement for Protein Synthesis During Induction of Competence

    PubMed Central

    Tomasz, Alexander

    1970-01-01

    Metabolic inhibitors have differential effects on various phases of genetic transformation in pneumococci. Evidence is presented suggesting that, in addition to the competence factor, another specific protein or class of proteins is essential for the development of cellular “competence.” The precise role of this protein(s) in genetic transformation is not known, but it seems essential for some function subsequent to the interaction of competence factor and cells. PMID:4392399

  2. Signals for the lysosome: a control center for cellular clearance and energy metabolism

    PubMed Central

    Settembre, Carmine; Fraldi, Alessandro; Medina, Diego L.

    2015-01-01

    Preface For a long time lysosomes were considered merely to be cellular “incinerators” involved in the degradation and recycling of cellular waste. However, there is now compelling evidence indicating that lysosomes have a much broader function and that they are involved in fundamental processes such as secretion, plasma membrane repair, signaling and energy metabolism. Furthermore, the essential role of lysosomes in the autophagic pathway puts these organelles at the crossroads of several cellular processes, with significant implications for health and disease. The identification of a master gene, transcription factor EB (TFEB), that regulates lysosomal biogenesis and autophagy, has revealed how the lysosome adapts to environmental cues, such as starvation, and suggests novel therapeutic strategies for modulating lysosomal function in human disease. PMID:23609508

  3. BAP1 inhibits the ER stress gene regulatory network and modulates metabolic stress response.

    PubMed

    Dai, Fangyan; Lee, Hyemin; Zhang, Yilei; Zhuang, Li; Yao, Hui; Xi, Yuanxin; Xiao, Zhen-Dong; You, M James; Li, Wei; Su, Xiaoping; Gan, Boyi

    2017-03-21

    The endoplasmic reticulum (ER) is classically linked to metabolic homeostasis via the activation of unfolded protein response (UPR), which is instructed by multiple transcriptional regulatory cascades. BRCA1 associated protein 1 (BAP1) is a tumor suppressor with de-ubiquitinating enzyme activity and has been implicated in chromatin regulation of gene expression. Here we show that BAP1 inhibits cell death induced by unresolved metabolic stress. This prosurvival role of BAP1 depends on its de-ubiquitinating activity and correlates with its ability to dampen the metabolic stress-induced UPR transcriptional network. BAP1 inhibits glucose deprivation-induced reactive oxygen species and ATP depletion, two cellular events contributing to the ER stress-induced cell death. In line with this, Bap1 KO mice are more sensitive to tunicamycin-induced renal damage. Mechanically, we show that BAP1 represses metabolic stress-induced UPR and cell death through activating transcription factor 3 (ATF3) and C/EBP homologous protein (CHOP), and reveal that BAP1 binds to ATF3 and CHOP promoters and inhibits their transcription. Taken together, our results establish a previously unappreciated role of BAP1 in modulating the cellular adaptability to metabolic stress and uncover a pivotal function of BAP1 in the regulation of the ER stress gene-regulatory network. Our study may also provide new conceptual framework for further understanding BAP1 function in cancer.

  4. BAP1 inhibits the ER stress gene regulatory network and modulates metabolic stress response

    PubMed Central

    Dai, Fangyan; Lee, Hyemin; Zhang, Yilei; Zhuang, Li; Yao, Hui; Xi, Yuanxin; Xiao, Zhen-Dong; You, M. James; Li, Wei; Su, Xiaoping; Gan, Boyi

    2017-01-01

    The endoplasmic reticulum (ER) is classically linked to metabolic homeostasis via the activation of unfolded protein response (UPR), which is instructed by multiple transcriptional regulatory cascades. BRCA1 associated protein 1 (BAP1) is a tumor suppressor with de-ubiquitinating enzyme activity and has been implicated in chromatin regulation of gene expression. Here we show that BAP1 inhibits cell death induced by unresolved metabolic stress. This prosurvival role of BAP1 depends on its de-ubiquitinating activity and correlates with its ability to dampen the metabolic stress-induced UPR transcriptional network. BAP1 inhibits glucose deprivation-induced reactive oxygen species and ATP depletion, two cellular events contributing to the ER stress-induced cell death. In line with this, Bap1 KO mice are more sensitive to tunicamycin-induced renal damage. Mechanically, we show that BAP1 represses metabolic stress-induced UPR and cell death through activating transcription factor 3 (ATF3) and C/EBP homologous protein (CHOP), and reveal that BAP1 binds to ATF3 and CHOP promoters and inhibits their transcription. Taken together, our results establish a previously unappreciated role of BAP1 in modulating the cellular adaptability to metabolic stress and uncover a pivotal function of BAP1 in the regulation of the ER stress gene-regulatory network. Our study may also provide new conceptual framework for further understanding BAP1 function in cancer. PMID:28275095

  5. 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.

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

    PubMed

    Horniblow, Richard D; Dowle, Miriam; Iqbal, Tariq H; Latunde-Dada, Gladys O; Palmer, Richard E; Pikramenou, Zoe; Tselepis, Chris

    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.

  7. 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

  8. 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

  9. 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.

  10. 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

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

    PubMed

    Vitkin, Edward; Shlomi, Tomer

    2012-11-29

    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.

  12. 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.

  13. An improved sample loading technique for cellular metabolic response monitoring under pressure

    NASA Astrophysics Data System (ADS)

    Gikunda, Millicent Nkirote

    To monitor cellular metabolism under pressure, a pressure chamber designed around a simple-to-construct capillary-based spectroscopic chamber coupled to a microliter-flow perfusion system is used in the laboratory. Although cyanide-induced metabolic responses from Saccharomyces cerevisiae (baker's yeast) could be controllably induced and monitored under pressure, previously used sample loading technique was not well controlled. An improved cell-loading technique which is based on use of a secondary inner capillary into which the sample is loaded then inserted into the capillary pressure chamber, has been developed. As validation, we demonstrate the ability to measure the chemically-induced metabolic responses at pressures of up to 500 bars. This technique is shown to be less prone to sample loss due to perfusive flow than the previous techniques used.

  14. 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.

  15. Enumeration of smallest intervention strategies in genome-scale metabolic networks.

    PubMed

    von Kamp, Axel; Klamt, Steffen

    2014-01-01

    One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly

  16. 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…

  17. Divergent effects of human cytomegalovirus and herpes simplex virus-1 on cellular metabolism.

    PubMed

    Vastag, Livia; Koyuncu, Emre; Grady, Sarah L; Shenk, Thomas E; Rabinowitz, Joshua D

    2011-07-01

    Viruses rely on the metabolic network of the host cell to provide energy and macromolecular precursors to fuel viral replication. Here we used mass spectrometry to examine the impact of two related herpesviruses, human cytomegalovirus (HCMV) and herpes simplex virus type-1 (HSV-1), on the metabolism of fibroblast and epithelial host cells. Each virus triggered strong metabolic changes that were conserved across different host cell types. The metabolic effects of the two viruses were, however, largely distinct. HCMV but not HSV-1 increased glycolytic flux. HCMV profoundly increased TCA compound levels and flow of two carbon units required for TCA cycle turning and fatty acid synthesis. HSV-1 increased anapleurotic influx to the TCA cycle through pyruvate carboxylase, feeding pyrimidine biosynthesis. Thus, these two related herpesviruses drive diverse host cells to execute distinct, virus-specific metabolic programs. Current drugs target nucleotide metabolism for treatment of both viruses. Although our results confirm that this is a robust target for HSV-1, therapeutic interventions at other points in metabolism might prove more effective for treatment of HCMV.

  18. Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

    PubMed Central

    Martín-Jiménez, Cynthia A.; Salazar-Barreto, Diego; Barreto, George E.; González, Janneth

    2017-01-01

    Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states. PMID:28243200

  19. Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network.

    PubMed

    Martín-Jiménez, Cynthia A; Salazar-Barreto, Diego; Barreto, George E; González, Janneth

    2017-01-01

    Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states.

  20. 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.

  1. 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.

  2. Topological analysis of metabolic networks based on petri net theory.

    PubMed

    Zevedei-Oancea, Ionela; Schuster, Stefan

    2011-01-01

    Petri net concepts provide additional tools for the modelling of metabolic networks. Here, the similarities between the counterparts in traditional biochemical modelling and Petri net theory are discussed. For example the stoichiometry matrix of a metabolic network corresponds to the incidence matrix of the Petri net. The flux modes and conservation relations have the T-invariants, respectively, P-invariants as counterparts. We reveal the biological meaning of some notions specific to the Petri net framework (traps, siphons, deadlocks, liveness). We focus on the topological analysis rather than on the analysis of the dynamic behaviour. The treatment of external metabolites is discussed. Some simple theoretical examples are presented for illustration. Also the Petri nets corresponding to some biochemical networks are built to support our results. For example, the role of triose phosphate isomerase (TPI) in Trypanosoma brucei metabolism is evaluated by detecting siphons and traps. All Petri net properties treated in this contribution are exemplified on a system extracted from nucleotide metabolism.

  3. 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.

  4. A novel alkyne cholesterol to trace cellular cholesterol metabolism and localization[S

    PubMed Central

    Hofmann, Kristina; Thiele, Christoph; Schött, Hans-Frieder; Gaebler, Anne; Schoene, Mario; Kiver, Yuriy; Friedrichs, Silvia; Lütjohann, Dieter; Kuerschner, Lars

    2014-01-01

    Cholesterol is an important lipid of mammalian cells and plays a fundamental role in many biological processes. Its concentration in the various cellular membranes differs and is tightly regulated. Here, we present a novel alkyne cholesterol analog suitable for tracing both cholesterol metabolism and localization. This probe can be detected by click chemistry employing various reporter azides. Alkyne cholesterol is accepted by cellular enzymes from different biological species (Brevibacterium, yeast, rat, human) and these enzymes include cholesterol oxidases, hydroxylases, and acyl transferases that generate the expected metabolites in in vitro and in vivo assays. Using fluorescence microscopy, we studied the distribution of cholesterol at subcellular resolution, detecting the lipid in the Golgi and at the plasma membrane, but also in the endoplasmic reticulum and mitochondria. In summary, alkyne cholesterol represents a versatile, sensitive, and easy-to-use tool for tracking cellular cholesterol metabolism and localization as it allows for manifold detection methods including mass spectrometry, thin-layer chromatography/fluorography, and fluorescence microscopy. PMID:24334219

  5. [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.

  6. 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.

  7. 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

  8. 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

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

    SciTech Connect

    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.

    2015-05-28

    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. In conclusion, 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.

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

    PubMed

    Robaina-Estévez, Semidán; Nikoloski, Zoran

    2016-05-01

    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.

  11. 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

  12. Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

    PubMed Central

    Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

    2016-01-01

    Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845

  13. 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

  14. 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.

  15. 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...

  16. 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.

  17. Metabolic changes in flatfish hepatic tumours revealed by NMR-based metabolomics and metabolic correlation networks.

    PubMed

    Southam, Andrew D; Easton, John M; Stentiford, Grant D; Ludwig, Christian; Arvanitis, Theodoros N; Viant, Mark R

    2008-12-01

    Histopathologically well-characterized fish liver was analyzed by 800 MHz 1H NMR metabolomics to identify metabolic changes between healthy and tumor tissue. Data were analyzed by multivariate statistics and metabolic correlation networks, and results revealed elevated anaerobic metabolism and reduced choline metabolism in tumor tissue. Significant negative correlations were observed between alanine-acetate (p = 3.0 x 10(-5)) and between proline-acetate (p = 0.003) in tumors only, suggesting alanine and proline are utilized as alternative energy sources in flatfish liver tumors.

  18. 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

  19. 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.

  20. Almost automorphic solutions for shunting inhibitory cellular neural networks with time-varying delays.

    PubMed

    Xu, Changjin; Liao, Maoxin

    2015-01-01

    This paper is concerned with the shunting inhibitory cellular neural networks with time-varying delays. Under some suitable conditions, we establish some criteria on the existence and global exponential stability of the almost automorphic solutions of the networks. Numerical simulations are given to support the theoretical findings.

  1. Networking with noise at the molecular, cellular, and population level

    NASA Astrophysics Data System (ADS)

    Vilar, Jose

    2002-03-01

    The intrinsic stochastic nature of biochemical reactions affects enzymatic and transcriptional networks at different levels. Yet, cells are able to function effectively and consistently amidst such random fluctuations. I will discuss some molecular mechanisms that are able to reduce the intrinsic noise of chemical reactions, how suitable designs can make networks resistant to noise, and what strategies can be used by populations to achieve precise functions.

  2. Detection of silent cells, synchronization and modulatory activity in developing cellular networks.

    PubMed

    Hjorth, Johannes J J; Dawitz, Julia; Kroon, Tim; Pires, Johny; Dassen, Valerie J; Berkhout, Janna A; Emperador Melero, Javier; Nadadhur, Aish G; Alevra, Mihai; Toonen, Ruud F; Heine, Vivi M; Mansvelder, Huibert D; Meredith, Rhiannon M

    2016-04-01

    Developing networks in the immature nervous system and in cellular cultures are characterized by waves of synchronous activity in restricted clusters of cells. Synchronized activity in immature networks is proposed to regulate many different developmental processes, from neuron growth and cell migration, to the refinement of synapses, topographic maps, and the mature composition of ion channels. These emergent activity patterns are not present in all cells simultaneously within the network and more immature "silent" cells, potentially correlated with the presence of silent synapses, are prominent in different networks during early developmental periods. Many current network analyses for detection of synchronous cellular activity utilize activity-based pixel correlations to identify cellular-based regions of interest (ROIs) and coincident cell activity. However, using activity-based correlations, these methods first underestimate or ignore the inactive silent cells within the developing network and second, are difficult to apply within cell-dense regions commonly found in developing brain networks. In addition, previous methods may ignore ROIs within a network that shows transient activity patterns comprising both inactive and active periods. We developed analysis software to semi-automatically detect cells within developing neuronal networks that were imaged using calcium-sensitive reporter dyes. Using an iterative threshold, modulation of activity was tracked within individual cells across the network. The distribution pattern of both inactive and active, including synchronous cells, could be determined based on distance measures to neighboring cells and according to different anatomical layers.

  3. 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.

  4. Comparative analysis of Salmonella susceptibility and tolerance to the biocide chlorhexidine identifies a complex cellular defense network

    PubMed Central

    Condell, Orla; Power, Karen A.; Händler, Kristian; Finn, Sarah; Sheridan, Aine; Sergeant, Kjell; Renaut, Jenny; Burgess, Catherine M.; Hinton, Jay C. D.; Nally, Jarlath E.; Fanning, Séamus

    2014-01-01

    Chlorhexidine is one of the most widely used biocides in health and agricultural settings as well as in the modern food industry. It is a cationic biocide of the biguanide class. Details of its mechanism of action are largely unknown. The frequent use of chlorhexidine has been questioned recently, amidst concerns that an overuse of this compound may select for bacteria displaying an altered susceptibility to antimicrobials, including clinically important anti-bacterial agents. We generated a Salmonella enterica serovar Typhimurium isolate (ST24CHX) that exhibited a high-level tolerant phenotype to chlorhexidine, following several rounds of in vitro selection, using sub-lethal concentrations of the biocide. This mutant showed altered suceptibility to a panel of clinically important antimicrobial compounds. Here we describe a genomic, transcriptomic, proteomic, and phenotypic analysis of the chlorhexidine tolerant S. Typhimurium compared with its isogenic sensitive progenitor. Results from this study describe a chlorhexidine defense network that functions in both the reference chlorhexidine sensitive isolate and the tolerant mutant. The defense network involved multiple cell targets including those associated with the synthesis and modification of the cell wall, the SOS response, virulence, and a shift in cellular metabolism toward anoxic pathways, some of which were regulated by CreB and Fur. In addition, results indicated that chlorhexidine tolerance was associated with more extensive modifications of the same cellular processes involved in this proposed network, as well as a divergent defense response involving the up-regulation of additional targets such as the flagellar apparatus and an altered cellular phosphate metabolism. These data show that sub-lethal concentrations of chlorhexidine induce distinct changes in exposed Salmonella, and our findings provide insights into the mechanisms of action and tolerance to this biocidal agent. PMID:25136333

  5. The effect of neurosphere culture conditions on the cellular metabolism of glioma cells.

    PubMed

    Kahlert, Ulf Dietrich; Koch, Katharina; Suwala, Abigail Kora; Hartmann, Rudolf; Cheng, Menglin; Maciaczyk, Donata; Willbold, Dieter; Eberhart, Charles G; Glunde, Kristine; Maciaczyk, Jarek

    2015-01-01

    Malignant gliomas, with an average survival time of 16-19 months after initial diagnosis, account for one of the most lethal tumours overall. Current standards in patient care provide only unsatisfying strategies in diagnostic and treatment for high-grade gliomas. Here we describe metabolic phenomena in the choline and glycine network associated with stem cell culture conditions in the classical glioma cell line U87. Using high-resolution proton magnetic resonance spectroscopy of cell culture metabolic extracts we compare the metabolic composition of U87 chronically propagated as adherent culture in medium supplemented with serum to serum-free neurosphere growth. We found that the switch to neurosphere growth, besides the increase of cells expressing the putative glioma stem cell marker CD133, modulated a number of intracellular metabolites including choline, creatine, glycine, and myo-inositol that have been previously reported as potential diagnostic markers in various tumours. These findings highlight the critical influence of culture conditions on glioma cell metabolism, and therefore particular caution should be drawn to the use of in vitro system research in order to investigate cancer metabolism.

  6. Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction: COMMUNITY DATA-DRIVEN METABOLIC NETWORK MODELING

    SciTech Connect

    Henry, Christopher S.; Bernstein, Hans C.; Weisenhorn, Pamela; Taylor, Ronald C.; Lee, Joon-Yong; Zucker, Jeremy; Song, Hyun-Seob

    2016-06-02

    Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources.

  7. 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.

  8. 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...

  9. 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

  10. Antidiabetic Drugs: Mechanisms of Action and Potential Outcomes on Cellular Metabolism.

    PubMed

    Meneses, Maria J; Silva, Branca M; Sousa, Mário; Sá, Rosália; Oliveira, Pedro F; Alves, Marco G

    2015-01-01

    Diabetes mellitus (DM) is one of the most prevalent chronic diseases and has been a leading cause of death in the last decades. Thus, methods to detect, prevent or delay this disease and its co-morbidities have long been a matter of discussion. Nowadays, DM patients, particularly those suffering with type 2 DM, are advised to alter their diet and physical exercise regimens and then proceed progressively from monotherapy, dual therapy, and multi-agent therapy to insulin administration, as the disease becomes more severe. Although progresses have been made, the pursuit for the "perfect" antidiabetic drug still continues. The complexity of DM and its impact on whole body homeodynamics are two of the main reasons why there is not yet such a drug. Moreover, the molecular mechanisms by which DM can be controlled are still under an intense debate. As the associated risks, disadvantages, side effects and mechanisms of action vary from drug to drug, the choice of the most suitable therapy needs to be thoroughly investigated. Herein we propose to discuss the different classes of antidiabetic drugs available, their applications and mechanisms of action, particularly those of the newer and/or most widely prescribed classes. A special emphasis will be made on their effects on cellular metabolism, since these drugs affect those pathways in several cellular systems and organs, promoting metabolic alterations responsible for either deleterious or beneficial effects. This is a crucial property that needs to be carefully investigated when prescribing an antidiabetic.

  11. 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.

  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. 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.

  14. 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

  15. 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…

  16. 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…

  17. Frequency-dependent micromechanics of cellularized biopolymer networks

    NASA Astrophysics Data System (ADS)

    Jones, Chris; Kim, Jihan; McIntyre, David; Sun, Bo

    Mechanical interactions between cells and the extracellular matrix (ECM) influence many cellular behaviors such as growth, differentiation, and migration. These are dynamic processes in which the cells actively remodel the ECM. Reconstituted collagen gel is a common model ECM for studying cell-ECM interactions in vitro because collagen is the most abundant component of mammalian ECM and gives the ECM its material stiffness. We embed micron-sized particles in collagen and use holographic optical tweezers to apply forces to the particles in multiple directions and over a range of frequencies up to 10 Hz. We calculate the local compliance and show that it is dependent on both the direction and frequency of the applied force. Performing the same measurement on many particles allows us to characterize the spatial inhomogeneity of the mechanical properties and shows that the compliance decreases at higher frequencies. Performing these measurements on cell-populated collagen gels shows that cellular remodeling of the ECM changes the mechanical properties of the collagen and we investigate whether this change is dependent on the local strain and distance from nearby cells.

  18. Investigating the Cellular and Metabolic Responses of World-Class Canoeists Training: A Sportomics Approach

    PubMed Central

    Coelho, Wagner Santos; Viveiros de Castro, Luis; Deane, Elizabeth; Magno-França, Alexandre; Bassini, Adriana; Cameron, Luiz-Claudio

    2016-01-01

    (1) Background: We have been using the Sportomics approach to evaluate biochemical and hematological changes in response to exercise. The aim of this study was to evaluate the metabolic and hematologic responses of world-class canoeists during a training session; (2) Methods: Blood samples were taken at different points and analyzed for their hematological properties, activities of selected enzymes, hormones, and metabolites; (3) Results: Muscle stress biomarkers were elevated in response to exercise which correlated with modifications in the profile of white blood cells, where a leukocyte rise was observed after the canoe session. These results were accompanied by an increase in other exercise intensity parameters such as lactatemia and ammonemia. Adrenocorticotropic hormone and cortisol increased during the exercise sessions. The acute rise in both erythrocytes and white blood profile were probably due to muscle cell damage, rather than hepatocyte integrity impairment; (4) Conclusion: The cellular and metabolic responses found here, together with effective nutrition support, are crucial to understanding the effects of exercise in order to assist in the creation of new training and recovery planning. Also we show that Sportomics is a primal tool for training management and performance improvement, as well as to the understanding of metabolic response to exercise. PMID:27845704

  19. Thioflavin T as a fluorescence probe for monitoring RNA metabolism at molecular and cellular levels

    PubMed Central

    Sugimoto, Shinya; Arita-Morioka, Ken-ichi; Mizunoe, Yoshimitsu; Yamanaka, Kunitoshi; Ogura, Teru

    2015-01-01

    The intrinsically stochastic dynamics of mRNA metabolism have important consequences on gene regulation and non-genetic cell-to-cell variability; however, no generally applicable methods exist for studying such stochastic processes quantitatively. Here, we describe the use of the amyloid-binding probe Thioflavin T (ThT) for monitoring RNA metabolism in vitro and in vivo. ThT fluoresced strongly in complex with bacterial total RNA than with genomic DNA. ThT bound purine oligoribonucleotides preferentially over pyrimidine oligoribonucleotides and oligodeoxyribonucleotides. This property enabled quantitative real-time monitoring of poly(A) synthesis and phosphorolysis by polyribonucleotide phosphorylase in vitro. Cellular analyses, in combination with genetic approaches and the transcription-inhibitor rifampicin treatment, demonstrated that ThT mainly stained mRNA in actively dividing Escherichia coli cells. ThT also facilitated mRNA metabolism profiling at the single-cell level in diverse bacteria. Furthermore, ThT can also be used to visualise transitions between non-persister and persister cell states, a phenomenon of isogenic subpopulations of antibiotic-sensitive bacteria that acquire tolerance to multiple antibiotics due to stochastically induced dormant states. Collectively, these results suggest that probing mRNA dynamics with ThT is a broadly applicable approach ranging from the molecular level to the single-cell level. PMID:25883145

  20. Flux balance analysis of myocardial mitochondrial metabolic network

    NASA Astrophysics Data System (ADS)

    Luo, Ruoyu; Liao, Sha; Liu, Bifeng; Liu, Manxi; Zhang, Hongming; Luo, Qingming

    2005-03-01

    A large number of biological information has been available from genome sequencing and bioinformatics. To further understand the qualities of the biological networks (such as metabolic network) in the complex biological system, representations of integrated function in silico have been widely investigated, and various modeling approaches have been designed, most of which are based on detailed kinetic information except flux balance analysis (FBA). FBA, just based on stoichimetrical information of reactions, is a suitable method for the study of metabolic pathways, and it analyzes the behaviors of the network from the viewpoint of the whole system. Herein, this modeling approach has been utilized to reconstruct the mitochondrial metabolic network to integrate and analyze its capability of producing energy. Besides, extreme pathways analysis (EPA) and shadow prices analysis have also been integrated to study the interior characters of the network. Our modeling results have indicated for the first time that the covalent regulative property of pyruvate dehydrogenase is restrained by the feedback of acetyl-CoA. Combined with the biological experiments, these simulations in silico could be pretty useful for the further understanding of functions and characters of the biological network as a complex system.

  1. 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

  2. Phosphoproteome Analysis Links Protein Phosphorylation to Cellular Remodeling and Metabolic Adaptation during Magnaporthe oryzae Appressorium Development.

    PubMed

    Franck, William L; Gokce, Emine; Randall, Shan M; Oh, Yeonyee; Eyre, Alex; Muddiman, David C; Dean, Ralph A

    2015-06-05

    The rice pathogen, Magnaporthe oryzae, undergoes a complex developmental process leading to formation of an appressorium prior to plant infection. In an effort to better understand phosphoregulation during appressorium development, a mass spectrometry based phosphoproteomics study was undertaken. A total of 2924 class I phosphosites were identified from 1514 phosphoproteins from mycelia, conidia, germlings, and appressoria of the wild type and a protein kinase A (PKA) mutant. Phosphoregulation during appressorium development was observed for 448 phosphosites on 320 phosphoproteins. In addition, a set of candidate PKA targets was identified encompassing 253 phosphosites on 227 phosphoproteins. Network analysis incorporating regulation from transcriptomic, proteomic, and phosphoproteomic data revealed new insights into the regulation of the metabolism of conidial storage reserves and phospholipids, autophagy, actin dynamics, and cell wall metabolism during appressorium formation. In particular, protein phosphorylation appears to play a central role in the regulation of autophagic recycling and actin dynamics during appressorium formation. Changes in phosphorylation were observed in multiple components of the cell wall integrity pathway providing evidence that this pathway is highly active during appressorium development. Several transcription factors were phosphoregulated during appressorium formation including the bHLH domain transcription factor MGG_05709. Functional analysis of MGG_05709 provided further evidence for the role of protein phosphorylation in regulation of glycerol metabolism and the metabolic reprogramming characteristic of appressorium formation. The data presented here represent a comprehensive investigation of the M. oryzae phosphoproteome and provide key insights on the role of protein phosphorylation during infection-related development.

  3. Parallel labeling experiments validate Clostridium acetobutylicum metabolic network model for (13)C metabolic flux analysis.

    PubMed

    Au, Jennifer; Choi, Jungik; Jones, Shawn W; Venkataramanan, Keerthi P; Antoniewicz, Maciek R

    2014-11-01

    In this work, we provide new insights into the metabolism of Clostridium acetobutylicum ATCC 824 obtained using a systematic approach for quantifying fluxes based on parallel labeling experiments and (13)C-metabolic flux analysis ((13)C-MFA). Here, cells were grown in parallel cultures with [1-(13)C]glucose and [U-(13)C]glucose as tracers and (13)C-MFA was used to quantify intracellular metabolic fluxes. Several metabolic network models were compared: an initial model based on current knowledge, and extended network models that included additional reactions that improved the fits of experimental data. While the initial network model did not produce a statistically acceptable fit of (13)C-labeling data, an extended network model with five additional reactions was able to fit all data with 292 redundant measurements. The model was subsequently trimmed to produce a minimal network model of C. acetobutylicum for (13)C-MFA, which could still reproduce all of the experimental data. The flux results provided valuable new insights into the metabolism of C. acetobutylicum. First, we found that TCA cycle was effectively incomplete, as there was no measurable flux between α-ketoglutarate and succinyl-CoA, succinate and fumarate, and malate and oxaloacetate. Second, an active pathway was identified from pyruvate to fumarate via aspartate. Third, we found that isoleucine was produced exclusively through the citramalate synthase pathway in C. acetobutylicum and that CAC3174 was likely responsible for citramalate synthase activity. These model predictions were confirmed in several follow-up tracer experiments. The validated metabolic network model established in this study can be used in future investigations for unbiased (13)C-flux measurements in C. acetobutylicum.

  4. 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.

  5. 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

  6. 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.

  7. Multi-Criteria Optimization of Regulation in Metabolic Networks

    PubMed Central

    Higuera, Clara; Villaverde, Alejandro F.; Banga, Julio R.; Ross, John; Morán, Federico

    2012-01-01

    Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different “environments” (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks. PMID:22848435

  8. 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

  9. Evolution of enzymes in metabolism: a network perspective.

    PubMed

    Alves, Rui; Chaleil, Raphael A G; Sternberg, Michael J E

    2002-07-19

    Several models have been proposed to explain the origin and evolution of enzymes in metabolic pathways. Initially, the retro-evolution model proposed that, as enzymes at the end of pathways depleted their substrates in the primordial soup, there was a pressure for earlier enzymes in pathways to be created, using the later ones as initial template, in order to replenish the pools of depleted metabolites. Later, the recruitment model proposed that initial templates from other pathways could be used as long as those enzymes were similar in chemistry or substrate specificity. These two models have dominated recent studies of enzyme evolution. These studies are constrained by either the small scale of the study or the artificial restrictions imposed by pathway definitions. Here, a network approach is used to study enzyme evolution in fully sequenced genomes, thus removing both constraints. We find that homologous pairs of enzymes are roughly twice as likely to have evolved from enzymes that are less than three steps away from each other in the reaction network than pairs of non-homologous enzymes. These results, together with the conservation of the type of chemical reaction catalyzed by evolutionarily related enzymes, suggest that functional blocks of similar chemistry have evolved within metabolic networks. One possible explanation for these observations is that this local evolution phenomenon is likely to cause less global physiological disruptions in metabolism than evolution of enzymes from other enzymes that are distant from them in the metabolic network.

  10. 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

  11. Metagenomics reveals flavour metabolic network of cereal vinegar microbiota.

    PubMed

    Wu, Lin-Huan; Lu, Zhen-Ming; Zhang, Xiao-Juan; Wang, Zong-Min; Yu, Yong-Jian; Shi, Jin-Song; Xu, Zheng-Hong

    2017-04-01

    Multispecies microbial community formed through centuries of repeated batch acetic acid fermentation (AAF) is crucial for the flavour quality of traditional vinegar produced from cereals. However, the metabolism to generate and/or formulate the essential flavours by the multispecies microbial community is hardly understood. Here we used metagenomic approach to clarify in situ metabolic network of key microbes responsible for flavour synthesis of a typical cereal vinegar, Zhenjiang aromatic vinegar, produced by solid-state fermentation. First, we identified 3 organic acids, 7 amino acids, and 20 volatiles as dominant vinegar metabolites. Second, we revealed taxonomic and functional composition of the microbiota by metagenomic shotgun sequencing. A total of 86 201 predicted protein-coding genes from 35 phyla (951 genera) were involved in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of Metabolism (42.3%), Genetic Information Processing (28.3%), and Environmental Information Processing (10.1%). Furthermore, a metabolic network for substrate breakdown and dominant flavour formation in vinegar microbiota was constructed, and microbial distribution discrepancy in different metabolic pathways was charted. This study helps elucidating different metabolic roles of microbes during flavour formation in vinegar microbiota.

  12. Molecular Signaling Network Motifs Provide a Mechanistic Basis for Cellular Threshold Responses

    PubMed Central

    Bhattacharya, Sudin; Conolly, Rory B.; Clewell, Harvey J.; Kaminski, Norbert E.; Andersen, Melvin E.

    2014-01-01

    Background: Increasingly, there is a move toward using in vitro toxicity testing to assess human health risk due to chemical exposure. As with in vivo toxicity testing, an important question for in vitro results is whether there are thresholds for adverse cellular responses. Empirical evaluations may show consistency with thresholds, but the main evidence has to come from mechanistic considerations. Objectives: Cellular response behaviors depend on the molecular pathway and circuitry in the cell and the manner in which chemicals perturb these circuits. Understanding circuit structures that are inherently capable of resisting small perturbations and producing threshold responses is an important step towards mechanistically interpreting in vitro testing data. Methods: Here we have examined dose–response characteristics for several biochemical network motifs. These network motifs are basic building blocks of molecular circuits underpinning a variety of cellular functions, including adaptation, homeostasis, proliferation, differentiation, and apoptosis. For each motif, we present biological examples and models to illustrate how thresholds arise from specific network structures. Discussion and Conclusion: Integral feedback, feedforward, and transcritical bifurcation motifs can generate thresholds. Other motifs (e.g., proportional feedback and ultrasensitivity)produce responses where the slope in the low-dose region is small and stays close to the baseline. Feedforward control may lead to nonmonotonic or hormetic responses. We conclude that network motifs provide a basis for understanding thresholds for cellular responses. Computational pathway modeling of these motifs and their combinations occurring in molecular signaling networks will be a key element in new risk assessment approaches based on in vitro cellular assays. Citation: Zhang Q, Bhattacharya S, Conolly RB, Clewell HJ III, Kaminski NE, Andersen ME. 2014. Molecular signaling network motifs provide a

  13. A new cellular automaton model for urban two-way road networks.

    PubMed

    Shi, Junqing; Cheng, Lin; Long, Jiancheng; Liu, Yuanlin

    2014-01-01

    A new cellular automaton (CA) model is proposed to simulate traffic dynamics in urban two-way road network systems. The NaSch rule is adopted to represent vehicle movements on road sections. Two novel rules are proposed to move the vehicles in intersection areas, and an additional rule is developed to avoid the "gridlock" phenomenon. Simulation results show that the network fundamental diagram is very similar to that of road traffic flow. We found that the randomization probability and the maximum vehicle speed have significant impact on network traffic mobility for free-flow state. Their effect may be weak when the network is congested.

  14. A New Cellular Automaton Model for Urban Two-Way Road Networks

    PubMed Central

    Shi, Junqing; Cheng, Lin; Liu, Yuanlin

    2014-01-01

    A new cellular automaton (CA) model is proposed to simulate traffic dynamics in urban two-way road network systems. The NaSch rule is adopted to represent vehicle movements on road sections. Two novel rules are proposed to move the vehicles in intersection areas, and an additional rule is developed to avoid the “gridlock” phenomenon. Simulation results show that the network fundamental diagram is very similar to that of road traffic flow. We found that the randomization probability and the maximum vehicle speed have significant impact on network traffic mobility for free-flow state. Their effect may be weak when the network is congested. PMID:25435868

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

    PubMed

    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

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Modeling psychiatric disorders at the cellular and network levels.

    PubMed

    Brennand, K J; Simone, A; Tran, N; Gage, F H

    2012-12-01

    Although psychiatric disorders such as autism spectrum disorders, schizophrenia and bipolar disorder affect a number of brain regions and produce a complex array of clinical symptoms, basic phenotypes likely exist at the level of single neurons and simple networks. Being highly heritable, it is hypothesized that these disorders are amenable to cell-based studies in vitro. Using induced pluripotent stem cell-derived neurons and/or induced neurons from fibroblasts, limitless numbers of live human neurons can now be generated from patients with a genetic background permissive to the disease state. We predict that cell-based studies will ultimately contribute to our understanding of the initiation, progression and treatment of these psychiatric disorders.

  2. 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.

  3. 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

  4. Revealing insect herbivory-induced phenolamide metabolism: from single genes to metabolic network plasticity analysis.

    PubMed

    Gaquerel, Emmanuel; Gulati, Jyotasana; Baldwin, Ian T

    2014-08-01

    The phenylpropanoid metabolic space comprises a network of interconnected metabolic branches that contribute to the biosynthesis of a large array of compounds with functions in plant development and stress adaptation. During biotic challenges, such as insect attack, a major rewiring of gene networks associated with phenylpropanoid metabolism is observed. This rapid reconfiguration of gene expression allows prioritized production of metabolites that help the plant solve ecological problems. Phenolamides are a group of phenolic derivatives that originate from diversion of hydroxycinnamoyl acids from the main phenylpropanoid pathway after N-acyltransferase-dependent conjugation to polyamines or aryl monoamines. These structurally diverse metabolites are abundant in the reproductive organs of many plants, and have recently been shown to play roles as induced defenses in vegetative tissues. In the wild tobacco, Nicotiana attenuata, in which herbivory-induced regulation of these metabolites has been studied, rapid elevations of the levels of phenolamides that function as induced defenses result from a multi-hormonal signaling network that re-shapes connected metabolic pathways. In this review, we summarize recent findings in the regulation of phenolamides obtained by mass spectrometry-based metabolomics profiling, and outline a conceptual framework for gene discovery in this pathway. We also introduce a multifactorial approach that is useful in deciphering metabolic pathway reorganizations among tissues in response to stress.

  5. Revealing insect herbivory-induced phenolamide metabolism: from single genes to metabolic network plasticity analysis

    PubMed Central

    Gaquerel, Emmanuel; Gulati, Jyotasana; Baldwin, Ian T.

    2016-01-01

    The phenylpropanoid metabolic space comprises a network of interconnected metabolic branches that contribute to the biosynthesis of a large array of compounds with functions in plant development and stress adaptation. During biotic challenges, such as insect attack, a major rewiring of gene networks associated with phenylpropanoid metabolism is observed. This rapid reconfiguration of gene expression allows for the prioritized production of metabolites that help the plant solve ecological problems. Phenolamides are a group of phenolic-derivatives that originate from the diversion of hydroxycinnamoyl acids from the main phenylpropanoid pathway after N-acyltransferase-dependent conjugation to polyamines or aryl-monoamines. These structurally diverse metabolites are abundant in reproductive organs of many plants and have recently been shown to play roles as induced defenses in vegetative tissues. In the wild tobacco, Nicotiana attenuata in which the herbivory-induced regulation of these metabolites has been studied, rapid elevations of phenolamide levels that function as induced defenses result from a multi-hormonal signaling network that reshapes connected metabolic pathways. In this review, we summarize recent findings in the regulation of phenolamides obtained by mass spectrometry-based metabolomics and outline a conceptual framework for gene discovery in this pathway. We finally introduce a multifactorial approach useful in deciphering metabolic pathway reorganizations among different tissues in response to stress. PMID:24617849

  6. Characterization of one-dimensional cellular automata rules through topological network features

    NASA Astrophysics Data System (ADS)

    D'Alotto, Lou; Pizzuti, Clara

    2016-10-01

    The paper investigates the relationship between the classification schemes, defined by Wolfram and Gilman, of one-dimensional cellular automata through concepts coming from network theory. An automaton is represented with a network, generated from the elementary rule defining its behavior. Characteristic features of this graph are computed and machine learning classification models are built. Such models allow to classify automaton rules and to compare Wolfram's and Gilman's classes by comparing the classes predicted by these models.

  7. 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.

  8. Iron-dependent changes in cellular energy metabolism: influence on citric acid cycle and oxidative phosphorylation.

    PubMed

    Oexle, H; Gnaiger, E; Weiss, G

    1999-11-10

    Iron modulates the expression of the critical citric acid cycle enzyme aconitase via a translational mechanism involving iron regulatory proteins. Thus, the present study was undertaken to investigate the consequences of iron perturbation on citric acid cycle activity, oxidative phosphorylation and mitochondrial respiration in the human cell line K-562. In agreement with previous data iron increases the activity of mitochondrial aconitase while it is reduced upon addition of the iron chelator desferrioxamine (DFO). Interestingly, iron also positively affects three other citric acid cycle enzymes, namely citrate synthase, isocitric dehydrogenase, and succinate dehydrogenase, while DFO decreases the activity of these enzymes. Consequently, iron supplementation results in increased formation of reducing equivalents (NADH) by the citric acid cycle, and thus in increased mitochondrial oxygen consumption and ATP formation via oxidative phosphorylation as shown herein. This in turn leads to downregulation of glucose utilization. In contrast, all these metabolic pathways are reduced upon iron depletion, and thus glycolysis and lactate formation are significantly increased in order to compensate for the decrease in ATP production via oxidative phosphorylation in the presence of DFO. Our results point to a complex interaction between iron homeostasis, oxygen supply and cellular energy metabolism in human cells.

  9. Dysregulation of Ezrin phosphorylation prevents metastasis and alters cellular metabolism in osteosarcoma

    PubMed Central

    Ren, Ling; Hong, Sung-Hyeok; Chen, Qing-Rong; Briggs, Joseph; Cassavaugh, Jessica; Srinivasan, Satish; Lizardo, Michael M.; Mendoza, Arnulfo; Xia, Ashley Y.; Avadhani, Narayan; Khan, Javed; Khanna, Chand

    2013-01-01

    Ezrin links the plasma membrane to the actin cytoskeleton where it plays a pivotal role in the metastatic progression of several human cancers (1, 2), however, the precise mechanistic basis for its role remains unknown. Here we define transitions between active (phosphorylated open) and inactive (dephosphorylated closed) forms of Ezrin that occur during metastatic progression in osteosarcoma. In our evaluation of these conformations we expressed C-terminal mutant forms of Ezrin that are open (phosphomimetic T567D) or closed (phosphodeficient T567A) and compared their biological characteristics to full length wild-type Ezrin in osteosarcoma cells. Unexpectedly, cells expressing open, active Ezrin could form neither primary orthotopic tumors nor lung metastases. In contrast, cells expressing closed, inactive Ezrin were also deficient in metastasis but were unaffected in their capacity for primary tumor growth. By imaging single metastatic cells in the lung, we found that cells expressing either open or closed Ezrin displayed increased levels of apoptosis early after their arrival in the lung. Gene expression analysis suggested dysregulation of genes that are functionally linked to carbohydrate and amino acid metabolism. In particular, cells expressing closed, inactive Ezrin exhibited reduced lactate production and basal or ATP-dependent oxygen consumption. Collectively, our results suggest that dynamic regulation of Ezrin phosphorylation at amino acid T567 that controls structural transitions of this protein plays a pivotal role in tumor progression and metastasis, possibly in part by altering cellular metabolism. PMID:22147261

  10. Detecting drug targets with minimum side effects in metabolic networks.

    PubMed

    Li, Z; Wang, R-S; Zhang, X-S; Chen, L

    2009-11-01

    High-throughput techniques produce massive data on a genome-wide scale which facilitate pharmaceutical research. Drug target discovery is a crucial step in the drug discovery process and also plays a vital role in therapeutics. In this study, the problem of detecting drug targets was addressed, which finds a set of enzymes whose inhibition stops the production of a given set of target compounds and meanwhile minimally eliminates non-target compounds in the context of metabolic networks. The model aims to make the side effects of drugs as small as possible and thus has practical significance of potential pharmaceutical applications. Specifically, by exploiting special features of metabolic systems, a novel approach was proposed to exactly formulate this drug target detection problem as an integer linear programming model, which ensures that optimal solutions can be found efficiently without any heuristic manipulations. To verify the effectiveness of our approach, computational experiments on both Escherichia coli and Homo sapiens metabolic pathways were conducted. The results show that our approach can identify the optimal drug targets in an exact and efficient manner. In particular, it can be applied to large-scale networks including the whole metabolic networks from most organisms.

  11. 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

  12. A mathematical model to study the dynamics of epithelial cellular networks.

    PubMed

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

    2012-01-01

    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).

  13. Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks.

    PubMed

    Kiparissides, A; Hatzimanikatis, V

    2017-01-01

    The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. Constraint based methods, such as Flux Balance Analysis (FBA) and Thermodynamics-based flux analysis (TFA), are commonly used to estimate the flow of metabolites through genome-wide metabolic networks, making it possible to identify the ranges of flux values that are consistent with the studied physiological and thermodynamic conditions. However, unless key intracellular fluxes and metabolite concentrations are known, constraint-based models lead to underdetermined problem formulations. This lack of information propagates as uncertainty in the estimation of fluxes and basic reaction properties such as the determination of reaction directionalities. Therefore, knowledge of which metabolites, if measured, would contribute the most to reducing this uncertainty can significantly improve our ability to define the internal state of the cell. In the present work we combine constraint based modeling, Design of Experiments (DoE) and Global Sensitivity Analysis (GSA) into the Thermodynamics-based Metabolite Sensitivity Analysis (TMSA) method. TMSA ranks metabolites comprising a metabolic network based on their ability to constrain the gamut of possible solutions to a limited, thermodynamically consistent set of internal states. TMSA is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements.

  14. 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.

  15. Altered cellular metabolism of HepG2 cells caused by microcystin-LR.

    PubMed

    Ma, Junguo; Feng, Yiyi; Jiang, Siyu; Li, Xiaoyu

    2017-03-21

    This study aimed to evaluate the possible effects of microcystin-LR (MC-LR) exposure on the metabolism and drug resistance of human hepatocellular carcinoma (HepG2) cells. For this purpose, we first conducted an experiment to make sure that MC-LR could penetrate the HepG2 cell membrane effectively. The transcriptional levels of phase I (such as CYP2E1, CYP3A4, and CYP26B1) and phase II (such as EPHX1, SULTs, and GSTM) enzymes and export pump genes (such as MRP1 and MDR1) were altered by MC-LR-exposure for 24 h, indicating that MC-LR treatment may destabilize the metabolism of HepG2 cells. Further research showed that the CYP inducers omeprazole, ethanol, and rifampicin inhibited cell viability, in particular, ethanol, a CYP2E1 inducer, induced ROS generation, lipid peroxidation, and apoptosis in HepG2 cells treated with MC-LR. The CYP2E1 inhibitor chlormethiazole inhibited ROS generation, mitochondrial membrane potential loss, caspase-3 activity, and cytotoxicity caused by MC-LR. Meanwhile, the results also showed that co-incubation with the ROS scavenger l-ascorbic acid and MC-LR decreased ROS levels and effectively prevented apoptosis. These findings provide an interesting mechanistic explanation of cellular metabolism associated with MC-LR, i.e., MC-LR-exposure exerted toxicity on HepG2 cells and induced apoptosis of HepG2 cells via promoting CYP2E1 expression and inducing excessive ROS in HepG2 cells.

  16. Cellular and Metabolic Origins of Flavoprotein Autofluorescence in the Cerebellar Cortex in vivo

    PubMed Central

    Reinert, Kenneth C.; Gao, Wangcai; Chen, Gang; Wang, Xinming; Peng, Yu-Ping

    2013-01-01

    Flavoprotein autofluorescence imaging, an intrinsic mitochondrial signal, has proven useful for monitoring neuronal activity. In the cerebellar cortex, parallel fiber stimulation evokes a beam-like response consisting of an initial, short-duration increase in fluorescence (on-beam light phase) followed by a longer duration decrease (on-beam dark phase). Also evoked are parasagittal bands of decreased fluorescence due to molecular layer inhibition. Previous work suggests that the on-beam light phase is due to oxidative metabolism in neurons. The present study further investigated the metabolic and cellular origins of the flavoprotein signal in vivo, testing the hypotheses that the dark phase is mediated by glia activation and the inhibitory bands reflect decreased flavoprotein oxidation and increased glycolysis in neurons. Blocking postsynaptic ionotropic and metabotropic glutamate receptors abolished the onbeam light phase and the parasagittal bands without altering the on-beam dark phase. Adding glutamate transporter blockers reduced the dark phase. Replacing glucose with lactate (or pyruvate) or adding lactate to the bathing media abolished the on-beam dark phase and reduced the inhibitory bands without affecting the light phase. Blocking monocarboxylate transporters eliminated the on-beam dark phase and increased the light phase. These results confirm that the on-beam light phase is due primarily to increased oxidative metabolism in neurons. They also show that the on-beam dark phase involves activation of glycolysis in glia resulting in the generation of lactate that is transferred to neurons. Oxidative savings in neurons contributes to the decrease in fluorescence characterizing the inhibitory bands. These findings provide strong in vivo support for the astrocyte–neuron lactate shuttle hypothesis. PMID:21503591

  17. 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

  18. Synaptoproteomic Analysis of a Rat Gene-Environment Model of Depression Reveals Involvement of Energy Metabolism and Cellular Remodeling Pathways

    PubMed Central

    Failler, Marion; Corna, Stefano; Racagni, Giorgio; Mathé, Aleksander A.; Popoli, Maurizio

    2015-01-01

    Background: Major depression is a severe mental illness that causes heavy social and economic burdens worldwide. A number of studies have shown that interaction between individual genetic vulnerability and environmental risk factors, such as stress, is crucial in psychiatric pathophysiology. In particular, the experience of stressful events in childhood, such as neglect, abuse, or parental loss, was found to increase the risk for development of depression in adult life. Here, to reproduce the gene x environment interaction, we employed an animal model that combines genetic vulnerability with early-life stress. Methods: The Flinders Sensitive Line rats (FSL), a validated genetic animal model of depression, and the Flinders Resistant Line (FRL) rats, their controls, were subjected to a standard protocol of maternal separation (MS) from postnatal days 2 to 14. A basal comparison between the two lines for the outcome of the environmental manipulation was performed at postnatal day 73, when the rats were into adulthood. We carried out a global proteomic analysis of purified synaptic terminals (synaptosomes), in order to study a subcellular compartment enriched in proteins involved in synaptic function. Two-dimensional gel electrophoresis (2-DE), mass spectrometry, and bioinformatic analysis were used to analyze proteins and related functional networks that were modulated by genetic susceptibility (FSL vs. FRL) or by exposure to early-life stress (FRL + MS vs. FRL and FSL + MS vs. FSL). Results: We found that, at a synaptic level, mainly proteins and molecular pathways related to energy metabolism and cellular remodeling were dysregulated. Conclusions: The present results, in line with previous works, suggest that dysfunction of energy metabolism and cytoskeleton dynamics at a synaptic level could be features of stress-related pathologies, in particular major depression. PMID:25522407

  19. 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.

  20. Functional modules, structural topology, and optimal activity in metabolic networks.

    PubMed

    Resendis-Antonio, Osbaldo; Hernández, Magdalena; Mora, Yolanda; Encarnación, Sergio

    2012-01-01

    Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris. To experimentally characterize the metabolic phenotype of this microorganism, we obtained the metabolic profile of 220 metabolites at two physiological stages: under free-living conditions, and during nitrogen fixation with P. vulgaris. By integrating these data into a constraint-based model, we built a refined computational platform with the capability to survey the metabolic activity underlying nitrogen fixation in R. etli. Topological analysis of the metabolic reconstruction led us to identify modular structures with functional activities. Consistent with modular activity in metabolism, we found that most of the metabolites experimentally detected in each module simultaneously increased their relative abundances during nitrogen fixation. In this work, we explore the relationships among topology, biological function, and optimal activity in the metabolism of R. etli through an integrative analysis based on modeling and metabolome data. Our findings suggest that the metabolic activity during nitrogen fixation is supported by interacting structural modules that correlate with three functional classifications: nucleic acids, peptides, and lipids. More fundamentally, we supply evidence that such modular organization during functional nitrogen fixation is

  1. Discrimination of liver cancer in cellular level based on backscatter micro-spectrum with PCA algorithm and BP neural network

    NASA Astrophysics Data System (ADS)

    Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona

    2016-10-01

    The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

  2. 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.

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

    DOE PAGES

    Gargouri, Mahmoud; Park, Jeong -Jin; Holguin, F. Omar; ...

    2015-05-28

    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 combinedmore » 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. In conclusion, 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.« less

  4. Multi-Cell Cooperation with Fairness Constraint in the Downlink OFDMA Cellular Networks

    NASA Astrophysics Data System (ADS)

    Li, Hongxing; Luo, Hanwen; Chen, Wen; Guo, Jia

    In this letter, we study cell cooperation in the downlink OFDMA cellular networks. The proposed cooperation scheme is based on fractional frequency reuse (FFR), where a cooperation group consists of three sector antennas from three adjacent cells and the subchannels of each cooperation group are allocated coordinately to users. Simulation results demonstrate the effectiveness of the proposed schemes in terms of throughput and fairness.

  5. 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.

  6. Redox Modulation of Cellular Signaling and Metabolism Through Reversible Oxidation of Methionine Sensors in Calcium Regulatory Proteins

    SciTech Connect

    Bigelow, Diana J.; Squier, Thomas C.

    2005-01-17

    Adaptive responses associated with environmental stressors are critical to cell survival. These involve the modulation of central signaling protein functions through site-specific and enzymatically reversible oxidative modifications of methionines to coordinate cellular metabolism, energy utilization, and calcium signaling. Under conditions when cellular redox and antioxidant defenses are overwhelmed, the selective oxidation of critical methionines within selected protein sensors functions to down-regulate energy metabolism and the further generation of reactive oxygen species (ROS). Mechanistically, these functional changes within protein sensors take advantage of the helix-breaking character of methionine sulfoxide. Thus, depending on either the ecological niche of the organism or the cellular milieu of different organ systems, cellular metabolism can be fine-tuned to maintain optimal function in the face of variable amounts of collateral oxidative damage. The sensitivity of several calcium regulatory proteins to oxidative modification provides cellular sensors that link oxidative stress to cellular response and recovery. Calmodulin (CaM) is one such critical calcium regulatory protein, which is functionally sensitive to methionine oxidation. Helix destabilization resulting from the oxidation of either Met{sup 144} or Met{sup 145} results in the nonproductive association between CaM and target proteins. The ability of oxidized CaM to stabilize its target proteins in an inhibited state with an affinity similar to that of native (unoxidized) CaM permits this central regulatory protein to function as a cellular rheostat that down-regulates energy metabolism in response to oxidative stress. Likewise, oxidation of a methionine within a critical switch region of the regulatory protein phospholamban is expected to destabilize the phosphorylationdependent helix formation necessary for the release of enzyme inhibition, resulting in a down-regulation of the Ca-ATPase in

  7. 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.

  8. 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

  9. 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.

  10. Bacterial Unculturability and the Formation of Intercellular Metabolic Networks.

    PubMed

    Pande, Samay; Kost, Christian

    2017-04-04

    The majority of known bacterial species cannot be cultivated under laboratory conditions. Here we argue that the adaptive emergence of obligate metabolic interactions in natural bacterial communities can explain this pattern. Bacteria commonly release metabolites into the external environment. Accumulating pools of extracellular metabolites create an ecological niche that benefits auxotrophic mutants, which have lost the ability to autonomously produce the corresponding metabolites. In addition to a diffusion-based metabolite transfer, auxotrophic cells can use contact-dependent means to obtain nutrients from other co-occurring cells. Spatial colocalisation and a continuous coevolution further increase the nutritional dependency and optimise fluxes through combined metabolic networks. Thus, bacteria likely function as networks of interacting cells that reciprocally exchange nutrients and biochemical functions rather than as physiologically autonomous units.

  11. 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

  12. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

    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. Here we give a detailed description of the employed rainfall retrieval algorithm and provide the corresponding code. Moreover, the code (in the scripting language "R") is made available including a data set of commercial microwave links. The purpose of this paper is to promote rainfall monitoring utilizing microwave links from cellular communication networks as an alternative or complementary means for global, continental-scale rainfall monitoring.

  14. 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.

  15. Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays

    PubMed Central

    Lu, Lin

    2016-01-01

    We investigate a class of memristor-based shunting inhibitory cellular neural networks with leakage delays. By applying a new Lyapunov function method, we prove that the neural network which has a unique almost periodic solution is globally exponentially stable. Moreover, the theoretical findings of this paper on the almost periodic solution are applied to prove the existence and stability of periodic solution for memristor-based shunting inhibitory cellular neural networks with leakage delays and periodic coefficients. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this paper are completely new and complement the previously known studies of Wu (2011) and Chen and Cao (2002). PMID:27840634

  16. Characterizing the Network of Drugs and Their Affected Metabolic Subpathways

    PubMed Central

    Li, Jing; Han, Junwei; Wang, Shuyuan; Yao, Qianlan; Wang, Yingying; Zhang, Yunpeng; Zhang, Chunlong; Xu, Yanjun; Jiang, Wei; Li, Xia

    2012-01-01

    A fundamental issue in biology and medicine is illustration of the overall drug impact which is always the consequence of changes in local regions of metabolic pathways (subpathways). To gain insights into the global relationship between drugs and their affected metabolic subpathways, we constructed a drug–metabolic subpathway network (DRSN). This network included 3925 significant drug–metabolic subpathway associations representing drug dual effects. Through analyses based on network biology, we found that if drugs were linked to the same subpathways in the DRSN, they tended to share the same indications and side effects. Furthermore, if drugs shared more subpathways, they tended to share more side effects. We then calculated the association score by integrating drug-affected subpathways and disease-related subpathways to quantify the extent of the associations between each drug class and disease class. The results showed some close drug–disease associations such as sex hormone drugs and cancer suggesting drug dual effects. Surprisingly, most drugs displayed close associations with their side effects rather than their indications. To further investigate the mechanism of drug dual effects, we classified all the subpathways in the DRSN into therapeutic and non-therapeutic subpathways representing drug therapeutic effects and side effects. Compared to drug side effects, the therapeutic effects tended to work through tissue-specific genes and these genes tend to be expressed in the adrenal gland, liver and kidney; while drug side effects always occurred in the liver, bone marrow and trachea. Taken together, the DRSN could provide great insights into understanding the global relationship between drugs and metabolic subpathways. PMID:23112813

  17. 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

  18. Hardware Implementation of a Desktop Supercomputer for High Performance Image Processing. Color Image Processing Using Cellular Neural Networks

    DTIC Science & Technology

    1994-11-01

    This report addresses the functional behavior of Cellular Neural Networks (CNN). The impact of variable convergence times on the proper operation of...The report discusses the new fault model, presents the algorithmic procedures and shows simulated testing results. Cellular neural Networks , Testing.

  19. Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis

    PubMed Central

    Goelzer, Anne; Bekkal Brikci, Fadia; Martin-Verstraete, Isabelle; Noirot, Philippe; Bessières, Philippe; Aymerich, Stéphane; Fromion, Vincent

    2008-01-01

    Background Few genome-scale models of organisms focus on the regulatory networks and none of them integrates all known levels of regulation. In particular, the regulations involving metabolite pools are often neglected. However, metabolite pools link the metabolic to the genetic network through genetic regulations, including those involving effectors of transcription factors or riboswitches. Consequently, they play pivotal roles in the global organization of the genetic and metabolic regulatory networks. Results We report the manually curated reconstruction of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis (transcriptional, translational and post-translational regulations and modulation of enzymatic activities). We provide a systematic graphic representation of regulations of each metabolic pathway based on the central role of metabolites in regulation. We show that the complex regulatory network of B. subtilis can be decomposed as sets of locally regulated modules, which are coordinated by global regulators. Conclusion This work reveals the strong involvement of metabolite pools in the general regulation of the metabolic network. Breaking the metabolic network down into modules based on the control of metabolite pools reveals the functional organization of the genetic and metabolic regulatory networks of B. subtilis. PMID:18302748

  20. 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.

  1. Current understanding of the formation and adaptation of metabolic systems based on network theory.

    PubMed

    Takemoto, Kazuhiro

    2012-07-12

    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.

  2. Quantum complexity: Quantum mutual information, complex networks, and emergent phenomena in quantum cellular automata

    NASA Astrophysics Data System (ADS)

    Vargas, David L.

    Emerging quantum simulator technologies provide a new challenge to quantum many body theory. Quantifying the emergent order in and predicting the dynamics of such complex quantum systems requires a new approach. We develop such an approach based on complex network analysis of quantum mutual information. First, we establish the usefulness of quantum mutual information complex networks by reproducing the phase diagrams of transverse Ising and Bose-Hubbard models. By quantifying the complexity of quantum cellular automata we then demonstrate the applicability of complex network theory to non-equilibrium quantum dynamics. We conclude with a study of student collaboration networks, correlating a student's role in a collaboration network with their grades. This work thus initiates a quantitative theory of quantum complexity and provides a new tool for physics education research. (Abstract shortened by ProQuest.).

  3. 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

  4. 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.

  5. 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.

  6. Stoichiometric network constraints on xylose metabolism by recombinant Saccharomyces cerevisiae.

    PubMed

    Jin, Yong-Su; Jeffries, Thomas W

    2004-07-01

    Metabolic pathway engineering is constrained by the thermodynamic and stoichiometric feasibility of enzymatic activities of introduced genes. Engineering of xylose metabolism in Saccharomyces cerevisiae has focused on introducing genes for the initial xylose assimilation steps from Pichia stipitis, a xylose-fermenting yeast, into S. cerevisiae, a yeast traditionally used in ethanol production from hexose. However, recombinant S. cerevisiae created in several laboratories have used xylose oxidatively rather than in the fermentative manner that this yeast metabolizes glucose. To understand the differences between glucose and engineered xylose metabolic networks, we performed a flux balance analysis (FBA) and calculated extreme pathways using a stoichiometric model that describes the biochemistry of yeast cell growth. FBA predicted that the ethanol yield from xylose exhibits a maximum under oxygen-limited conditions, and a fermentation experiment confirmed this finding. Fermentation results were largely consistent with in silico phenotypes based on calculated extreme pathways, which displayed several phases of metabolic phenotype with respect to oxygen availability from anaerobic to aerobic conditions. However, in contrast to the model prediction, xylitol production continued even after the optimum aeration level for ethanol production was attained. These results suggest that oxygen (or some other electron accepting system) is required to resolve the redox imbalance caused by cofactor difference between xylose reductase and xylitol dehydrogenase, and that other factors limit glycolytic flux when xylose is the sole carbon source.

  7. 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.

  8. The role of field coupling in nano-scale cellular nonlinear networks.

    PubMed

    Porod, Wolfgang; Csaba, Gyorgy; Csurgay, Arpad

    2003-12-01

    We review some of our previous work on field-coupling in nano-scale cellular arrays. Electronic devices based on metallic and magnetic nanoscale dots and molecular structures have been suggested, however, no technologically viable architecture for nanoelectronic circuit integration has emerged so far. A natural architecture on the nanoscale appears to be near-neighbor cellular networking, and we explore promising alternative ways of integrating nanodevices by direct physical field coupling, i.e. either by Coulomb or by magnetic interactions. We review new architectures for such field-coupled nanocircuits.

  9. Cellular and Network Contributions to Excitability of Layer 5 Neocortical Pyramidal Neurons in the Rat

    PubMed Central

    Bar-Yehuda, Dan; Korngreen, Alon

    2007-01-01

    There is a considerable gap between investigating the dynamics of single neurons and the computational aspects of neural networks. A growing number of studies have attempted to overcome this gap using the excitation in brain slices elicited by various chemical manipulations of the bath solution. However, there has been no quantitative study on the effects of these manipulations on the cellular and network factors controlling excitability. Using the whole-cell configuration of the patch-clamp technique we recorded the membrane potential from the soma of layer 5 pyramidal neurons in acute brain slices from the somatosensory cortex of young rats at 22°C and 35°C. Using blockers of synaptic transmission, we show distinct changes in cellular properties following modification of the ionic composition of the artificial cerebrospinal fluid (ACSF). Thus both cellular and network changes may contribute to the observed effects of slice excitation solutions on the physiology of single neurons. Furthermore, our data suggest that the difference in the ionic composition of current standard ACSF from that of CSF measured in vivo cause ACSF to depress network activity in acute brain slices. This may affect outcomes of experiments investigating biophysical and physiological properties of neurons in such preparations. Our results strongly advocate the necessity of redesigning experiments routinely carried out in the quiescent acute brain slice preparation. PMID:18030343

  10. 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

  11. 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.

  12. Synthesis, metabolism and cellular permeability of enzymatically stable dipeptide prodrugs of acyclovir.

    PubMed

    Talluri, Ravi S; Samanta, Swapan K; Gaudana, Ripal; Mitra, Ashim K

    2008-09-01

    The objective of this study was to synthesize and evaluate novel enzymatically stable dipeptide prodrugs for improved absorption of acyclovir. l-Valine-l-valine-acyclovir (LLACV), l-valine-d-valine-acyclovir (LDACV), d-valine-l-valine-acyclovir (DLACV) and d-valine-d-valine-acyclovir (DDACV) were successfully synthesized. The uptake and transport studies were conducted on a Caco-2 cell line. Buffer stability and metabolism of the prodrugs in Caco-2, rat intestine and liver homogenates were studied. Structure and purity of the all compounds were confirmed with LC-MS/MS and NMR spectroscopy. Uptake and transport of [(3)H] glycylsarcosine was inhibited by all prodrugs except DDACV. DLACV and DDACV exhibited no measurable degradation in Caco-2 homogenate. Except DDACV other three prodrugs were hydrolyzed in rat intestine and liver homogenates. The order of permeability across Caco-2 was LDACV>LLACV>DDACV>DLACV. A linear correlation between the amount of prodrug transported and over all permeability of acyclovir was established. This study shows that the incorporation of one d-valine in a dipeptide did not abolish its affinity towards peptide transporters (PEPT). Moreover, it enhanced enzymatic stability of prodrug to a certain extent depending on the position in a dipeptide conjugate. This strategy improved both the cellular permeability and the amount of intact prodrug transported which would enable targeting the nutrient transporters at blood ocular barrier (BOB).

  13. 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.

  14. Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks.

    PubMed

    Beber, Moritz Emanuel; Fretter, Christoph; Jain, Shubham; Sonnenschein, Nikolaus; Müller-Hannemann, Matthias; Hütt, Marc-Thorsten

    2012-12-07

    Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over- or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges.

  15. Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks

    PubMed Central

    Beber, Moritz Emanuel; Fretter, Christoph; Jain, Shubham; Sonnenschein, Nikolaus; Müller-Hannemann, Matthias; Hütt, Marc-Thorsten

    2012-01-01

    Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over- or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges. PMID:22896565

  16. Joint Mode Selection and Resource Allocation for Cellular Controlled Short-Range Communication in OFDMA Networks

    NASA Astrophysics Data System (ADS)

    Deng, Hui; Tao, Xiaoming; Ge, Ning; Lu, Jianhua

    This letter studies cellular controlled short-range communication in OFDMA networks. The network needs to decide when to allow direct communication between a closely located device-to-device (D2D) pair instead of conveying data from one device to the other via the base station and when not to, in addition to subchannel and power allocation. Our goal is to maximize the total network throughput while guaranteeing the rate requirements of all users. For that purpose, we formulate an optimization problem subject to subchannel and power constraints. A scheme which combines a joint mode selection and subchannel allocation algorithm based on equal power allocation with a power reallocation scheme is proposed. Simulation results show that our proposed scheme can improve the network throughput and outage probability compared with other schemes.

  17. Optimal control of metabolic networks with saturable enzyme kinetics.

    PubMed

    Oyarzuun, D A

    2011-03-01

    This note addresses the optimal control of non-linear metabolic networks by means of time-dependent enzyme synthesis rates. The authors consider networks with general topologies described by a control-affine dynamical system coupled with a linear model for enzyme synthesis and degradation. The problem formulation accounts for transitions between two metabolic equilibria, which typically arise in metabolic adaptations to environmental changes, and the minimisation of a quadratic functional that weights the cost/benefit relation between the transcriptional effort required for enzyme synthesis and the transition to the new phenotype. Using a linear time-variant approximation of the non-linear dynamics, the problem is recast as a sequence of linear-quadratic problems, the solution of which involves a sequence of differential Lyapunov equations. The authors provide conditions for convergence to an approximate solution of the original problem, which are naturally satisfied by a wide class of models for saturable enzyme kinetics. As a case study the authors use the method to examine the robustness of an optimal just-in-time gene expression pattern with respect to heterogeneity in the biosynthetic costs of individual proteins.

  18. Adding learning to cellular genetic algorithms for training recurrent neural networks.

    PubMed

    Ku, K W; Mak, M W; Siu, W C

    1999-01-01

    This paper proposes a hybrid optimization algorithm which combines the efforts of local search (individual learning) and cellular genetic algorithms (GA's) for training recurrent neural networks (RNN's). Each weight of an RNN is encoded as a floating point number, and a concatenation of the numbers forms a chromosome. Reproduction takes place locally in a square grid with each grid point representing a chromosome. Two approaches, Lamarckian and Baldwinian mechanisms, for combining cellular GA's and learning have been compared. Different hill-climbing algorithms are incorporated into the cellular GA's as learning methods. These include the real-time recurrent learning (RTRL) and its simplified versions, and the delta rule. The RTRL algorithm has been successively simplified by freezing some of the weights to form simplified versions. The delta rule, which is the simplest form of learning, has been implemented by considering the RNN's as feedforward networks during learning. The hybrid algorithms are used to train the RNN's to solve a long-term dependency problem. The results show that Baldwinian learning is inefficient in assisting the cellular GA. It is conjectured that the more difficult it is for genetic operations to produce the genotypic changes that match the phenotypic changes due to learning, the poorer is the convergence of Baldwinian learning. Most of the combinations using the Lamarckian mechanism show an improvement in reducing the number of generations required for an optimum network; however, only a few can reduce the actual time taken. Embedding the delta rule in the cellular GA's has been found to be the fastest method. It is also concluded that learning should not be too extensive if the hybrid algorithm is to be benefit from learning.

  19. Sensitivity analysis of FBMC-based multi-cellular networks to synchronization errors and HPA nonlinearities

    NASA Astrophysics Data System (ADS)

    Elmaroud, Brahim; Faqihi, Ahmed; Aboutajdine, Driss

    2017-01-01

    In this paper, we study the performance of asynchronous and nonlinear FBMC-based multi-cellular networks. The considered system includes a reference mobile perfectly synchronized with its reference base station (BS) and K interfering BSs. Both synchronization errors and high-power amplifier (HPA) distortions will be considered and a theoretical analysis of the interference signal will be conducted. On the basis of this analysis, we will derive an accurate expression of signal-to-noise-plus-interference ratio (SINR) and bit error rate (BER) in the presence of a frequency-selective channel. In order to reduce the computational complexity of the BER expression, we applied an interesting lemma based on the moment generating function of the interference power. Finally, the proposed model is evaluated through computer simulations which show a high sensitivity of the asynchronous FBMC-based multi-cellular network to HPA nonlinear distortions.

  20. Complete stability of cellular neural networks with unbounded time-varying delays.

    PubMed

    Wang, Lili; Chen, Tianping

    2012-12-01

    In this paper, we are concerned with the delayed cellular neural networks (DCNNs) in the case that the time-varying delays are unbounded. Under some conditions, it shows that the DCNNs can exhibit 3(n) equilibrium points. Then, we track the dynamics of u(t)(t>0) in two cases with respect to different types of subset regions in which u(0) is located. It concludes that every solution trajectory u(t) would converge to one of the equilibrium points despite the time-varying delays, that is, the delayed cellular neural networks are completely stable. The method is novel and the results obtained extend the existing ones. In addition, two illustrative examples are presented to verify the effectiveness of our results.

  1. Two programmed replicative lifespans of Saccharomyces cerevisiae formed by the endogenous molecular-cellular network.

    PubMed

    Hu, Jie; Zhu, Xiaomei; Wang, Xinan; Yuan, Ruoshi; Zheng, Wei; Xu, Minjuan; Ao, Ping

    2014-12-07

    Cellular replicative capacity is a therapeutic target for regenerative medicine as well as cancer treatment. The mechanism of replicative senescence and cell immortality is still unclear. We investigated the diauxic growth of Saccharomyces cerevisiae and demonstrate that the replicative capacity revealed by the yeast growth curve can be understood by using the dynamical property of the molecular-cellular network regulating S. cerevisiae. The endogenous network we proposed has a limit cycle when pheromone signaling is disabled, consistent with the exponential growth phase with an infinite replicative capacity. In the post-diauxic phase, the cooperative effect of the pheromone activated mitogen-activated protein kinase (MAPK) signaling pathway with the cell cycle leads to a fixed point attractor instead of the limit cycle. The cells stop dividing after several generations counting from the beginning of the post-diauxic growth. By tuning the MAPK pathway, S. cerevisiae therefore programs the number of offsprings it replicates.

  2. 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

  3. Cellular neural network implementation using a phase-only joint transform correlator

    NASA Astrophysics Data System (ADS)

    Zhang, Shuqun; Karim, Mohammad A.

    1999-04-01

    A phase-only joint transform correlator (JTC) is used to realize cellular neural networks (CNNs). The operation of summing cross-correlations of bipolar data in CNNs can be realized in parallel by phase-encoding bipolar data. Compared to other optical systems for implementing CNNs, the proposed method offers the advantages of easier implementation and robustness in terms of system alignment, and requires neither electronic precalculation nor data rearrangement. Simulation results of the proposed optical CNNs for edge detection are provided.

  4. Exponential stability of delayed and impulsive cellular neural networks with partially Lipschitz continuous activation functions.

    PubMed

    Song, Xueli; Xin, Xing; Huang, Wenpo

    2012-05-01

    The paper discusses exponential stability of distributed delayed and impulsive cellular neural networks with partially Lipschitz continuous activation functions. By relative nonlinear measure method, some novel criteria are obtained for the uniqueness and exponential stability of the equilibrium point. Our method abandons usual assumptions on global Lipschitz continuity, boundedness and monotonicity of activation functions. Our results are generalization and improvement of some existing ones. Finally, two examples and their simulations are presented to illustrate the correctness of our analysis.

  5. Cellular Neural Network Models of Growth and Immune of Effector Cells Response to Cancer

    NASA Astrophysics Data System (ADS)

    Su, Yongmei; Min, Lequan

    Four reaction-diffusion cellular neural network (R-D CNN) models are set up based on the differential equation models for the growths of effector cells and cancer cells, and the model of the immune response to cancer proposed by Allison et al. The CNN models have different reaction-diffusion coefficients and coupling parameters. The R-D CNN models may provide possible quantitative interpretations, and are good in agreement with the in vitro experiment data reported by Allison et al.

  6. 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.

  7. 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.

  8. Measuring information flow in cellular networks by the systems biology method through microarray data

    PubMed Central

    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. PMID:26082788

  9. Alteration of cellular lipids and lipid metabolism markers in RTL-W1 cells exposed to model endocrine disrupters.

    PubMed

    Dimastrogiovanni, Giorgio; Córdoba, Marlon; Navarro, Isabel; Jáuregui, Olga; Porte, Cinta

    2015-08-01

    This work investigates the suitability of the rainbow trout liver cell line (RTL-W1) as an in-vitro model to study the ability of model endocrine disrupters, namely TBT, TPT, 4-NP, BPA and DEHP, to act as metabolic disrupters by altering cellular lipids and markers of lipid metabolism. Among the tested compounds, BPA and DEHP significantly increased the intracellular accumulation of triacylglycerols (TAGs), while all the compounds -apart from TPT-, altered membrane lipids - phosphatidylcholines (PCs) and plasmalogen PCs - indicating a strong interaction of the toxicants with cell membranes and cell signaling. RTL-W1 expressed a number of genes involved in lipid metabolism that were modulated by exposure to BPA, TBT and TPT (up-regulation of FATP1 and FAS) and 4-NP and DEHP (down-regulation of FAS and LPL). Multiple and complex modes of action of these chemicals were observed in RTL-W1 cells, both in terms of expression of genes related to lipid metabolism and alteration of cellular lipids. Although further characterization is needed, this might be a useful model for the detection of chemicals leading to steatosis or other diseases associated with lipid metabolism in fish.

  10. 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.

  11. 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.

  12. 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

  13. Ginkgo biloba extract induces gene expression changes in xenobiotics metabolism and the Myc-centered network.

    PubMed

    Guo, Lei; Mei, Nan; Liao, Wayne; Chan, Po-Chuen; Fu, Peter P

    2010-02-01

    The use of herbal dietary supplements in the United States is rapidly growing, and it is crucial that the quality and safety of these preparations be ensured. To date, it is still a challenge to determine the mechanisms of toxicity induced by mixtures containing many chemical components, such as herbal dietary supplements. We previously proposed that analyses of the gene expression profiles using microarrays in the livers of rodents treated with herbal dietary supplements is a potentially practical approach for understanding the mechanism of toxicity. In this study, we utilized microarrays to analyze gene expression changes in the livers of male B6C3F1 mice administered Ginkgo biloba leaf extract (GBE) by gavage for 2 years, and to determine pathways and mechanisms associated with GBE treatments. Analysis of 31,802 genes revealed that there were 129, 289, and 2,011 genes significantly changed in the 200, 600, and 2,000 mg/kg treatment groups, respectively, when compared with control animals. Drug metabolizing genes were significantly altered in response to GBE treatments. Pathway and network analyses were applied to investigate the gene relationships, functional clustering, and mechanisms involved in GBE exposure. These analyses indicate alteration in the expression of genes coding for drug metabolizing enzymes, the NRF2-mediated oxidative stress response pathway, and the Myc gene-centered network named "cell cycle, cellular movement, and cancer" were found. These results indicate that Ginkgo biloba-related drug metabolizing enzymes may cause herb-drug interactions and contribute to hepatotoxicity. In addition, the outcomes of pathway and network analysis may be used to elucidate the toxic mechanisms of Ginkgo biloba.

  14. Hybrid RF and Digital Beamformer for Cellular Networks: Algorithms, Microwave Architectures, and Measurements

    NASA Astrophysics Data System (ADS)

    Venkateswaran, Vijay; Pivit, Florian; Guan, Lei

    2016-07-01

    Modern wireless communication networks, particularly cellular networks utilize multiple antennas to improve the capacity and signal coverage. In these systems, typically an active transceiver is connected to each antenna. However, this one-to-one mapping between transceivers and antennas will dramatically increase the cost and complexity of a large phased antenna array system. In this paper, firstly we propose a \\emph{partially adaptive} beamformer architecture where a reduced number of transceivers with a digital beamformer (DBF) is connected to an increased number of antennas through an RF beamforming network (RFBN). Then, based on the proposed architecture, we present a methodology to derive the minimum number of transceivers that are required for marco-cell and small-cell base stations, respectively. Subsequently, in order to achieve optimal beampatterns with given cellular standard requirements and RF operational constraints, we propose efficient algorithms to jointly design DBF and RFBN. Starting from the proposed algorithms, we specify generic microwave RFBNs for optimal marco-cell and small-cell networks. In order to verify the proposed approaches, we compare the performance of RFBN using simulations and anechoic chamber measurements. Experimental measurement results confirm the robustness and performance of the proposed hybrid DBF-RFBN concept eventually ensuring that theoretical multi-antenna capacity and coverage are achieved at a little incremental cost.

  15. Motion adaptive vertical handoff in cellular/WLAN heterogeneous wireless network.

    PubMed

    Li, Limin; 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.

  16. 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

  17. 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.

  18. A Memristive Multilayer Cellular Neural Network With Applications to Image Processing.

    PubMed

    Hu, Xiaofang; Feng, Gang; Duan, Shukai; Liu, Lu

    2016-05-13

    The memristor has been extensively studied in electrical engineering and biological sciences as a means to compactly implement the synaptic function in neural networks. The cellular neural network (CNN) is one of the most implementable artificial neural network models and capable of massively parallel analog processing. In this paper, a novel memristive multilayer CNN (Mm-CNN) model is presented along with its performance analysis and applications. In this new CNN design, the memristor crossbar circuit acts as the synapse, which realizes one signed synaptic weight with a pair of memristors and performs the synaptic weighting compactly and linearly. Moreover, the complex weighted summation is executed in an efficient way with a proper design of Mm-CNN cell circuits. The proposed Mm-CNN has several merits, such as compactness, nonvolatility, versatility, and programmability of synaptic weights. Its performance in several image processing applications is illustrated through simulations.

  19. Micro-connectomics: probing the organization of neuronal networks at the cellular scale.

    PubMed

    Schröter, Manuel; Paulsen, Ole; Bullmore, Edward T

    2017-03-01

    Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.

  20. 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.

  1. Increasing the coverage area through relay node deployment in long term evolution advanced cellular networks

    NASA Astrophysics Data System (ADS)

    Aldhaibani, Jaafar A.; Ahmad, R. B.; Yahya, A.; Azeez, Suzan A.

    2015-05-01

    Wireless multi-hop relay networks have become very important technologies in mobile communications. These networks ensure high throughput and coverage extension with a low cost. The poor capacity at cell edges is not enough to meet with growing demand of high capacity and throughput irrespective of user's placement in the cellular network. In this paper we propose optimal placement of relay node that provides maximum achievable rate at users and enhances the throughput and coverage at cell edge region. The proposed scheme is based on the outage probability at users and taken on account the interference between nodes. Numerical analyses along with simulation results indicated there are an improvement in capacity for users at the cell edge is 40% increment from all cell capacity.

  2. A global genetic interaction network maps a wiring diagram of cellular function.

    PubMed

    Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N; Baryshnikova, Anastasia; Pons, Carles; Tan, Guihong; Wang, Wen; Usaj, Matej; Hanchard, Julia; Lee, Susan D; Pelechano, Vicent; Styles, Erin B; Billmann, Maximilian; van Leeuwen, Jolanda; van Dyk, Nydia; Lin, Zhen-Yuan; Kuzmin, Elena; Nelson, Justin; Piotrowski, Jeff S; Srikumar, Tharan; Bahr, Sondra; Chen, Yiqun; Deshpande, Raamesh; Kurat, Christoph F; Li, Sheena C; Li, Zhijian; Usaj, Mojca Mattiazzi; Okada, Hiroki; Pascoe, Natasha; San Luis, Bryan-Joseph; Sharifpoor, Sara; Shuteriqi, Emira; Simpkins, Scott W; Snider, Jamie; Suresh, Harsha Garadi; Tan, Yizhao; Zhu, Hongwei; Malod-Dognin, Noel; Janjic, Vuk; Przulj, Natasa; Troyanskaya, Olga G; Stagljar, Igor; Xia, Tian; Ohya, Yoshikazu; Gingras, Anne-Claude; Raught, Brian; Boutros, Michael; Steinmetz, Lars M; Moore, Claire L; Rosebrock, Adam P; Caudy, Amy A; Myers, Chad L; Andrews, Brenda; Boone, Charles

    2016-09-23

    We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

  3. 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.

  4. Lethality and synthetic lethality in the genome-wide metabolic network of Escherichia coli.

    PubMed

    Ghim, Cheol-Min; Goh, Kwang-Il; Kahng, Byungnam

    2005-12-21

    Recent genomic analyses on the cellular metabolic network show that reaction flux across enzymes are diverse and exhibit power-law behavior in its distribution. While intuition might suggest that the reactions with larger fluxes are more likely to be lethal under the blockade of its catalysing gene products or gene knockouts, we find, by in silico flux analysis, that the lethality rarely has correlations with the flux level owing to the widespread backup pathways innate in the genome-wide metabolism of Escherichia coli. Lethal reactions, of which the deletion generates cascading failure of following reactions up to the biomass reaction, are identified in terms of the Boolean network scheme as well as the flux balance analysis. The avalanche size of a reaction, defined as the number of subsequently blocked reactions after its removal, turns out to be a useful measure of lethality. As a means to elucidate phenotypic robustness to a single deletion, we investigate synthetic lethality in reaction level, where simultaneous deletion of a pair of nonlethal reactions leads to the failure of the biomass reaction. Synthetic lethals identified via flux balance and Boolean scheme are consistently shown to act in parallel pathways, working in such a way that the backup machinery is compromised.

  5. Diquat-induced cellular pyridine nucleotide redox changes and alteration of metabolic enzyme activities in colonic carcinoma cells.

    PubMed

    Circu, Magdalena L; Maloney, Ronald E; Aw, Tak Yee

    2017-02-25

    Previously we have shown that the redox cycler menadione (MQ) induced cellular pyridine nucleotide redox imbalance that was linked to a decrease in aerobic glycolysis and perturbation of the mitochondrial respiratory activity due to the redox cycling of the compound; these processes were potentiated by low glucose. In this study, we investigated how colonic epithelial cells maintained pyridine nucleotide (NAD(+)/NADH and NADP(+)/NADPH) redox homeostasis upon acute metabolic variation and exposure to the redox cycling diquat (DQ). Our results show that DQ challenge disrupted cellular NADH/NAD(+) redox status and enhanced cellular NADPH generation. Notably, DQ-induced NADH decrease was associated with enhanced lactate production, a process that was potentiated by glucose availability, but not by the mitochondrial substrates, succinate or malate/glutamate. In addition, DQ increased glucose 6-phoshate dehydrogenase (G6PDH) activity consistent with glucose diversion towards pentose phosphate pathway. As a consequence, steady-state NADPH levels were maintained during MQ challenge at normal glucose. In contrast and despite increased G6PDH and malic enzyme (ME) activities, DQ induced cellular NADPH-to-NADP(+) shift at low glucose, a situation that was reversed by mitochondrial substrates. Collectively, these results are consistent with increased aerobic glycolysis by DQ and specific metabolic changes leading to enhanced NADPH generation upon oxidative challenge.

  6. From the Cover: Design of artificial cell-cell communication using gene and metabolic networks

    NASA Astrophysics Data System (ADS)

    Bulter, Thomas; Lee, Sun-Gu; Waichun Wong, Wilson; Fung, Eileen; Connor, Michael R.; Liao, James C.

    2004-02-01

    Artificial transcriptional networks have been used to achieve novel, nonnative behavior in bacteria. Typically, these artificial circuits are isolated from cellular metabolism and are designed to function without intercellular communication. To attain concerted biological behavior in a population, synchronization through intercellular communication is highly desirable. Here we demonstrate the design and construction of a gene-metabolic circuit that uses a common metabolite to achieve tunable artificial cell-cell communication. This circuit uses a threshold concentration of acetate to induce gene expression by acetate kinase and part of the nitrogen-regulation two-component system. As one application of the cell-cell communication circuit we created an artificial quorum sensor. Engineering of carbon metabolism in Escherichia coli made acetate secretion proportional to cell density and independent of oxygen availability. In these cells the circuit induced gene expression in response to a threshold cell density. This threshold can be tuned effectively by controlling pH over the cell membrane, which determines the partition of acetate between medium and cells. Mutagenesis of the enhancer sequence of the glnAp2 promoter produced variants of the circuit with changed sensitivity demonstrating tunability of the circuit by engineering of its components. The behavior of the circuit shows remarkable predictability based on a mathematical design model.

  7. 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.

  8. Cellular pulse-coupled neural network with adaptive weights for image segmentation and its VLSI implementation

    NASA Astrophysics Data System (ADS)

    Schreiter, Juerg; Ramacher, Ulrich; Heittmann, Arne; Matolin, Daniel; Schuffny, Rene

    2004-05-01

    We present a cellular pulse coupled neural network with adaptive weights and its analog VLSI implementation. The neural network operates on a scalar image feature, such as grey scale or the output of a spatial filter. It detects segments and marks them with synchronous pulses of the corresponding neurons. The network consists of integrate-and-fire neurons, which are coupled to their nearest neighbors via adaptive synaptic weights. Adaptation follows either one of two empirical rules. Both rules lead to spike grouping in wave like patterns. This synchronous activity binds groups of neurons and labels the corresponding image segments. Applications of the network also include feature preserving noise removal, image smoothing, and detection of bright and dark spots. The adaptation rules are insensitive for parameter deviations, mismatch and non-ideal approximation of the implied functions. That makes an analog VLSI implementation feasible. Simulations showed no significant differences in the synchronization properties between networks using the ideal adaptation rules and networks resembling implementation properties such as randomly distributed parameters and roughly implemented adaptation functions. A prototype is currently being designed and fabricated using an Infineon 130nm technology. It comprises a 128 × 128 neuron array, analog image memory, and an address event representation pulse output.

  9. Multifunctional Superelastic Foam-Like Boron Nitride Nanotubular Cellular-Network Architectures.

    PubMed

    Xue, Yanming; Dai, Pengcheng; Zhou, Min; Wang, Xi; Pakdel, Amir; Zhang, Chao; Weng, Qunhong; Takei, Toshiaki; Fu, Xiuwei; Popov, Zakhar I; Sorokin, Pavel B; Tang, Chengchun; Shimamura, Kiyoshi; Bando, Yoshio; Golberg, Dmitri

    2017-01-24

    Construction of cellular architectures has been expected to enhance materials' mechanical tolerance and to stimulate and broaden their efficient utilizations in many potential fields. However, hitherto, there have been rather scarce developments in boron nitride (BN)-type cellular architectures because of well-known difficulties in the syntheses of BN-based structures. Herein, cellular-network multifunctional foams made of interconnective nanotubular hexagonal BN (h-BN) architectures are developed using carbothermal reduction-assisted in situ chemical vapor deposition conversion from N-doped tubular graphitic cellular foams. These ultralight, chemically inert, thermally stable, and robust-integrity (supporting about 25,000 times of their own weight) three-dimensional-BN foams exhibit a 98.5% porosity, remarkable shape recovery (even after cycling compressions with 90% deformations), excellent resistance to water intrusion, thermal diffusion stability, and high strength and stiffness. They remarkably reduce the coefficient of thermal expansion and dielectric constant of polymeric poly(methyl methacrylate) composites, greatly contribute to their thermal conductivity improvement, and effectively limit polymeric composite softening at elevated temperatures. The foams also demonstrate high-capacity adsorption-separation and removal ability for a wide range of oils and organic chemicals in oil/water systems and reliable recovery under their cycling usage as organic adsorbers. These created multifunctional foams should be valuable in many high-end practical applications.

  10. 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

  11. 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.

  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. 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.

  14. 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.

  15. Aluminium-induced excessive ROS causes cellular damage and metabolic shifts in black gram Vigna mungo (L.) Hepper.

    PubMed

    Chowra, Umakanta; Yanase, Emiko; Koyama, Hiroyuki; Panda, Sanjib Kumar

    2017-01-01

    Aluminium-induced oxidative damage caused by excessive ROS production was evaluated in black gram pulse crop. Black gram plants were treated with different aluminium (Al(3+)) concentrations (10, 50 and 100 μM with pH 4.7) and further the effects of Al(3+) were characterised by means of root growth inhibition, histochemical assay, ROS content analysis, protein carbonylation quantification and (1)H-NMR analysis. The results showed that aluminium induces excessive ROS production which leads to cellular damage, root injury, stunt root growth and other metabolic shifts. In black gram, Al(3+) induces cellular damage at the earliest stage of stress which was characterised from histochemical analysis. From this study, it was observed that prolonged stress can activate certain aluminium detoxification defence mechanism. Probably excessive ROS triggers such defence mechanism in black gram. Al(3+) can induce excessive ROS initially in the root region then transported to other parts of the plant. As much as the Al(3+) concentration increases, the rate of cellular injury and ROS production also increases. But after 72 h of stress, plants showed a lowered ROS level and cellular damage which indicates the upregulation of defensive mechanisms. Metabolic shift analysis also showed that the black gram plant under stress has less metabolic content after 24 h of treatment, but gradually, it was increased after 72 h of treatment. It was assumed that ROS played the most important role as a signalling molecule for aluminium stress in black gram.

  16. High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels.

    PubMed

    Müller, Jan; Ballini, Marco; Livi, Paolo; Chen, Yihui; Radivojevic, Milos; Shadmani, Amir; Viswam, Vijay; Jones, Ian L; Fiscella, Michele; Diggelmann, Roland; Stettler, Alexander; Frey, Urs; Bakkum, Douglas J; Hierlemann, Andreas

    2015-07-07

    Studies on information processing and learning properties of neuronal networks would benefit from simultaneous and parallel access to the activity of a large fraction of all neurons in such networks. Here, we present a CMOS-based device, capable of simultaneously recording the electrical activity of over a thousand cells in in vitro neuronal networks. The device provides sufficiently high spatiotemporal resolution to enable, at the same time, access to neuronal preparations on subcellular, cellular, and network level. The key feature is a rapidly reconfigurable array of 26 400 microelectrodes arranged at low pitch (17.5 μm) within a large overall sensing area (3.85 × 2.10 mm(2)). An arbitrary subset of the electrodes can be simultaneously connected to 1024 low-noise readout channels as well as 32 stimulation units. Each electrode or electrode subset can be used to electrically stimulate or record the signals of virtually any neuron on the array. We demonstrate the applicability and potential of this device for various different experimental paradigms: large-scale recordings from whole networks of neurons as well as investigations of axonal properties of individual neurons.

  17. In Absence of the Cellular Prion Protein, Alterations in Copper Metabolism and Copper-Dependent Oxidase Activity Affect Iron Distribution.

    PubMed

    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.

  18. 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

  19. 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.

  20. Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

    NASA Astrophysics Data System (ADS)

    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.

  1. A new method for the re-implementation of threshold logic functions with cellular neural networks.

    PubMed

    Bénédic, Y; Wira, P; Mercklé, J

    2008-08-01

    A new strategy is presented for the implementation of threshold logic functions with binary-output Cellular Neural Networks (CNNs). The objective is to optimize the CNNs weights to develop a robust implementation. Hence, the concept of generative set is introduced as a convenient representation of any linearly separable Boolean function. Our analysis of threshold logic functions leads to a complete algorithm that automatically provides an optimized generative set. New weights are deduced and a more robust CNN template assuming the same function can thus be implemented. The strategy is illustrated by a detailed example.

  2. Moving object segmentation algorithm based on cellular neural networks in the H.264 compressed domain

    NASA Astrophysics Data System (ADS)

    Feng, Jie; Chen, Yaowu; Tian, Xiang

    2009-07-01

    A cellular neural network (CNN)-based moving object segmentation algorithm in the H.264 compressed domain is proposed. This algorithm mainly utilizes motion vectors directly extracted from H.264 bitstreams. To improve the robustness of the motion vector information, the intramodes in I-frames are used for smooth and nonsmooth region classification, and the residual coefficient energy of P-frames is used to update the classification results first. Then, an adaptive motion vector filter is used according to interpartition modes. Finally, many CNN models are applied to implement moving object segmentation based on motion vector fields. Experiment results are presented to verify the efficiency and the robustness of this algorithm.

  3. 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.

  4. 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.

  5. Toxic influence of organophosphate, carbamate, and organochlorine pesticides on cellular metabolism of lipids, proteins, and carbohydrates: a systematic review.

    PubMed

    Karami-Mohajeri, Somayyeh; Abdollahi, Mohammad

    2011-09-01

    Pesticides, including organophosphate (OP), organochlorine (OC), and carbamate (CB) compounds, are widely used in agricultural and indoor purposes. OP and CB act as acetyl cholinesterase (AChE) inhibitors that affect lots of organs such as peripheral and central nervous systems, muscles, liver, pancreas, and brain, whereas OC are neurotoxic involved in alteration of ion channels. There are several reports about metabolic disorders, hyperglycemia, and also oxidative stress in acute and chronic exposures to pesticides that are linked with diabetes and other metabolic disorders. In this respect, there are several in vitro and in vivo but few clinical studies about mechanism underlying these effects. Bibliographic databases were searched for the years 1963-2010 and resulted in 1652 articles. After elimination of duplicates or irrelevant papers, 204 papers were included and reviewed. Results indicated that OP and CB impair the enzymatic pathways involved in metabolism of carbohydrates, fats and protein within cytoplasm, mitochondria, and proxisomes. It is believed that OP and CB show this effect through inhibition of AChE or affecting target organs directly. OC mostly affect lipid metabolism in the adipose tissues and change glucose pathway in other cells. As a shared mechanism, all OP, CB and OC induce cellular oxidative stress via affecting mitochondrial function and therefore disrupt neuronal and hormonal status of the body. Establishing proper epidemiological studies to explore exact relationships between exposure levels to these pesticides and rate of resulted metabolic disorders in human will be helpful.

  6. Cellular metabolic rates in cultured primary dermal fibroblasts and myoblast cells from fast-growing and control Coturnix quail.

    PubMed

    Jimenez, Ana Gabriela; Cooper-Mullin, Clara; Anthony, Nicholas B; Williams, Joseph B

    2014-05-01

    Fibroblast cells have been extensively used in research, including in medicine, physiology, physiological-ecology, and conservation biology. However, whether the physiology of fibroblasts reflects the physiology of other cell types in the same animal is unknown. Dermal fibroblasts are responsible for generating connective tissue and involved in wound healing, but generally, this cell type is thought to be metabolically inactive until it is required at the site of tissue damage. Thus, one might question whether fibroblasts are a representative model system to portray the metabolic profile of the whole organism, as compared with cells isolated from other tissues, like muscle, brain or kidneys. To explore whether fibroblasts have the same metabolic profile as do myoblast cells, we cultured cells from day-old chicks of quail (Coturnix coturnix japonica) selected for fast-growth or normal growth (our control group). Our results suggest that isolated primary fibroblasts and myoblast cells had higher rates of glycolysis, oxygen consumption and more mitochondria in the fast-growing line than in the control line. Our findings lend support for the idea that fibroblasts are a representative cell system to characterize the whole organism metabolic signature at the cellular-level. These data are striking, however, because fibroblasts had higher rates of metabolism for every parameter measured than myoblast cells isolated from the same individuals.

  7. 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

  8. 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

  9. 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.

  10. Modeling Immune Network Through Cellular Automata:. a Unified Mechanism of Immunological Memory

    NASA Astrophysics Data System (ADS)

    Chowdhury, Debashish; Deshpande, Varsha; Stauffer, Dietrich

    The populations of the various types of immunocompetent cells in the immune system are described as cellular automata and the population dynamics of these cells are formulated in terms of dynamical maps in discrete time. Both intra-clonal interactions (i.e., interactions among the cell types belonging to the same clone) and inter-clonal interactions (i.e., interactions among the cell types belonging to different clones) are included in the models proposed here. While the intra-clonal interactions are shown to play a crucial role in the primary response of some clones and in the formation of the immunological memory, the inter-clonal interactions are responsible for retaining the memory through a dynamical process driven by the mutual stimulation of the clones. We present the results for two different types of connectivity, namely, a “necklace” network and a network in “shape space”.

  11. Bactericidal Antibiotics Induce Toxic Metabolic Perturbations that Lead to Cellular Damage.

    PubMed

    Belenky, Peter; Ye, Jonathan D; Porter, Caroline B M; Cohen, Nadia R; Lobritz, Michael A; Ferrante, Thomas; Jain, Saloni; Korry, Benjamin J; Schwarz, Eric G; Walker, Graham C; Collins, James J

    2015-11-03

    Understanding how antibiotics impact bacterial metabolism may provide insight into their mechanisms of action and could lead to enhanced therapeutic methodologies. Here, we profiled the metabolome of Escherichia coli after treatment with three different classes of bactericidal antibiotics (?-lactams, aminoglycosides, quinolones). These treatments induced a similar set of metabolic changes after 30 min that then diverged into more distinct profiles at later time points. The most striking changes corresponded to elevated concentrations of central carbon metabolites, active breakdown of the nucleotide pool, reduced lipid levels, and evidence of an elevated redox state. We examined potential end-target consequences of these metabolic perturbations and found that antibiotic-treated cells exhibited cytotoxic changes indicative of oxidative stress, including higher levels of protein carbonylation, malondialdehyde adducts, nucleotide oxidation, and double-strand DNA breaks. This work shows that bactericidal antibiotics induce a complex set of metabolic changes that are correlated with the buildup of toxic metabolic by-products.

  12. 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.

  13. Modeling of Diffusion through a Network: A New Approach using Cellular Automata and Network Science Techniques

    DTIC Science & Technology

    2010-05-01

    to Professor Chris Arney and LTC Donovan Phillips for providing valuable feedback on this project. vii MODELING OF DIFFUSION THROUGH A...only does the study of networks afford the U.S. Army greater information sharing abilities, it could also give a better understanding of enemy...These models are named for the conditions by which a node changes state. The first model gives each node its own threshold which must be reached before

  14. 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.

  15. Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality

    DOE PAGES

    Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; ...

    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

  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. 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.

  18. 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.

  19. The effect of fluid mechanical stress on cellular arachidonic acid metabolism

    NASA Technical Reports Server (NTRS)

    Mcintire, L. V.; Frangos, J. A.; Rhee, B. G.; Eskin, S. G.; Hall, E. R.

    1987-01-01

    The effect of sublytic levels of mechanical perturations of cells on cell metabolism were investigated by analyzing the products of arachidonic acid (used as a marker metabolite) in blood platelets, polymorphonuclear leucocytes, and cultured umbilical-vein endothelial cells after the suspensions of these cells were subjected to a shear stress in a modified viscometer. It is shown that the sublytic levels of mechanical stress stimulated the arachidonic acid metabolism in all these cell types. Possible biological implications of this stress-metabolism coupling are discussed.

  20. ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis.

    PubMed

    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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. Reconstruction and analysis of human liver-specific metabolic network based on CNHLPP data.

    PubMed

    Zhao, Jing; Geng, Chao; Tao, Lin; Zhang, Duanfeng; Jiang, Ying; Tang, Kailin; Zhu, Ruixin; Yu, Hong; Zhang, Weidong; He, Fuchu; Li, Yixue; Cao, Zhiwei

    2010-04-05

    Liver is the largest internal organ in the body that takes central roles in metabolic homeostasis, detoxification of various substances, as well as in the synthesis and storage of nutrients. To fulfill these complex tasks, thousands of biochemical reactions are going on in liver to cope with a wide range of foods and environmental variations, which are densely interconnected into an intricate metabolic network. Here, the first human liver-specific metabolic network was reconstructed according to proteomics data from Chinese Human Liver Proteome Project (CNHLPP), and then investigated in the context of the genome-scale metabolic network of Homo sapiens. Topological analysis shows that this organ-specific metabolic network exhibits similar features as organism-specific networks, such as power-law degree distribution, small-world property, and bow-tie structure. Furthermore, the structure of liver network exhibits a modular organization where the modules are formed around precursors from primary metabolism or hub metabolites from derivative metabolism, respectively. Most of the modules are dominated by one major category of metabolisms, while enzymes within same modules have a tendency of being expressed concertedly at protein level. Network decomposition and comparison suggest that the liver network overlays a predominant area in the global metabolic network of H. sapiens genome; meanwhile the human network may develop extra modules to gain more specialized functionality out of liver. The results of this study would permit a high-level interpretation of the metabolite information flow in human liver and provide a basis for modeling the physiological and pathological metabolic states of liver.

  9. Multiple applications of Alamar Blue as an indicator of metabolic function and cellular health in cell viability bioassays.

    PubMed

    Rampersad, Sephra N

    2012-01-01

    Accurate prediction of the adverse effects of test compounds on living systems, detection of toxic thresholds, and expansion of experimental data sets to include multiple toxicity end-point analysis are required for any robust screening regime. Alamar Blue is an important redox indicator that is used to evaluate metabolic function and cellular health. The Alamar Blue bioassay has been utilized over the past 50 years to assess cell viability and cytotoxicity in a range of biological and environmental systems and in a number of cell types including bacteria, yeast, fungi, protozoa and cultured mammalian and piscine cells. It offers several advantages over other metabolic indicators and other cytotoxicity assays. However, as with any bioassay, suitability must be determined for each application and cell model. This review seeks to highlight many of the important considerations involved in assay use and design in addition to the potential pitfalls.

  10. Metabolic network structure and function in bacteria goes beyond conserved enzyme components

    PubMed Central

    Bazurto, Jannell V.; Downs, Diana M.

    2016-01-01

    For decades, experimental work has laid the foundation for our understanding of the linear and branched pathways that are integrated to form the metabolic networks on which life is built. Genetic and biochemical approaches applied in model organisms generate empirical data that correlate genes, gene products and their biological activities. In the post-genomic era, these results have served as the basis for the genome annotation that is routinely used to infer the metabolic capabilities of an organism and mathematically model the presumed metabolic network structure. At large, genome annotation and metabolic network reconstructions have demystified genomic content of non-culturable microorganisms and allowed researchers to explore the breadth of metabolisms in silico. Mis-annotation aside, it is unclear whether in silico reconstructions of metabolic structure from component parts accurately captures the higher levels of network organization and flux distribution. For this approach to provide accurate predictions, one must assume that the conservation of metabolic components leads to conservation of metabolic network architecture and function. This assumption has not been rigorously tested. Here we describe the implications of a recent study (MBio 5;7(1): e01840-15), which demonstrated that conservation of metabolic components was not sufficient to predict network structure and function. PMID:28357363

  11. Study on incompatibility of traditional chinese medicine: evidence from formula network, chemical space, and metabolism room.

    PubMed

    Long, Wei; Zhang, Xiao-Dong; Wu, Hong-Ying; Jin, Jin; Yu, Guang-Yun; He, Xin; Wang, Hao; Shen, Xiu; Zhou, Ze-Wei; Liu, Pei-Xun; Fan, Sai-Jun

    2013-01-01

    A traditional Chinese medicine (TCM) formula network including 362 TCM formulas was built by using complex network methodologies. The properties of this network were analyzed including network diameter, average distance, clustering coefficient, and average degree. Meanwhile, we built a TCM chemical space and a TCM metabolism room under the theory of chemical space. The properties of chemical space and metabolism room were calculated and analyzed. The properties of the medicine pairs in "eighteen antagonisms and nineteen mutual inhibitors," an ancient rule for TCM incompatibility, were studied based on the TCM formula network, chemical space, and metabolism room. The results showed that the properties of these incompatible medicine pairs are different from those of the other TCM based on the analysis of the TCM formula network, chemical space, and metabolism room. The lines of evidence derived from our work demonstrated that the ancient rule of TCM incompatibility, "eighteen antagonisms and nineteen mutual inhibitors," is probably scientifically based.

  12. 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.

  13. The novel choline kinase inhibitor ICL-CCIC-0019 reprograms cellular metabolism and inhibits cancer cell growth

    PubMed Central

    Trousil, Sebastian; Kaliszczak, Maciej; Schug, Zachary; Nguyen, Quang-De; Tomasi, Giampaolo; Favicchio, Rosy; Brickute, Diana; Fortt, Robin; Twyman, Frazer J.; Carroll, Laurence; Kalusa, Andrew; Navaratnam, Naveenan; Adejumo, Thomas; Carling, David; Gottlieb, Eyal; Aboagye, Eric O.

    2016-01-01

    The glycerophospholipid phosphatidylcholine is the most abundant phospholipid species of eukaryotic membranes and essential for structural integrity and signaling function of cell membranes required for cancer cell growth. Inhibition of choline kinase alpha (CHKA), the first committed step to phosphatidylcholine synthesis, by the selective small-molecule ICL-CCIC-0019, potently suppressed growth of a panel of 60 cancer cell lines with median GI50 of 1.12 μM and inhibited tumor xenograft growth in mice. ICL-CCIC-0019 decreased phosphocholine levels and the fraction of labeled choline in lipids, and induced G1 arrest, endoplasmic reticulum stress and apoptosis. Changes in phosphocholine cellular levels following treatment could be detected non-invasively in tumor xenografts by [18F]-fluoromethyl-[1,2–2H4]-choline positron emission tomography. Herein, we reveal a previously unappreciated effect of choline metabolism on mitochondria function. Comparative metabolomics demonstrated that phosphatidylcholine pathway inhibition leads to a metabolically stressed phenotype analogous to mitochondria toxin treatment but without reactive oxygen species activation. Drug treatment decreased mitochondria function with associated reduction of citrate synthase expression and AMPK activation. Glucose and acetate uptake were increased in an attempt to overcome the metabolic stress. This study indicates that choline pathway pharmacological inhibition critically affects the metabolic function of the cell beyond reduced synthesis of phospholipids. PMID:27206796

  14. A second target of the antimalarial and antibacterial agent fosmidomycin revealed by cellular metabolic profiling†

    PubMed Central

    Zhang, Baichen; Watts, Kristin M.; Hodge, Dana; Kemp, Lisa M.; Hunstad, David A.; Hicks, Leslie M.; Odom, Audrey R.

    2011-01-01

    Antimicrobial drug resistance is an urgent problem in control and treatment of many of the world's most serious infections, including Plasmodium falciparum malaria, tuberculosis, and healthcare-associated infections with Gram-negative bacteria. Because the non-mevalonate pathway of isoprenoid biosynthesis is essential in eubacteria and P. falciparum, and this pathway is not present in humans, there is great interest in targeting the enzymes of non-mevalonate metabolism for antibacterial and antiparasitic drug development. Fosmidomycin is a broad-spectrum antimicrobial agent currently in clinical trials of combination therapies to treat malaria. In vitro, fosmidomycin is known to inhibit the deoxyxylulose phosphate reductoisomerase (DXR) enzyme of isoprenoid biosynthesis from multiple pathogenic organisms. To define the in vivo metabolic response to fosmidomycin, we developed a novel mass spectrometry method to quantitate six metabolites of non-mevalonate isoprenoid metabolism from complex biological samples. Using this technique, we validate that the biological effects of fosmidomycin are mediated through blockade of de novo isoprenoid biosynthesis in both P. falciparum malaria parasites and E. coli bacteria: in both organisms, metabolic profiling demonstrated a block in isoprenoid metabolism following fosmidomycin treatment, and growth inhibition due to fosmidomycin was rescued by media supplemented with isoprenoid metabolites. Isoprenoid metabolism proceeded through DXR even in the presence of fosmidomycin, but was inhibited at the level of the downstream enzyme, methylerythritol phosphate cytidyltransferase (IspD). Overexpression of IspD in E. coli conferred fosmidomycin resistance, and fosmidomycin was found to inhibit IspD in vitro. This work has validated fosmidomycin as a biological reagent to block non-mevalonate isoprenoid metabolism, and suggests a second in vivo target for fosmidomycin within isoprenoid biosynthesis, in two evolutionarily diverse

  15. IMMUNOSUPPRESSANT NEUROTOXICITY IN RAT BRAIN MODELS: OXIDATIVE STRESS AND CELLULAR METABOLISM

    PubMed Central

    Klawitter, Jelena; Gottschalk, Sven; Hainz, Carsten; Leibfritz, Dieter; Christians, Uwe; Serkova, Natalie J.

    2010-01-01

    Co-administration of the calcineurin inhibitor cyclosporine (CsA) and the mTOR inhibitors sirolimus (SRL) or everolimus (RAD) increases efficacy of immunosuppression after organ transplantation. Neurotoxicity of CsA is a major clinical problem. Our goal was to assess the effects of CsA, SRL and RAD on the brain cell metabolism. The studies included the comparison of immunosuppressant-mediated effects on glucose metabolism, energy production and reactive oxygen species (ROS) formation in perfused rat brain slices, primary rat astrocytes and C6-glioma cells. In brain slices and astrocytes, CsA inhibited Krebs cycle metabolism, while activating anaerobic glycolysis most likely to compensate for the inhibition of mitochondrial energy production. SRL and RAD inhibited cytosolic glycolysis, but did not cause changes in mitochondrial energy production. CsA+SRL inhibited Krebs cycle and glycolysis, thus reducing the ability of the cell to compensate for the negative effects of CsA on mitochondrial nucleoside triphosphate synthesis. In contrast to SRL at the concentrations tested, RAD reduced the CsA-induced ROS formation and antagonized CsA-induced effects on glucose and energy metabolism. Surprisingly, in C6 cells, SRL and RAD exposure resulted in high ROS concentrations without significant impairment of cell metabolism. Our results suggested that SRL enhances CsA-induced ROS formation and negative metabolic effects in brain cells, while RAD seems to antagonize the CsA effects. However, the three models showed different metabolic responses when challenged with the study drugs. In contrast to SRL, RAD enhances ROS formation in C6 glioma cells, but has only minor effects on normal rat brain tissue. PMID:20148532

  16. Material balance studies on animal cell metabolism using a stoichiometrically based reaction network.

    PubMed

    Xie, L; Wang, D I

    1996-12-05

    A detailed reaction network of mammalian cell metabolism contains hundreds of enzymatic reactions. By grouping serial reactions into single overall reactions and separating overlapped pathways into independent reactions, the total number of reactions of the network is significantly reduced. This strategy of manipulating the reaction network avoids the manipulations of a large number of reactions otherwise needed to determine the reaction extents. A stoichiometric material balance model is developed based on the stoichiometry of the simplified reaction network. Closures of material balances on glucose and each of the 20 amino acids are achieved using experimental data from three controlled fed-batch and one-batch hybridoma cultures. Results show that the critical role of essential amino acids, except glutamine, is to provide precursors for protein synthesis. The catabolism of some of the essential amino acids, particularly isoleucine and leucine, is observed when an excess amount of these amino acids is available in the culture medium. It was found that the reduction of glutamine utilization (for reducing ammonia production) is accompanied by an increase in the uptake of nonessential amino acids (NAAs) from the culture medium. This suggests that NAAs are necessary even though they are not essential for cell growth. A glutamine balance shows that less than 20% of the glutamine nitrogen is utilized for essential roles, such as protein and nucleotide syntheses. A relatively constant percentage (about 45%) of the glutamine nitrogen is utilized for NAA biosynthesis, despite the fact that the absolute amount varies among the four experiments. As to the carbon skeleton of glutamine, a significant portion enters the tricarboxylic acid (TCA) cycle. A material balance on glucose shows that most of the glucose (81%) is converted into lactate when glucose is in excess. On the other hand, when glucose is limited, lactate production is considerably reduced, while a major portion

  17. 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

  18. 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

  19. 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

    2016-08-25

    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.

  20. Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

    NASA Astrophysics Data System (ADS)

    Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie

    2016-10-01

    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.

  1. 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

  2. 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.

  3. System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max).

    PubMed

    Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

    2014-01-01

    Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome

  4. Global analysis of the role of autophagy in cellular metabolism and energy homeostasis in Arabidopsis seedlings under carbon starvation.

    PubMed

    Avin-Wittenberg, Tamar; Bajdzienko, Krzysztof; Wittenberg, Gal; Alseekh, Saleh; Tohge, Takayuki; Bock, Ralph; Giavalisco, Patrick; Fernie, Alisdair R

    2015-02-01

    Germination and early seedling establishment are developmental stages in which plants face limited nutrient supply as their photosynthesis mechanism is not yet active. For this reason, the plant must mobilize the nutrient reserves provided by the mother plant in order to facilitate growth. Autophagy is a catabolic process enabling the bulk degradation of cellular constituents in the vacuole. The autophagy mechanism is conserved among eukaryotes, and homologs of many autophagy-related (ATG) genes have been found in Arabidopsis thaliana. T-DNA insertion mutants (atg mutants) of these genes display higher sensitivity to various stresses, particularly nutrient starvation. However, the direct impact of autophagy on cellular metabolism has not been well studied. In this work, we used etiolated Arabidopsis seedlings as a model system for carbon starvation. atg mutant seedlings display delayed growth in response to carbon starvation compared with wild-type seedlings. High-throughput metabolomic, lipidomic, and proteomic analyses were performed, as well as extensive flux analyses, in order to decipher the underlying causes of the phenotype. Significant differences between atg mutants and wild-type plants have been demonstrated, suggesting global effects of autophagy on central metabolism during carbon starvation as well as severe energy deprivation, resulting in a morphological phenotype.

  5. Observation conflict resolution in steady-state metabolic network dynamics analysis.

    PubMed

    Cicek, A Ercument; Ozsoyoglu, Gultekin

    2012-02-01

    Steady state metabolic network dynamics analysis (SMDA) is a recently proposed computational metabolomics tool that (i) captures a metabolic network and its rules via a metabolic network database, (ii) mimics the reasoning of a biochemist, given a set of metabolic observations, and (iii) locates efficiently all possible metabolic activation/inactivation (flux) alternatives. However, a number of factors may cause the SMDA algorithm to eliminate feasible flux scenarios. These factors include (i) inherent error margins in observations (measurements), (ii) lack of knowledge to classify measurements as normal versus abnormal, and (iii) choosing a highly constrained metabolic subnetwork to query against. In this work, we first present and formalize these obstacles. Then, we propose techniques to eliminate them and present an experimental evaluation of our proposed techniques.

  6. 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. The main advantage is that this ultimately enables the targeting of intracellular control variables such as metabolic reactions or metabolic pathways directly linked with productivity and product quality. We analyze in particular design methods that target elementary modes of metabolic networks. The topics covered include the analysis of the structure of metabolic networks, computation and reduction of elementary modes, measurement methods for the envirome, envirome-guided metabolic reconstruction, and macroscopic dynamic modeling and control. These topics are illustrated with applications to a cultivation process of a recombinant Pichia pastoris X33 strain expressing a single-chain antibody fragment (scFv).

  7. Viral Replication Protein Inhibits Cellular Cofilin Actin Depolymerization Factor to Regulate the Actin Network and Promote Viral Replicase Assembly

    PubMed Central

    Kovalev, Nikolay; de Castro Martín, Isabel Fernández; Barajas, Daniel; Risco, Cristina; Nagy, Peter D.

    2016-01-01

    RNA viruses exploit host cells by co-opting host factors and lipids and escaping host antiviral responses. Previous genome-wide screens with Tomato bushy stunt virus (TBSV) in the model host yeast have identified 18 cellular genes that are part of the actin network. In this paper, we show that the p33 viral replication factor interacts with the cellular cofilin (Cof1p), which is an actin depolymerization factor. Using temperature-sensitive (ts) Cof1p or actin (Act1p) mutants at a semi-permissive temperature, we find an increased level of TBSV RNA accumulation in yeast cells and elevated in vitro activity of the tombusvirus replicase. We show that the large p33 containing replication organelle-like structures are located in the close vicinity of actin patches in yeast cells or around actin cable hubs in infected plant cells. Therefore, the actin filaments could be involved in VRC assembly and the formation of large viral replication compartments containing many individual VRCs. Moreover, we show that the actin network affects the recruitment of viral and cellular components, including oxysterol binding proteins and VAP proteins to form membrane contact sites for efficient transfer of sterols to the sites of replication. Altogether, the emerging picture is that TBSV, via direct interaction between the p33 replication protein and Cof1p, controls cofilin activities to obstruct the dynamic actin network that leads to efficient subversion of cellular factors for pro-viral functions. In summary, the discovery that TBSV interacts with cellular cofilin and blocks the severing of existing filaments and the formation of new actin filaments in infected cells opens a new window to unravel the way by which viruses could subvert/co-opt cellular proteins and lipids. By regulating the functions of cofilin and the actin network, which are central nodes in cellular pathways, viruses could gain supremacy in subversion of cellular factors for pro-viral functions. PMID:26863541

  8. The Molecular and Cellular Effect of Homocysteine Metabolism Imbalance on Human Health

    PubMed Central

    Škovierová, Henrieta; Vidomanová, Eva; Mahmood, Silvia; Sopková, Janka; Drgová, Anna; Červeňová, Tatiana; Halašová, Erika; Lehotský, Ján

    2016-01-01

    Homocysteine (Hcy) is a sulfur-containing non-proteinogenic amino acid derived in methionine metabolism. The increased level of Hcy in plasma, hyperhomocysteinemia, is considered to be an independent risk factor for cardio and cerebrovascular diseases. However, it is still not clear if Hcy is a marker or a causative agent of diseases. More and more research data suggest that Hcy is an important indicator for overall health status. This review represents the current understanding of molecular mechanism of Hcy metabolism and its link to hyperhomocysteinemia-related pathologies in humans. The aberrant Hcy metabolism could lead to the redox imbalance and oxidative stress resulting in elevated protein, nucleic acid and carbohydrate oxidation and lipoperoxidation, products known to be involved in cytotoxicity. Additionally, we examine the role of Hcy in thiolation of proteins, which results in their molecular and functional modifications. We also highlight the relationship between the imbalance in Hcy metabolism and pathogenesis of diseases, such as cardiovascular diseases, neurological and psychiatric disorders, chronic kidney disease, bone tissue damages, gastrointestinal disorders, cancer, and congenital defects. PMID:27775595

  9. Network dynamics: quantitative analysis of complex behavior in metabolism, organelles, and cells, from experiments to models and back.

    PubMed

    Kurz, Felix T; Kembro, Jackelyn M; Flesia, Ana G; Armoundas, Antonis A; Cortassa, Sonia; Aon, Miguel A; Lloyd, David

    2017-01-01

    Advancing from two core traits of biological systems: multilevel network organization and nonlinearity, we review a host of novel and readily available techniques to explore and analyze their complex dynamic behavior within the framework of experimental-computational synergy. In the context of concrete biological examples, analytical methods such as wavelet, power spectra, and metabolomics-fluxomics analyses, are presented, discussed, and their strengths and limitations highlighted. Further shown is how time series from stationary and nonstationary biological variables and signals, such as membrane potential, high-throughput metabolomics, O2 and CO2 levels, bird locomotion, at the molecular, (sub)cellular, tissue, and whole organ and animal levels, can reveal important information on the properties of the underlying biological networks. Systems biology-inspired computational methods start to pave the way for addressing the integrated functional dynamics of metabolic, organelle and organ networks. As our capacity to unravel the control and regulatory properties of these networks and their dynamics under normal or pathological conditions broadens, so is our ability to address endogenous rhythms and clocks to improve health-span in human aging, and to manage complex metabolic disorders, neurodegeneration, and cancer. WIREs Syst Biol Med 2017, 9:e1352. doi: 10.1002/wsbm.1352 For further resources related to this article, please visit the WIREs website.

  10. Global detection of live virtual machine migration based on cellular neural networks.

    PubMed

    Xie, Kang; Yang, Yixian; Zhang, Ling; Jing, Maohua; Xin, Yang; Li, Zhongxian

    2014-01-01

    In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation allowing the VM migration detection to be performed better.

  11. Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks

    NASA Astrophysics Data System (ADS)

    Ozcan, H. Kurtulus; Bilgili, Erdem; Sahin, Ulku; Ucan, O. Nuri; Bayat, Cuma

    2007-09-01

    Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.

  12. A novel method to assess human population exposure induced by a wireless cellular network.

    PubMed

    Varsier, Nadège; Plets, David; Corre, Yoann; Vermeeren, Günter; Joseph, Wout; Aerts, Sam; Martens, Luc; Wiart, Joe

    2015-09-01

    This paper presents a new metric to evaluate electromagnetic exposure induced by wireless cellular networks. This metric takes into account the exposure induced by base station antennas as well as exposure induced by wireless devices to evaluate average global exposure of the population in a specific geographical area. The paper first explains the concept and gives the formulation of the Exposure Index (EI). Then, the EI computation is illustrated through simple phone call scenarios (indoor office, in train) and a complete macro urban data long-term evolution scenario showing how, based on simulations, radio-planning predictions, realistic population statistics, user traffic data, and specific absorption rate calculations can be combined to assess the index. Bioelectromagnetics. 36:451-463, 2015. © 2015 Wiley Periodicals, Inc.

  13. Parallelism on the Intel 860 Hypercube:. Ising Magnets, Hydrodynamical Cellular Automata and Neural Networks

    NASA Astrophysics Data System (ADS)

    Kohring, G. A.; Stauffer, D.

    Geometric parallelization was tested on the Intel Hypercube with 32 MIMD processors of 1860 type, each with 16 Mbytes of distributed memory. We applied it to Ising models in two and three dimensions as well as to neural networks and two-dimensional hydrodynamic cellular automata. For system sizes suited to this machine, up to 60960*60960 and 1410*1410*1408 Ising spins, we found nearly hundred percent parallel efficiency in spite of the needed inter-processor communications. For small systems, the observed deviations from full efficiency were compared with the scaling concepts of Heermann and Burkitt and of Jakobs and Gerling. For Ising models, we determined the Glauber kinetic exponent z≃2.18 in two dimensions and confirmed the stretched exponential relaxation of the magnetization towards the spontaneous magnetization below Tc. For three dimensions we found z≃2.09 and simple exponential relaxation.

  14. Spike integration and cellular memory in a rhythmic network from Na+/K+ pump current dynamics

    PubMed Central

    Pulver, Stefan R.

    2009-01-01

    The output of a neural circuit results from an interaction between the intrinsic properties of neurons within the circuit and the features of the synaptic connections between them. The plasticity of intrinsic properties has been primarily attributed to modification of ion channel function and/or number. In this study, we demonstrate a mechanism for intrinsic plasticity in rhythmically active Drosophila neurons that is not conductance-based. Larval motor neurons show a long lasting sodium-dependent afterhyperpolarization (AHP) following bursts of action potentials that is mediated by the electrogenic activity of Na+/K+ ATPase. This AHP persists for multiple seconds following volleys of action potentials and is able to function as a pattern-insensitive integrator of spike number that is independent of external calcium. This current also interacts with endogenous Shal K+ conductances to modulate spike timing for multiple seconds following rhythmic activity, providing a cellular memory of network activity on a behaviorally relevant time scale. PMID:19966842

  15. Adaptive call admission control and resource allocation in multi server wireless/cellular network

    NASA Astrophysics Data System (ADS)

    Jain, Madhu; Mittal, Ragini

    2016-11-01

    The ever increasing demand of the subscribers has put pressure on the capacity of wireless networks around the world. To utilize the scare resources, in the present paper we propose an optimal allocation scheme for an integrated wireless/cellular model with handoff priority and handoff guarantee services. The suggested algorithm optimally allocates the resources in each cell and dynamically adjust threshold to control the admission. To give the priority to handoff calls over the new calls, the provision of guard channels and subrating scheme is taken into consideration. The handoff voice call may balk and renege from the system while waiting in the buffer. An iterative algorithm is implemented to generate the arrival rate of the handoff calls in each cell. Various performance indices are established in term of steady state probabilities. The sensitivity analysis has also been carried out to examine the tractability of algorithms and to explore the effects of system descriptors on the performance indices.

  16. Smart-Pixel Array Processors Based on Optimal Cellular Neural Networks for Space Sensor Applications

    NASA Technical Reports Server (NTRS)

    Fang, Wai-Chi; Sheu, Bing J.; Venus, Holger; Sandau, Rainer

    1997-01-01

    A smart-pixel cellular neural network (CNN) with hardware annealing capability, digitally programmable synaptic weights, and multisensor parallel interface has been under development for advanced space sensor applications. The smart-pixel CNN architecture is a programmable multi-dimensional array of optoelectronic neurons which are locally connected with their local neurons and associated active-pixel sensors. Integration of the neuroprocessor in each processor node of a scalable multiprocessor system offers orders-of-magnitude computing performance enhancements for on-board real-time intelligent multisensor processing and control tasks of advanced small satellites. The smart-pixel CNN operation theory, architecture, design and implementation, and system applications are investigated in detail. The VLSI (Very Large Scale Integration) implementation feasibility was illustrated by a prototype smart-pixel 5x5 neuroprocessor array chip of active dimensions 1380 micron x 746 micron in a 2-micron CMOS technology.

  17. BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling

    PubMed Central

    Feng, Song; Ollivier, Julien F.; Swain, Peter S.; Soyer, Orkun S.

    2015-01-01

    Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx. PMID:26101250

  18. A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks.

    PubMed

    Merlet, Benjamin; Paulhe, Nils; Vinson, Florence; Frainay, Clément; Chazalviel, Maxime; Poupin, Nathalie; Gloaguen, Yoann; Giacomoni, Franck; Jourdan, Fabien

    2016-01-01

    This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

  19. A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks

    PubMed Central

    Merlet, Benjamin; Paulhe, Nils; Vinson, Florence; Frainay, Clément; Chazalviel, Maxime; Poupin, Nathalie; Gloaguen, Yoann; Giacomoni, Franck; Jourdan, Fabien

    2016-01-01

    This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities. PMID:26909353

  20. Roadmap to cellular reprogramming--manipulating transcriptional networks with DNA, RNA, proteins and small molecules.

    PubMed

    Wörsdörfer, P; Thier, M; Kadari, A; Edenhofer, F

    2013-06-01

    Recent reports demonstrate that the plasticity of mammalian somatic cells is much higher than previously assumed and that ectopic expression of transcription factors may have the potential to induce the conversion of any cell type into another. Fibroblast cells can be converted into embryonic stem cell-like cells, neural cells, cardiomyocytes, macrophage-like cells as well as blood progenitors. Additionally, the conversion of astrocytes into neurons or neural stem cells into monocytes has been demonstrated. Nowadays, in the era of systems biology, continuously growing holistic data sets are providing increasing insights into core transcriptional networks and cellular signaling pathways. This knowledge enables cell biologists to understand how cellular fate is determined and how it could be manipulated. As a consequence for biomedical applications, it might be soon possible to convert patient specific somatic cells directly into desired transplantable other cell types. The clinical value, however, of such reprogrammed cells is currently limited due to the invasiveness of methods applied to induce reprogramming factor activity. This review will focus on experimental strategies to ectopically induce cell fate modulators. We will emphasize those strategies that enable efficient and robust overexpression of transcription factors by minimal genetic alterations of the host genome. Furthermore, we will discuss procedures devoid of any genomic manipulation, such as the direct delivery of mRNA, proteins, or the use of small molecules. By this, we aim to give a comprehensive overview on state of the art techniques that harbor the potential to generate safe reprogrammed cells for clinical applications.

  1. Intragranular cellular segregation network structure strengthening 316L stainless steel prepared by selective laser melting

    NASA Astrophysics Data System (ADS)

    Zhong, Yuan; Liu, Leifeng; Wikman, Stefan; Cui, Daqing; Shen, Zhijian

    2016-03-01

    A feasibility study was performed to fabricate ITER In-Vessel components by Selective Laser Melting (SLM) supported by Fusion for Energy (F4E). Almost fully dense 316L stainless steel (SS316L) components were prepared from gas-atomized powder and with optimized SLM processing parameters. Tensile tests and Charpy-V tests were carried out at 22 °C and 250 °C and the results showed that SLM SS316L fulfill the RCC-MR code. Microstructure characterization reveals the presence of hierarchical macro-, micro- and nano-structures in as-built samples that were very different from SS316L microstructures prepared by other established methods. The formation of a characteristic intragranular cellular segregation network microstructure appears to contribute to the increase of yield strength without losing ductility. Silicon oxide nano-inclusions were formed during the SLM process that generated a micro-hardness fluctuation in the building direction. The combined influence of a cellular microstructure and the nano-inclusions constraints the size of ductile dimples to nano-scale. The crack propagation is hindered by a pinning effect that improves the defect-tolerance of the SLM SS316L. This work proves that it was possible to manufacture SS316L with properties suitable for ITER First Wall panels. Further studies on irradiation properties of SLM SS316L and manufacturing of larger real-size components are needed.

  2. Computerized detection of pulmonary nodules using cellular neural networks in CT images

    NASA Astrophysics Data System (ADS)

    Zhang, Xiangwei; McLennan, Geoffrey; Hoffman, Eric A.; Sonka, Milan

    2004-05-01

    The purpose of this study is to develop a computer-aided diagnosis (CAD) system to detect small-sized (from 2mm to 10mm) non-pleural pulmonary nodules in high resolution helical CT scans. A new 3D automated scheme using cellular neural networks is presented. Different from most previous methods, this scheme employed the local shape property to perform voxel classification. The shape index feature successfully captured the local shape difference between nodules and non-nodules, especially vessels. A 3D discrete-time cellular neural network (DTCNN) was constructed to give a reliable voxel classification by collecting information in a neighborhood. To tailor it for lung nodule detection, this DTCNN was trained using genetic algorithms (GAs) to derive the shape index variation pattern of nodules. 19 clinical thoracic CT cases involving a total of 4838 sectional images were used in this work, with 2 scans forming the training set, and the remaining 17 cases being the testing set. The evaluation was composed of two stages. During the first stage, a pulmonologist and our CAD system independently detected nodules in the testing set. Then, the suspected nodule areas located by the computer were reviewed by the pulmonologist to confirm nodules missed by the human in the first review. There were 32 true nodules detected by the computer but missed by the pulmonologist in the first review, in which 30 non-juxtapleural nodules were found. Considering the nodules detected by the pulmonologist during the first and second reviews as the truth, 52 of 62 non-pleural nodules were detected by the CAD system (sensitivity being 83.9%), with the number of false positives being 3.47 per case.

  3. A toolbox model of evolution of metabolic pathways on networks of arbitrary topology.

    PubMed

    Pang, Tin Yau; Maslov, Sergei

    2011-05-01

    In prokaryotic genomes the number of transcriptional regulators is known to be proportional to the square of the total number of protein-coding genes. A toolbox model of evolution was recently proposed to explain this empirical scaling for metabolic enzymes and their regulators. According to its rules, the metabolic network of an organism evolves by horizontal transfer of pathways from other species. These pathways are part of a larger "universal" network formed by the union of all species-specific networks. It remained to be understood, however, how the topological properties of this universal network influence the scaling law of functional content of genomes in the toolbox model. Here we answer this question by first analyzing the scaling properties of the toolbox model on arbitrary tree-like universal networks. We prove that critical branching topology, in which the average number of upstream neighbors of a node is equal to one, is both necessary and sufficient for quadratic scaling. We further generalize the rules of the model to incorporate reactions with multiple substrates/products as well as branched and cyclic metabolic pathways. To achieve its metabolic tasks, the new model employs evolutionary optimized pathways with minimal number of reactions. Numerical simulations of this realistic model on the universal network of all reactions in the KEGG database produced approximately quadratic scaling between the number of regulated pathways and the size of the metabolic network. To quantify the geometrical structure of individual pathways, we investigated the relationship between their number of reactions, byproducts, intermediate, and feedback metabolites. Our results validate and explain the ubiquitous appearance of the quadratic scaling for a broad spectrum of topologies of underlying universal metabolic networks. They also demonstrate why, in spite of "small-world" topology, real-life metabolic networks are characterized by a broad distribution of pathway

  4. Quantitative petri net model of gene regulated metabolic networks in the cell.

    PubMed

    Chen, Ming; Hofestädt, Ralf

    2011-01-01

    A method to exploit hybrid Petri nets (HPN) for quantitatively modeling and simulating gene regulated metabolic networks is demonstrated. A global kinetic modeling strategy and Petri net modeling algorithm are applied to perform the bioprocess functioning and model analysis. With the model, the interrelations between pathway analysis and metabolic control mechanism are outlined. Diagrammatical results of the dynamics of metabolites are simulated and observed by implementing a HPN tool, Visual Object Net ++. An explanation of the observed behavior of the urea cycle is proposed to indicate possibilities for metabolic engineering and medical care. Finally, the perspective of Petri nets on modeling and simulation of metabolic networks is discussed.

  5. Metabolic networking in Brunfelsia calycina petals after flower opening.

    PubMed

    Bar-Akiva, Ayelet; Ovadia, Rinat; Rogachev, Ilana; Bar-Or, Carmiya; Bar, Einat; Freiman, Zohar; Nissim-Levi, Ada; Gollop, Natan; Lewinsohn, Efraim; Aharoni, Asaph; Weiss, David; Koltai, Hinanit; Oren-Shamir, Michal

    2010-03-01

    Brunfelsia calycina flowers change colour from purple to white due to anthocyanin degradation, parallel to an increase in fragrance and petal size. Here it was tested whether the production of the fragrant benzenoids is dependent on induction of the shikimate pathway, or if they are formed from the anthocyanin degradation products. An extensive characterization of the events taking place in Brunfelsia flowers is presented. Anthocyanin characterization was performed using ultraperfomance liquid chromatography-quadrupole time of flight-tandem mass specrometry (UPLC-QTOF-MS/MS). Volatiles emitted were identified by headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Accumulated proteins were identified by 2D gel electrophoresis. Transcription profiles were characterized by cross-species hybridization of Brunfelsia cDNAs to potato cDNA microarrays. Identification of accumulated metabolites was performed by UPLC-QTOF-MS non-targeted metabolite analysis. The results include characterization of the nine main anthocyanins in Brunfelsia flowers. In addition, 146 up-regulated genes, 19 volatiles, seven proteins, and 17 metabolites that increased during anthocyanin degradation were identified. A multilevel analysis suggests induction of the shikimate pathway. This pathway is the most probable source of the phenolic acids, which in turn are precursors of both the benzenoid and lignin production pathways. The knowledge obtained is valuable for future studies on degradation of anthocyanins, formation of volatiles, and the network of secondary metabolism in Brunfelsia and related species.

  6. Interplay between Cellular Methyl Metabolism and Adaptive Efflux during Oncogenic Transformation from Chronic Arsenic Exposure in Human Cells*S⃞

    PubMed Central

    Coppin, Jean-François; Qu, Wei; Waalkes, Michael P.

    2008-01-01

    After protracted low level arsenic exposure, the normal human prostate epithelial cell line RWPE-1 acquires a malignant phenotype with DNA hypomethylation, indicative of disrupted methyl metabolism, and shows arsenic adaptation involving glutathione overproduction and enhanced arsenic efflux. Thus, the interplay between methyl and glutathione metabolism during this progressive arsenic adaptation was studied. Arsenic-treated cells showed a time-dependent increase in LC50 and a marked increase in homocysteine (Hcy) levels. A marked suppression of S-adenosylmethionine (SAM) levels occurred with decreased methionine adenosyltransferase 2A (converts methionine to SAM) expression and increased negative regulator methionine adenosyltransferase B, suggesting reduced conversion of Hcy to SAM. Consistent with Hcy overproduction, activity and expression of S-adenosylhomocysteine hydrolase (converts S-adenosylhomocysteine to Hcy) were both increased. Expression of cystathionine β-synthase, a key gene in the transsulfuration pathway, and various glutathione production genes were increased, resulting in a 5-fold increase in glutathione. Arsenic efflux increased along with expression of ATP-binding cassette protein C1, which effluxes arsenic as a glutathione conjugate. Evidence of genomic DNA hypomethylation was observed during early arsenic exposure, indicating that the disruption in methyl metabolism had a potential impact related to oncogenesis. Thus, cellular arsenic adaptation is a dynamic, progressive process that involves decreased SAM recycling and concurrent accumulation of Hcy, which is channeled via transsulfuration to increase glutathione and enhance arsenic efflux but may also impact the carcinogenic process. PMID:18487201

  7. Input-dependent wave propagations in asymmetric cellular automata: possible behaviors of feed-forward loop in biological reaction network.

    PubMed

    Awazu, Akinori

    2008-07-01

    Dynamical aspects of the asymmetric cellular automata were investigated to consider the signaling processes in biological systems. As a meta-model of the cascade of feed-forward loop type network motifs in biological reaction networks, we consider the one dimensional asymmetric cellular automata where the state of each cell is controlled by a trio of cells, the cell itself, the nearest upstream cell and the next nearest upstream cell. Through the systematic simulations, some novel input-dependent wave propagations were found in certain asymmetric CA, which may be useful for the signaling processes like the distinction, the filtering and the memory of external stimuli.

  8. Development of a Clickable Probe for Profiling of Protein Glutathionylation in the Central Cellular Metabolism of E. coli and Drosophila.

    PubMed

    Feng, Shan; Chen, Yuling; Yang, Fan; Zhang, Lei; Gong, Yiyi; Adilijiang, Gulishana; Gao, Yan; Deng, Haiteng

    2015-11-19

    Protein glutathionylation is an important post-translational modification that regulates many cellular processes, including energy metabolism, signal transduction, and protein homeostasis. Global profiling of glutathionylated proteins (denoted as glutathionylome) is crucial for understanding redox-regulated signal transduction. Here, we developed a novel method based on click reaction and proteomics to enrich and identify the glutathionylated peptides in Escherichia coli and Drosophila lysates, in which 937 and 1,930 potential glutathionylated peptides were identified, respectively. Bioinformatics analysis showed that the cysteine residue next to negatively charged amino acid residues has a higher frequency of glutathionylation. Importantly, we found that most proteins associated with metabolic pathways were glutathionylated and that the glutathionylation sites of metabolic enzymes were highly conserved among different species. Our results indicate that the glutathione analog is a useful tool to characterize protein glutathionylation, and glutathionylation of metabolic enzymes, which play important roles in regulating cellular metabolism, is conserved.

  9. Differential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosis.

    PubMed

    Bonde, Bhushan K; Beste, Dany J V; Laing, Emma; Kierzek, Andrzej M; McFadden, Johnjoe

    2011-06-01

    A general paucity of knowledge about the metabolic state of Mycobacterium tuberculosis within the host environment is a major factor impeding development of novel drugs against tuberculosis. Current experimental methods do not allow direct determination of the global metabolic state of a bacterial pathogen in vivo, but the transcriptional activity of all encoded genes has been investigated in numerous microarray studies. We describe a novel algorithm, Differential Producibility Analysis (DPA) that uses a metabolic network to extract metabolic signals from transcriptome data. The method utilizes Flux Balance Analysis (FBA) to identify the set of genes that affect the ability to produce each metabolite in the network. Subsequently, Rank Product Analysis is used to identify those metabolites predicted to be most affected by a transcriptional signal. We first apply DPA to investigate the metabolic response of E. coli to both anaerobic growth and inactivation of the FNR global regulator. DPA successfully extracts metabolic signals that correspond to experimental data and provides novel metabolic insights. We next apply DPA to investigate the metabolic response of M. tuberculosis to the macrophage environment, human sputum and a range of in vitro environmental perturbations. The analysis revealed a previously unrecognized feature of the response of M. tuberculosis to the macrophage environment: a down-regulation of genes influencing metabolites in central metabolism and concomitant up-regulation of genes that influence synthesis of cell wall components and virulence factors. DPA suggests that a significant feature of the response of the tubercle bacillus to the intracellular environment is a channeling of resources towards remodeling of its cell envelope, possibly in preparation for attack by host defenses. DPA may be used to unravel the mechanisms of virulence and persistence of M. tuberculosis and other pathogens and may have general application for extracting

  10. Reversible Nuclear-Lipid-Droplet Morphology Induced by Oleic Acid: A Link to Cellular-Lipid Metabolism

    PubMed Central

    Lagrutta, Lucía C.; Montero-Villegas, Sandra; Layerenza, Juan P.; Sisti, Martín S.; García de Bravo, Margarita M.

    2017-01-01

    Neutral lipids—involved in many cellular processes—are stored as lipid droplets (LD), those mainly cytosolic (cLD) along with a small nuclear population (nLD). nLD could be involved in nuclear-lipid homeostasis serving as an endonuclear buffering system that would provide or incorporate lipids and proteins involved in signalling pathways as transcription factors and as enzymes of lipid metabolism and nuclear processes. Our aim was to determine if nLD constituted a dynamic domain. Oleic-acid (OA) added to rat hepatocytes or HepG2 cells in culture produced cellular-phenotypic LD modifications: increases in TAG, CE, C, and PL content and in cLD and nLD numbers and sizes. LD increments were reversed on exclusion of OA and were prevented by inhibition of acyl-CoA synthetase (with Triacsin C) and thus lipid biosynthesis. Under all conditions, nLD corresponded to a small population (2–10%) of total cellular LD. The anabolism triggered by OA, involving morphologic and size changes within the cLD and nLD populations, was reversed by a net balance of catabolism, upon eliminating OA. These catabolic processes included lipolysis and the mobilization of hydrolyzed FA from the LD to cytosolic-oxidation sites. These results would imply that nLD are actively involved in nuclear processes that include lipids. In conclusion, nLD are a dynamic nuclear domain since they are modified by OA through a reversible mechanism in combination with cLD; this process involves acyl-CoA-synthetase activity; ongoing TAG, CE, and PL biosynthesis. Thus, liver nLD and cLD are both dynamic cellular organelles. PMID:28125673

  11. IAPP modulates cellular autophagy, apoptosis, and extracellular matrix metabolism in human intervertebral disc cells

    PubMed Central

    Wu, Xinghuo; Song, Yu; Liu, Wei; Wang, Kun; Gao, Yong; Li, Shuai; Duan, Zhenfeng; Shao, Zengwu; Yang, Shuhua; Yang, Cao

    2017-01-01

    The pathogenic process of intervertebral disc degeneration (IDD) is characterized by imbalance in the extracellular matrix (ECM) metabolism. Nucleus pulposus (NP) cells have important roles in maintaining the proper structure and tissue homeostasis of disc ECM. These cells need adequate supply of glucose and oxygen. Islet amyloid polypeptide (IAPP) exerts its biological effects by regulating glucose metabolism. The purpose of this study was to investigate the expression of IAPP in degenerated IVD tissue, and IAPP modulation of ECM metabolism in human NP cells, especially the crosstalk mechanism between apoptosis and autophagy in these cells. We found that the expression of IAPP and Calcr-RAMP decreased considerably during IDD progression, along with the decrease in the expression of AG, BG, and Col2A1. Induction of IAPP in NP cells by transfection with pLV-IAPP enhanced the synthesis of aggrecan and Col2A1 and attenuated the expression of pro-inflammatory factors, tumor necrosis factor (TNF)-α, and interleukin (IL)-1. Upregulation of IAPP also affected the expression of the catabolic markers—matrix metalloproteinases (MMPs) 3, 9 and 13 and ADAMTS 4 and 5. Downregulation of IAPP by siRNA inhibited the expression of anabolic genes but increased the expression of catabolic genes and inflammatory factors. The expressions of autophagic and apoptotic markers in NP cells transfected with pLV-IAPP were upregulated, including BECLIN1, ATG5, ATG7, LC3 II/I and Bcl-2, while significantly increase in the expression of Bax and Caspase-3 in NP cells transfected with pLV-siIAPP. Mechanistically, PI3K/AKT-mTOR and p38/JNK MAPK signal pathways were involved. We propose that IAPP might play a pivotal role in the development of IDD, by regulating ECM metabolism and controlling the crosstalk between apoptosis and autophagy in NP, thus potentially offering a novel therapeutic approach to the treatment of IDD. PMID:28149534

  12. Genome-scale reconstruction of the metabolic network in Yersinia pestis CO92

    NASA Astrophysics Data System (ADS)

    Navid, Ali; Almaas, Eivind

    2007-03-01

    The gram-negative bacterium Yersinia pestis is the causative agent of bubonic plague. Using publicly available genomic, biochemical and physiological data, we have developed a constraint-based flux balance model of metabolism in the CO92 strain (biovar Orientalis) of this organism. The metabolic reactions were appropriately compartmentalized, and the model accounts for the exchange of metabolites, as well as the import of nutrients and export of waste products. We have characterized the metabolic capabilities and phenotypes of this organism, after comparing the model predictions with available experimental observations to evaluate accuracy and completeness. We have also begun preliminary studies into how cellular metabolism affects virulence.

  13. Effect of Nutrient Starvation on the Cellular Composition and Metabolic Capacity of Saccharomyces cerevisiae▿

    PubMed Central

    Albers, Eva; Larsson, Christer; Andlid, Thomas; Walsh, Michael C.; Gustafsson, Lena

    2007-01-01

    This investigation addresses the following question: what are the important factors for maintenance of a high catabolic capacity under various starvation conditions? Saccharomyces cerevisiae was cultured in aerobic batch cultures, and during the diauxic shift cells were transferred and subjected to 24 h of starvation. The following conditions were used: carbon starvation, nitrogen starvation in the presence of glucose or ethanol, and both carbon starvation and nitrogen starvation. During the starvation period changes in biomass composition (including protein, carbohydrate, lipid, and nucleic acid contents), metabolic activity, sugar transport kinetics, and the levels of selected enzymes were recorded. Subsequent to the starvation period the remaining catabolic capacity was measured by addition of 50 mM glucose. The results showed that the glucose transport capacity is a key factor for maintenance of high metabolic capacity in many, but not all, cases. The results for cells starved of carbon, carbon and nitrogen, or nitrogen in the presence of glucose all indicated that the metabolic capacity was indeed controlled by the glucose transport ability, perhaps with some influence of hexokinase, phosphofructokinase, aldolase, and enolase levels. However, it was also demonstrated that there was no such correlation when nitrogen starvation occurred in the presence of ethanol instead of glucose. PMID:17545328

  14. On the cellular metabolism of the click chemistry probe 19-alkyne arachidonic acid.

    PubMed

    Robichaud, Philippe Pierre; Poirier, Samuel J; Boudreau, Luc H; Doiron, Jérémie A; Barnett, David A; Boilard, Eric; Surette, Marc E

    2016-10-01

    Alkyne and azide analogs of natural compounds that can be coupled to sensitive tags by click chemistry are powerful tools to study biological processes. Arachidonic acid (AA) is a FA precursor to biologically active compounds. 19-Alkyne-AA (AA-alk) is a sensitive clickable AA analog; however, its use as a surrogate to study AA metabolism requires further evaluation. In this study, AA-alk metabolism was compared with that of AA in human cells. Jurkat cell uptake of AA was 2-fold greater than that of AA-alk, but significantly more AA-Alk was elongated to 22:4. AA and AA-alk incorporation into and remodeling between phospholipid (PL) classes was identical indicating equivalent CoA-independent AA-PL remodeling. Platelets stimulated in the pre-sence of AA-alk synthesized significantly less 12-lipoxygenase (12-LOX) and cyclooxygenase products than in the presence of AA. Ionophore-stimulated neutrophils produced significantly more 5-LOX products in the presence of AA-alk than AA. Neutrophils stimulated with only exogenous AA-alk produced significantly less 5-LOX products compared with AA, and leukotriene B4 (LTB4)-alk was 12-fold less potent at stimulating neutrophil migration than LTB4, collectively indicative of weaker leukotriene B4 receptor 1 agonist activity of LTB4-alk. Overall, these results suggest that the use of AA-alk as a surrogate for the study of AA metabolism should be carried out with caution.

  15. STAT3-Mediated Metabolic Reprograming in Cellular Transformation and Implications for Drug Resistance

    PubMed Central

    Poli, Valeria; Camporeale, Annalisa

    2015-01-01

    Signal transducer and activator of transcription (STAT)3 mediates the signaling downstream of cytokine and growth factor receptors, regulating the expression of target genes. It is constitutively phosphorylated on tyrosine (Y-P) in many tumors, where its transcriptional activity can induce a metabolic switch toward aerobic glycolysis and down-regulate mitochondrial activity, a prominent metabolic feature of most cancer cells, correlating with reduced production of ROS, delayed senescence, and protection from apoptosis. STAT3 can, however, also localize to mitochondria, where its serine-phosphorylated (S-P) form preserves mitochondrial oxidative phosphorylation and controls the opening of the mitochondrial permeability transition pore, also promoting survival and resistance to apoptosis in response to specific signals/oncogenes such as RAS. Thus, downstream of different signals, both nuclear, Y-P STAT3, and mitochondrial, S-P STAT3, can act by promoting cell survival and reducing ROS production. Here, we discuss these properties in the light of potential connections between STAT3-driven alterations of mitochondrial metabolism and the development of drug resistance in cancer patients. PMID:26106584

  16. Limited Influence of Oxygen on the Evolution of Chemical Diversity in Metabolic Networks

    PubMed Central

    Takemoto, Kazuhiro; Yoshitake, Ikumi

    2013-01-01

    Oxygen is thought to promote species and biomolecule diversity. Previous studies have suggested that oxygen expands metabolic networks by acquiring metabolites with different chemical properties (higher hydrophobicity, for example). However, such conclusions are typically based on biased evaluation, and are therefore non-conclusive. Thus, we re-investigated the effect of oxygen on metabolic evolution using a phylogenetic comparative method and metadata analysis to reduce the bias as much as possible. Notably, we found no difference in metabolic network expansion between aerobes and anaerobes when evaluating phylogenetic relationships. Furthermore, we showed that previous studies have overestimated or underestimated the degrees of differences in the chemical properties (e.g., hydrophobicity) between oxic and anoxic metabolites in metabolic networks of unicellular organisms; however, such overestimation was not observed when considering the metabolic networks of multicellular organisms. These findings indicate that the contribution of oxygen to increased chemical diversity in metabolic networks is lower than previously thought; rather, phylogenetic signals and cell-cell communication result in increased chemical diversity. However, this conclusion does not contradict the effect of oxygen on metabolic evolution; instead, it provides a deeper understanding of how oxygen contributes to metabolic evolution despite several limitations in data analysis methods. PMID:24958261

  17. Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites.

    PubMed

    Hadadi, Noushin; Hafner, Jasmin; Soh, Keng Cher; Hatzimanikatis, Vassily

    2017-01-01

    Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite-level studies to atom-level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom-mapped reactions to atom-mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom-level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom-mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom-mapped reactions of the KEGG database and we provide an example of an atom-level representation of the core metabolic network of E. coli.

  18. Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks

    PubMed Central

    Foremski, Paweł; Gorawski, Michał; Grochla, Krzysztof; Polys, Konrad

    2015-01-01

    The paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage