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Sample records for metabolic network preferentially

  1. Though with constraints imposed by endosymbiosis, preferential attachment is still a plausible mechanism responsible for the evolution of the chloroplast metabolic network.

    PubMed

    Wang, Z; Zhu, X-G; Chang, X; Chen, Y Z; Li, Y X; Liu, L

    2009-01-01

    Chloroplasts evolved as a result of endosymbiosis, during which sophisticated mechanisms evolved to translocate nucleus-encoded plastid-targeted enzymes into the chloroplast to form the chloroplast metabolic network. Given the constraints and complexity of endosymbiosis, will preferential attachment still be a plausible mechanism for chloroplast metabolic network evolution? We answer this question by analysing the metabolic network properties of the chloroplast and a cyanobacterium, Synechococcus sp. WH8102 (syw). First, we found that enzymes related to more ancient pathways are more connected, and synthetases have the highest connectivity. Most of the enzymes shared by the two densest cores between the chloroplast and syw are synthetases. Second, the highly conserved functional modules mainly consist of highly connected enzymes. Finally, isozymes and enzymes from endosymbiotic gene transfer (EGT) were distributed mainly in conserved modules and showed higher connectivity than nonisozymes or non-EGT enzymes. These results suggest that even with severe evolutionary constraints imposed by endosymbiosis, preferential attachment is still a plausible mechanism responsible for the evolution of the chloroplast metabolic network. However, the current analysis may not completely differentiate whether the chloroplast network properties reflect the evolution of the chloroplast network through preferential attachment or has been inherited from its cyanobacterial ancestor. To fully differentiate these two possibilities, further analyses of the metabolic network structure properties of organisms at various intermediate evolutionary stages between cyanobacteria and the chloroplast are needed.

  2. Preferential urn model and nongrowing complex networks.

    PubMed

    Ohkubo, Jun; Yasuda, Muneki; Tanaka, Kazuyuki

    2005-12-01

    A preferential urn model, which is based on the concept "the rich get richer," is proposed. From a relationship between a nongrowing model for complex networks and the preferential urn model in regard to degree distributions, it is revealed that a fitness parameter in the nongrowing model is interpreted as an inverse local temperature in the preferential urn model. Furthermore, it is clarified that the preferential urn model with randomness generates a fat-tailed occupation distribution; the concept of the local temperature enables us to understand the fat-tailed occupation distribution intuitively. Since the preferential urn model is a simple stochastic model, it can be applied to research on not only the nongrowing complex networks, but also many other fields such as econophysics and social sciences.

  3. Reverse preferential spread in complex networks

    NASA Astrophysics Data System (ADS)

    Toyoizumi, Hiroshi; Tani, Seiichi; Miyoshi, Naoto; Okamoto, Yoshio

    2012-08-01

    Large-degree nodes may have a larger influence on the network, but they can be bottlenecks for spreading information since spreading attempts tend to concentrate on these nodes and become redundant. We discuss that the reverse preferential spread (distributing information inversely proportional to the degree of the receiving node) has an advantage over other spread mechanisms. In large uncorrelated networks, we show that the mean number of nodes that receive information under the reverse preferential spread is an upper bound among any other weight-based spread mechanisms, and this upper bound is indeed a logistic growth independent of the degree distribution.

  4. Discovering Preferential Patterns in Sectoral Trade Networks.

    PubMed

    Cingolani, Isabella; Piccardi, Carlo; Tajoli, Lucia

    2015-01-01

    We analyze the patterns of import/export bilateral relations, with the aim of assessing the relevance and shape of "preferentiality" in countries' trade decisions. Preferentiality here is defined as the tendency to concentrate trade on one or few partners. With this purpose, we adopt a systemic approach through the use of the tools of complex network analysis. In particular, we apply a pattern detection approach based on community and pseudocommunity analysis, in order to highlight the groups of countries within which most of members' trade occur. The method is applied to two intra-industry trade networks consisting of 221 countries, relative to the low-tech "Textiles and Textile Articles" and the high-tech "Electronics" sectors for the year 2006, to look at the structure of world trade before the start of the international financial crisis. It turns out that the two networks display some similarities and some differences in preferential trade patterns: they both include few significant communities that define narrow sets of countries trading with each other as preferential destinations markets or supply sources, and they are characterized by the presence of similar hierarchical structures, led by the largest economies. But there are also distinctive features due to the characteristics of the industries examined, in which the organization of production and the destination markets are different. Overall, the extent of preferentiality and partner selection at the sector level confirm the relevance of international trade costs still today, inducing countries to seek the highest efficiency in their trade patterns.

  5. Complex Cooperative Networks from Evolutionary Preferential Attachment

    PubMed Central

    Poncela, Julia; Gómez-Gardeñes, Jesús; Floría, Luis M.; Sánchez, Angel; Moreno, Yamir

    2008-01-01

    In spite of its relevance to the origin of complex networks, the interplay between form and function and its role during network formation remains largely unexplored. While recent studies introduce dynamics by considering rewiring processes of a pre-existent network, we study network growth and formation by proposing an evolutionary preferential attachment model, its main feature being that the capacity of a node to attract new links depends on a dynamical variable governed in turn by the node interactions. As a specific example, we focus on the problem of the emergence of cooperation by analyzing the formation of a social network with interactions given by the Prisoner's Dilemma. The resulting networks show many features of real systems, such as scale-free degree distributions, cooperative behavior and hierarchical clustering. Interestingly, results such as the cooperators being located mostly on nodes of intermediate degree are very different from the observations of cooperative behavior on static networks. The evolutionary preferential attachment mechanism points to an evolutionary origin of scale-free networks and may help understand similar feedback problems in the dynamics of complex networks by appropriately choosing the game describing the interaction of nodes. PMID:18560601

  6. Discovering Preferential Patterns in Sectoral Trade Networks

    PubMed Central

    Cingolani, Isabella; Piccardi, Carlo; Tajoli, Lucia

    2015-01-01

    We analyze the patterns of import/export bilateral relations, with the aim of assessing the relevance and shape of “preferentiality” in countries’ trade decisions. Preferentiality here is defined as the tendency to concentrate trade on one or few partners. With this purpose, we adopt a systemic approach through the use of the tools of complex network analysis. In particular, we apply a pattern detection approach based on community and pseudocommunity analysis, in order to highlight the groups of countries within which most of members’ trade occur. The method is applied to two intra-industry trade networks consisting of 221 countries, relative to the low-tech “Textiles and Textile Articles” and the high-tech “Electronics” sectors for the year 2006, to look at the structure of world trade before the start of the international financial crisis. It turns out that the two networks display some similarities and some differences in preferential trade patterns: they both include few significant communities that define narrow sets of countries trading with each other as preferential destinations markets or supply sources, and they are characterized by the presence of similar hierarchical structures, led by the largest economies. But there are also distinctive features due to the characteristics of the industries examined, in which the organization of production and the destination markets are different. Overall, the extent of preferentiality and partner selection at the sector level confirm the relevance of international trade costs still today, inducing countries to seek the highest efficiency in their trade patterns. PMID:26485163

  7. 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.

  8. Network Evolution by Relevance and Importance Preferential Attachment

    DTIC Science & Technology

    2014-08-06

    improved preferential attachment (PA) algorithm to take in consideration the relevance between vertices of the network measured by a given metric. We...Network Evolution by Relevance and Importance Preferential Attachment The views, opinions and/or findings contained in this report are those of the... attachment by importance and relevance REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR/MONITOR’S ACRONYM(S) ARO 8

  9. Consensus of synchronization-preferential scale-free networks

    NASA Astrophysics Data System (ADS)

    Hongyong, Yang; Lan, Lu; Siying, Zhang

    2010-08-01

    In this paper, the relations of the scale-free network topology and the moving consensus of multi-agent systems are studied. A synchronization-preferential BA (SPBA) network with the combinatorial preferential attachment is presented for the prestissimo consensus. The effects of the SPBA network on the algebraic connectivity of the topology graph are compared with the standard BA network. The robustness gain to delay is analyzed for variable network topologies with the same size. The characteristics of dramatic increase for the algebraic connectivity and slightly increase for the robustness gain to delay are unfolded in SPBA network. By comparing the convergence time of the different network structures, the consensuses are studied for multiagent systems with and without communication delays.

  10. Preferential survival in models of complex ad hoc networks

    NASA Astrophysics Data System (ADS)

    Kong, Joseph S.; Roychowdhury, Vwani P.

    2008-05-01

    There has been a rich interplay in recent years between (i) empirical investigations of real-world dynamic networks, (ii) analytical modeling of the microscopic mechanisms that drive the emergence of such networks, and (iii) harnessing of these mechanisms to either manipulate existing networks, or engineer new networks for specific tasks. We continue in this vein, and study the deletion phenomenon in the web by the following two different sets of websites (each comprising more than 150,000 pages) over a one-year period. Empirical data show that there is a significant deletion component in the underlying web networks, but the deletion process is not uniform. This motivates us to introduce a new mechanism of preferential survival (PS), where nodes are removed according to the degree-dependent deletion kernel, D(k)∝k, with α≥0. We use the mean-field rate equation approach to study a general dynamic model driven by Preferential Attachment (PA), Double PA (DPA), and a tunable PS (i.e., with any α>0), where c nodes ( c<1) are deleted per node added to the network, and verify our predictions via large-scale simulations. One of our results shows that, unlike in the case of uniform deletion (i.e., where α=0), the PS kernel when coupled with the standard PA mechanism, can lead to heavy-tailed power-law networks even in the presence of extreme turnover in the network. Moreover, a weak DPA mechanism, coupled with PS, can help to make the network even more heavy-tailed, especially in the limit when deletion and insertion rates are almost equal, and the overall network growth is minimal. The dynamics reported in this work can be used to design and engineer stable ad hoc networks and explain the stability of the power-law exponents observed in real-world networks.

  11. Homophily versus preferential attachment: Evolutionary mechanisms of scientific collaboration networks

    NASA Astrophysics Data System (ADS)

    Wang, Zhen-Zhen; Zhu, Jonathan J. H.

    2014-12-01

    Homophily and preferential attachment are among the most recognized mechanisms of network evolution. Instead of examining the two mechanisms separately, this study considers them jointly in a scholarly collaboration network. Specifically, when a new scholar enters a field, how does he/she choose the first collaborator from the pool of available scholars? We find that new scholars tend to collaborate with someone who works in the same institution (which is called constrained acceptance), shares similar specialty interests (active choice), or has already worked with many collaborators (random action). We view constrained acceptance and active choice as supporting evidence for homophily (because similarity is attractive) and random action as supporting evidence for preferential attachment (because popularity is attractive). As such, both homophily and preferential attachment affect the evolution of collaboration networks. Furthermore, the influences vary over time with random action, constrained acceptance, and active choice taking turns to act the dominant force at the beginning, middle and later phases of the evolution process, respectively.

  12. Soil organic carbon, macropore networks and preferential transport

    NASA Astrophysics Data System (ADS)

    Larsbo, Mats; Koestel, John; Kätterer, Thomas; Jarvis, Nick

    2016-04-01

    Agricultural management practices such as tillage, crop rotations, residue management and fertilization can have a strong influence on soil organic carbon (SOC) stocks. An increase in SOC content will generally improve soil structure, which in turn determines the solute transport pathways through the soil. The aim of this study was to quantify the architecture of macropore networks in undisturbed soil columns (15 cm high, 12.7 cm diameter) sampled along a transect with natural variations in SOC using X-ray tomography and to relate the network characteristics to the degree of preferential transport in the columns. Two tracer experiments were carried out at constant irrigation rates of 2 and 5 mm h-1. We used the normalised 5% arrival time which reflects the tendency for early arrival of the solutes as a measure of the degree of preferential transport. The soil macropore networks were analysed in cylindrical sub-volumes (8 cm high, 10 cm diameter) located centrally within the soil columns. These sub-volumes were considered unaffected by sampling artefacts. Analyses were also carried out the for whole sample volumes to enable comparisons with the results from the transport experiments. Image processing and analysis were carried out in ImageJ and R. The same grey value threshold was applied to all images after harmonisation of grey values using the PVC column walls and the air outside the columns. This approach resulted in a satisfactory separation between the pore space and the surrounding soil matrix and organic matter. The SOC content along the transect, which varied from 4.2 to 15% , was correlated to all measures of the pore network for the sub-volumes except for the connectivity probability. Columns with high SOC content were associated with large macroporosities (both total and connected), large specific surface areas, large fractal dimensions and small mean pore thicknesses. The SOC content for whole sample volumes was positively correlated to 5% arrival times

  13. Complex networks as an emerging property of hierarchical preferential attachment

    NASA Astrophysics Data System (ADS)

    Hébert-Dufresne, Laurent; Laurence, Edward; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J.

    2015-12-01

    Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties universally emerge. We propose that an important class of complex systems can be modeled as an organization of many embedded levels (potentially infinite in number), all of them following the same universal growth principle known as preferential attachment. We give examples of such hierarchy in real systems, for instance, in the pyramid of production entities of the film industry. More importantly, we show how real complex networks can be interpreted as a projection of our model, from which their scale independence, their clustering, their hierarchy, their fractality, and their navigability naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of their unobserved hierarchical nature.

  14. Granger causality stock market networks: Temporal proximity and preferential attachment

    NASA Astrophysics Data System (ADS)

    Výrost, Tomáš; Lyócsa, Štefan; Baumöhl, Eduard

    2015-06-01

    The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-samples revealed three significant findings. First, after the recent financial crisis the impact of the US stock market has declined. Second, spatial probit models confirmed the role of the temporal proximity between market closing times for return spillovers, i.e. the time distance between national stock markets matters. Third, a preferential attachment between stock markets exists, i.e. the probability of the presence of spillover effects between any given two markets increases with their degree of connectedness to others.

  15. 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

  16. Context dependent preferential attachment model for complex networks

    NASA Astrophysics Data System (ADS)

    Pandey, Pradumn Kumar; Adhikari, Bibhas

    2015-10-01

    In this paper, we propose a growing random complex network model, which we call context dependent preferential attachment model (CDPAM), when the preference of a new node to get attached to old nodes is determined by the local and global property of the old nodes. We consider that local and global properties of a node as the degree and relative average degree of the node respectively. We prove that the degree distribution of complex networks generated by CDPAM follow power law with exponent lies in the interval [2,3] and the expected diameter grows logarithmically with the size of new nodes added to the initial small network. Numerical results show that the expected diameter stabilizes when alike weights to the local and global properties are assigned by the new nodes. Computing various measures including clustering coefficient, assortativity, number of triangles, algebraic connectivity, spectral radius, we show that the proposed model replicates properties of real networks when alike weights are given to local and global property. Finally, we observe that the BA model is a limiting case of CDPAM when new nodes tend to give large weight to the local property compared to the weight given to the global property during link formation.

  17. Testing the hypothesis of preferential attachment in social network formation.

    PubMed

    House, Thomas; Read, Jonathan M; Danon, Leon; Keeling, Matthew J

    The hypothesis of preferential attachment (PA) - whereby better connected individuals make more connections - is hotly debated, particularly in the context of epidemiological networks. The simplest models of PA, for example, are incompatible with the eradication of any disease through population-level control measures such as random vaccination. Typically, evidence has been sought for the presence or absence of preferential attachment via asymptotic power-law behaviour. Here, we present a general statistical method to test directly for evidence of PA in count data and apply this to data for contacts relevant to the spread of respiratory diseases. We find that while standard methods for model selection prefer a form of PA, careful analysis of the best fitting PA models allows for a level of contact heterogeneity that in fact allows control of respiratory diseases. Our approach is based on a flexible but numerically cheap likelihood-based model that could in principle be applied to other integer data where the hypothesis of PA is of interest.

  18. 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.

  19. Spreading dynamics of an e-commerce preferential information model on scale-free networks

    NASA Astrophysics Data System (ADS)

    Wan, Chen; Li, Tao; Guan, Zhi-Hong; Wang, Yuanmei; Liu, Xiongding

    2017-02-01

    In order to study the influence of the preferential degree and the heterogeneity of underlying networks on the spread of preferential e-commerce information, we propose a novel susceptible-infected-beneficial model based on scale-free networks. The spreading dynamics of the preferential information are analyzed in detail using the mean-field theory. We determine the basic reproductive number and equilibria. The theoretical analysis indicates that the basic reproductive number depends mainly on the preferential degree and the topology of the underlying networks. We prove the global stability of the information-elimination equilibrium. The permanence of preferential information and the global attractivity of the information-prevailing equilibrium are also studied in detail. Some numerical simulations are presented to verify the theoretical results.

  20. Transcriptional Network Growing Models Using Motif-Based Preferential Attachment.

    PubMed

    Abdelzaher, Ahmed F; Al-Musawi, Ahmad F; Ghosh, Preetam; Mayo, Michael L; Perkins, Edward J

    2015-01-01

    Understanding relationships between architectural properties of gene-regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics, as it can provide insights into, e.g., disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs - i.e., small-node subgraphs that occur more abundantly in GRNs than expected from chance alone. Because these transcriptional modules represent "building blocks" of complex networks and exhibit a wide range of functional and dynamical properties, they may contribute to the remarkable robustness and dynamical stability associated with the whole of GRNs. Here, we developed network-construction models to better understand this relationship, which produce randomized GRNs by using transcriptional motifs as the fundamental growth unit in contrast to other methods that construct similar networks on a node-by-node basis. Because this model produces networks with a prescribed lower bound on the number of choice transcriptional motifs (e.g., downlinks, feed-forward loops), its fidelity to the motif distributions observed in model organisms represents an improvement over existing methods, which we validated by contrasting their resultant motif and degree distributions against existing network-growth models and data from the model organism of the bacterium Escherichia coli. These models may therefore serve as novel testbeds for further elucidating relationships between the topology of transcriptional motifs and network-wide dynamical properties.

  1. Joint estimation of preferential attachment and node fitness in growing complex networks

    PubMed Central

    Pham, Thong; Sheridan, Paul; Shimodaira, Hidetoshi

    2016-01-01

    Complex network growth across diverse fields of science is hypothesized to be driven in the main by a combination of preferential attachment and node fitness processes. For measuring the respective influences of these processes, previous approaches make strong and untested assumptions on the functional forms of either the preferential attachment function or fitness function or both. We introduce a Bayesian statistical method called PAFit to estimate preferential attachment and node fitness without imposing such functional constraints that works by maximizing a log-likelihood function with suitably added regularization terms. We use PAFit to investigate the interplay between preferential attachment and node fitness processes in a Facebook wall-post network. While we uncover evidence for both preferential attachment and node fitness, thus validating the hypothesis that these processes together drive complex network evolution, we also find that node fitness plays the bigger role in determining the degree of a node. This is the first validation of its kind on real-world network data. But surprisingly the rate of preferential attachment is found to deviate from the conventional log-linear form when node fitness is taken into account. The proposed method is implemented in the R package PAFit. PMID:27601314

  2. Joint estimation of preferential attachment and node fitness in growing complex networks

    NASA Astrophysics Data System (ADS)

    Pham, Thong; Sheridan, Paul; Shimodaira, Hidetoshi

    2016-09-01

    Complex network growth across diverse fields of science is hypothesized to be driven in the main by a combination of preferential attachment and node fitness processes. For measuring the respective influences of these processes, previous approaches make strong and untested assumptions on the functional forms of either the preferential attachment function or fitness function or both. We introduce a Bayesian statistical method called PAFit to estimate preferential attachment and node fitness without imposing such functional constraints that works by maximizing a log-likelihood function with suitably added regularization terms. We use PAFit to investigate the interplay between preferential attachment and node fitness processes in a Facebook wall-post network. While we uncover evidence for both preferential attachment and node fitness, thus validating the hypothesis that these processes together drive complex network evolution, we also find that node fitness plays the bigger role in determining the degree of a node. This is the first validation of its kind on real-world network data. But surprisingly the rate of preferential attachment is found to deviate from the conventional log-linear form when node fitness is taken into account. The proposed method is implemented in the R package PAFit.

  3. Feedback mechanism in network dynamics with preferential flow

    NASA Astrophysics Data System (ADS)

    Fan, H.; Wang, Z.; Chen, L.; Aihara, K.

    2009-02-01

    We study complex systems or networks in which each node holds an internal dynamics and interacts with other nodes through some kinds of topologies. Collective behavior with dynamical fluctuations is analyzed in complex systems. The dynamical fluctuations of a node can be divided into two parts: one is the internal dynamical fluctuation of the node and the other is the external dynamical fluctuation caused by other nodes. Based on a theoretical analysis, a hidden feedback mechanism is identified in complex systems, which is illustrated in a macroeconomic network and in a city-population network. Furthermore, we study the effect of the topology of the networks on the feedback mechanism. The feedback mechanism is preserved for hub nodes in the networks with a scale-free topology as well as in the networks with an evolving topology. By the hidden feedback mechanism, the observation data can be utilized to judge directly whether the system of each node is with positive feedback or with negative feedback even without knowing its dynamical model.

  4. A coevolving model based on preferential triadic closure for social media networks

    PubMed Central

    Li, Menghui; Zou, Hailin; Guan, Shuguang; Gong, Xiaofeng; Li, Kun; Di, Zengru; Lai, Choy-Heng

    2013-01-01

    The dynamical origin of complex networks, i.e., the underlying principles governing network evolution, is a crucial issue in network study. In this paper, by carrying out analysis to the temporal data of Flickr and Epinions–two typical social media networks, we found that the dynamical pattern in neighborhood, especially the formation of triadic links, plays a dominant role in the evolution of networks. We thus proposed a coevolving dynamical model for such networks, in which the evolution is only driven by the local dynamics–the preferential triadic closure. Numerical experiments verified that the model can reproduce global properties which are qualitatively consistent with the empirical observations. PMID:23979061

  5. Network growth with preferential attachment and without “rich get richer” mechanism

    NASA Astrophysics Data System (ADS)

    Lachgar, A.; Achahbar, A.

    2016-08-01

    We propose a simple preferential attachment model of growing network using the complementary probability of Barabási-Albert (BA) model, i.e. Π(ki)∝1-ki∑jkj. In this network, new nodes are preferentially attached to not well connected nodes. Numerical simulations, in perfect agreement with the master equation solution, give an exponential degree distribution. This suggests that the power law degree distribution is a consequence of preferential attachment probability together with “rich get richer” phenomena. We also calculate the average degree of a target node at time t() and its fluctuations, to have a better view of the microscopic evolution of the network, and we also compare the results with BA model.

  6. 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.

  7. Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks

    NASA Astrophysics Data System (ADS)

    Sendiña-Nadal, I.; Danziger, M. M.; Wang, Z.; Havlin, S.; Boccaletti, S.

    2016-02-01

    Real-world networks have distinct topologies, with marked deviations from purely random networks. Many of them exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Though microscopic mechanisms have been suggested for the emergence of other topological features, assortativity has proven elusive. Assortativity can be artificially implanted in a network via degree-preserving link permutations, however this destroys the graph’s hierarchical clustering and does not correspond to any microscopic mechanism. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties in degree and clustering distributions and tunable realistic assortativity. Two distinct populations of nodes are incrementally added to an initial network by selecting a subgraph to connect to at random. One population (the followers) follows preferential attachment, while the other population (the potential leaders) connects via anti-preferential attachment: they link to lower degree nodes when added to the network. By selecting the lower degree nodes, the potential leader nodes maintain high visibility during the growth process, eventually growing into hubs. The evolution of links in Facebook empirically validates the connection between the initial anti-preferential attachment and long term high degree. In this way, our work sheds new light on the structure and evolution of social networks.

  8. Preferential metabolism of N-nitrosodiethylamine by two cell lines derived from human pulmonary adenocarcinomas

    SciTech Connect

    Falzon, M.; McMahon, J.B.; Gazdar, A.F.; Schuller, H.M.

    1986-01-01

    Diethylnitrosamine (DEN), in common with other nitrosamines, is a carcinogenic agent which produces tumors in a wide variety of tissues in experimental animals. The pulmonary Clara cell is a major target of N-nitrosamine-induced carcinogenesis in hamsters and rats. DEN is believed to require metabolic activation to elicit its carcinogenic effects. The metabolism of (/sup 14/C)DEN was studied in two cell lines derived from human lung adenocarcinomas and two cell lines derived from human small cell lung cancers by monitoring /sup 14/CO/sub 2/ production and covalent binding of radiolabel from (/sup 14/C)DEN to the cell protein and DNA fractions. (/sup 14/C)DEN was metabolized by adenocarcinoma-derived NCI-H322 (with Clara cell features) and NCI-H358 (with features of alveolar type II cells) but not by NCI-H69 and NCI-H128 (derived from small cell carcinoma). Metabolism was markedly inhibited by heat denaturation of the cell protein. (/sup 14/C)DEN metabolism by NCI-H322 was greatly decreased when the incubation was carried out under anaerobic conditions and in the presence of a carbon monoxide enriched atmosphere. These results suggested the involvement of the cytochrome P-450-dependent monooxygenase enzyme system. Metabolism by NCI-H358 was also decreased in the absence of oxygen or presence of carbon monoxide although the effects were relatively small compared with the results with NCI-H322. On the other hand, aspirin or indomethacin, which are inhibitors of the fatty acid cyclooxygenase component of prostaglandin endoperoxide synthetase, preferentially inhibited (/sup 14/C)DEN metabolism by NIC-H358. There were little or no effects of these inhibitors on the metabolism of DEN in NCI-H322. The data suggest that DEN metabolism in different lung cell types may be carried out by different enzyme systems which in turn may contribute to the selective effect of DEN in the lung.

  9. Succinate is a preferential metabolic stimulus-coupling signal for glucose-induced proinsulin biosynthesis translation.

    PubMed

    Alarcon, Cristina; Wicksteed, Barton; Prentki, Marc; Corkey, Barbara E; Rhodes, Christopher J

    2002-08-01

    The secondary signals emanating from increased glucose metabolism, which lead to specific increases in proinsulin biosynthesis translation, remain elusive. It is known that signals for glucose-stimulated insulin secretion and proinsulin biosynthesis diverge downstream of glycolysis. Consequently, the mitochondrial products ATP, Krebs cycle intermediates, glutamate, and acetoacetate were investigated as candidate stimulus-coupling signals specific for glucose-induced proinsulin biosynthesis in rat islets. Decreasing ATP levels by oxidative phosphorylation inhibitors showed comparable effects on proinsulin biosynthesis and total protein synthesis. Although it is a cofactor, ATP is unlikely to be a metabolic stimulus-coupling signal specific for glucose-induced proinsulin biosynthesis. Neither glutamic acid methyl ester nor acetoacetic acid methyl ester showed a specific effect on glucose-stimulated proinsulin biosynthesis. Interestingly, among Krebs cycle intermediates, only succinic acid monomethyl ester specifically stimulated proinsulin biosynthesis. Malonic acid methyl ester, an inhibitor of succinate dehydrogenase, also specifically increased glucose-induced proinsulin biosynthesis without affecting islet ATP levels or insulin secretion. Glucose caused a 40% increase in islet intracellular succinate levels, but malonic acid methyl ester showed no further effect, probably due to efficient conversion of succinate to succinyl-CoA. In this regard, a GTP-dependent succinyl-CoA synthetase activity was found in cytosolic fractions of pancreatic islets. Thus, succinate and/or succinyl-CoA appear to be preferential metabolic stimulus-coupling factors for glucose-induced proinsulin biosynthesis translation.

  10. 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.

  11. Relations between macropore network characteristics and the degree of preferential solute transport

    NASA Astrophysics Data System (ADS)

    Larsbo, M.; Koestel, J.; Jarvis, N.

    2014-12-01

    The characteristics of the soil macropore network determine the potential for fast transport of agrochemicals and contaminants through the soil. The objective of this study was to examine the relationships between macropore network characteristics, hydraulic properties and state variables and measures of preferential transport. Experiments were carried out under near-saturated conditions on undisturbed columns sampled from four agricultural topsoils of contrasting texture and structure. Macropore network characteristics were computed from 3-D X-ray tomography images of the soil pore system. Non-reactive solute transport experiments were carried out at five steady-state water flow rates from 2 to 12 mm h-1. The degree of preferential transport was evaluated by the normalised 5% solute arrival time and the apparent dispersivity calculated from the resulting breakthrough curves. Near-saturated hydraulic conductivities were measured on the same samples using a tension disc infiltrometer placed on top of the columns. Results showed that many of the macropore network characteristics were inter-correlated. For example, large macroporosities were associated with larger specific macropore surface areas and better local connectivity of the macropore network. Generally, an increased flow rate resulted in earlier solute breakthrough and a shifting of the arrival of peak concentration towards smaller drained volumes. Columns with smaller macroporosities, poorer local connectivity of the macropore network and smaller near-saturated hydraulic conductivities exhibited a greater degree of preferential transport. This can be explained by the fact that, with only two exceptions, global (i.e. sample scale) continuity of the macropore network was still preserved at low macroporosities. Thus, for any given flow rate, pores of larger diameter were actively conducting solute in soils of smaller near-saturated hydraulic conductivity. This was associated with larger local transport

  12. Common mycorrhizal networks amplify competition by preferential mineral nutrient allocation to large host plants.

    PubMed

    Weremijewicz, Joanna; Sternberg, Leonel da Silveira Lobo O'Reilly; Janos, David P

    2016-10-01

    Arbuscular mycorrhizal (AM) fungi interconnect plants in common mycorrhizal networks (CMNs) which can amplify competition among neighbors. Amplified competition might result from the fungi supplying mineral nutrients preferentially to hosts that abundantly provide fixed carbon, as suggested by research with organ-cultured roots. We examined whether CMNs supplied (15) N preferentially to large, nonshaded, whole plants. We conducted an intraspecific target-neighbor pot experiment with Andropogon gerardii and several AM fungi in intact, severed or prevented CMNs. Neighbors were supplied (15) N, and half of the target plants were shaded. Intact CMNs increased target dry weight (DW), intensified competition and increased size inequality. Shading decreased target weight, but shaded plants in intact CMNs had mycorrhizal colonization similar to that of sunlit plants. AM fungi in intact CMNs acquired (15) N from the substrate of neighbors and preferentially allocated it to sunlit, large, target plants. Sunlit, intact CMN, target plants acquired as much as 27% of their nitrogen from the vicinity of their neighbors, but shaded targets did not. These results suggest that AM fungi in CMNs preferentially provide mineral nutrients to those conspecific host individuals best able to provide them with fixed carbon or representing the strongest sinks, thereby potentially amplifying asymmetric competition below ground.

  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. Nonlinear preferential rewiring in fixed-size networks as a diffusion process.

    PubMed

    Johnson, Samuel; Torres, Joaquín J; Marro, Joaquín

    2009-05-01

    We present an evolving network model in which the total numbers of nodes and edges are conserved, but in which edges are continuously rewired according to nonlinear preferential detachment and reattachment. Assuming power-law kernels with exponents alpha and beta , the stationary states which the degree distributions evolve toward exhibit a second-order phase transition-from relatively homogeneous to highly heterogeneous (with the emergence of starlike structures) at alpha=beta . Temporal evolution of the distribution in this critical regime is shown to follow a nonlinear diffusion equation, arriving at either pure or mixed power laws of exponents -alpha and 1-alpha .

  15. Design of pathway-preferential estrogens that provide beneficial metabolic and vascular effects without stimulating reproductive tissues

    PubMed Central

    Madak-Erdogan, Zeynep; Kim, Sung-Hoon; Gong, Ping; Zhao, Yiru C.; Zhang, Hui; Chambliss, Ken L.; Carlson, Kathryn E.; Mayne, Christopher G.; Shaul, Philip W.; Korach, Kenneth S.; Katzenellenbogen, John A.; Katzenellenbogen, Benita S.

    2016-01-01

    There is great medical need for estrogens with favorable pharmacological profiles, that support desirable activities for menopausal women such as metabolic and vascular protection but that lack stimulatory activities on the breast and uterus. Here, we report the development of structurally novel estrogens that preferentially activate a subset of estrogen receptor (ER) signaling pathways and result in favorable target tissue-selective activity. Through a process of structural alteration of estrogenic ligands that was designed to preserve their essential chemical and physical features but greatly reduced their binding affinity for ERs, we obtained “Pathway Preferential Estrogens” (PaPEs) which interacted with ERs to activate the extranuclear-initiated signaling pathway preferentially over the nuclear-initiated pathway. PaPEs elicited a pattern of gene regulation and cellular and biological processes that did not stimulate reproductive and mammary tissues or breast cancer cells. However, in ovariectomized mice, PaPEs triggered beneficial responses both in metabolic tissues (adipose tissue and liver) that reduced body weight gain and fat accumulation and in the vasculature that accelerated repair of endothelial damage. This process of designed ligand structure alteration represents a novel approach to develop ligands that shift the balance in ER-mediated extranuclear and nuclear pathways to obtain tissue-selective, non-nuclear pathway-preferential estrogens, which may be beneficial for postmenopausal hormone replacement. The approach may also have broad applicability for other members of the nuclear hormone receptor superfamily. PMID:27221711

  16. 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

  17. 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.

  18. 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.

  19. 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

  20. Preferential attachment in the growth of social networks: the internet encyclopedia Wikipedia.

    PubMed

    Capocci, A; Servedio, V D P; Colaiori, F; Buriol, L S; Donato, D; Leonardi, S; Caldarelli, G

    2006-09-01

    We present an analysis of the statistical properties and growth of the free on-line encyclopedia Wikipedia. By describing topics by vertices and hyperlinks between them as edges, we can represent this encyclopedia as a directed graph. The topological properties of this graph are in close analogy with those of the World Wide Web, despite the very different growth mechanism. In particular, we measure a scale-invariant distribution of the in and out degree and we are able to reproduce these features by means of a simple statistical model. As a major consequence, Wikipedia growth can be described by local rules such as the preferential attachment mechanism, though users, who are responsible of its evolution, can act globally on the network.

  1. Network growth with arbitrary initial conditions: Degree dynamics for uniform and preferential attachment

    NASA Astrophysics Data System (ADS)

    Fotouhi, Babak; Rabbat, Michael G.

    2013-12-01

    This paper provides time-dependent expressions for the expected degree distribution of a given network that is subject to growth. We consider both uniform attachment, where incoming nodes form links to existing nodes selected uniformly at random, and preferential attachment, where probabilities are assigned proportional to the degrees of the existing nodes. We consider the cases of single and multiple links being formed by each newly introduced node. The initial conditions are arbitrary, that is, the solution depends on the degree distribution of the initial graph which is the substrate of the growth. Previous work in the literature focuses on the asymptotic state, that is, when the number of nodes added to the initial graph tends to infinity, rendering the effect of the initial graph negligible. Our contribution provides a solution for the expected degree distribution as a function of time, for arbitrary initial condition. Previous results match our results in the asymptotic limit. The results are discrete in the degree domain and continuous in the time domain, where the addition of new nodes to the graph are approximated by a continuous arrival rate.

  2. Preferential Effect of Synchrotron Microbeam Radiation Therapy on Intracerebral 9L Gliosarcoma Vascular Networks

    SciTech Connect

    Bouchet, Audrey; Lemasson, Benjamin; Le Duc, Geraldine; Maisin, Cecile; Braeuer-Krisch, Elke; Siegbahn, Erik Albert; Renaud, Luc; Khalil, Enam; Remy, Chantal; Poillot, Cathy; Bravin, Alberto; Laissue, Jean A.; Barbier, Emmanuel L.; Serduc, Raphael

    2010-12-01

    Purpose: Synchrotron microbeam radiation therapy (MRT) relies on spatial fractionation of the incident photon beam into parallel micron-wide beams. Our aim was to analyze the effects of MRT on normal brain and 9L gliosarcoma tissues, particularly on blood vessels. Methods and Materials: Responses to MRT (two arrays, one lateral, one anteroposterior (2 x 400 Gy), intersecting orthogonally in the tumor region) were studied during 6 weeks using MRI, immunohistochemistry, and vascular endothelial growth factor Western blot. Results: MRT increased the median survival time of irradiated rats (x3.25), significantly increased blood vessel permeability, and inhibited tumor growth; a cytotoxic effect on 9L cells was detected 5 days after irradiation. Significant decreases in tumoral blood volume fraction and vessel diameter were measured from 8 days after irradiation, due to loss of endothelial cells in tumors as detected by immunochemistry. Edema was observed in the normal brain exposed to both crossfired arrays about 6 weeks after irradiation. This edema was associated with changes in blood vessel morphology and an overexpression of vascular endothelial growth factor. Conversely, vascular parameters and vessel morphology in brain regions exposed to one of the two arrays were not damaged, and there was no loss of vascular endothelia. Conclusions: We show for the first time that preferential damage of MRT to tumor vessels versus preservation of radioresistant normal brain vessels contributes to the efficient palliation of 9L gliosarcomas in rats. Molecular pathways of repair mechanisms in normal and tumoral vascular networks after MRT may be essential for the improvement of such differential effects on the vasculature.

  3. 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.

  4. 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

  5. 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

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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...

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Cytochrome P450 27A1 Deficiency and Regional Differences in Brain Sterol Metabolism Cause Preferential Cholestanol Accumulation in the Cerebellum.

    PubMed

    Mast, Natalia; Anderson, Kyle W; Lin, Joseph B; Li, Yong; Turko, Illarion V; Tatsuoka, Curtis; Bjorkhem, Ingemar; Pikuleva, Irina A

    2017-02-11

    Cytochrome P450 27A1 (CYP27A1 or sterol 27-hydroxylase) is a ubiquitous, multifunctional enzyme catalyzing regio- and stereo-specific hydroxylation of different sterols. In humans, complete CYP27A1 deficiency leads to cerebrotendinous xanthomatosis or nodule formation in tendons and brain (preferentially in the cerebellum) rich in cholesterol and cholestanol, the 5α-saturated analog of cholesterol. In Cyp27a1(-/-) mice, xanthomas are not formed, despite a significant cholestanol increase in the brain and cerebellum. The mechanism behind cholestanol production has been clarified, yet little is known about its metabolism, except that CYP27A1 might metabolize cholestanol. It also is unclear why CYP27A1 deficiency results in preferential cholestanol accumulation in the cerebellum. We hypothesized that cholestanol might be metabolized by CYP46A1, the principal cholesterol 24-hydroxylase in the brain. We quantified sterols along with CYP27A1 and CYP46A1 in mouse models (Cyp27a1(-/-) , Cyp46a1(-/-) , Cyp27a1(-/-)Cyp46a1(-/-) , and two wild type strains) and human brain specimens. In vitro experiments with purified P450s were conducted as well. We demonstrate that CYP46A1 is involved in cholestanol removal from the brain, and that several factors contribute to the preferential increase in cholestanol in the cerebellum arising from CYP27A1 deficiency. These factors include: (i) low cerebellar abundance of CYP46A1 and high cerebellar abundance of CYP27A1, whose lack likely selectively increases the cerebellar cholestanol production; (ii) spatial separation in the cerebellum of cholesterol/cholestanol-metabolizing P450s from a pool of metabolically available cholestanol; and (iii) weak cerebellar regulation of cholesterol biosynthesis. We identified a new physiological role of CYP46A1, an important brain enzyme and cytochrome P450 that could be activated pharmacologically.

  2. 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 .

  3. Protein-Protein interaction networks: why static MpK model works and preferential attachment does not

    NASA Astrophysics Data System (ADS)

    Zhang, Jingshan; Shakhnovich, Eugene

    2007-03-01

    Various approaches have been proposed to explain the observed scale free structure p(k) ˜k^-γ of protein-protein interaction networks. We argue that the preferential attachment coming from gene duplication[1] is questionable. A static ``MpK'' model produces the scale free structure via computer simulations[2] for unexplained reasons. On the other hand, it was analytically proved[3] that deterministic threshold models produce scale free networks (with γ≡2) if fitness distributions are exponential. We study the static MpK model further and find the above analytical proof applicable with extensions, and γ dependent on the threshold parameter. This work not only predicts the dependence of γ on protein concentrations, but also provides a generic mechanism of scale free networks. The clustering coefficient distribution in the model is interpreted by a simple picture. [1] A.-L. Barab'asi and Z. N. Oltvai, Nature Reviews Genetics 5, 101 (2004). [2] E. J. Deeds, O. Ashenberg, E. I. Shakhnovich, Proc. Natl. Acad. Sci. USA 103, 311 (2006). [3] G. Caldarelli, A. Capocci, P. De Los Rios, and M. A. Muñoz, Phys. Rev. Lett. 89, 258702 (2002).

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  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. 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.

  10. 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.

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

    PubMed

    Kruger, Nicholas J; Ratcliffe, R George

    2015-01-01

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

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. On the growth of directed complex networks with preferential attachment: Effect upon the prohibition of multiple links

    NASA Astrophysics Data System (ADS)

    Esquivel-Gómez, J.; Balderas-Navarro, R. E.; Ugalde, Edgardo; Acosta-Elías, J.

    2015-11-01

    Several real-world directed networks do not have multiple links. For example, in a paper citation network a paper does not cite two identical references, and in a network of friends there exists only a single link between two individuals. This suggest that the growth and evolution models of complex networks should take into account such feature in order to approximate the topological properties of this class of networks. The aim of this paper is to propose a growth model of directed complex networks that takes into account the prohibition of the existence multiple links. It is shown through numerical experiments that when multiple links are forbidden, the exponent γ of the in-degree connectivity distribution, P(k{ in}) ˜ k{ in}-γ , takes values ranging from 1 to ∞. In particular, the proposed multi-link free (MLF) model is able to predict exponents occurring in real-world complex networks, which range 1.05 < γ < 3.51. As an example, the MLF reproduces somxe topological properties exhibited by the network of flights between airports of the world (NFAW); i.e. γ ≈ 1.74. With this result, we believe that the multiple links prohibition might be one of the local processes accounting for the existence of exponents γ < 2 found in some real complex networks.

  14. 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.

  15. 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...

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. Classifying Membrane Proteins in the Proteome by Using Artificial Neural Networks Based on the Preferential Parameters of Amino Acids

    NASA Astrophysics Data System (ADS)

    Bose, Subrata K.; Browne, Antony; Kazemian, Hassan; White, Kenneth

    Membrane proteins (MPs) are large set of biological macromolecules that play a fundamental role in physiology and pathophysiology for survival. From a pharma-economical perspective, though it is the fact that MPs constitute ˜75% of possible targets for novel drugs but MPs are one of the most understudied groups of proteins in biochemical research. This is mainly because of the technical difficulties of obtaining structural information about trans-membrane regions (these are small sequences that crossways the bilayer lipid membrane). It is quite useful to predict the location of transmembrane segments down the sequence, since these are the elementary structural building blocks defining their topology. There have been several attempts over the last 20 years to develop tools for predicting membrane-spanning regions but current tools are far away from achieving a considerable reliability in prediction. This study aims to exploit the knowledge and current understanding in the field of artificial neural networks (ANNs) in particular data representation through the development of a system to identify and predict membrane-spanning regions by analysing primary amino acids sequence. In this paper we present a novel neural network (NNs) architecture and algorithms for predicting membrane spanning regions from primary amino acids sequences by using their preference parameters.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. 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

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. A generalized theory of preferential linking

    NASA Astrophysics Data System (ADS)

    Hu, Haibo; Guo, Jinli; Liu, Xuan; Wang, Xiaofan

    2014-12-01

    There are diverse mechanisms driving the evolution of social networks. A key open question dealing with understanding their evolution is: How do various preferential linking mechanisms produce networks with different features? In this paper we first empirically study preferential linking phenomena in an evolving online social network, find and validate the linear preference. We propose an analyzable model which captures the real growth process of the network and reveals the underlying mechanism dominating its evolution. Furthermore based on preferential linking we propose a generalized model reproducing the evolution of online social networks, and present unified analytical results describing network characteristics for 27 preference scenarios. We study the mathematical structure of degree distributions and find that within the framework of preferential linking analytical degree distributions can only be the combinations of finite kinds of functions which are related to rational, logarithmic and inverse tangent functions, and extremely complex network structure will emerge even for very simple sublinear preferential linking. This work not only provides a verifiable origin for the emergence of various network characteristics in social networks, but bridges the micro individuals' behaviors and the global organization of social networks.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. Emergence of tempered preferential attachment from optimization

    PubMed Central

    D'Souza, Raissa M.; Borgs, Christian; Chayes, Jennifer T.; Berger, Noam; Kleinberg, Robert D.

    2007-01-01

    We show how preferential attachment can emerge in an optimization framework, resolving a long-standing theoretical controversy. We also show that the preferential attachment model so obtained has two novel features, saturation and viability, which have natural interpretations in the underlying network and lead to a power-law degree distribution with exponential cutoff. Moreover, we consider a generalized version of this preferential attachment model with independent saturation and viability, leading to a broader class of power laws again with exponential cutoff. We present a collection of empirical observations from social, biological, physical, and technological networks, for which such degree distributions give excellent fits. We suggest that, in general, optimization models that give rise to preferential attachment with saturation and viability effects form a good starting point for the analysis of many networks. PMID:17395721

  12. 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

  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. 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.

  15. 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.

  16. Controlled CO preferential oxidation

    DOEpatents

    Meltser, M.A.; Hoch, M.M.

    1997-06-10

    Method is described for controlling the supply of air to a PROX (PReferential OXidation for CO cleanup) reactor for the preferential oxidation in the presence of hydrogen wherein the concentration of the hydrogen entering and exiting the PROX reactor is monitored, the difference there between correlated to the amount of air needed to minimize such difference, and based thereon the air supply to the PROX reactor adjusted to provide such amount and minimize such difference. 2 figs.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. MicroRNA regulation of bovine monocyte inflammatory and metabolic networks in an in vivo infection model.

    PubMed

    Lawless, Nathan; Reinhardt, Timothy A; Bryan, Kenneth; Baker, Mike; Pesch, Bruce; Zimmerman, Duane; Zuelke, Kurt; Sonstegard, Tad; O'Farrelly, Cliona; Lippolis, John D; Lynn, David J

    2014-01-27

    Bovine mastitis is an inflammation-driven disease of the bovine mammary gland that costs the global dairy industry several billion dollars per year. Because disease susceptibility is a multifactorial complex phenotype, an integrative biology approach is required to dissect the molecular networks involved. Here, we report such an approach using next-generation sequencing combined with advanced network and pathway biology methods to simultaneously profile mRNA and miRNA expression at multiple time points (0, 12, 24, 36 and 48 hr) in milk and blood FACS-isolated CD14(+) monocytes from animals infected in vivo with Streptococcus uberis. More than 3700 differentially expressed (DE) genes were identified in milk-isolated monocytes (MIMs), a key immune cell recruited to the site of infection during mastitis. Upregulated genes were significantly enriched for inflammatory pathways, whereas downregulated genes were enriched for nonglycolytic metabolic pathways. Monocyte transcriptional changes in the blood, however, were more subtle but highlighted the impact of this infection systemically. Genes upregulated in blood-isolated monocytes (BIMs) showed a significant association with interferon and chemokine signaling. Furthermore, 26 miRNAs were DE in MIMs and three were DE in BIMs. Pathway analysis revealed that predicted targets of downregulated miRNAs were highly enriched for roles in innate immunity (FDR < 3.4E-8), particularly TLR signaling, whereas upregulated miRNAs preferentially targeted genes involved in metabolism. We conclude that during S. uberis infection miRNAs are key amplifiers of monocyte inflammatory response networks and repressors of several metabolic pathways.

  4. 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.

  5. 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

  6. 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.

  7. 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

  8. Information filtering via preferential diffusion

    NASA Astrophysics Data System (ADS)

    Lü, Linyuan; Liu, Weiping

    2011-06-01

    Recommender systems have shown great potential in addressing the information overload problem, namely helping users in finding interesting and relevant objects within a huge information space. Some physical dynamics, including the heat conduction process and mass or energy diffusion on networks, have recently found applications in personalized recommendation. Most of the previous studies focus overwhelmingly on recommendation accuracy as the only important factor, while overlooking the significance of diversity and novelty that indeed provide the vitality of the system. In this paper, we propose a recommendation algorithm based on the preferential diffusion process on a user-object bipartite network. Numerical analyses on two benchmark data sets, MovieLens and Netflix, indicate that our method outperforms the state-of-the-art methods. Specifically, it can not only provide more accurate recommendations, but also generate more diverse and novel recommendations by accurately recommending unpopular objects.

  9. Information filtering via preferential diffusion.

    PubMed

    Lü, Linyuan; Liu, Weiping

    2011-06-01

    Recommender systems have shown great potential in addressing the information overload problem, namely helping users in finding interesting and relevant objects within a huge information space. Some physical dynamics, including the heat conduction process and mass or energy diffusion on networks, have recently found applications in personalized recommendation. Most of the previous studies focus overwhelmingly on recommendation accuracy as the only important factor, while overlooking the significance of diversity and novelty that indeed provide the vitality of the system. In this paper, we propose a recommendation algorithm based on the preferential diffusion process on a user-object bipartite network. Numerical analyses on two benchmark data sets, MovieLens and Netflix, indicate that our method outperforms the state-of-the-art methods. Specifically, it can not only provide more accurate recommendations, but also generate more diverse and novel recommendations by accurately recommending unpopular objects.

  10. 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

  11. 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.

  12. 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.

  13. Design of pathway-level bioprocess monitoring and control strategies supported by metabolic networks.

    PubMed

    Isidro, Inês A; Ferreira, Ana R; Clemente, João J; Cunha, António E; Dias, João M L; Oliveira, Rui

    2013-01-01

    In this chapter we explore the basic tools for the design of bioprocess monitoring, optimization, and control algorithms that incorporate a priori knowledge of metabolic networks. 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).

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. 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.

  19. 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

  20. 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

  1. 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.

  2. Controlled CO preferential oxidation

    DOEpatents

    Meltser, Mark A.; Hoch, Martin M.

    1997-01-01

    Method for controlling the supply of air to a PROX reactor for the preferential oxidation in the presence of hydrogen wherein the concentration of the hydrogen entering and exiting the PROX reactor is monitored, the difference therebetween correlated to the amount of air needed to minimize such difference, and based thereon the air supply to the PROX reactor adjusted to provide such amount and minimize such difference.

  3. 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.

  4. Phenotypic constraints promote latent versatility and carbon efficiency in metabolic networks.

    PubMed

    Bardoscia, Marco; Marsili, Matteo; Samal, Areejit

    2015-07-01

    System-level properties of metabolic networks may be the direct product of natural selection or arise as a by-product of selection on other properties. Here we study the effect of direct selective pressure for growth or viability in particular environments on two properties of metabolic networks: latent versatility to function in additional environments and carbon usage efficiency. Using a Markov chain Monte Carlo (MCMC) sampling based on flux balance analysis (FBA), we sample from a known biochemical universe random viable metabolic networks that differ in the number of directly constrained environments. We find that the latent versatility of sampled metabolic networks increases with the number of directly constrained environments and with the size of the networks. We then show that the average carbon wastage of sampled metabolic networks across the constrained environments decreases with the number of directly constrained environments and with the size of the networks. Our work expands the growing body of evidence about nonadaptive origins of key functional properties of biological networks.

  5. Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions

    PubMed Central

    2012-01-01

    Background The iJO1366 reconstruction of the metabolic network of Escherichia coli is one of the most complete and accurate metabolic reconstructions available for any organism. Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network. Results A new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions. Conclusions Numerous improvements to the iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking. PMID:22548736

  6. A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism.

    PubMed

    Yuan, Huili; Cheung, C Y Maurice; Poolman, Mark G; Hilbers, Peter A J; van Riel, Natal A W

    2016-01-01

    Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses.

  7. Genome-scale analysis of the metabolic networks of oleaginous Zygomycete fungi.

    PubMed

    Vongsangnak, Wanwipa; Ruenwai, Rawisara; Tang, Xin; Hu, Xinjie; Zhang, Hao; Shen, Bairong; Song, Yuanda; Laoteng, Kobkul

    2013-05-25

    Microbial lipids are becoming an attractive option for the industrial production of foods and oleochemicals. To investigate the lipid physiology of the oleaginous microorganisms, at the system level, genome-scale metabolic networks of Mortierella alpina and Mucor circinelloides were constructed using bioinformatics and systems biology. As scaffolds for integrated data analysis focusing on lipid production, consensus metabolic routes governing fatty acid synthesis, and lipid storage and mobilisation were identified by comparative analysis of developed metabolic networks. Unique metabolic features were identified in individual fungi, particularly in NADPH metabolism and sterol biosynthesis, which might be related to differences in fungal lipid phenotypes. The frameworks detailing the metabolic relationship between M. alpina and M. circinelloides generated in this study is useful for further elucidation of the microbial oleaginicity, which might lead to the production improvement of microbial oils as alternative feedstocks for oleochemical industry.

  8. Review of metabolic pathways activated in cancer cells as determined through isotopic labeling and network analysis.

    PubMed

    Dong, Wentao; Keibler, Mark A; Stephanopoulos, Gregory

    2017-02-10

    Cancer metabolism has emerged as an indispensable part of contemporary cancer research. During the past 10 years, the use of stable isotopic tracers and network analysis have unveiled a number of metabolic pathways activated in cancer cells. Here, we review such pathways along with the particular tracers and labeling observations that led to the discovery of their rewiring in cancer cells. The list of such pathways comprises the reductive metabolism of glutamine, altered glycolysis, serine and glycine metabolism, mutant isocitrate dehydrogenase (IDH) induced reprogramming and the onset of acetate metabolism. Additionally, we demonstrate the critical role of isotopic labeling and network analysis in identifying these pathways. The alterations described in this review do not constitute a complete list, and future research using these powerful tools is likely to discover other cancer-related pathways and new metabolic targets for cancer therapy.

  9. 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.

  10. Metabolic network reconstruction, growth characterization and 13C-metabolic flux analysis of the extremophile Thermus thermophilus HB8.

    PubMed

    Swarup, Aditi; Lu, Jing; DeWoody, Kathleen C; Antoniewicz, Maciek R

    2014-07-01

    Thermus thermophilus is an extremely thermophilic bacterium with significant biotechnological potential. In this work, we have characterized aerobic growth characteristics of T. thermophilus HB8 at temperatures between 50 and 85°C, constructed a metabolic network model of its central carbon metabolism and validated the model using (13)C-metabolic flux analysis ((13)C-MFA). First, cells were grown in batch cultures in custom constructed mini-bioreactors at different temperatures to determine optimal growth conditions. The optimal temperature for T. thermophilus grown on defined medium with glucose was 81°C. The maximum growth rate was 0.25h(-1). Between 50 and 81°C the growth rate increased by 7-fold and the temperature dependence was described well by an Arrhenius model with an activation energy of 47kJ/mol. Next, we performed a (13)C-labeling experiment with [1,2-(13)C] glucose as the tracer and calculated intracellular metabolic fluxes using (13)C-MFA. The results provided support for the constructed network model and highlighted several interesting characteristics of T. thermophilus metabolism. We found that T. thermophilus largely uses glycolysis and TCA cycle to produce biosynthetic precursors, ATP and reducing equivalents needed for cells growth. Consistent with its proposed metabolic network model, we did not detect any oxidative pentose phosphate pathway flux or Entner-Doudoroff pathway activity. The biomass precursors erythrose-4-phosphate and ribose-5-phosphate were produced via the non-oxidative pentose phosphate pathway, and largely via transketolase, with little contribution from transaldolase. The high biomass yield on glucose that was measured experimentally was also confirmed independently by (13)C-MFA. The results presented here provide a solid foundation for future studies of T. thermophilus and its metabolic engineering applications.

  11. Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network.

    PubMed

    Galhardo, Mafalda; Sinkkonen, Lasse; Berninger, Philipp; Lin, Jake; Sauter, Thomas; Heinäniemi, Merja

    2014-02-01

    Metabolic diseases and comorbidities represent an ever-growing epidemic where multiple cell types impact tissue homeostasis. Here, the link between the metabolic and gene regulatory networks was studied through experimental and computational analysis. Integrating gene regulation data with a human metabolic network prompted the establishment of an open-sourced web portal, IDARE (Integrated Data Nodes of Regulation), for visualizing various gene-related data in context of metabolic pathways. Motivated by increasing availability of deep sequencing studies, we obtained ChIP-seq data from widely studied human umbilical vein endothelial cells. Interestingly, we found that association of metabolic genes with multiple transcription factors (TFs) enriched disease-associated genes. To demonstrate further extensions enabled by examining these networks together, constraint-based modeling was applied to data from human preadipocyte differentiation. In parallel, data on gene expression, genome-wide ChIP-seq profiles for peroxisome proliferator-activated receptor (PPAR) γ, CCAAT/enhancer binding protein (CEBP) α, liver X receptor (LXR) and H3K4me3 and microRNA target identification for miR-27a, miR-29a and miR-222 were collected. Disease-relevant key nodes, including mitochondrial glycerol-3-phosphate acyltransferase (GPAM), were exposed from metabolic pathways predicted to change activity by focusing on association with multiple regulators. In both cell types, our analysis reveals the convergence of microRNAs and TFs within the branched chain amino acid (BCAA) metabolic pathway, possibly providing an explanation for its downregulation in obese and diabetic conditions.

  12. A pivoting algorithm for metabolic networks in the presence of thermodynamic constraints.

    PubMed

    Nigam, R; Liang, S

    2005-01-01

    A linear programming algorithm is presented to constructively compute thermodynamically feasible fluxes and change in chemical potentials of reactions for a metabolic network. It is based on physical laws of mass conservation and the second law of thermodynamics that all chemical reactions should satisfy. As a demonstration, the algorithm has been applied to the core metabolic pathway of E. coli.

  13. 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.

  14. The AT-hook motif-encoding gene METABOLIC NETWORK MODULATOR 1 underlies natural variation in Arabidopsis primary metabolism

    PubMed Central

    Li, Baohua; Kliebenstein, Daniel J.

    2014-01-01

    Regulation of primary metabolism is a central mechanism by which plants coordinate their various responses to biotic and abiotic challenge. To identify genes responsible for natural variation in primary metabolism, we focused on cloning a locus from Arabidopsis thaliana that influences the level of TCA cycle metabolites in planta. We found that the Met.V.67 locus was controlled by natural variation in METABOLIC NETWORK MODULATOR 1 (MNM1), which encoded an AT-hook motif-containing protein that was unique to the Brassicales lineage. MNM1 had wide ranging effects on plant metabolism and displayed a tissue expression pattern that was suggestive of a function in sink tissues. Natural variation within MNM1 had differential effects during a diurnal time course, and this temporal dependency was supported by analysis of T-DNA insertion and over-expression lines for MNM1. Thus, the cloning of a natural variation locus specifically associated with primary metabolism allowed us to identify MNM1 as a lineage-specific modulator of primary metabolism, suggesting that the regulation of primary metabolism can change during evolution. PMID:25202318

  15. 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.

  16. Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

    NASA Technical Reports Server (NTRS)

    Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)

    2001-01-01

    Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.

  17. Dead End Metabolites - Defining the Known Unknowns of the E. coli Metabolic Network

    PubMed Central

    Mackie, Amanda; Keseler, Ingrid M.; Nolan, Laura; Karp, Peter D.; Paulsen, Ian T.

    2013-01-01

    The EcoCyc database is an online scientific database which provides an integrated view of the metabolic and regulatory network of the bacterium Escherichia coli K-12 and facilitates computational exploration of this important model organism. We have analysed the occurrence of dead end metabolites within the database – these are metabolites which lack the requisite reactions (either metabolic or transport) that would account for their production or consumption within the metabolic network. 127 dead end metabolites were identified from the 995 compounds that are contained within the EcoCyc metabolic network. Their presence reflects either a deficit in our representation of the network or in our knowledge of E. coli metabolism. Extensive literature searches resulted in the addition of 38 transport reactions and 3 metabolic reactions to the database and led to an improved representation of the pathway for Vitamin B12 salvage. 39 dead end metabolites were identified as components of reactions that are not physiologically relevant to E. coli K-12 – these reactions are properties of purified enzymes in vitro that would not be expected to occur in vivo. Our analysis led to improvements in the software that underpins the database and to the program that finds dead end metabolites within EcoCyc. The remaining dead end metabolites in the EcoCyc database likely represent deficiencies in our knowledge of E. coli metabolism. PMID:24086468

  18. Discovering missing reactions of metabolic networks by using gene co-expression data

    PubMed Central

    Hosseini, Zhaleh; Marashi, Sayed-Amir

    2017-01-01

    Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts. PMID:28150713

  19. Discovering missing reactions of metabolic networks by using gene co-expression data

    NASA Astrophysics Data System (ADS)

    Hosseini, Zhaleh; Marashi, Sayed-Amir

    2017-02-01

    Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.

  20. Construction and analysis of a genome-scale metabolic network for Bacillus licheniformis WX-02.

    PubMed

    Guo, Jing; Zhang, Hong; Wang, Cheng; Chang, Ji-Wei; Chen, Ling-Ling

    2016-05-01

    We constructed the genome-scale metabolic network of Bacillus licheniformis (B. licheniformis) WX-02 by combining genomic annotation, high-throughput phenotype microarray (PM) experiments and literature-based metabolic information. The accuracy of the metabolic network was assessed by an OmniLog PM experiment. The final metabolic model iWX1009 contains 1009 genes, 1141 metabolites and 1762 reactions, and the predicted metabolic phenotypes showed an agreement rate of 76.8% with experimental PM data. In addition, key metabolic features such as growth yield, utilization of different substrates and essential genes were identified by flux balance analysis. A total of 195 essential genes were predicted from LB medium, among which 149 were verified with the experimental essential gene set of B. subtilis 168. With the removal of 5 reactions from the network, pathways for poly-γ-glutamic acid (γ-PGA) synthesis were optimized and the γ-PGA yield reached 83.8 mmol/h. Furthermore, the important metabolites and pathways related to γ-PGA synthesis and bacterium growth were comprehensively analyzed. The present study provides valuable clues for exploring the metabolisms and metabolic regulation of γ-PGA synthesis in B. licheniformis WX-02.

  1. Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome

    PubMed Central

    Loughran, Thomas P.; Papin, Jason A.; Albert, Reka

    2015-01-01

    We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth. PMID:26102287

  2. Putting The Plant Metabolic Network pathway databases to work: going offline to gain new capabilities.

    PubMed

    Dreher, Kate

    2014-01-01

    Metabolic databases such as The Plant Metabolic Network/MetaCyc and KEGG PATHWAY are publicly accessible resources providing organism-specific information on reactions and metabolites. KEGG PATHWAY depicts metabolic networks as wired, electronic circuit-like maps, whereas the MetaCyc family of databases uses a canonical textbook-like representation. The first MetaCyc-based database for a plant species was AraCyc, which describes metabolism in the model plant Arabidopsis. This database was created over 10 years ago and has since then undergone extensive manual curation to reflect updated information on enzymes and pathways in Arabidopsis. This chapter describes accessing and using AraCyc and its underlying Pathway Tools software. Specifically, methods for (1) navigating Pathway Tools, (2) visualizing omics data and superimposing the data on a metabolic pathway map, and (3) creating pathways and pathway components are discussed.

  3. An algorithm for rapid computational construction of metabolic networks: a cholesterol biosynthesis example.

    PubMed

    Belič, Aleš; Pompon, Denis; Monostory, Katalin; Kelly, Diane; Kelly, Steven; Rozman, Damjana

    2013-06-01

    Alternative pathways of metabolic networks represent the escape routes that can reduce drug efficacy and can cause severe adverse effects. In this paper we introduce a mathematical algorithm and a coding system for rapid computational construction of metabolic networks. The initial data for the algorithm are the source substrate code and the enzyme/metabolite interaction tables. The major strength of the algorithm is the adaptive coding system of the enzyme-substrate interactions. A reverse application of the algorithm is also possible, when optimisation algorithm is used to compute the enzyme/metabolite rules from the reference network structure. The coding system is user-defined and must be adapted to the studied problem. The algorithm is most effective for computation of networks that consist of metabolites with similar molecular structures. The computation of the cholesterol biosynthesis metabolic network suggests that 89 intermediates can theoretically be formed between lanosterol and cholesterol, only 20 are presently considered as cholesterol intermediates. Alternative metabolites may represent links with other metabolic networks both as precursors and metabolites of cholesterol. A possible cholesterol-by-pass pathway to bile acids metabolism through cholestanol is suggested.

  4. NExT: integration of thermodynamic constraints and metabolomics data into a metabolic network.

    PubMed

    Martínez, Verónica Sofía; Nielsen, Lars K

    2014-01-01

    Thermodynamic constraints are widely used in metabolic modelling such that calculated flux phenotypes are closer to real cell behavior. If metabolic data is also included in the analysis, a check of the thermodynamic consistency of the data can be realized and subsequently use the metabolic data to further constrain the solution space, giving a more specific representation of the cell metabolism under the studied conditions. Here NExT, a software based on network-embedded thermodynamic analysis, is presented, to integrate thermodynamics constraints and metabolomics data in the estimation of intracellular fluxes. New irreversible reactions can be inferred by calculating the thermodynamically feasible range of metabolite concentrations and Gibbs energy of reactions.

  5. Effect of substrate competition in kinetic models of metabolic networks.

    PubMed

    Schäuble, Sascha; Stavrum, Anne Kristin; Puntervoll, Pål; Schuster, Stefan; Heiland, Ines

    2013-09-02

    Substrate competition can be found in many types of biological processes, ranging from gene expression to signal transduction and metabolic pathways. Although several experimental and in silico studies have shown the impact of substrate competition on these processes, it is still often neglected, especially in modelling approaches. Using toy models that exemplify different metabolic pathway scenarios, we show that substrate competition can influence the dynamics and the steady state concentrations of a metabolic pathway. We have additionally derived rate laws for substrate competition in reversible reactions and summarise existing rate laws for substrate competition in irreversible reactions.

  6. 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

  7. Reconstruction of Tissue-Specific Metabolic Networks Using CORDA

    PubMed Central

    Schultz, André; Qutub, Amina A.

    2016-01-01

    Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation. PMID:26942765

  8. A general model for metabolic scaling in self-similar asymmetric networks

    PubMed Central

    Savage, Van M.; Enquist, Brian J.

    2017-01-01

    How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE) model argues that these two principles (space-filling and energy minimization) are (i) general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii) can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber’s Law can still be attained within many asymmetric networks. PMID:28319153

  9. A general model for metabolic scaling in self-similar asymmetric networks.

    PubMed

    Brummer, Alexander Byers; Savage, Van M; Enquist, Brian J

    2017-03-01

    How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE) model argues that these two principles (space-filling and energy minimization) are (i) general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii) can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber's Law can still be attained within many asymmetric networks.

  10. Theoretical Basis for Dynamic Label Propagation in Stationary Metabolic Networks under Step and Periodic Inputs.

    PubMed

    Sokol, Serguei; Portais, Jean-Charles

    2015-01-01

    The dynamics of label propagation in a stationary metabolic network during an isotope labeling experiment can provide highly valuable information on the network topology, metabolic fluxes, and on the size of metabolite pools. However, major issues, both in the experimental set-up and in the accompanying numerical methods currently limit the application of this approach. Here, we propose a method to apply novel types of label inputs, sinusoidal or more generally periodic label inputs, to address both the practical and numerical challenges of dynamic labeling experiments. By considering a simple metabolic system, i.e. a linear, non-reversible pathway of arbitrary length, we develop mathematical descriptions of label propagation for both classical and novel label inputs. Theoretical developments and computer simulations show that the application of rectangular periodic pulses has both numerical and practical advantages over other approaches. We applied the strategy to estimate fluxes in a simulated experiment performed on a complex metabolic network (the central carbon metabolism of Escherichia coli), to further demonstrate its value in conditions which are close to those in real experiments. This study provides a theoretical basis for the rational interpretation of label propagation curves in real experiments, and will help identify the strengths, pitfalls and limitations of such experiments. The cases described here can also be used as test cases for more general numerical methods aimed at identifying network topology, analyzing metabolic fluxes or measuring concentrations of metabolites.

  11. Metabolic profiling during peach fruit development and ripening reveals the metabolic networks that underpin each developmental stage.

    PubMed

    Lombardo, Verónica A; Osorio, Sonia; Borsani, Julia; Lauxmann, Martin A; Bustamante, Claudia A; Budde, Claudio O; Andreo, Carlos S; Lara, María V; Fernie, Alisdair R; Drincovich, María F

    2011-12-01

    Fruit from rosaceous species collectively display a great variety of flavors and textures as well as a generally high content of nutritionally beneficial metabolites. However, relatively little analysis of metabolic networks in rosaceous fruit has been reported. Among rosaceous species, peach (Prunus persica) has stone fruits composed of a juicy mesocarp and lignified endocarp. Here, peach mesocarp metabolic networks were studied across development using metabolomics and analysis of key regulatory enzymes. Principal component analysis of peach metabolic composition revealed clear metabolic shifts from early through late development stages and subsequently during postharvest ripening. Early developmental stages were characterized by a substantial decrease in protein abundance and high levels of bioactive polyphenols and amino acids, which are substrates for the phenylpropanoid and lignin pathways during stone hardening. Sucrose levels showed a large increase during development, reflecting translocation from the leaf, while the importance of galactinol and raffinose is also inferred. Our study further suggests that posttranscriptional mechanisms are key for metabolic regulation at early stages. In contrast to early developmental stages, a decrease in amino acid levels is coupled to an induction of transcripts encoding amino acid and organic acid catabolic enzymes during ripening. These data are consistent with the mobilization of amino acids to support respiration. In addition, sucrose cycling, suggested by the parallel increase of transcripts encoding sucrose degradative and synthetic enzymes, appears to operate during postharvest ripening. When taken together, these data highlight singular metabolic programs for peach development and may allow the identification of key factors related to agronomic traits of this important crop species.

  12. 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

  13. A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information

    PubMed Central

    Nägele, Thomas; Fürtauer, Lisa; Nagler, Matthias; Weiszmann, Jakob; Weckwerth, Wolfram

    2016-01-01

    The functional connection of experimental metabolic time series data with biochemical network information is an important, yet complex, issue in systems biology. Frequently, experimental analysis of diurnal, circadian, or developmental dynamics of metabolism results in a comprehensive and multidimensional data matrix comprising information about metabolite concentrations, protein levels, and/or enzyme activities. While, irrespective of the type of organism, the experimental high-throughput analysis of the transcriptome, proteome, and metabolome has become a common part of many systems biological studies, functional data integration in a biochemical and physiological context is still challenging. Here, an approach is presented which addresses the functional connection of experimental time series data with biochemical network information which can be inferred, for example, from a metabolic network reconstruction. Based on a time-continuous and variance-weighted regression analysis of experimental data, metabolic functions, i.e., first-order derivatives of metabolite concentrations, were related to time-dependent changes in other biochemically relevant metabolic functions, i.e., second-order derivatives of metabolite concentrations. This finally revealed time points of perturbed dependencies in metabolic functions indicating a modified biochemical interaction. The approach was validated using previously published experimental data on a diurnal time course of metabolite levels, enzyme activities, and metabolic flux simulations. To support and ease the presented approach of functional time series analysis, a graphical user interface including a test data set and a manual is provided which can be run within the numerical software environment Matlab®. PMID:27014700

  14. A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information.

    PubMed

    Nägele, Thomas; Fürtauer, Lisa; Nagler, Matthias; Weiszmann, Jakob; Weckwerth, Wolfram

    2016-01-01

    The functional connection of experimental metabolic time series data with biochemical network information is an important, yet complex, issue in systems biology. Frequently, experimental analysis of diurnal, circadian, or developmental dynamics of metabolism results in a comprehensive and multidimensional data matrix comprising information about metabolite concentrations, protein levels, and/or enzyme activities. While, irrespective of the type of organism, the experimental high-throughput analysis of the transcriptome, proteome, and metabolome has become a common part of many systems biological studies, functional data integration in a biochemical and physiological context is still challenging. Here, an approach is presented which addresses the functional connection of experimental time series data with biochemical network information which can be inferred, for example, from a metabolic network reconstruction. Based on a time-continuous and variance-weighted regression analysis of experimental data, metabolic functions, i.e., first-order derivatives of metabolite concentrations, were related to time-dependent changes in other biochemically relevant metabolic functions, i.e., second-order derivatives of metabolite concentrations. This finally revealed time points of perturbed dependencies in metabolic functions indicating a modified biochemical interaction. The approach was validated using previously published experimental data on a diurnal time course of metabolite levels, enzyme activities, and metabolic flux simulations. To support and ease the presented approach of functional time series analysis, a graphical user interface including a test data set and a manual is provided which can be run within the numerical software environment Matlab®.

  15. Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency

    NASA Astrophysics Data System (ADS)

    Weng, Rui; Shen, Sensen; Tian, Yonglu; Burton, Casey; Xu, Xinyuan; Liu, Yi; Chang, Cuilan; Bai, Yu; Liu, Huwei

    2015-07-01

    Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer’s disease and Parkinson’s disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states.

  16. Consistent abnormalities in metabolic network activity in idiopathic rapid eye movement sleep behaviour disorder.

    PubMed

    Wu, Ping; Yu, Huan; Peng, Shichun; Dauvilliers, Yves; Wang, Jian; Ge, Jingjie; Zhang, Huiwei; Eidelberg, David; Ma, Yilong; Zuo, Chuantao

    2014-12-01

    Rapid eye movement sleep behaviour disorder has been evaluated using Parkinson's disease-related metabolic network. It is unknown whether this disorder is itself associated with a unique metabolic network. 18F-fluorodeoxyglucose positron emission tomography was performed in 21 patients (age 65.0±5.6 years) with idiopathic rapid eye movement sleep behaviour disorder and 21 age/gender-matched healthy control subjects (age 62.5±7.5 years) to identify a disease-related pattern and examine its evolution in 21 hemi-parkinsonian patients (age 62.6±5.0 years) and 16 moderate parkinsonian patients (age 56.9±12.2 years). We identified a rapid eye movement sleep behaviour disorder-related metabolic network characterized by increased activity in pons, thalamus, medial frontal and sensorimotor areas, hippocampus, supramarginal and inferior temporal gyri, and posterior cerebellum, with decreased activity in occipital and superior temporal regions. Compared to the healthy control subjects, network expressions were elevated (P<0.0001) in the patients with this disorder and in the parkinsonian cohorts but decreased with disease progression. Parkinson's disease-related network activity was also elevated (P<0.0001) in the patients with rapid eye movement sleep behaviour disorder but lower than in the hemi-parkinsonian cohort. Abnormal metabolic networks may provide markers of idiopathic rapid eye movement sleep behaviour disorder to identify those at higher risk to develop neurodegenerative parkinsonism.

  17. Comparative analysis of Salmonella genomes identifies a metabolic network for escalating growth in the inflamed gut.

    PubMed

    Nuccio, Sean-Paul; Bäumler, Andreas J

    2014-03-18

    The Salmonella genus comprises a group of pathogens associated with illnesses ranging from gastroenteritis to typhoid fever. We performed an in silico analysis of comparatively reannotated Salmonella genomes to identify genomic signatures indicative of disease potential. By removing numerous annotation inconsistencies and inaccuracies, the process of reannotation identified a network of 469 genes involved in central anaerobic metabolism, which was intact in genomes of gastrointestinal pathogens but degrading in genomes of extraintestinal pathogens. This large network contained pathways that enable gastrointestinal pathogens to utilize inflammation-derived nutrients as well as many of the biochemical reactions used for the enrichment and biochemical discrimination of Salmonella serovars. Thus, comparative genome analysis identifies a metabolic network that provides clues about the strategies for nutrient acquisition and utilization that are characteristic of gastrointestinal pathogens. IMPORTANCE While some Salmonella serovars cause infections that remain localized to the gut, others disseminate throughout the body. Here, we compared Salmonella genomes to identify characteristics that distinguish gastrointestinal from extraintestinal pathogens. We identified a large metabolic network that is functional in gastrointestinal pathogens but decaying in extraintestinal pathogens. While taxonomists have used traits from this network empirically for many decades for the enrichment and biochemical discrimination of Salmonella serovars, our findings suggest that it is part of a "business plan" for growth in the inflamed gastrointestinal tract. By identifying a large metabolic network characteristic of Salmonella serovars associated with gastroenteritis, our in silico analysis provides a blueprint for potential strategies to utilize inflammation-derived nutrients and edge out competing gut microbes.

  18. TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

    PubMed Central

    2011-01-01

    Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR) relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data. PMID:21943338

  19. Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions.

    PubMed

    Blais, Edik M; Rawls, Kristopher D; Dougherty, Bonnie V; Li, Zhuo I; Kolling, Glynis L; Ye, Ping; Wallqvist, Anders; Papin, Jason A

    2017-02-08

    The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications.

  20. Interferon Control of the Sterol Metabolic Network: Bidirectional Molecular Circuitry-Mediating Host Protection

    PubMed Central

    Robertson, Kevin A.; Ghazal, Peter

    2016-01-01

    The sterol metabolic network is emerging center stage in inflammation and immunity. Historically, observational clinical studies show that hypocholesterolemia is a common side effect of interferon (IFN) treatment. More recently, comprehensive systems-wide investigations of the macrophage IFN response reveal a direct molecular link between cholesterol metabolism and infection. Upon infection, flux through the sterol metabolic network is acutely moderated by the IFN response at multiple regulatory levels. The precise mechanisms by which IFN regulates the mevalonate-sterol pathway—the spine of the network—are beginning to be unraveled. In this review, we discuss our current understanding of the multifactorial mechanisms by which IFN regulates the sterol pathway. We also consider bidirectional communications resulting in sterol metabolism regulation of immunity. Finally, we deliberate on how this fundamental interaction functions as an integral element of host protective responses to infection and harmful inflammation. PMID:28066443

  1. Metabolic network rewiring of propionate flux compensates vitamin B12 deficiency in C. elegans.

    PubMed

    Watson, Emma; Olin-Sandoval, Viridiana; Hoy, Michael J; Li, Chi-Hua; Louisse, Timo; Yao, Victoria; Mori, Akihiro; Holdorf, Amy D; Troyanskaya, Olga G; Ralser, Markus; Walhout, Albertha Jm

    2016-07-06

    Metabolic network rewiring is the rerouting of metabolism through the use of alternate enzymes to adjust pathway flux and accomplish specific anabolic or catabolic objectives. Here, we report the first characterization of two parallel pathways for the breakdown of the short chain fatty acid propionate in Caenorhabditis elegans. Using genetic interaction mapping, gene co-expression analysis, pathway intermediate quantification and carbon tracing, we uncover a vitamin B12-independent propionate breakdown shunt that is transcriptionally activated on vitamin B12 deficient diets, or under genetic conditions mimicking the human diseases propionic- and methylmalonic acidemia, in which the canonical B12-dependent propionate breakdown pathway is blocked. Our study presents the first example of transcriptional vitamin-directed metabolic network rewiring to promote survival under vitamin deficiency. The ability to reroute propionate breakdown according to B12 availability may provide C. elegans with metabolic plasticity and thus a selective advantage on different diets in the wild.

  2. Investigation of the central carbon metabolism of Sorangium cellulosum: metabolic network reconstruction and quantification of pathway fluxes.

    PubMed

    Bolten, Christoph J; Heinzle, Elmar; Müller, Rolf; Wittmann, Christoph

    2009-01-01

    In the present work, the metabolic network of primary metabolism of the slow-growing myxobacterium Sorangium cellulosum was reconstructed from the annotated genome sequence of the type strain So ce56. During growth on glucose as the carbon source and asparagine as the nitrogen source, So ce56 showed a very low growth rate of 0.23 d-(1), equivalent to a doubling time of 3 days. Based on a complete stoichiometric and isotopomer model of the central metabolism, 13C metabolic flux analysis was carried out for growth with glucose as carbon and asparagine as nitrogen sources. Normalized to the uptake flux for glucose (100%), cells recruited glycolysis (51%) and the pentose phosphate pathway (48%) as major catabolic pathways. The Entner-Doudoroff pathway and glyoxylate shunt were not active. A high flux through the TCA cycle (118%) enabled a strong formation of ATP, but cells revealed a rather low yield for biomass. Inspection of fluxes linked to energy metabolism revealed that S. cellulosum utilized only 10% of the ATP formed for growth, whereas 90% is required for maintenance. This explains the apparent discrepancy between the relatively low biomass yield and the high flux through the energy-delivering TCA cycle. The total flux of NADPH supply (216%) was higher than the demand for anabolism (156%), indicating additional reactions for balancing of NADPH. The cells further exhibited a highly active metabolic cycle, interconverting C3 and C4 metabolites of glycolysis and the TCA cycle. The present work provides the first insight into fluxes of the primary metabolism of myxobacteria, especially for future investigation on the supply of cofactors, building blocks, and energy in myxobacteria, producing natural compounds of biotechnological interest.

  3. Understanding the control of acyl flux through the lipid metabolic network of plant oil biosynthesis.

    PubMed

    Bates, Philip D

    2016-09-01

    Plant oil biosynthesis involves a complex metabolic network with multiple subcellular compartments, parallel pathways, cycles, and pathways that have a dual function to produce essential membrane lipids and triacylglycerol. Modern molecular biology techniques provide tools to alter plant oil compositions through bioengineering, however with few exceptions the final composition of triacylglycerol cannot be predicted. One reason for limited success in oilseed bioengineering is the inadequate understanding of how to control the flux of fatty acids through various fatty acid modification, and triacylglycerol assembly pathways of the lipid metabolic network. This review focuses on the mechanisms of acyl flux through the lipid metabolic network, and highlights where uncertainty resides in our understanding of seed oil biosynthesis. This article is part of a Special Issue entitled: Plant Lipid Biology edited by Kent D. Chapman and Ivo Feussner.

  4. Energy balance for analysis of complex metabolic networks.

    PubMed Central

    Beard, Daniel A; Liang, Shou-dan; Qian, Hong

    2002-01-01

    Predicting behavior of large-scale biochemical networks represents one of the greatest challenges of bioinformatics and computational biology. Computational tools for predicting fluxes in biochemical networks are applied in the fields of integrated and systems biology, bioinformatics, and genomics, and to aid in drug discovery and identification of potential drug targets. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are promising tools for the analysis of large complex networks. Here we introduce energy balance analysis (EBA)--the theory and methodology for enforcing the laws of thermodynamics in such simulations--making the results more physically realistic and revealing greater insight into the regulatory and control mechanisms operating in complex large-scale systems. We show that EBA eliminates thermodynamically infeasible results associated with FBA. PMID:12080101

  5. Detection of driver metabolites in the human liver metabolic network using structural controllability analysis

    PubMed Central

    2014-01-01

    Background Abnormal states in human liver metabolism are major causes of human liver diseases ranging from hepatitis to hepatic tumor. The accumulation in relevant data makes it feasible to derive a large-scale human liver metabolic network (HLMN) and to discover important biological principles or drug-targets based on network analysis. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis (which is a newly prevailed concept in networks) to biological networks. The exploration on the connections between structural controllability theory and the HLMN could be used to uncover valuable information on the human liver metabolism from a fresh perspective. Results We applied structural controllability analysis to the HLMN and detected driver metabolites. The driver metabolites tend to have strong ability to influence the states of other metabolites and weak susceptibility to be influenced by the states of others. In addition, the metabolites were classified into three classes: critical, high-frequency and low-frequency driver metabolites. Among the identified 36 critical driver metabolites, 27 metabolites were found to be essential; the high-frequency driver metabolites tend to participate in different metabolic pathways, which are important in regulating the whole metabolic systems. Moreover, we explored some other possible connections between the structural controllability theory and the HLMN, and find that transport reactions and the environment play important roles in the human liver metabolism. Conclusion There are interesting connections between the structural controllability theory and the human liver metabolism: driver metabolites have essential biological functions; the crucial role of extracellular metabolites and transport reactions in controlling the HLMN highlights the importance of the environment in the health of human liver metabolism. PMID:24885538

  6. 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

  7. Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

    PubMed

    Granger, Brian R; Chang, Yi-Chien; Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

    2016-04-01

    The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

  8. Visualization of metabolic interaction networks in microbial communities using VisANT 5.0

    SciTech Connect

    Granger, Brian R.; Chang, Yi -Chien; Wang, Yan; DeLisi, Charles; Segre, Daniel; Hu, Zhenjun

    2016-04-15

    Here, the complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique meta-graph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues.

  9. Towards stable kinetics of large metabolic networks: Nonequilibrium potential function approach

    NASA Astrophysics Data System (ADS)

    Chen, Yong-Cong; Yuan, Ruo-Shi; Ao, Ping; Xu, Min-Juan; Zhu, Xiao-Mei

    2016-06-01

    While the biochemistry of metabolism in many organisms is well studied, details of the metabolic dynamics are not fully explored yet. Acquiring adequate in vivo kinetic parameters experimentally has always been an obstacle. Unless the parameters of a vast number of enzyme-catalyzed reactions happened to fall into very special ranges, a kinetic model for a large metabolic network would fail to reach a steady state. In this work we show that a stable metabolic network can be systematically established via a biologically motivated regulatory process. The regulation is constructed in terms of a potential landscape description of stochastic and nongradient systems. The constructed process draws enzymatic parameters towards stable metabolism by reducing the change in the Lyapunov function tied to the stochastic fluctuations. Biologically it can be viewed as interplay between the flux balance and the spread of workloads on the network. Our approach allows further constraints such as thermodynamics and optimal efficiency. We choose the central metabolism of Methylobacterium extorquens AM1 as a case study to demonstrate the effectiveness of the approach. Growth efficiency on carbon conversion rate versus cell viability and futile cycles is investigated in depth.

  10. An Evidence-Based Review of Related Metabolites and Metabolic Network Research on Cerebral Ischemia

    PubMed Central

    Liu, Mengting; Tang, Liying; Liu, Xin; Fang, Jing; Zhan, Hao; Wu, Hongwei; Yang, Hongjun

    2016-01-01

    In recent years, metabolomics analyses have been widely applied to cerebral ischemia research. This paper introduces the latest proceedings of metabolomics research on cerebral ischemia. The main techniques, models, animals, and biomarkers of cerebral ischemia will be discussed. With analysis help from the MBRole website and the KEGG database, the altered metabolites in rat cerebral ischemia were used for metabolic pathway enrichment analyses. Our results identify the main metabolic pathways that are related to cerebral ischemia and further construct a metabolic network. These results will provide useful information for elucidating the pathogenesis of cerebral ischemia, as well as the discovery of cerebral ischemia biomarkers. PMID:27274780

  11. Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach

    PubMed Central

    Kaltdorf, Martin; Srivastava, Mugdha; Gupta, Shishir K.; Liang, Chunguang; Binder, Jasmin; Dietl, Anna-Maria; Meir, Zohar; Haas, Hubertus; Osherov, Nir; Krappmann, Sven; Dandekar, Thomas

    2016-01-01

    New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness (“hubs”), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines. PMID:27379244

  12. [Controlling arachidonic acid metabolic network: from single- to multi-target inhibitors of key enzymes].

    PubMed

    Liu, Ying; Chen, Zheng; Shang, Er-chang; Yang, Kun; Wei, Deng-guo; Zhou, Lu; Jiang, Xiao-lu; He, Chong; Lai, Lu-hua

    2009-03-01

    Inflammatory diseases are common medical conditions seen in disorders of human immune system. There is a great demand for anti-inflammatory drugs. There are major inflammatory mediators in arachidonic acid metabolic network. Several enzymes in this network have been used as key targets for the development of anti-inflammatory drugs. However, specific single-target inhibitors can not sufficiently control the network balance and may cause side effects at the same time. Most inflammation induced diseases come from the complicated coupling of inflammatory cascades involving multiple targets. In order to treat these complicated diseases, drugs that can intervene multi-targets at the same time attracted much attention. The goal of this review is mainly focused on the key enzymes in arachidonic acid metabolic network, such as phospholipase A2, cyclooxygenase, 5-lipoxygenase and eukotriene A4 hydrolase. Advance in single target and multi-targe inhibitors is summarized.

  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. [Gene networks that regulate secondary metabolism in actinomycetes: pleiotropic regulators].

    PubMed

    Rabyk, M V; Ostash, B O; Fedorenko, V O

    2014-01-01

    Current advances in the research and practical applications of pleiotropic regulatory genes for antibiotic production in actinomycetes are reviewed. The basic regulatory mechanisms found in these bacteria are outlined. Examples described in the review show the importance of the manipulation of regulatory systems that affect the synthesis of antibiotics for the metabolic engineering of the actinomycetes. Also, the study of these genes is the basis for the development of genetic engineering approaches towards the induction of "cryptic" part of the actinomycetes secondary metabolome, which capacity for production of biologically active compounds is much bigger than the diversity of antibiotics underpinned by traditional microbiological screening. Besides the practical problems, the study of regulatory genes for antibiotic biosynthesis will provide insights into the process of evolution of complex regulatory systems that coordinate the expression of gene operons, clusters and regulons, involved in the control of secondary metabolism and morphogenesis of actinomycetes.

  15. Identification of candidate network hubs involved in metabolic adjustments of rice under drought stress by integrating transcriptome data and genome-scale metabolic network.

    PubMed

    Mohanty, Bijayalaxmi; Kitazumi, Ai; Cheung, C Y Maurice; Lakshmanan, Meiyappan; de los Reyes, Benildo G; Jang, In-Cheol; Lee, Dong-Yup

    2016-01-01

    In this study, we have integrated a rice genome-scale metabolic network and the transcriptome of a drought-tolerant rice line, DK151, to identify the major transcriptional regulators involved in metabolic adjustments necessary for adaptation to drought. This was achieved by examining the differential expressions of transcription factors and metabolic genes in leaf, root and young panicle of rice plants subjected to drought stress during tillering, booting and panicle elongation stages. Critical transcription factors such as AP2/ERF, bZIP, MYB and NAC that control the important nodes in the gene regulatory pathway were identified through correlative analysis of the patterns of spatio-temporal expression and cis-element enrichment. We showed that many of the candidate transcription factors involved in metabolic adjustments were previously linked to phenotypic variation for drought tolerance. This approach represents the first attempt to integrate models of transcriptional regulation and metabolic pathways for the identification of candidate regulatory genes for targeted selection in rice breeding.

  16. An extended bioreaction database that significantly improves reconstruction and analysis of genome-scale metabolic networks.

    PubMed

    Stelzer, Michael; Sun, Jibin; Kamphans, Tom; Fekete, Sándor P; Zeng, An-Ping

    2011-11-01

    The bioreaction database established by Ma and Zeng (Bioinformatics, 2003, 19, 270-277) for in silico reconstruction of genome-scale metabolic networks has been widely used. Based on more recent information in the reference databases KEGG LIGAND and Brenda, we upgrade the bioreaction database in this work by almost doubling the number of reactions from 3565 to 6851. Over 70% of the reactions have been manually updated/revised in terms of reversibility, reactant pairs, currency metabolites and error correction. For the first time, 41 spontaneous sugar mutarotation reactions are introduced into the biochemical database. The upgrade significantly improves the reconstruction of genome scale metabolic networks. Many gaps or missing biochemical links can be recovered, as exemplified with three model organisms Homo sapiens, Aspergillus niger, and Escherichia coli. The topological parameters of the constructed networks were also largely affected, however, the overall network structure remains scale-free. Furthermore, we consider the problem of computing biologically feasible shortest paths in reconstructed metabolic networks. We show that these paths are hard to compute and present solutions to find such paths in networks of small and medium size.

  17. On dynamically generating relevant elementary flux modes in a metabolic network using optimization.

    PubMed

    Oddsdóttir, Hildur Æsa; Hagrot, Erika; Chotteau, Véronique; Forsgren, Anders

    2015-10-01

    Elementary flux modes (EFMs) are pathways through a metabolic reaction network that connect external substrates to products. Using EFMs, a metabolic network can be transformed into its macroscopic counterpart, in which the internal metabolites have been eliminated and only external metabolites remain. In EFMs-based metabolic flux analysis (MFA) experimentally determined external fluxes are used to estimate the flux of each EFM. It is in general prohibitive to enumerate all EFMs for complex networks, since the number of EFMs increases rapidly with network complexity. In this work we present an optimization-based method that dynamically generates a subset of EFMs and solves the EFMs-based MFA problem simultaneously. The obtained subset contains EFMs that contribute to the optimal solution of the EFMs-based MFA problem. The usefulness of our method was examined in a case-study using data from a Chinese hamster ovary cell culture and two networks of varied complexity. It was demonstrated that the EFMs-based MFA problem could be solved at a low computational cost, even for the more complex network. Additionally, only a fraction of the total number of EFMs was needed to compute the optimal solution.

  18. Preferential Remedies for Employment Discrimination

    ERIC Educational Resources Information Center

    Edwards, Harry T.; Zaretsky, Barry L.

    1975-01-01

    An overview of the problem of preferential remedies to achieve equal employment opportunities for women and minority groups. Contends that "color blindness" will not end discrimination but that some form of "color conscious" affirmative action program must be employed. Temporary preferential treatment is justified, according to…

  19. (Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis

    PubMed Central

    2013-01-01

    Background Metabolic control analysis (MCA) and supply–demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply–demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. Results This study integrates control engineering and classical MCA augmented with supply–demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the ‘integral control’ (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of ‘integral control’ should rarely be expected to lead to the ‘perfect adaptation’: although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. Conclusions A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and

  20. Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism.

    PubMed

    Tabe-Bordbar, Shayan; Marashi, Sayed-Amir

    2013-12-01

    Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing EMs may hamper their applicability as, in most genome-scale metabolic models, no EM can be computed due to running out of memory. In this study, we present a method for computing randomly sampled EMs. In this approach, a network reduction algorithm is used for EM computation, which is based on flux balance-based methods. We show that this approach can be used to recover the EMs in the medium- and genome-scale metabolic network models, while the EMs are sampled in an unbiased way. The applicability of such results is shown by computing “estimated” control-effective flux values in Escherichia coli metabolic network.

  1. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

    PubMed

    Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

    2016-05-01

    Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine.

  2. Ongoing resolution of duplicate gene functions shapes the diversification of a metabolic network

    PubMed Central

    Kuang, Meihua Christina; Hutchins, Paul D; Russell, Jason D; Coon, Joshua J; Hittinger, Chris Todd

    2016-01-01

    The evolutionary mechanisms leading to duplicate gene retention are well understood, but the long-term impacts of paralog differentiation on the regulation of metabolism remain underappreciated. Here we experimentally dissect the functions of two pairs of ancient paralogs of the GALactose sugar utilization network in two yeast species. We show that the Saccharomyces uvarum network is more active, even as over-induction is prevented by a second co-repressor that the model yeast Saccharomyces cerevisiae lacks. Surprisingly, removal of this repression system leads to a strong growth arrest, likely due to overly rapid galactose catabolism and metabolic overload. Alternative sugars, such as fructose, circumvent metabolic control systems and exacerbate this phenotype. We further show that S. cerevisiae experiences homologous metabolic constraints that are subtler due to how the paralogs have diversified. These results show how the functional differentiation of paralogs continues to shape regulatory network architectures and metabolic strategies long after initial preservation. DOI: http://dx.doi.org/10.7554/eLife.19027.001 PMID:27690225

  3. Visualization of metabolic interaction networks in microbial communities using VisANT 5.0

    DOE PAGES

    Granger, Brian R.; Chang, Yi -Chien; Wang, Yan; ...

    2016-04-15

    Here, the complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique meta-graph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction networkmore » between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues.« less

  4. Glucose Metabolic Brain Networks in Early-Onset vs. Late-Onset Alzheimer's Disease

    PubMed Central

    Chung, Jinyong; Yoo, Kwangsun; Kim, Eunjoo; Na, Duk L.; Jeong, Yong

    2016-01-01

    Objective: Early-onset Alzheimer's disease (EAD) shows distinct features from late-onset Alzheimer's disease (LAD). To explore the characteristics of EAD, clinical, neuropsychological, and functional imaging studies have been conducted. However, differences between EAD and LAD are not clear, especially in terms of brain connectivity and networks. In this study, we investigated the differences in metabolic connectivity between EAD and LAD by adopting graph theory measures. Methods: We analyzed 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) images to investigate the distinct features of metabolic connectivity between EAD and LAD. Using metabolic connectivity and graph theory analysis, metabolic network differences between LAD and EAD were explored. Results: Results showed the decreased connectivity centered in the cingulate gyri and occipital regions in EAD, whereas decreased connectivity in the occipital and temporal regions as well as increased connectivity in the supplementary motor area were observed in LAD when compared with age-matched control groups. Global efficiency and clustering coefficients were decreased in EAD but not in LAD. EAD showed progressive network deterioration as a function of disease severity and clinical dementia rating (CDR) scores, mainly in terms of connectivity between the cingulate gyri and occipital regions. Global efficiency and clustering coefficients were also decreased along with disease severity. Conclusion: These results indicate that EAD and LAD have distinguished features in terms of metabolic connectivity, with EAD demonstrating more extensive and progressive deterioration. PMID:27445800

  5. Quantitative Tools for Dissection of Hydrogen-Producing Metabolic Networks-Final Report

    SciTech Connect

    Rabinowitz, Joshua D.; Dismukes, G.Charles.; Rabitz, Herschel A.; Amador-Noguez, Daniel

    2012-10-19

    During this project we have pioneered the development of integrated experimental-computational technologies for the quantitative dissection of metabolism in hydrogen and biofuel producing microorganisms (i.e. C. acetobutylicum and various cyanobacteria species). The application of these new methodologies resulted in many significant advances in the understanding of the metabolic networks and metabolism of these organisms, and has provided new strategies to enhance their hydrogen or biofuel producing capabilities. As an example, using mass spectrometry, isotope tracers, and quantitative flux-modeling we mapped the metabolic network structure in C. acetobutylicum. This resulted in a comprehensive and quantitative understanding of central carbon metabolism that could not have been obtained using genomic data alone. We discovered that biofuel production in this bacterium, which only occurs during stationary phase, requires a global remodeling of central metabolism (involving large changes in metabolite concentrations and fluxes) that has the effect of redirecting resources (carbon and reducing power) from biomass production into solvent production. This new holistic, quantitative understanding of metabolism is now being used as the basis for metabolic engineering strategies to improve solvent production in this bacterium. In another example, making use of newly developed technologies for monitoring hydrogen and NAD(P)H levels in vivo, we dissected the metabolic pathways for photobiological hydrogen production by cyanobacteria Cyanothece sp. This investigation led to the identification of multiple targets for improving hydrogen production. Importantly, the quantitative tools and approaches that we have developed are broadly applicable and we are now using them to investigate other important biofuel producers, such as cellulolytic bacteria.

  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 Modeling of Microbial Interactions in Natural and Engineered Environmental Systems.

    PubMed

    Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

    2016-01-01

    We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can

  11. Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

    PubMed Central

    Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

    2016-01-01

    We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can

  12. 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.

  13. The long-chain alkane metabolism network of Alcanivorax dieselolei.

    PubMed

    Wang, Wanpeng; Shao, Zongze

    2014-12-12

    Alkane-degrading bacteria are ubiquitous in marine environments, but little is known about how alkane degradation is regulated. Here we investigate alkane sensing, chemotaxis, signal transduction, uptake and pathway regulation in Alcanivorax dieselolei. The outer membrane protein OmpS detects the presence of alkanes and triggers the expression of an alkane chemotaxis complex. The coupling protein CheW2 of the chemotaxis complex, which is induced only by long-chain (LC) alkanes, sends signals to trigger the expression of Cyo, which participates in modulating the expression of the negative regulator protein AlmR. This change in turn leads to the expression of ompT1 and almA, which drive the selective uptake and hydroxylation of LC alkanes, respectively. AlmA is confirmed as a hydroxylase of LC alkanes. Additional factors responsible for the metabolism of medium-chain-length alkanes are also identified, including CheW1, OmpT1 and OmpT2. These results provide new insights into alkane metabolism pathways from alkane sensing to degradation.

  14. Hepatokines: unlocking the multi-organ network in metabolic diseases.

    PubMed

    Iroz, Alison; Couty, Jean-Pierre; Postic, Catherine

    2015-08-01

    In the face of urbanisation, surplus energy intake, sedentary habits and obesity, type 2 diabetes has developed into a major health concern worldwide. Commonly overlooked in contemporary obesity research, the liver is emerging as a central regulator of whole body energy homeostasis. Liver-derived proteins known as hepatokines are now considered attractive targets for the development of novel type 2 diabetes treatments. This commentary presents examples of three leading hepatokines: fetuin-A, the first to be described and correlated with increased inflammation and insulin resistance; angiopoietin-like protein (ANGPTL)8/betatrophin, initially proposed for its action on beta cell proliferation, although this effect has recently been brought into question; and fibroblast growth factor 21 (FGF21), an insulin-sensitising hormone that is an appealing drug target because of its beneficial metabolic actions. Novel discoveries in hepatokine research may lead to promising biomarkers and treatments for metabolic disorders and type 2 diabetes. This is one of a series of commentaries under the banner '50 years forward', giving personal opinions on future perspectives in diabetes, to celebrate the 50th anniversary of Diabetologia (1965-2015).

  15. SIRT5 regulates the mitochondrial lysine succinylome and metabolic networks.

    PubMed

    Rardin, Matthew J; He, Wenjuan; Nishida, Yuya; Newman, John C; Carrico, Chris; Danielson, Steven R; Guo, Ailan; Gut, Philipp; Sahu, Alexandria K; Li, Biao; Uppala, Radha; Fitch, Mark; Riiff, Timothy; Zhu, Lei; Zhou, Jing; Mulhern, Daniel; Stevens, Robert D; Ilkayeva, Olga R; Newgard, Christopher B; Jacobson, Matthew P; Hellerstein, Marc; Goetzman, Eric S; Gibson, Bradford W; Verdin, Eric

    2013-12-03

    Reversible posttranslational modifications are emerging as critical regulators of mitochondrial proteins and metabolism. Here, we use a label-free quantitative proteomic approach to characterize the lysine succinylome in liver mitochondria and its regulation by the desuccinylase SIRT5. A total of 1,190 unique sites were identified as succinylated, and 386 sites across 140 proteins representing several metabolic pathways including β-oxidation and ketogenesis were significantly hypersuccinylated in Sirt5(-/-) animals. Loss of SIRT5 leads to accumulation of medium- and long-chain acylcarnitines and decreased β-hydroxybutyrate production in vivo. In addition, we demonstrate that SIRT5 regulates succinylation of the rate-limiting ketogenic enzyme 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) both in vivo and in vitro. Finally, mutation of hypersuccinylated residues K83 and K310 on HMGCS2 to glutamic acid strongly inhibits enzymatic activity. Taken together, these findings establish SIRT5 as a global regulator of lysine succinylation in mitochondria and present a mechanism for inhibition of ketogenesis through HMGCS2.

  16. A Maize Gene Regulatory Network for Phenolic Metabolism.

    PubMed

    Yang, Fan; Li, Wei; Jiang, Nan; Yu, Haidong; Morohashi, Kengo; Ouma, Wilberforce Zachary; Morales-Mantilla, Daniel E; Gomez-Cano, Fabio Andres; Mukundi, Eric; Prada-Salcedo, Luis Daniel; Velazquez, Roberto Alers; Valentin, Jasmin; Mejía-Guerra, Maria Katherine; Gray, John; Doseff, Andrea I; Grotewold, Erich

    2017-03-06

    The translation of the genotype into phenotype, represented for example by the expression of genes encoding enzymes required for the biosynthesis of phytochemicals that are important for interaction of plants with the environment, is largely carried out by transcription factors (TFs) that recognize specific cis-regulatory elements in the genes that they control. TFs and their target genes are organized in gene regulatory networks (GRNs), and thus uncovering GRN architecture presents an important biological challenge necessary to explain gene regulation. Linking TFs to the genes they control, central to understanding GRNs, can be carried out using gene- or TF-centered approaches. In this study, we employed a gene-centered approach utilizing the yeast one-hybrid assay to generate a network of protein-DNA interactions that participate in the transcriptional control of genes involved in the biosynthesis of maize phenolic compounds including general phenylpropanoids, lignins, and flavonoids. We identified 1100 protein-DNA interactions involving 54 phenolic gene promoters and 568 TFs. A set of 11 TFs recognized 10 or more promoters, suggesting a role in coordinating pathway gene expression. The integration of the gene-centered network with information derived from TF-centered approaches provides a foundation for a phenolics GRN characterized by interlaced feed-forward loops that link developmental regulators with biosynthetic genes.

  17. Cold adaptation shapes the robustness of metabolic networks in Drosophila melanogaster

    PubMed Central

    Williams, CM; Watanabe, M; Guarracino, MR; Ferraro, MB; Edison, AS; Morgan, TJ; Boroujerdi, AFB; Hahn, DA

    2015-01-01

    When ectotherms are exposed to low temperatures, they enter a cold-induced coma (chill coma) that prevents resource acquisition, mating, oviposition, and escape from predation. There is substantial variation in time taken to recover from chill coma both within and among species, and this variation is correlated with habitat temperatures such that insects from cold environments recover more quickly. This suggests an adaptive response, but the mechanisms underlying variation in recovery times are unknown, making it difficult to decisively test adaptive hypotheses. We use replicated lines of Drosophila melanogaster selected in the laboratory for fast (hardy) or slow (susceptible) chill-coma recovery times to investigate modifications to metabolic profiles associated with cold adaptation. We measured metabolite concentrations of flies before, during, and after cold exposure using NMR spectroscopy to test the hypotheses that hardy flies maintain metabolic homeostasis better during cold exposure and recovery, and that their metabolic networks are more robust to cold-induced perturbations. The metabolites of cold-hardy flies were less cold responsive and their metabolic networks during cold exposure were more robust, supporting our hypotheses. Metabolites involved in membrane lipid synthesis, tryptophan metabolism, oxidative stress, energy balance, and proline metabolism were altered by selection on cold tolerance. We discuss the potential significance of these alterations. PMID:25308124

  18. c-Myc activates multiple metabolic networks to generate substrates for cell-cycle entry.

    PubMed

    Morrish, F; Isern, N; Sadilek, M; Jeffrey, M; Hockenbery, D M

    2009-07-09

    Cell proliferation requires the coordinated activity of cytosolic and mitochondrial metabolic pathways to provide ATP and building blocks for DNA, RNA and protein synthesis. Many metabolic pathway genes are targets of the c-myc oncogene and cell-cycle regulator. However, the contribution of c-Myc to the activation of cytosolic and mitochondrial metabolic networks during cell-cycle entry is unknown. Here, we report the metabolic fates of [U-(13)C] glucose in serum-stimulated myc(-/-) and myc(+/+) fibroblasts by (13)C isotopomer NMR analysis. We demonstrate that endogenous c-myc increased (13)C labeling of ribose sugars, purines and amino acids, indicating partitioning of glucose carbons into C1/folate and pentose phosphate pathways, and increased tricarboxylic acid cycle turnover at the expense of anaplerotic flux. Myc expression also increased global O-linked N-acetylglucosamine protein modification, and inhibition of hexosamine biosynthesis selectively reduced growth of Myc-expressing cells, suggesting its importance in Myc-induced proliferation. These data reveal a central organizing function for the Myc oncogene in the metabolism of cycling cells. The pervasive deregulation of this oncogene in human cancers may be explained by its function in directing metabolic networks required for cell proliferation.

  19. 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.

  20. Identifying all moiety conservation laws in genome-scale metabolic networks.

    PubMed

    De Martino, Andrea; De Martino, Daniele; Mulet, Roberto; Pagnani, Andrea

    2014-01-01

    The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

  1. Cooccurrence of free-living amoebae and nontuberculous Mycobacteria in hospital water networks, and preferential growth of Mycobacterium avium in Acanthamoeba lenticulata.

    PubMed

    Ovrutsky, Alida R; Chan, Edward D; Kartalija, Marinka; Bai, Xiyuan; Jackson, Mary; Gibbs, Sara; Falkinham, Joseph O; Iseman, Michael D; Reynolds, Paul R; McDonnell, Gerald; Thomas, Vincent

    2013-05-01

    The incidence of lung and other diseases due to nontuberculous mycobacteria (NTM) is increasing. NTM sources include potable water, especially in households where NTM populate pipes, taps, and showerheads. NTM share habitats with free-living amoebae (FLA) and can grow in FLA as parasites or as endosymbionts. FLA containing NTM may form cysts that protect mycobacteria from disinfectants and antibiotics. We first assessed the presence of FLA and NTM in water and biofilm samples collected from a hospital, confirming the high prevalence of NTM and FLA in potable water systems, particularly in biofilms. Acanthamoeba spp. (genotype T4) were mainly recovered (8/17), followed by Hartmannella vermiformis (7/17) as well as one isolate closely related to the genus Flamella and one isolate only distantly related to previously described species. Concerning mycobacteria, Mycobacterium gordonae was the most frequently found isolate (9/17), followed by Mycobacterium peregrinum (4/17), Mycobacterium chelonae (2/17), Mycobacterium mucogenicum (1/17), and Mycobacterium avium (1/17). The propensity of Mycobacterium avium hospital isolate H87 and M. avium collection strain 104 to survive and replicate within various FLA was also evaluated, demonstrating survival of both strains in all amoebal species tested but high replication rates only in Acanthamoeba lenticulata. As A. lenticulata was frequently recovered from environmental samples, including drinking water samples, these results could have important consequences for the ecology of M. avium in drinking water networks and the epidemiology of disease due to this species.

  2. Cooccurrence of Free-Living Amoebae and Nontuberculous Mycobacteria in Hospital Water Networks, and Preferential Growth of Mycobacterium avium in Acanthamoeba lenticulata

    PubMed Central

    Ovrutsky, Alida R.; Kartalija, Marinka; Bai, Xiyuan; Jackson, Mary; Gibbs, Sara; Falkinham, Joseph O.; Iseman, Michael D.; Reynolds, Paul R.; McDonnell, Gerald

    2013-01-01

    The incidence of lung and other diseases due to nontuberculous mycobacteria (NTM) is increasing. NTM sources include potable water, especially in households where NTM populate pipes, taps, and showerheads. NTM share habitats with free-living amoebae (FLA) and can grow in FLA as parasites or as endosymbionts. FLA containing NTM may form cysts that protect mycobacteria from disinfectants and antibiotics. We first assessed the presence of FLA and NTM in water and biofilm samples collected from a hospital, confirming the high prevalence of NTM and FLA in potable water systems, particularly in biofilms. Acanthamoeba spp. (genotype T4) were mainly recovered (8/17), followed by Hartmannella vermiformis (7/17) as well as one isolate closely related to the genus Flamella and one isolate only distantly related to previously described species. Concerning mycobacteria, Mycobacterium gordonae was the most frequently found isolate (9/17), followed by Mycobacterium peregrinum (4/17), Mycobacterium chelonae (2/17), Mycobacterium mucogenicum (1/17), and Mycobacterium avium (1/17). The propensity of Mycobacterium avium hospital isolate H87 and M. avium collection strain 104 to survive and replicate within various FLA was also evaluated, demonstrating survival of both strains in all amoebal species tested but high replication rates only in Acanthamoeba lenticulata. As A. lenticulata was frequently recovered from environmental samples, including drinking water samples, these results could have important consequences for the ecology of M. avium in drinking water networks and the epidemiology of disease due to this species. PMID:23475613

  3. Data-driven integration of genome-scale regulatory and metabolic network models.

    PubMed

    Imam, Saheed; Schäuble, Sascha; Brooks, Aaron N; Baliga, Nitin S; Price, Nathan D

    2015-01-01

    Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert-a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

  4. Data-driven integration of genome-scale regulatory and metabolic network models

    DOE PAGES

    Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.; ...

    2015-05-05

    Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less

  5. Data-driven integration of genome-scale regulatory and metabolic network models

    SciTech Connect

    Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.; Baliga, Nitin S.; Price, Nathan D.

    2015-05-05

    Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

  6. Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

    PubMed

    Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-03-01

    "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory.

  7. Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy.

    PubMed

    Quinn, Robert A; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C

    2017-02-01

    Molecular networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chemistry of molecules dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related molecules are likely to display similar fragment ion spectra. Molecular networking organizes the MS/MS data as a relational spectral network thereby mapping the chemistry that was detected in an MS/MS-based metabolomics experiment. Although the wider utility of molecular networking is just beginning to be recognized, in this review we highlight the principles behind molecular networking and its use for the discovery of therapeutic leads, monitoring drug metabolism, clinical diagnostics, and emerging applications in precision medicine.

  8. Invariant features of metabolic networks: a data analysis application on scaling properties of biochemical pathways

    NASA Astrophysics Data System (ADS)

    Giuliani, Alessandro; Zbilut, Joseph P.; Conti, Filippo; Manetti, Cesare; Miccheli, Alfredo

    2004-06-01

    The network metaphor is currently one of the most common general paradigms in biological sciences: this paradigm spans different scales of definition going from gene regulation to protein-protein interaction studies and metabolic regulation networks. Generally, the networks are defined by the nature of the connected elements (nodes) and their relative relations (edges). In this paper we demonstrate how the same biochemical regulation network can assume different shapes in terms of both constituting elements and intervening relations while remaining recognizable as a specific entity. This behaviour can be explained by the general scaling properties of biological networks and points to regulation pathways as emergent features of biochemical systems posited at a different hierarchical level with respect to the intervening metabolites.

  9. An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models

    PubMed Central

    Chindelevitch, Leonid; Trigg, Jason; Regev, Aviv; Berger, Bonnie

    2014-01-01

    Constraint-based models are currently the only methodology that allows the study of metabolism at the whole-genome scale. Flux balance analysis is commonly used to analyse constraint-based models. Curiously, the results of this analysis vary with the software being run, a situation that we show can be remedied by using exact rather than floating-point arithmetic. Here we introduce MONGOOSE, a toolbox for analysing the structure of constraint-based metabolic models in exact arithmetic. We apply MONGOOSE to the analysis of 98 existing metabolic network models and find that the biomass reaction is surprisingly blocked (unable to sustain non-zero flux) in nearly half of them. We propose a principled approach for unblocking these reactions and extend it to the problems of identifying essential and synthetic lethal reactions and minimal media. Our structural insights enable a systematic study of constraint-based metabolic models, yielding a deeper understanding of their possibilities and limitations. PMID:25291352

  10. Network thermodynamic curation of human and yeast genome-scale metabolic models.

    PubMed

    Martínez, Verónica S; Quek, Lake-Ee; Nielsen, Lars K

    2014-07-15

    Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties.

  11. Growth states of catalytic reaction networks exhibiting energy metabolism

    NASA Astrophysics Data System (ADS)

    Kondo, Yohei; Kaneko, Kunihiko

    2011-07-01

    All cells derive nutrition by absorbing some chemical and energy resources from the environment; these resources are used by the cells to reproduce the chemicals within them, which in turn leads to an increase in their volume. In this study we introduce a protocell model exhibiting catalytic reaction dynamics, energy metabolism, and cell growth. Results of extensive simulations of this model show the existence of four phases with regard to the rates of both the influx of resources and cell growth. These phases include an active phase with high influx and high growth rates, an inefficient phase with high influx but low growth rates, a quasistatic phase with low influx and low growth rates, and a death phase with negative growth rate. A mean field model well explains the transition among these phases as bifurcations. The statistical distribution of the active phase is characterized by a power law, and that of the inefficient phase is characterized by a nearly equilibrium distribution. We also discuss the relevance of the results of this study to distinct states in the existing cells.

  12. A data integration and visualization resource for the metabolic network of Synechocystis sp. PCC 6803.

    PubMed

    Maarleveld, Timo R; Boele, Joost; Bruggeman, Frank J; Teusink, Bas

    2014-03-01

    Data integration is a central activity in systems biology. The integration of genomic, transcript, protein, metabolite, flux, and computational data yields unprecedented information about the system level functioning of organisms. Often, data integration is done purely computationally, leaving the user with little insight in addition to statistical information. In this article, we present a visualization tool for the metabolic network of Synechocystis sp. PCC 6803, an important model cyanobacterium for sustainable biofuel production. We illustrate how this metabolic map can be used to integrate experimental and computational data for Synechocystis sp. PCC 6803 systems biology and metabolic engineering studies. Additionally, we discuss how this map, and the software infrastructure that we supply with it, can be used in the development of other organism-specific metabolic network visualizations. In addition to the Python console package VoNDA (http://vonda.sf.net), we provide a working demonstration of the interactive metabolic map and the associated Synechocystis sp. PCC 6803 genome-scale stoichiometric model, as well as various ready-to-visualize microarray data sets, at http://f-a-m-e.org/synechocytis.

  13. 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.

  14. AMBIENT: Active Modules for Bipartite Networks - using high-throughput transcriptomic data to dissect metabolic response

    PubMed Central

    2013-01-01

    Background With the continued proliferation of high-throughput biological experiments, there is a pressing need for tools to integrate the data produced in ways that produce biologically meaningful conclusions. Many microarray studies have analysed transcriptomic data from a pathway perspective, for instance by testing for KEGG pathway enrichment in sets of upregulated genes. However, the increasing availability of species-specific metabolic models provides the opportunity to analyse these data in a more objective, system-wide manner. Results Here we introduce ambient (Active Modules for Bipartite Networks), a simulated annealing approach to the discovery of metabolic subnetworks (modules) that are significantly affected by a given genetic or environmental change. The metabolic modules returned by ambient are connected parts of the bipartite network that change coherently between conditions, providing a more detailed view of metabolic changes than standard approaches based on pathway enrichment. Conclusions ambient is an effective and flexible tool for the analysis of high-throughput data in a metabolic context. The same approach can be applied to any system in which reactions (or metabolites) can be assigned a score based on some biological observation, without the limitation of predefined pathways. A Python implementation of ambient is available at http://www.theosysbio.bio.ic.ac.uk/ambient. PMID:23531303

  15. 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

  16. Genome-scale reconstruction of metabolic networks of Lactobacillus casei ATCC 334 and 12A.

    PubMed

    Vinay-Lara, Elena; Hamilton, Joshua J; Stahl, Buffy; Broadbent, Jeff R; Reed, Jennifer L; Steele, James L

    2014-01-01

    Lactobacillus casei strains are widely used in industry and the utility of this organism in these industrial applications is strain dependent. Hence, tools capable of predicting strain specific phenotypes would have utility in the selection of strains for specific industrial processes. Genome-scale metabolic models can be utilized to better understand genotype-phenotype relationships and to compare different organisms. To assist in the selection and development of strains with enhanced industrial utility, genome-scale models for L. casei ATCC 334, a well characterized strain, and strain 12A, a corn silage isolate, were constructed. Draft models were generated from RAST genome annotations using the Model SEED database and refined by evaluating ATP generating cycles, mass-and-charge-balances of reactions, and growth phenotypes. After the validation process was finished, we compared the metabolic networks of these two strains to identify metabolic, genetic and ortholog differences that may lead to different phenotypic behaviors. We conclude that the metabolic capabilities of the two networks are highly similar. The L. casei ATCC 334 model accounts for 1,040 reactions, 959 metabolites and 548 genes, while the L. casei 12A model accounts for 1,076 reactions, 979 metabolites and 640 genes. The developed L. casei ATCC 334 and 12A metabolic models will enable better understanding of the physiology of these organisms and be valuable tools in the development and selection of strains with enhanced utility in a variety of industrial applications.

  17. Genome –Scale Reconstruction of Metabolic Networks of Lactobacillus casei ATCC 334 and 12A

    PubMed Central

    Vinay-Lara, Elena; Hamilton, Joshua J.; Stahl, Buffy; Broadbent, Jeff R.; Reed, Jennifer L.; Steele, James L.

    2014-01-01

    Lactobacillus casei strains are widely used in industry and the utility of this organism in these industrial applications is strain dependent. Hence, tools capable of predicting strain specific phenotypes would have utility in the selection of strains for specific industrial processes. Genome-scale metabolic models can be utilized to better understand genotype-phenotype relationships and to compare different organisms. To assist in the selection and development of strains with enhanced industrial utility, genome-scale models for L. casei ATCC 334, a well characterized strain, and strain 12A, a corn silage isolate, were constructed. Draft models were generated from RAST genome annotations using the Model SEED database and refined by evaluating ATP generating cycles, mass-and-charge-balances of reactions, and growth phenotypes. After the validation process was finished, we compared the metabolic networks of these two strains to identify metabolic, genetic and ortholog differences that may lead to different phenotypic behaviors. We conclude that the metabolic capabilities of the two networks are highly similar. The L. casei ATCC 334 model accounts for 1,040 reactions, 959 metabolites and 548 genes, while the L. casei 12A model accounts for 1,076 reactions, 979 metabolites and 640 genes. The developed L. casei ATCC 334 and 12A metabolic models will enable better understanding of the physiology of these organisms and be valuable tools in the development and selection of strains with enhanced utility in a variety of industrial applications. PMID:25365062

  18. 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

  19. Multi-omics approach for estimating metabolic networks using low-order partial correlations.

    PubMed

    Kayano, Mitsunori; Imoto, Seiya; Yamaguchi, Rui; Miyano, Satoru

    2013-08-01

    Two typical purposes of metabolome analysis are to estimate metabolic pathways and to understand the regulatory systems underlying the metabolism. A powerful source of information for these analyses is a set of multi-omics data for RNA, proteins, and metabolites. However, integrated methods that analyze multi-omics data simultaneously and unravel the systems behind metabolisms have not been well established. We developed a statistical method based on low-order partial correlations with a robust correlation coefficient for estimating metabolic networks from metabolome, proteome, and transcriptome data. Our method is defined by the maximum of low-order, particularly first-order, partial correlations (MF-PCor) in order to assign a correct edge with the highest correlation and to detect the factors that strongly affect the correlation coefficient. First, through numerical experiments with real and synthetic data, we showed that the use of protein and transcript data of enzymes improved the accuracy of the estimated metabolic networks in MF-PCor. In these experiments, the effectiveness of the proposed method was also demonstrated by comparison with a correlation network (Cor) and a Gaussian graphical model (GGM). Our theoretical investigation confirmed that the performance of MF-PCor could be superior to that of the competing methods. In addition, in the real data analysis, we investigated the role of metabolites, enzymes, and enzyme genes that were identified as important factors in the network established by MF-PCor. We then found that some of them corresponded to specific reactions between metabolites mediated by catalytic enzymes that were difficult to be identified by analysis based on metabolite data alone.

  20. Patterns of metabolite changes identified from large-scale gene perturbations in Arabidopsis using a genome-scale metabolic network.

    PubMed

    Kim, Taehyong; Dreher, Kate; Nilo-Poyanco, Ricardo; Lee, Insuk; Fiehn, Oliver; Lange, Bernd Markus; Nikolau, Basil J; Sumner, Lloyd; Welti, Ruth; Wurtele, Eve S; Rhee, Seung Y

    2015-04-01

    Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.

  1. Modeling and Robustness Analysis of Biochemical Networks of Glycerol Metabolism by Klebsiella Pneumoniae

    NASA Astrophysics Data System (ADS)

    Ye, Jianxiong; Feng, Enmin; Wang, Lei; Xiu, Zhilong; Sun, Yaqin

    Glycerol bioconversion to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae (K. pneumoniae) can be characterized by an intricate network of interactions among biochemical fluxes, metabolic compounds, key enzymes and genetic regulatory. To date, there still exist some uncertain factors in this complex network because of the limitation in bio-techniques, especially in measuring techniques for intracellular substances. In this paper, among these uncertain factors, we aim to infer the transport mechanisms of glycerol and 1,3-PD across the cell membrane, which have received intensive interest in recent years. On the basis of different inferences of the transport mechanisms, we reconstruct various metabolic networks correspondingly and subsequently develop their dynamical systems (S-systems). To determine the most reasonable metabolic network from all possible ones, we establish a quantitative definition of biological robustness and undertake parameter identification and robustness analysis for each system. Numerical results show that it is most possible that both glycerol and 1,3-PD pass the cell membrane by active transport and passive diffusion.

  2. Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus

    PubMed Central

    Pathania, Shivalika; Bagler, Ganesh; Ahuja, Paramvir S.

    2016-01-01

    Comparative co-expression analysis of multiple species using high-throughput data is an integrative approach to determine the uniformity as well as diversification in biological processes. Rauvolfia serpentina and Catharanthus roseus, both members of Apocyanacae family, are reported to have remedial properties against multiple diseases. Despite of sharing upstream of terpenoid indole alkaloid pathway, there is significant diversity in tissue-specific synthesis and accumulation of specialized metabolites in these plants. This led us to implement comparative co-expression network analysis to investigate the modules and genes responsible for differential tissue-specific expression as well as species-specific synthesis of metabolites. Toward these goals differential network analysis was implemented to identify candidate genes responsible for diversification of metabolites profile. Three genes were identified with significant difference in connectivity leading to differential regulatory behavior between these plants. These genes may be responsible for diversification of secondary metabolism, and thereby for species-specific metabolite synthesis. The network robustness of R. serpentina, determined based on topological properties, was also complemented by comparison of gene-metabolite networks of both plants, and may have evolved to have complex metabolic mechanisms as compared to C. roseus under the influence of various stimuli. This study reveals evolution of complexity in secondary metabolism of R. serpentina, and key genes that contribute toward diversification of specific metabolites. PMID:27588023

  3. 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

  4. A FDG-PET Study of Metabolic Networks in Apolipoprotein E ε4 Allele Carriers.

    PubMed

    Yao, Zhijun; Hu, Bin; Zheng, Jiaxiang; Zheng, Weihao; Chen, Xuejiao; Gao, Xiang; Xie, Yuanwei; Fang, Lei

    2015-01-01

    Recently, some studies have applied the graph theory in brain network analysis in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). However, relatively little research has specifically explored the properties of the metabolic network in apolipoprotein E (APOE) ε4 allele carriers. In our study, all the subjects, including ADs, MCIs and NCs (normal controls) were divided into 165 APOE ε4 carriers and 165 APOE ε4 noncarriers. To establish the metabolic network for all brain regions except the cerebellum, cerebral glucose metabolism data obtained from FDG-PET (18F-fluorodeoxyglucose positron emission tomography) were segmented into 90 areas with automated anatomical labeling (AAL) template. Then, the properties of the networks were computed to explore the between-group differences. Our results suggested that both APOE ε4 carriers and noncarriers showed the small-world properties. Besides, compared with APOE ε4 noncarriers, the carriers showed a lower clustering coefficient. In addition, significant changes in 6 hub brain regions were found in between-group nodal centrality. Namely, compared with APOE ε4 noncarriers, significant decreases of the nodal centrality were found in left insula, right insula, right anterior cingulate, right paracingulate gyri, left cuneus, as well as significant increases in left paracentral lobule and left heschl gyrus in APOE ε4 carriers. Increased local short distance interregional correlations and disrupted long distance interregional correlations were found, which may support the point that the APOE ε4 carriers were more similar with AD or MCI in FDG uptake. In summary, the organization of metabolic network in APOE ε4 carriers indicated a less optimal pattern and APOE ε4 might be a risk factor for AD.

  5. 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.

  6. Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

    PubMed Central

    Prigent, Sylvain; Frioux, Clémence; Dittami, Simon M.; Larhlimi, Abdelhalim; Collet, Guillaume; Gutknecht, Fabien; Got, Jeanne; Eveillard, Damien; Bourdon, Jérémie; Plewniak, Frédéric; Tonon, Thierry; Siegel, Anne

    2017-01-01

    Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to

  7. Metabolic network driven analysis of genome-wide transcription data from Aspergillus nidulans

    PubMed Central

    David, Helga; Hofmann, Gerald; Oliveira, Ana Paula; Jarmer, Hanne; Nielsen, Jens

    2006-01-01

    Background Aspergillus nidulans (the asexual form of Emericella nidulans) is a model organism for aspergilli, which are an important group of filamentous fungi that encompasses human and plant pathogens as well as industrial cell factories. Aspergilli have a highly diversified metabolism and, because of their medical, agricultural and biotechnological importance, it would be valuable to have an understanding of how their metabolism is regulated. We therefore conducted a genome-wide transcription analysis of A. nidulans grown on three different carbon sources (glucose, glycerol, and ethanol) with the objective of identifying global regulatory structures. Furthermore, we reconstructed the complete metabolic network of this organism, which resulted in linking 666 genes to metabolic functions, as well as assigning metabolic roles to 472 genes that were previously uncharacterized. Results Through combination of the reconstructed metabolic network and the transcription data, we identified subnetwork structures that pointed to coordinated regulation of genes that are involved in many different parts of the metabolism. Thus, for a shift from glucose to ethanol, we identified coordinated regulation of the complete pathway for oxidation of ethanol, as well as upregulation of gluconeogenesis and downregulation of glycolysis and the pentose phosphate pathway. Furthermore, on change in carbon source from glucose to ethanol, the cells shift from using the pentose phosphate pathway as the major source of NADPH (nicotinamide adenine dinucleotide phosphatase, reduced form) for biosynthesis to use of the malic enzyme. Conclusion Our analysis indicates that some of the genes are regulated by common transcription factors, making it possible to establish new putative links between known transcription factors and genes through clustering. PMID:17107606

  8. Habitat variability does not generally promote metabolic network modularity in flies and mammals.

    PubMed

    Takemoto, Kazuhiro

    2016-01-01

    The evolution of species habitat range is an important topic over a wide range of research fields. In higher organisms, habitat range evolution is generally associated with genetic events such as gene duplication. However, the specific factors that determine habitat variability remain unclear at higher levels of biological organization (e.g., biochemical networks). One widely accepted hypothesis developed from both theoretical and empirical analyses is that habitat variability promotes network modularity; however, this relationship has not yet been directly tested in higher organisms. Therefore, I investigated the relationship between habitat variability and metabolic network modularity using compound and enzymatic networks in flies and mammals. Contrary to expectation, there was no clear positive correlation between habitat variability and network modularity. As an exception, the network modularity increased with habitat variability in the enzymatic networks of flies. However, the observed association was likely an artifact, and the frequency of gene duplication appears to be the main factor contributing to network modularity. These findings raise the question of whether or not there is a general mechanism for habitat range expansion at a higher level (i.e., above the gene scale). This study suggests that the currently widely accepted hypothesis for habitat variability should be reconsidered.

  9. 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.

  10. Comparative genomic reconstruction of transcriptional networks controlling central metabolism in the Shewanella genus

    PubMed Central

    2011-01-01

    Background Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. The Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria. Results To explore conservation and variations in the Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. Multiple variations in regulatory strategies between the Shewanella spp. and E. coli include regulon contraction and expansion (as in the case of PdhR, HexR, FadR), numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. PsrA for fatty acid degradation) and, conversely, orthologous regulators to control distinct pathways (e.g. TyrR, ArgR, Crp). Conclusions We tentatively defined the first reference collection of ~100 transcriptional regulons in 16 Shewanella genomes. The resulting regulatory network contains ~600 regulated genes per genome that are mostly involved in metabolism of carbohydrates, amino acids, fatty acids, vitamins, metals, and stress responses. Several reconstructed regulons including NagR for N-acetylglucosamine catabolism were experimentally validated in S. oneidensis MR-1. Analysis of

  11. Computing Smallest Intervention Strategies for Multiple Metabolic Networks in a Boolean Model

    PubMed Central

    Lu, Wei; Song, Jiangning; Akutsu, Tatsuya

    2015-01-01

    Abstract This article considers the problem whereby, given two metabolic networks N1 and N2, a set of source compounds, and a set of target compounds, we must find the minimum set of reactions whose removal (knockout) ensures that the target compounds are not producible in N1 but are producible in N2. Similar studies exist for the problem of finding the minimum knockout with the smallest side effect for a single network. However, if technologies of external perturbations are advanced in the near future, it may be important to develop methods of computing the minimum knockout for multiple networks (MKMN). Flux balance analysis (FBA) is efficient if a well-polished model is available. However, that is not always the case. Therefore, in this article, we study MKMN in Boolean models and an elementary mode (EM)-based model. Integer linear programming (ILP)-based methods are developed for these models, since MKMN is NP-complete for both the Boolean model and the EM-based model. Computer experiments are conducted with metabolic networks of clostridium perfringens SM101 and bifidobacterium longum DJO10A, respectively known as bad bacteria and good bacteria for the human intestine. The results show that larger networks are more likely to have MKMN solutions. However, solving for these larger networks takes a very long time, and often the computation cannot be completed. This is reasonable, because small networks do not have many alternative pathways, making it difficult to satisfy the MKMN condition, whereas in large networks the number of candidate solutions explodes. Our developed software minFvskO is available online. PMID:25684199

  12. Computing smallest intervention strategies for multiple metabolic networks in a boolean model.

    PubMed

    Lu, Wei; Tamura, Takeyuki; Song, Jiangning; Akutsu, Tatsuya

    2015-02-01

    This article considers the problem whereby, given two metabolic networks N1 and N2, a set of source compounds, and a set of target compounds, we must find the minimum set of reactions whose removal (knockout) ensures that the target compounds are not producible in N1 but are producible in N2. Similar studies exist for the problem of finding the minimum knockout with the smallest side effect for a single network. However, if technologies of external perturbations are advanced in the near future, it may be important to develop methods of computing the minimum knockout for multiple networks (MKMN). Flux balance analysis (FBA) is efficient if a well-polished model is available. However, that is not always the case. Therefore, in this article, we study MKMN in Boolean models and an elementary mode (EM)-based model. Integer linear programming (ILP)-based methods are developed for these models, since MKMN is NP-complete for both the Boolean model and the EM-based model. Computer experiments are conducted with metabolic networks of clostridium perfringens SM101 and bifidobacterium longum DJO10A, respectively known as bad bacteria and good bacteria for the human intestine. The results show that larger networks are more likely to have MKMN solutions. However, solving for these larger networks takes a very long time, and often the computation cannot be completed. This is reasonable, because small networks do not have many alternative pathways, making it difficult to satisfy the MKMN condition, whereas in large networks the number of candidate solutions explodes. Our developed software minFvskO is available online.

  13. Simultaneous Parameters Identifiability and Estimation of an E. coli Metabolic Network Model

    PubMed Central

    Alberton, André Luís; Di Maggio, Jimena Andrea; Estrada, Vanina Gisela; Díaz, María Soledad; Secchi, Argimiro Resende

    2015-01-01

    This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available. PMID:25654103

  14. 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.

  15. Inter-Tissue Gene Co-Expression Networks between Metabolically Healthy and Unhealthy Obese Individuals

    PubMed Central

    Kogelman, Lisette J. A.; Fu, Jingyuan; Franke, Lude; Greve, Jan Willem; Hofker, Marten; Rensen, Sander S.; Kadarmideen, Haja N.

    2016-01-01

    Background Obesity is associated with severe co-morbidities such as type 2 diabetes and nonalcoholic steatohepatitis. However, studies have shown that 10–25 percent of the severely obese individuals are metabolically healthy. To date, the identification of genetic factors underlying the metabolically healthy obese (MHO) state is limited. Systems genetics approaches have led to the identification of genes and pathways in complex diseases. Here, we have used such approaches across tissues to detect genes and pathways involved in obesity-induced disease development. Methods Expression data of 60 severely obese individuals was accessible, of which 28 individuals were MHO and 32 were metabolically unhealthy obese (MUO). A whole genome expression profile of four tissues was available: liver, muscle, subcutaneous adipose tissue and visceral adipose tissue. Using insulin-related genes, we used the weighted gene co-expression network analysis (WGCNA) method to build within- and inter-tissue gene networks. We identified genes that were differentially connected between MHO and MUO individuals, which were further investigated by homing in on the modules they were active in. To identify potentially causal genes, we integrated genomic and transcriptomic data using an eQTL mapping approach. Results Both IL-6 and IL1B were identified as highly differentially co-expressed genes across tissues between MHO and MUO individuals, showing their potential role in obesity-induced disease development. WGCNA showed that those genes were clustering together within tissues, and further analysis showed different co-expression patterns between MHO and MUO subnetworks. A potential causal role for metabolic differences under similar obesity state was detected for PTPRE, IL-6R and SLC6A5. Conclusions We used a novel integrative approach by integration of co-expression networks across tissues to elucidate genetic factors related to obesity-induced metabolic disease development. The identified

  16. 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.

  17. Metabolism

    MedlinePlus

    Metabolism refers to all the physical and chemical processes in the body that convert or use energy, ... Tortora GJ, Derrickson BH. Metabolism. In: Tortora GJ, Derrickson ... Physiology . 14th ed. Hoboken, NJ: John Wiley & Sons; 2014:chap ...

  18. Metabolism

    MedlinePlus

    ... El metabolismo Metabolism Basics Our bodies get the energy they need from food through metabolism, the chemical ... that convert the fuel from food into the energy needed to do everything from moving to thinking ...

  19. 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.

  20. Metabolic Network Analysis-Based Identification of Antimicrobial Drug Targets in Category A Bioterrorism Agents

    PubMed Central

    Ahn, Yong-Yeol; Lee, Deok-Sun; Burd, Henry; Blank, William; Kapatral, Vinayak

    2014-01-01

    The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nucleic acid pathways common to all strains across the three distinct genera were identified as targets. Antimicrobial agents against some of these enzymes are available. Thus, a combination of cross species-specific antibiotics and common antimicrobials against shared targets may represent a useful combinatorial therapeutic approach against all Category A bioterrorism agents. PMID:24454817

  1. Plasticity of metabolic networks and the evolution of C4 photosynthesis

    NASA Astrophysics Data System (ADS)

    Bogart, Eli; Myers, Chris

    2012-02-01

    Over 50 groups of plants have independently developed a common mechanism (C4 photosynthesis) for increasing the efficiency of photosynthetic carbon dioxide assimilation. Understanding the high degree of evolvability of the C4 system could offer useful guidance for attempts to introduce it artificially to other plants. Previously, the nonlinear relationship between carbon dioxide levels and rates of carbon assimilation and photorespiration has prevented the application of genome-scale metabolic models to the problem of the evolution of the pathway. We apply a nonlinear optimization method to find feasible flux distributions in a plant metabolic model, allowing us to explore the plasticity of the metabolic network and characterize the fitness landscape of the transition from C3 to C4 photosynthesis.

  2. The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy.

    PubMed

    Papin, Jason A; Price, Nathan D; Edwards, Jeremy S; Palsson B, Bernhard Ø

    2002-03-07

    Genome-scale metabolic networks can be characterized by a set of systemically independent and unique extreme pathways. These extreme pathways span a convex, high-dimensional space that circumscribes all potential steady-state flux distributions achievable by the defined metabolic network. Genome-scale extreme pathways associated with the production of non-essential amino acids in Haemophilus influenzae were computed. They offer valuable insight into the functioning of its metabolic network. Three key results were obtained. First, there were multiple internal flux maps corresponding to externally indistinguishable states. It was shown that there was an average of 37 internal states per unique exchange flux vector in H. influenzae when the network was used to produce a single amino acid while allowing carbon dioxide and acetate as carbon sinks. With the inclusion of succinate as an additional output, this ratio increased to 52, a 40% increase. Second, an analysis of the carbon fates illustrated that the extreme pathways were non-uniformly distributed across the carbon fate spectrum. In the detailed case study, 45% of the distinct carbon fate values associated with lysine production represented 85% of the extreme pathways. Third, this distribution fell between distinct systemic constraints. For lysine production, the carbon fate values that represented 85% of the pathways described above corresponded to only 2 distinct ratios of 1:1 and 4:1 between carbon dioxide and acetate. The present study analysed single outputs from one organism, and provides a start to genome-scale extreme pathways studies. These emergent system-level characterizations show the significance of metabolic extreme pathway analysis at the genome-scale.

  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. Metabolic network rewiring of propionate flux compensates vitamin B12 deficiency in C. elegans

    PubMed Central

    Watson, Emma; Olin-Sandoval, Viridiana; Hoy, Michael J; Li, Chi-Hua; Louisse, Timo; Yao, Victoria; Mori, Akihiro; Holdorf, Amy D; Troyanskaya, Olga G; Ralser, Markus; Walhout, Albertha JM

    2016-01-01

    Metabolic network rewiring is the rerouting of metabolism through the use of alternate enzymes to adjust pathway flux and accomplish specific anabolic or catabolic objectives. Here, we report the first characterization of two parallel pathways for the breakdown of the short chain fatty acid propionate in Caenorhabditis elegans. Using genetic interaction mapping, gene co-expression analysis, pathway intermediate quantification and carbon tracing, we uncover a vitamin B12-independent propionate breakdown shunt that is transcriptionally activated on vitamin B12 deficient diets, or under genetic conditions mimicking the human diseases propionic- and methylmalonic acidemia, in which the canonical B12-dependent propionate breakdown pathway is blocked. Our study presents the first example of transcriptional vitamin-directed metabolic network rewiring to promote survival under vitamin deficiency. The ability to reroute propionate breakdown according to B12 availability may provide C. elegans with metabolic plasticity and thus a selective advantage on different diets in the wild. DOI: http://dx.doi.org/10.7554/eLife.17670.001 PMID:27383050

  5. Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

    PubMed Central

    Blais, Edik M.; Rawls, Kristopher D.; Dougherty, Bonnie V.; Li, Zhuo I.; Kolling, Glynis L.; Ye, Ping; Wallqvist, Anders; Papin, Jason A.

    2017-01-01

    The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications. PMID:28176778

  6. Integration of Posttranscriptional Gene Networks into Metabolic Adaptation and Biofilm Maturation in Candida albicans

    PubMed Central

    Harrison, Paul F.; Lo, Tricia L.; Quenault, Tara; Dagley, Michael J.; Bellousoff, Matthew; Powell, David R.; Beilharz, Traude H.; Traven, Ana

    2015-01-01

    The yeast Candida albicans is a human commensal and opportunistic pathogen. Although both commensalism and pathogenesis depend on metabolic adaptation, the regulatory pathways that mediate metabolic processes in C. albicans are incompletely defined. For example, metabolic change is a major feature that distinguishes community growth of C. albicans in biofilms compared to suspension cultures, but how metabolic adaptation is functionally interfaced with the structural and gene regulatory changes that drive biofilm maturation remains to be fully understood. We show here that the RNA binding protein Puf3 regulates a posttranscriptional mRNA network in C. albicans that impacts on mitochondrial biogenesis, and provide the first functional data suggesting evolutionary rewiring of posttranscriptional gene regulation between the model yeast Saccharomyces cerevisiae and C. albicans. A proportion of the Puf3 mRNA network is differentially expressed in biofilms, and by using a mutant in the mRNA deadenylase CCR4 (the enzyme recruited to mRNAs by Puf3 to control transcript stability) we show that posttranscriptional regulation is important for mitochondrial regulation in biofilms. Inactivation of CCR4 or dis-regulation of mitochondrial activity led to altered biofilm structure and over-production of extracellular matrix material. The extracellular matrix is critical for antifungal resistance and immune evasion, and yet of all biofilm maturation pathways extracellular matrix biogenesis is the least understood. We propose a model in which the hypoxic biofilm environment is sensed by regulators such as Ccr4 to orchestrate metabolic adaptation, as well as the regulation of extracellular matrix production by impacting on the expression of matrix-related cell wall genes. Therefore metabolic changes in biofilms might be intimately linked to a key biofilm maturation mechanism that ultimately results in untreatable fungal disease. PMID:26474309

  7. A meta-metabolome network of carbohydrate metabolism: interactions between gut microbiota and host.

    PubMed

    Ibrahim, Maziya; Anishetty, Sharmila

    2012-11-16

    With the current knowledge of the multitude of microbes that inhabit the human body, it is increasingly clear that they constitute an integral component of the host. The gut microbiota community is principally involved in the metabolism of dietary constituents such as carbohydrates which account for majority of the energy intake from diet. Diet has gained an important role in shaping the composition of gut inhabitants. The quantity and type of food consumed is recognized as a causal factor for metabolic disorders such as obesity and diabetes. Analysis of host-microbe interactions can thus contribute to the understanding of such metabolic disorders. In this study, data from Kyoto Encyclopedia of Genes and Genomes and Carbohydrate Active EnZYmes Database was utilized as a starting point. Enzyme information from the host Homo sapiens coupled with details of the three predominant phyla of gut bacteria, namely Firmicutes, Bacteroidetes and Actinobacteria were used in the creation of a comprehensive metabolic network, which we refer to as 'meta-metabolome'. This 'meta-metabolome' provides a perspective of the degree to which microbes influence carbohydrate metabolism, in conjunction with host specific enzymes. Analysis of reactions in the network reveals the amplification of monosaccharide content brought about by microbial enzyme activity. The framework outlined in this study provides a holistic approach to assess host-microbe symbiosis. It also provides us with a means of analyzing how diet can be modulated to provide beneficial effects to the host or how probiotics can potentially be used to relieve certain metabolic disorders.

  8. 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.

  9. Coevolution trumps pleiotropy: carbon assimilation traits are independent of metabolic network structure in budding yeast.

    PubMed

    Opulente, Dana A; Morales, Christopher M; Carey, Lucas B; Rest, Joshua S

    2013-01-01

    Phenotypic traits may be gained and lost together because of pleiotropy, the involvement of common genes and networks, or because of simultaneous selection for multiple traits across environments (multiple-trait coevolution). However, the extent to which network pleiotropy versus environmental coevolution shapes shared responses has not been addressed. To test these alternatives, we took advantage of the fact that the genus Saccharomyces has variation in habitat usage and diversity in the carbon sources that a given strain can metabolize. We examined patterns of gain and loss in carbon utilization traits across 488 strains of Saccharomyces to investigate whether the structure of metabolic pathways or selection pressure from common environments may have caused carbon utilization traits to be gained and lost together. While most carbon sources were gained and lost independently of each other, we found four clusters that exhibit non-random patterns of gain and loss across strains. Contrary to the network pleiotropy hypothesis, we did not find that these patterns are explained by the structure of metabolic pathways or shared enzymes. Consistent with the hypothesis that common environments shape suites of phenotypes, we found that the environment a strain was isolated from partially predicts the carbon sources it can assimilate.

  10. Metabolic Covariant Network in Relation to Nigrostriatal Degeneration in Carbon Monoxide Intoxication-Related Parkinsonism

    PubMed Central

    Chang, Chiung-Chih; Hsu, Jung-Lung; Chang, Wen-Neng; Huang, Shu-Hua; Huang, Chi-Wei; Chang, Ya-Ting; Chen, Nai-Ching; Lui, Chun-Chung; Lee, Chen-Chang; Hsu, Shih-Wei

    2016-01-01

    Presence of parkinsonian features after carbon monoxide (CO) intoxication is well known and the severity was found to relate to the pre-synaptic dopaminergic deficits. There is no systemic study to analyse the functional network involved in CO-related Parkinsonism. Forty-five CO-related parkinsonism patients and 25 aged-matched controls completed the 3D T1-weighted imaging and 18F-fluoro-2-deoxyglucose positron emission tomography (FDG-PET). Voxel-based morphometry (VBM) was performed to assess the structural and functional brain differences between the patients and controls. Spatial covariant networks responsible for distinguishing patients and controls were constructed using independent component analysis. For validation, the pre-synaptic dopaminergic functional network was established by regression model using striatal TRODAT-1 SPECT as the independent variable. The clinical significance of both networks was determined by correlation with the Unified Parkinson's Disease Rating Scale (UPDRS). Compared with controls, the spatial covariant signals of FDG-PET were significantly lower in the medial and lateral frontal, caudate nucleus, dorsomedial prefrontal areas, and temporal-parietal regions while the spatial intensities correlated significantly with UPDRS total scores. The functional network that correlated with striatum pre-synaptic dopaminergic uptakes included the midbrain, thalamus, caudate, lateral frontal cortex, ventral striatum, ventral, or dorsal anterior cingulate cortex. Both networks overlapped considerably and the topographies reflected structural damage pattern. Our study provides evidence that glucose metabolism in CO-parkinsonism patients pertains to an organized covariant pattern in the cortical regions that is spatially coherent with the cortical map of pre-synaptic dopamine deficits. As the fronto-temporal, striatum, and temporal-parietal areas were involved, the unique metabolic covariant network suggests a different pathophysiology in CO

  11. Hierarchical organization of fluxes in Escherichia coli metabolic network: using flux coupling analysis for understanding the physiological properties of metabolic genes.

    PubMed

    Hosseini, Zhaleh; Marashi, Sayed-Amir

    2015-05-01

    Flux coupling analysis is a method for investigating the connections between reactions of metabolic networks. Here, we construct the hierarchical flux coupling graph for the reactions of the Escherichia coli metabolic network model to determine the level of each reaction in the graph. This graph is constructed based on flux coupling analysis of metabolic network: if zero flux through reaction a results in zero flux through reaction b (and not vice versa), then reaction a is located at the top of reaction b in the flux coupling graph. We show that in general, more important, older and essential reactions are located at the top of the graph. Strikingly, genes corresponding to these reactions are found to be the genes which are most regulated.

  12. Counting and correcting thermodynamically infeasible flux cycles in genome-scale metabolic networks.

    PubMed

    De Martino, Daniele; Capuani, Fabrizio; Mori, Matteo; De Martino, Andrea; Marinari, Enzo

    2013-10-14

    Thermodynamics constrains the flow of matter in a reaction network to occur through routes along which the Gibbs energy decreases, implying that viable steady-state flux patterns should be void of closed reaction cycles. Identifying and removing cycles in large reaction networks can unfortunately be a highly challenging task from a computational viewpoint. We propose here a method that accomplishes it by combining a relaxation algorithm and a Monte Carlo procedure to detect loops, with ad hoc rules (discussed in detail) to eliminate them. As test cases, we tackle (a) the problem of identifying infeasible cycles in the E. coli metabolic network and (b) the problem of correcting thermodynamic infeasibilities in the Flux-Balance-Analysis solutions for 15 human cell-type-specific metabolic networks. Results for (a) are compared with previous analyses of the same issue, while results for (b) are weighed against alternative methods to retrieve thermodynamically viable flux patterns based on minimizing specific global quantities. Our method, on the one hand, outperforms previous techniques and, on the other, corrects loopy solutions to Flux Balance Analysis. As a byproduct, it also turns out to be able to reveal possible inconsistencies in model reconstructions.

  13. Cerebral energy metabolism and the brain's functional network architecture: an integrative review

    PubMed Central

    Lord, Louis-David; Expert, Paul; Huckins, Jeremy F; Turkheimer, Federico E

    2013-01-01

    Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain's ‘functional connectivity' is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks. PMID:23756687

  14. New insights into Dehalococcoides mccartyi metabolism from a reconstructed metabolic network-based systems-level analysis of D. mccartyi transcriptomes.

    PubMed

    Islam, M Ahsanul; Waller, Alison S; Hug, Laura A; Provart, Nicholas J; Edwards, Elizabeth A; Mahadevan, Radhakrishnan

    2014-01-01

    Organohalide respiration, mediated by Dehalococcoides mccartyi, is a useful bioremediation process that transforms ground water pollutants and known human carcinogens such as trichloroethene and vinyl chloride into benign ethenes. Successful application of this process depends on the fundamental understanding of the respiration and metabolism of D. mccartyi. Reductive dehalogenases, encoded by rdhA genes of these anaerobic bacteria, exclusively catalyze organohalide respiration and drive metabolism. To better elucidate D. mccartyi metabolism and physiology, we analyzed available transcriptomic data for a pure isolate (Dehalococcoides mccartyi strain 195) and a mixed microbial consortium (KB-1) using the previously developed pan-genome-scale reconstructed metabolic network of D. mccartyi. The transcriptomic data, together with available proteomic data helped confirm transcription and expression of the majority genes in D. mccartyi genomes. A composite genome of two highly similar D. mccartyi strains (KB-1 Dhc) from the KB-1 metagenome sequence was constructed, and operon prediction was conducted for this composite genome and other single genomes. This operon analysis, together with the quality threshold clustering analysis of transcriptomic data helped generate experimentally testable hypotheses regarding the function of a number of hypothetical proteins and the poorly understood mechanism of energy conservation in D. mccartyi. We also identified functionally enriched important clusters (13 for strain 195 and 11 for KB-1 Dhc) of co-expressed metabolic genes using information from the reconstructed metabolic network. This analysis highlighted some metabolic genes and processes, including lipid metabolism, energy metabolism, and transport that potentially play important roles in organohalide respiration. Overall, this study shows the importance of an organism's metabolic reconstruction in analyzing various "omics" data to obtain improved understanding of the

  15. Comparative analysis of metabolic networks provides insight into the evolution of plant pathogenic and nonpathogenic lifestyles in Pseudomonas.

    PubMed

    Mithani, Aziz; Hein, Jotun; Preston, Gail M

    2011-01-01

    Plant pathogenic pseudomonads such as Pseudomonas syringae colonize plant surfaces and tissues and have been reported to be nutritionally specialized relative to nonpathogenic pseudomonads. We performed comparative analyses of metabolic networks reconstructed from genome sequence data in order to investigate the hypothesis that P. syringae has evolved to be metabolically specialized for a plant pathogenic lifestyle. We used the metabolic network comparison tool Rahnuma and complementary bioinformatic analyses to compare the distribution of 1,299 metabolic reactions across nine genome-sequenced strains of Pseudomonas, including three strains of P. syringae. The two pathogenic Pseudomonas species analyzed, P. syringae and the opportunistic human pathogen P. aeruginosa, each displayed a high level of intraspecies metabolic similarity compared with nonpathogenic Pseudomonas. The three P. syringae strains lacked a significant number of reactions predicted to be present in all other Pseudomonas strains analyzed, which is consistent with the hypothesis that P. syringae is adapted for growth in a nutritionally constrained environment. Pathway predictions demonstrated that some of the differences detected in metabolic network comparisons could account for differences in amino acid assimilation ability reported in experimental analyses. Parsimony analysis and reaction neighborhood approaches were used to model the evolution of metabolic networks and amino acid assimilation pathways in pseudomonads. Both methods supported a model of Pseudomonas evolution in which the common ancestor of P. syringae had experienced a significant number of deletion events relative to other nonpathogenic pseudomonads. We discuss how the characteristic metabolic features of P. syringae could reflect adaptation to a pathogenic lifestyle.

  16. Galvez-Markov network transferability indices: review of classic theory and new model for perturbations in metabolic reactions.

    PubMed

    Vergara-Galicia, Jorge; Prado-Prado, Francisco J; Gonzalez-Diaz, Humberto

    2014-01-01

    Topological Indices (TIs) are numerical parameters useful to carry out Quantitative Structure-Property Relationships (QSPR) analysis and predict the effect of perturbations in many types of Complex Networks. This work, focuses on a very powerful class of TIs called Galvez charge transfer indices. First, we review the classic concept and some applications of these indices. Next, we review the Galvez-Markov TIs of order k (GMk), a recent generalization to these TIs introduced by us. We also reviewed some previous examples of calculation of GMk values for different classes of networks, including metabolic networks. Here, we also demonstrated that Galvez- Markov TIs are useful to predict perturbations and the transferability of biochemical patterns forms metabolic networks of species to others. We report a linear QSPR-Perturbation theory model that predicts more than 300,000 perturbations in metabolic networks with 85 - 99% of good classification in training and validation series.

  17. Strategies for investigating the plant metabolic network with steady-state metabolic flux analysis: lessons from an Arabidopsis cell culture and other systems.

    PubMed

    Kruger, N J; Masakapalli, S K; Ratcliffe, R G

    2012-03-01

    Steady-state (13)C metabolic flux analysis (MFA) is currently the experimental method of choice for generating flux maps of the compartmented network of primary metabolism in heterotrophic and mixotrophic plant tissues. While statistically robust protocols for the application of steady-state MFA to plant tissues have been developed by several research groups, the implementation of the method is still far from routine. The effort required to produce a flux map is more than justified by the information that it contains about the metabolic phenotype of the system, but it remains the case that steady-state MFA is both analytically and computationally demanding. This article provides an overview of principles that underpin the implementation of steady-state MFA, focusing on the definition of the metabolic network responsible for redistribution of the label, experimental considerations relating to data collection, the modelling process that allows a set of metabolic fluxes to be deduced from the labelling data, and the interpretation of flux maps. The article draws on published studies of Arabidopsis cell cultures and other systems, including developing oilseeds, with the aim of providing practical guidance and strategies for handling the issues that arise when applying steady-state MFA to the complex metabolic networks encountered in plants.

  18. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    PubMed

    Biggs, Matthew B; Papin, Jason A

    2017-03-06

    Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  19. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA

    PubMed Central

    2017-01-01

    Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository. PMID:28263984

  20. Integrated Modeling of Gene Regulatory and Metabolic Networks in Mycobacterium tuberculosis.

    PubMed

    Ma, Shuyi; Minch, Kyle J; Rustad, Tige R; Hobbs, Samuel; Zhou, Suk-Lin; Sherman, David R; Price, Nathan D

    2015-11-01

    Mycobacterium tuberculosis (MTB) is the causative bacterium of tuberculosis, a disease responsible for over a million deaths worldwide annually with a growing number of strains resistant to antibiotics. The development of better therapeutics would greatly benefit from improved understanding of the mechanisms associated with MTB responses to different genetic and environmental perturbations. Therefore, we expanded a genome-scale regulatory-metabolic model for MTB using the Probabilistic Regulation of Metabolism (PROM) framework. Our model, MTBPROM2.0, represents a substantial knowledge base update and extension of simulation capability. We incorporated a recent ChIP-seq based binding network of 2555 interactions linking to 104 transcription factors (TFs) (representing a 3.5-fold expansion of TF coverage). We integrated this expanded regulatory network with a refined genome-scale metabolic model that can correctly predict growth viability over 69 source metabolite conditions and predict metabolic gene essentiality more accurately than the original model. We used MTBPROM2.0 to simulate the metabolic consequences of knocking out and overexpressing each of the 104 TFs in the model. MTBPROM2.0 improves performance of knockout growth defect predictions compared to the original PROM MTB model, and it can successfully predict growth defects associated with TF overexpression. Moreover, condition-specific models of MTBPROM2.0 successfully predicted synergistic growth consequences of overexpressing the TF whiB4 in the presence of two standard anti-TB drugs. MTBPROM2.0 can screen in silico condition-specific transcription factor perturbations to generate putative targets of interest that can help prioritize future experiments for therapeutic development efforts.

  1. Polycyclic Aromatic Hydrocarbon Metabolic Network in Mycobacterium vanbaaleniiPYR-1 ▿ †

    PubMed Central

    Kweon, Ohgew; Kim, Seong-Jae; Holland, Ricky D.; Chen, Hongyan; Kim, Dae-Wi; Gao, Yuan; Yu, Li-Rong; Baek, Songjoon; Baek, Dong-Heon; Ahn, Hongsik; Cerniglia, Carl E.

    2011-01-01

    This study investigated a metabolic network (MN) from Mycobacterium vanbaaleniiPYR-1 for polycyclic aromatic hydrocarbons (PAHs) from the perspective of structure, behavior, and evolution, in which multilayer omics data are integrated. Initially, we utilized a high-throughput proteomic analysis to assess the protein expression response of M. vanbaaleniiPYR-1 to seven different aromatic compounds. A total of 3,431 proteins (57.38% of the genome-predicted proteins) were identified, which included 160 proteins that seemed to be involved in the degradation of aromatic hydrocarbons. Based on the proteomic data and the previous metabolic, biochemical, physiological, and genomic information, we reconstructed an experiment-based system-level PAH-MN. The structure of PAH-MN, with 183 metabolic compounds and 224 chemical reactions, has a typical scale-free nature. The behavior and evolution of the PAH-MN reveals a hierarchical modularity with funnel effects in structure/function and intimate association with evolutionary modules of the functional modules, which are the ring cleavage process (RCP), side chain process (SCP), and central aromatic process (CAP). The 189 commonly upregulated proteins in all aromatic hydrocarbon treatments provide insights into the global adaptation to facilitate the PAH metabolism. Taken together, the findings of our study provide the hierarchical viewpoint from genes/proteins/metabolites to the network via functional modules of the PAH-MN equipped with the engineering-driven approaches of modularization and rationalization, which may expand our understanding of the metabolic potential of M. vanbaaleniiPYR-1 for bioremediation applications. PMID:21725022

  2. Genome-Scale Reconstruction and Analysis of the Metabolic Network in the Hyperthermophilic Archaeon Sulfolobus Solfataricus

    PubMed Central

    Ulas, Thomas; Riemer, S. Alexander; Zaparty, Melanie; Siebers, Bettina; Schomburg, Dietmar

    2012-01-01

    We describe the reconstruction of a genome-scale metabolic model of the crenarchaeon Sulfolobus solfataricus, a hyperthermoacidophilic microorganism. It grows in terrestrial volcanic hot springs with growth occurring at pH 2–4 (optimum 3.5) and a temperature of 75–80°C (optimum 80°C). The genome of Sulfolobus solfataricus P2 contains 2,992,245 bp on a single circular chromosome and encodes 2,977 proteins and a number of RNAs. The network comprises 718 metabolic and 58 transport/exchange reactions and 705 unique metabolites, based on the annotated genome and available biochemical data. Using the model in conjunction with constraint-based methods, we simulated the metabolic fluxes induced by different environmental and genetic conditions. The predictions were compared to experimental measurements and phenotypes of S. solfataricus. Furthermore, the performance of the network for 35 different carbon sources known for S. solfataricus from the literature was simulated. Comparing the growth on different carbon sources revealed that glycerol is the carbon source with the highest biomass flux per imported carbon atom (75% higher than glucose). Experimental data was also used to fit the model to phenotypic observations. In addition to the commonly known heterotrophic growth of S. solfataricus, the crenarchaeon is also able to grow autotrophically using the hydroxypropionate-hydroxybutyrate cycle for bicarbonate fixation. We integrated this pathway into our model and compared bicarbonate fixation with growth on glucose as sole carbon source. Finally, we tested the robustness of the metabolism with respect to gene deletions using the method of Minimization of Metabolic Adjustment (MOMA), which predicted that 18% of all possible single gene deletions would be lethal for the organism. PMID:22952675

  3. EnzDP: improved enzyme annotation for metabolic network reconstruction based on domain composition profiles.

    PubMed

    Nguyen, Nam-Ninh; Srihari, Sriganesh; Leong, Hon Wai; Chong, Ket-Fah

    2015-10-01

    Determining the entire complement of enzymes and their enzymatic functions is a fundamental step for reconstructing the metabolic network of cells. High quality enzyme annotation helps in enhancing metabolic networks reconstructed from the genome, especially by reducing gaps and increasing the enzyme coverage. Currently, structure-based and network-based approaches can only cover a limited number of enzyme families, and the accuracy of homology-based approaches can be further improved. Bottom-up homology-based approach improves the coverage by rebuilding Hidden Markov Model (HMM) profiles for all known enzymes. However, its clustering procedure relies firmly on BLAST similarity score, ignoring protein domains/patterns, and is sensitive to changes in cut-off thresholds. Here, we use functional domain architecture to score the association between domain families and enzyme families (Domain-Enzyme Association Scoring, DEAS). The DEAS score is used to calculate the similarity between proteins, which is then used in clustering procedure, instead of using sequence similarity score. We improve the enzyme annotation protocol using a stringent classification procedure, and by choosing optimal threshold settings and checking for active sites. Our analysis shows that our stringent protocol EnzDP can cover up to 90% of enzyme families available in Swiss-Prot. It achieves a high accuracy of 94.5% based on five-fold cross-validation. EnzDP outperforms existing methods across several testing scenarios. Thus, EnzDP serves as a reliable automated tool for enzyme annotation and metabolic network reconstruction. Available at: www.comp.nus.edu.sg/~nguyennn/EnzDP .

  4. Revealing the cerebral regions and networks mediating vulnerability to depression: oxidative metabolism mapping of rat brain.

    PubMed

    Harro, Jaanus; Kanarik, Margus; Kaart, Tanel; Matrov, Denis; Kõiv, Kadri; Mällo, Tanel; Del Río, Joaquin; Tordera, Rosa M; Ramirez, Maria J

    2014-07-01

    The large variety of available animal models has revealed much on the neurobiology of depression, but each model appears as specific to a significant extent, and distinction between stress response, pathogenesis of depression and underlying vulnerability is difficult to make. Evidence from epidemiological studies suggests that depression occurs in biologically predisposed subjects under impact of adverse life events. We applied the diathesis-stress concept to reveal brain regions and functional networks that mediate vulnerability to depression and response to chronic stress by collapsing data on cerebral long term neuronal activity as measured by cytochrome c oxidase histochemistry in distinct animal models. Rats were rendered vulnerable to depression either by partial serotonergic lesion or by maternal deprivation, or selected for a vulnerable phenotype (low positive affect, low novelty-related activity or high hedonic response). Environmental adversity was brought about by applying chronic variable stress or chronic social defeat. Several brain regions, most significantly median raphe, habenula, retrosplenial cortex and reticular thalamus, were universally implicated in long-term metabolic stress response, vulnerability to depression, or both. Vulnerability was associated with higher oxidative metabolism levels as compared to resilience to chronic stress. Chronic stress, in contrast, had three distinct patterns of effect on oxidative metabolism in vulnerable vs. resilient animals. In general, associations between regional activities in several brain circuits were strongest in vulnerable animals, and chronic stress disrupted this interrelatedness. These findings highlight networks that underlie resilience to stress, and the distinct response to stress that occurs in vulnerable subjects.

  5. Metabolic networks to generate pyruvate, PEP and ATP from glycerol in Pseudomonas fluorescens.

    PubMed

    Alhasawi, Azhar; Thomas, Sean C; Appanna, Vasu D

    2016-04-01

    Glycerol is a major by-product of the biodiesel industry. In this study we report on the metabolic networks involved in its transformation into pyruvate, phosphoenolpyruvate (PEP) and ATP. When the nutritionally-versatile Pseudomonas fluorescens was exposed to hydrogen peroxide (H2O2) in a mineral medium with glycerol as the sole carbon source, the microbe reconfigured its metabolism to generate adenosine triphosphate (ATP) primarily via substrate-level phosphorylation (SLP). This alternative ATP-producing stratagem resulted in the synthesis of copious amounts of PEP and pyruvate. The production of these metabolites was mediated via the enhanced activities of such enzymes as pyruvate carboxylase (PC) and phosphoenolpyruvate carboxylase (PEPC). The high energy PEP was subsequently converted into ATP with the aid of pyruvate phosphate dikinase (PPDK), phosphoenolpyruvate synthase (PEPS) and pyruvate kinase (PK) with the concomitant formation of pyruvate. The participation of the phospho-transfer enzymes like adenylate kinase (AK) and acetate kinase (ACK) ensured the efficiency of this O2-independent energy-generating machinery. The increased activity of glycerol dehydrogenase (GDH) in the stressed bacteria provided the necessary precursors to fuel this process. This H2O2-induced anaerobic life-style fortuitously evokes metabolic networks to an effective pathway that can be harnessed into the synthesis of ATP, PEP and pyruvate. The bioconversion of glycerol to pyruvate will offer interesting economic benefit.

  6. Integrated Analysis of Metabolite and Transcript Levels Reveals the Metabolic Shifts That Underlie Tomato Fruit Development and Highlight Regulatory Aspects of Metabolic Network Behavior1[W

    PubMed Central

    Carrari, Fernando; Baxter, Charles; Usadel, Björn; Urbanczyk-Wochniak, Ewa; Zanor, Maria-Ines; Nunes-Nesi, Adriano; Nikiforova, Victoria; Centero, Danilo; Ratzka, Antje; Pauly, Markus; Sweetlove, Lee J.; Fernie, Alisdair R.

    2006-01-01

    Tomato (Solanum lycopersicum) is a well-studied model of fleshy fruit development and ripening. Tomato fruit development is well understood from a hormonal-regulatory perspective, and developmental changes in pigment and cell wall metabolism are also well characterized. However, more general aspects of metabolic change during fruit development have not been studied despite the importance of metabolism in the context of final composition of the ripe fruit. In this study, we quantified the abundance of a broad range of metabolites by gas chromatography-mass spectrometry, analyzed a number of the principal metabolic fluxes, and in parallel analyzed transcriptomic changes during tomato fruit development. Metabolic profiling revealed pronounced shifts in the abundance of metabolites of both primary and secondary metabolism during development. The metabolite changes were reflected in the flux analysis that revealed a general decrease in metabolic activity during ripening. However, there were several distinct patterns of metabolite profile, and statistical analysis demonstrated that metabolites in the same (or closely related) pathways changed in abundance in a coordinated manner, indicating a tight regulation of metabolic activity. The metabolite data alone allowed investigations of likely routes through the metabolic network, and, as an example, we analyze the operational feasibility of different pathways of ascorbate synthesis. When combined with the transcriptomic data, several aspects of the regulation of metabolism during fruit ripening were revealed. First, it was apparent that transcript abundance was less strictly coordinated by functional group than metabolite abundance, suggesting that posttranslational mechanisms dominate metabolic regulation. Nevertheless, there were some correlations between specific transcripts and metabolites, and several novel associations were identified that could provide potential targets for manipulation of fruit compositional traits

  7. Pseudomonas fluorescens induces strain-dependent and strain-independent host plant responses in defense networks, primary metabolism and photosynthesis

    SciTech Connect

    Pelletier, Dale A; Morrell-Falvey, Jennifer L; Karve, Abhijit A; Lu, Tse-Yuan S; Tschaplinski, Timothy J; Tuskan, Gerald A; Chen, Jay; Martin, Madhavi Z; Jawdy, Sara; Weston, David; Doktycz, Mitchel John; Schadt, Christopher Warren

    2012-01-01

    Colonization of plants by nonpathogenic Pseudomonas fluorescens strains can confer enhanced defense capacity against a broad spectrum of pathogens. Few studies, however, have linked defense pathway regulation to primary metabolism and physiology. In this study, physiological data, metabolites, and transcript profiles are integrated to elucidate how molecular networks initiated at the root-microbe interface influence shoot metabolism and whole-plant performance. Experiments with Arabidopsis thaliana were performed using the newly identified P. fluorescens GM30 or P. fluorescens Pf-5 strains. Co-expression networks indicated that Pf-5 and GM30 induced a subnetwork specific to roots enriched for genes participating in RNA regulation, protein degradation, and hormonal metabolism. In contrast, only GM30 induced a subnetwork enriched for calcium signaling, sugar and nutrient signaling, and auxin metabolism, suggesting strain dependence in network architecture. In addition, one subnetwork present in shoots was enriched for genes in secondary metabolism, photosynthetic light reactions, and hormone metabolism. Metabolite analysis indicated that this network initiated changes in carbohydrate and amino acid metabolism. Consistent with this, we observed strain-specific responses in tryptophan and phenylalanine abundance. Both strains reduced host plant carbon gain and fitness, yet provided a clear fitness benefit when plants were challenged with the pathogen P. syringae DC3000.

  8. Uniform Sampling of Steady States in Metabolic Networks: Heterogeneous Scales and Rounding

    PubMed Central

    De Martino, Daniele; Mori, Matteo; Parisi, Valerio

    2015-01-01

    The uniform sampling of convex polytopes is an interesting computational problem with many applications in inference from linear constraints, but the performances of sampling algorithms can be affected by ill-conditioning. This is the case of inferring the feasible steady states in models of metabolic networks, since they can show heterogeneous time scales. In this work we focus on rounding procedures based on building an ellipsoid that closely matches the sampling space, that can be used to define an efficient hit-and-run (HR) Markov Chain Monte Carlo. In this way the uniformity of the sampling of the convex space of interest is rigorously guaranteed, at odds with non markovian methods. We analyze and compare three rounding methods in order to sample the feasible steady states of metabolic networks of three models of growing size up to genomic scale. The first is based on principal component analysis (PCA), the second on linear programming (LP) and finally we employ the Lovazs ellipsoid method (LEM). Our results show that a rounding procedure dramatically improves the performances of the HR in these inference problems and suggest that a combination of LEM or LP with a subsequent PCA perform the best. We finally compare the distributions of the HR with that of two heuristics based on the Artificially Centered hit-and-run (ACHR), gpSampler and optGpSampler. They show a good agreement with the results of the HR for the small network, while on genome scale models present inconsistencies. PMID:25849140

  9. Topological Organization of Metabolic Brain Networks in Pre-Chemotherapy Cancer with Depression: A Resting-State PET Study

    PubMed Central

    An, Jianping; Chen, Xuejiao; Xie, Yuanwei; Zhao, Hui; Mao, Junfeng; Liang, Wangsheng; Ma, Xiangxing

    2016-01-01

    This study aimed to investigate the metabolic brain network and its relationship with depression symptoms using 18F-fluorodeoxyglucose positron emission tomography data in 78 pre-chemotherapy cancer patients with depression and 80 matched healthy subjects. Functional and structural imbalance or disruption of brain networks frequently occur following chemotherapy in cancer patients. However, few studies have focused on the topological organization of the metabolic brain network in cancer with depression, especially those without chemotherapy. The nodal and global parameters of the metabolic brain network were computed for cancer patients and healthy subjects. Significant decreases in metabolism were found in the frontal and temporal gyri in cancer patients compared with healthy subjects. Negative correlations between depression and metabolism were found predominantly in the inferior frontal and cuneus regions, whereas positive correlations were observed in several regions, primarily including the insula, hippocampus, amygdala, and middle temporal gyri. Furthermore, a higher clustering efficiency, longer path length, and fewer hubs were found in cancer patients compared with healthy subjects. The topological organization of the whole-brain metabolic networks may be disrupted in cancer. Finally, the present findings may provide a new avenue for exploring the neurobiological mechanism, which plays a key role in lessening the depression effects in pre-chemotherapy cancer patients. PMID:27832148

  10. A Network Flow Analysis of the Nitrogen Metabolism in Beijing, China.

    PubMed

    Zhang, Yan; Lu, Hanjing; Fath, Brian D; Zheng, Hongmei; Sun, Xiaoxi; Li, Yanxian

    2016-08-16

    Rapid urbanization results in high nitrogen flows and subsequent environmental consequences. In this study, we identified the main metabolic components (nitrogen inputs, flows, and outputs) and used ecological network analysis to track the direct and integral (direct + indirect) metabolic flows of nitrogen in Beijing, China, from 1996 to 2012 and to quantify the structure of Beijing's nitrogen metabolic processes. We found that Beijing's input of new reactive nitrogen (Q, which represents nitrogen obtained from the atmosphere or nitrogen-containing materials used in production and consumption to support human activities) increased from 431 Gg in 1996 to 507 Gg in 2012. Flows to the industry, atmosphere, and household, and components of the system were clearly largest, with total integrated inputs plus outputs from these nodes accounting for 31, 29, and 15%, respectively, of the total integral flows for all paths. The flows through the sewage treatment and transportation components showed marked growth, with total integrated inputs plus outputs increasing to 3.7 and 5.2 times their 1996 values, respectively. Our results can help policymakers to locate the key nodes and pathways in an urban nitrogen metabolic system so they can monitor and manage these components of the system.

  11. Network environ perspective for urban metabolism and carbon emissions: a case study of Vienna, Austria.

    PubMed

    Chen, Shaoqing; Chen, Bin

    2012-04-17

    Cities are considered major contributors to global warming, where carbon emissions are highly embedded in the overall urban metabolism. To examine urban metabolic processes and emission trajectories we developed a carbon flux model based on Network Environ Analysis (NEA). The mutual interactions and control situation within the urban ecosystem of Vienna were examined, and the system-level properties of the city's carbon metabolism were assessed. Regulatory strategies to minimize carbon emissions were identified through the tracking of the possible pathways that affect these emission trajectories. Our findings suggest that indirect flows have a strong bearing on the mutual and control relationships between urban sectors. The metabolism of a city is considered self-mutualistic and sustainable only when the local and distal environments are embraced. Energy production and construction were found to be two factors with a major impact on carbon emissions, and whose regulation is only effective via ad-hoc pathways. In comparison with the original life-cycle tracking, the application of NEA was better at revealing details from a mechanistic aspect, which is crucial for informed sustainable urban management.

  12. Networks of energetic and metabolic interactions define dynamics in microbial communities

    PubMed Central

    Embree, Mallory; Liu, Joanne K.; Al-Bassam, Mahmoud M.; Zengler, Karsten

    2015-01-01

    Microorganisms form diverse communities that have a profound impact on the environment and human health. Recent technological advances have enabled elucidation of community diversity at high resolution. Investigation of microbial communities has revealed that they often contain multiple members with complementing and seemingly redundant metabolic capabilities. An understanding of the communal impacts of redundant metabolic capabilities is currently lacking; specifically, it is not known whether metabolic redundancy will foster competition or motivate cooperation. By investigating methanogenic populations, we identified the multidimensional interspecies interactions that define composition and dynamics within syntrophic communities that play a key role in the global carbon cycle. Species-specific genomes were extracted from metagenomic data using differential coverage binning. We used metabolic modeling leveraging metatranscriptomic information to reveal and quantify a complex intertwined system of syntrophic relationships. Our results show that amino acid auxotrophies create additional interdependencies that define community composition and control carbon and energy flux through the system while simultaneously contributing to overall community robustness. Strategic use of antimicrobials further reinforces this intricate interspecies network. Collectively, our study reveals the multidimensional interactions in syntrophic communities that promote high species richness and bolster community stability during environmental perturbations. PMID:26621749

  13. Comparative metabolic network analysis of two xylose fermenting recombinant Saccharomyces cerevisiae strains.

    PubMed

    Grotkjaer, Thomas; Christakopoulos, Paul; Nielsen, Jens; Olsson, Lisbeth

    2005-01-01

    The recombinant xylose fermenting strain Saccharomyces cerevisiae TMB3001 can grow on xylose, but the xylose utilisation rate is low. One important reason for the inefficient fermentation of xylose to ethanol is believed to be the imbalance of redox co-factors. In the present study, a metabolic flux model was constructed for two recombinant S. cerevisiae strains: TMB3001 and CPB.CR4 which in addition to xylose metabolism have a modulated redox metabolism, i.e. ammonia assimilation was shifted from being NADPH to NADH dependent by deletion of gdh1 and over-expression of GDH2. The intracellular fluxes were estimated for both strains in anaerobic continuous cultivations when the growth limiting feed consisted of glucose (2.5 g L-1) and xylose (13 g L-1). The metabolic network analysis with 13C labelled glucose showed that there was a shift in the specific xylose reductase activity towards use of NADH as co-factor rather than NADPH. This shift is beneficial for solving the redox imbalance and it can therefore partly explain the 25% increase in the ethanol yield observed for CPB.CR4. Furthermore, the analysis indicated that the glyoxylate cycle was activated in CPB.CR4.

  14. Networks of energetic and metabolic interactions define dynamics in microbial communities.

    PubMed

    Embree, Mallory; Liu, Joanne K; Al-Bassam, Mahmoud M; Zengler, Karsten

    2015-12-15

    Microorganisms form diverse communities that have a profound impact on the environment and human health. Recent technological advances have enabled elucidation of community diversity at high resolution. Investigation of microbial communities has revealed that they often contain multiple members with complementing and seemingly redundant metabolic capabilities. An understanding of the communal impacts of redundant metabolic capabilities is currently lacking; specifically, it is not known whether metabolic redundancy will foster competition or motivate cooperation. By investigating methanogenic populations, we identified the multidimensional interspecies interactions that define composition and dynamics within syntrophic communities that play a key role in the global carbon cycle. Species-specific genomes were extracted from metagenomic data using differential coverage binning. We used metabolic modeling leveraging metatranscriptomic information to reveal and quantify a complex intertwined system of syntrophic relationships. Our results show that amino acid auxotrophies create additional interdependencies that define community composition and control carbon and energy flux through the system while simultaneously contributing to overall community robustness. Strategic use of antimicrobials further reinforces this intricate interspecies network. Collectively, our study reveals the multidimensional interactions in syntrophic communities that promote high species richness and bolster community stability during environmental perturbations.

  15. Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond

    PubMed Central

    Pfau, Thomas; Pacheco, Maria Pires; Sauter, Thomas

    2016-01-01

    Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted. PMID:26615025

  16. Amino Acid Flux from Metabolic Network Benefits Protein Translation: the Role of Resource Availability.

    PubMed

    Hu, Xiao-Pan; Yang, Yi; Ma, Bin-Guang

    2015-06-09

    Protein translation is a central step in gene expression and affected by many factors such as codon usage bias, mRNA folding energy and tRNA abundance. Despite intensive previous studies, how metabolic amino acid supply correlates with protein translation efficiency remains unknown. In this work, we estimated the amino acid flux from metabolic network for each protein in Escherichia coli and Saccharomyces cerevisiae by using Flux Balance Analysis. Integrated with the mRNA expression level, protein abundance and ribosome profiling data, we provided a detailed description of the role of amino acid supply in protein translation. Our results showed that amino acid supply positively correlates with translation efficiency and ribosome density. Moreover, with the rank-based regression model, we found that metabolic amino acid supply facilitates ribosome utilization. Based on the fact that the ribosome density change of well-amino-acid-supplied genes is smaller than poorly-amino-acid-supply genes under amino acid starvation, we reached the conclusion that amino acid supply may buffer ribosome density change against amino acid starvation and benefit maintaining a relatively stable translation environment. Our work provided new insights into the connection between metabolic amino acid supply and protein translation process by revealing a new regulation strategy that is dependent on resource availability.

  17. Investigating host-pathogen behavior and their interaction using genome-scale metabolic network models.

    PubMed

    Sadhukhan, Priyanka P; Raghunathan, Anu

    2014-01-01

    Genome Scale Metabolic Modeling methods represent one way to compute whole cell function starting from the genome sequence of an organism and contribute towards understanding and predicting the genotype-phenotype relationship. About 80 models spanning all the kingdoms of life from archaea to eukaryotes have been built till date and used to interrogate cell phenotype under varying conditions. These models have been used to not only understand the flux distribution in evolutionary conserved pathways like glycolysis and the Krebs cycle but also in applications ranging from value added product formation in Escherichia coli to predicting inborn errors of Homo sapiens metabolism. This chapter describes a protocol that delineates the process of genome scale metabolic modeling for analysing host-pathogen behavior and interaction using flux balance analysis (FBA). The steps discussed in the process include (1) reconstruction of a metabolic network from the genome sequence, (2) its representation in a precise mathematical framework, (3) its translation to a model, and (4) the analysis using linear algebra and optimization. The methods for biological interpretations of computed cell phenotypes in the context of individual host and pathogen models and their integration are also discussed.

  18. 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

  19. Towards finding the linkage between metabolic and age-related disorders using semantic gene data network analysis

    PubMed Central

    Uzzal Hossain, Mohammad; Zaffar Shibly, Abu; Md. Omar, Taimur; Tous Zohora, Fatama; Sara Santona, Umme; Hossain, Md. Jakir; Hosen Khoka, Md. Sadek; Ara Keya, Chaman; Salimullah, Md.

    2016-01-01

    A metabolic disorder (MD) occurs when the metabolic process is disturbed. This process is carried out by thousands of enzymes participating in numerous inter-dependent metabolic pathways. Critical biochemical reactions that involve the processing and transportation of carbohydrates, proteins and lipids are affected in metabolic diseases. Therefore, it is of interest to identify the common pathways of metabolic disorders by building protein-protein interactions (PPI) for network analysis. The molecular network linkages between MD and age related diseases (ARD) are intriguing. Hence, we created networks of protein-protein interactions that are related with MD and ARD using relevant known data in the public domain. The network analysis identified known MD associated proteins and predicted genes and or its products of ARD in common pathways. The genes in the common pathways were isolated from the network and further analyzed for their co-localization and shared domains. Thus, a model hypothesis is proposed using interaction networks that are linked between MD and ARD. This data even if less conclusive finds application in understanding the molecular mechanism of known diseases in relation to observed molecular events PMID:27212841

  20. Preferential Nucleation during Polymorphic Transformations

    PubMed Central

    Sharma, H.; Sietsma, J.; Offerman, S. E.

    2016-01-01

    Polymorphism is the ability of a solid material to exist in more than one phase or crystal structure. Polymorphism may occur in metals, alloys, ceramics, minerals, polymers, and pharmaceutical substances. Unresolved are the conditions for preferential nucleation during polymorphic transformations in which structural relationships or special crystallographic orientation relationships (OR’s) form between the nucleus and surrounding matrix grains. We measured in-situ and simultaneously the nucleation rates of grains that have zero, one, two, three and four special OR’s with the surrounding parent grains. These experiments show a trend in which the activation energy for nucleation becomes smaller – and therefore nucleation more probable - with increasing number of special OR’s. These insights contribute to steering the processing of polymorphic materials with tailored properties, since preferential nucleation affects which crystal structure forms, the average grain size and texture of the material, and thereby - to a large extent - the final properties of the material. PMID:27484579

  1. Preferential Nucleation during Polymorphic Transformations.

    PubMed

    Sharma, H; Sietsma, J; Offerman, S E

    2016-08-03

    Polymorphism is the ability of a solid material to exist in more than one phase or crystal structure. Polymorphism may occur in metals, alloys, ceramics, minerals, polymers, and pharmaceutical substances. Unresolved are the conditions for preferential nucleation during polymorphic transformations in which structural relationships or special crystallographic orientation relationships (OR's) form between the nucleus and surrounding matrix grains. We measured in-situ and simultaneously the nucleation rates of grains that have zero, one, two, three and four special OR's with the surrounding parent grains. These experiments show a trend in which the activation energy for nucleation becomes smaller - and therefore nucleation more probable - with increasing number of special OR's. These insights contribute to steering the processing of polymorphic materials with tailored properties, since preferential nucleation affects which crystal structure forms, the average grain size and texture of the material, and thereby - to a large extent - the final properties of the material.

  2. Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO2 Levels.

    PubMed

    Levering, Jennifer; Dupont, Christopher L; Allen, Andrew E; Palsson, Bernhard O; Zengler, Karsten

    2017-01-01

    Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean's primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom's metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and shared metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum's response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. IMPORTANCE Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum's metabolism.

  3. Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO2 Levels

    PubMed Central

    Dupont, Christopher L.; Allen, Andrew E.; Palsson, Bernhard O.

    2017-01-01

    ABSTRACT Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean’s primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom’s metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and shared metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum’s response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. IMPORTANCE Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum’s metabolism. PMID:28217746

  4. Reconstruction and In Silico Analysis of Metabolic Network for an Oleaginous Yeast, Yarrowia lipolytica

    PubMed Central

    Pan, Pengcheng; Hua, Qiang

    2012-01-01

    With the emergence of energy scarcity, the use of renewable energy sources such as biodiesel is becoming increasingly necessary. Recently, many researchers have focused their minds on Yarrowia lipolytica, a model oleaginous yeast, which can be employed to accumulate large amounts of lipids that could be further converted to biodiesel. In order to understand the metabolic characteristics of Y. lipolytica at a systems level and to examine the potential for enhanced lipid production, a genome-scale compartmentalized metabolic network was reconstructed based on a combination of genome annotation and the detailed biochemical knowledge from multiple databases such as KEGG, ENZYME and BIGG. The information about protein and reaction associations of all the organisms in KEGG and Expasy-ENZYME database was arranged into an EXCEL file that can then be regarded as a new useful database to generate other reconstructions. The generated model iYL619_PCP accounts for 619 genes, 843 metabolites and 1,142 reactions including 236 transport reactions, 125 exchange reactions and 13 spontaneous reactions. The in silico model successfully predicted the minimal media and the growing abilities on different substrates. With flux balance analysis, single gene knockouts were also simulated to predict the essential genes and partially essential genes. In addition, flux variability analysis was applied to design new mutant strains that will redirect fluxes through the network and may enhance the production of lipid. This genome-scale metabolic model of Y. lipolytica can facilitate system-level metabolic analysis as well as strain development for improving the production of biodiesels and other valuable products by Y. lipolytica and other closely related oleaginous yeasts. PMID:23236514

  5. Computing minimal nutrient sets from metabolic networks via linear constraint solving

    PubMed Central

    2013-01-01

    Background As more complete genome sequences become available, bioinformatics challenges arise in how to exploit genome sequences to make phenotypic predictions. One type of phenotypic prediction is to determine sets of compounds that will support the growth of a bacterium from the metabolic network inferred from the genome sequence of that organism. Results We present a method for computationally determining alternative growth media for an organism based on its metabolic network and transporter complement. Our method predicted 787 alternative anaerobic minimal nutrient sets for Escherichia coli K–12 MG1655 from the EcoCyc database. The program automatically partitioned the nutrients within these sets into 21 equivalence classes, most of which correspond to compounds serving as sources of carbon, nitrogen, phosphorous, and sulfur, or combinations of these essential elements. The nutrient sets were predicted with 72.5% accuracy as evaluated by comparison with 91 growth experiments. Novel aspects of our approach include (a) exhaustive consideration of all combinations of nutrients rather than assuming that all element sources can substitute for one another(an assumption that can be invalid in general) (b) leveraging the notion of a machinery-duplicating constraint, namely, that all intermediate metabolites used in active reactions must be produced in increasing concentrations to prevent successive dilution from cell division, (c) the use of Satisfiability Modulo Theory solvers rather than Linear Programming solvers, because our approach cannot be formulated as linear programming, (d) the use of Binary Decision Diagrams to produce an efficient implementation. Conclusions Our method for generating minimal nutrient sets from the metabolic network and transporters of an organism combines linear constraint solving with binary decision diagrams to efficiently produce solution sets to provided growth problems. PMID:23537498

  6. 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.

  7. On the Effect of Preferential Sampling in Spatial Prediction

    EPA Science Inventory

    The choice of the sampling locations in a spatial network is often guided by practical demands. In particular, typically, locations are preferentially chosen to capture high values of a response, for example, air pollution levels in environmental monitoring. Then, model estimatio...

  8. Integrating whole-genome expression results into metabolic networks with Pathway Processor.

    PubMed

    Cavalieri, Duccio; Grosu, Paul

    2004-05-01

    Genes never act alone in a biological system, but participate in a cascade of networks. As a result, analyzing microarray data from a pathway perspective leads to a new level of understanding the system. The authors' group has recently developed Pathway Processor (http://cgr.harvard.edu/cavalieri/pp.html), an automatic statistical method to determine which pathways are most affected by transcriptional changes and to map expression data from multiple whole-genome expression experiments on metabolic pathways. This unit presents applications of the Pathway Processor software.

  9. Metabolism

    MedlinePlus

    ... and intestines. Several of the hormones of the endocrine system are involved in controlling the rate and direction ... For Kids For Parents MORE ON THIS TOPIC Endocrine System What Can I Do About My High Metabolism? ...

  10. Metabolism

    MedlinePlus

    ... symptoms. Metabolic diseases and conditions include: Hyperthyroidism (pronounced: hi-per-THIGH-roy-dih-zum). Hyperthyroidism is caused ... or through surgery or radiation treatments. Hypothyroidism (pronounced: hi-po-THIGH-roy-dih-zum). Hypothyroidism is caused ...

  11. Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies.

    PubMed

    Horvat, Predrag; Koller, Martin; Braunegg, Gerhart

    2015-09-01

    A review of the use of elementary flux modes (EFMs) and their applications in metabolic engineering covered with yield space analysis (YSA) is presented. EFMs are an invaluable tool in mathematical modeling of biochemical processes. They are described from their inception in 1994, followed by various improvements of their computation in later years. YSA constitutes another precious tool for metabolic network modeling, and is presented in details along with EFMs in this article. The application of these techniques is discussed for several case studies of metabolic network modeling provided in respective original articles. The article is concluded by some case studies in which the application of EFMs and YSA turned out to be most useful, such as the analysis of intracellular polyhydroxyalkanoate (PHA) formation and consumption in Cupriavidus necator, including the constraint-based description of the steady-state flux cone of the strain's metabolic network, the profound analysis of a continuous five-stage bioreactor cascade for PHA production by C. necator using EFMs and, finally, the study of metabolic fluxes in the metabolic network of C. necator cultivated on glycerol.

  12. Kriging-Based Parameter Estimation Algorithm for Metabolic Networks Combined with Single-Dimensional Optimization and Dynamic Coordinate Perturbation.

    PubMed

    Wang, Hong; Wang, Xicheng; Li, Zheng; Li, Keqiu

    2016-01-01

    The metabolic network model allows for an in-depth insight into the molecular mechanism of a particular organism. Because most parameters of the metabolic network cannot be directly measured, they must be estimated by using optimization algorithms. However, three characteristics of the metabolic network model, i.e., high nonlinearity, large amount parameters, and huge variation scopes of parameters, restrict the application of many traditional optimization algorithms. As a result, there is a growing demand to develop efficient optimization approaches to address this complex problem. In this paper, a Kriging-based algorithm aiming at parameter estimation is presented for constructing the metabolic networks. In the algorithm, a new infill sampling criterion, named expected improvement and mutual information (EI&MI), is adopted to improve the modeling accuracy by selecting multiple new sample points at each cycle, and the domain decomposition strategy based on the principal component analysis is introduced to save computing time. Meanwhile, the convergence speed is accelerated by combining a single-dimensional optimization method with the dynamic coordinate perturbation strategy when determining the new sample points. Finally, the algorithm is applied to the arachidonic acid metabolic network to estimate its parameters. The obtained results demonstrate the effectiveness of the proposed algorithm in getting precise parameter values under a limited number of iterations.

  13. Systematic Analysis of Conservation Relations in Escherichia coli Genome-Scale Metabolic Network Reveals Novel Growth Media

    PubMed Central

    Imieliński, Marcin; Belta, Calin; Rubin, Harvey; Halász, Ádam

    2006-01-01

    A biochemical species is called producible in a constraints-based metabolic model if a feasible steady-state flux configuration exists that sustains its nonzero concentration during growth. Extreme semipositive conservation relations (ESCRs) are the simplest semipositive linear combinations of species concentrations that are invariant to all metabolic flux configurations. In this article, we outline a fundamental relationship between the ESCRs of a metabolic network and the producibility of a biochemical species under a nutrient media. We exploit this relationship in an algorithm that systematically enumerates all minimal nutrient sets that render an objective species weakly producible (i.e., producible in the absence of thermodynamic constraints) through a simple traversal of ESCRs. We apply our results to a recent genome scale model of Escherichia coli metabolism, in which we traverse the 51 anhydrous ESCRs of the metabolic network to determine all 928 minimal aqueous nutrient media that render biomass weakly producible. Applying irreversibility constraints, we find 287 of these 928 nutrient sets to be thermodynamically feasible. We also find that an additional 365 of these nutrient sets are thermodynamically feasible in the presence of oxygen. Since biomass producibility is commonly used as a surrogate for growth in genome scale metabolic models, our results represent testable hypotheses of alternate growth media derived from in silico analysis of the E. coli genome scale metabolic network. PMID:16461408

  14. A disease-specific metabolic brain network associated with corticobasal degeneration.

    PubMed

    Niethammer, Martin; Tang, Chris C; Feigin, Andrew; Allen, Patricia J; Heinen, Lisette; Hellwig, Sabine; Amtage, Florian; Hanspal, Era; Vonsattel, Jean Paul; Poston, Kathleen L; Meyer, Philipp T; Leenders, Klaus L; Eidelberg, David

    2014-11-01

    Corticobasal degeneration is an uncommon parkinsonian variant condition that is diagnosed mainly on clinical examination. To facilitate the differential diagnosis of this disorder, we used metabolic brain imaging to characterize a specific network that can be used to discriminate corticobasal degeneration from other atypical parkinsonian syndromes. Ten non-demented patients (eight females/two males; age 73.9 ± 5.7 years) underwent metabolic brain imaging with (18)F-fluorodeoxyglucose positron emission tomography for atypical parkinsonism. These individuals were diagnosed clinically with probable corticobasal degeneration. This diagnosis was confirmed in the three subjects who additionally underwent post-mortem examination. Ten age-matched healthy subjects (five females/five males; age 71.7 ± 6.7 years) served as controls for the imaging studies. Spatial covariance analysis was applied to scan data from the combined group to identify a significant corticobasal degeneration-related metabolic pattern that discriminated (P < 0.001) the patients from the healthy control group. This pattern was characterized by bilateral, asymmetric metabolic reductions involving frontal and parietal cortex, thalamus, and caudate nucleus. These pattern-related changes were greater in magnitude in the cerebral hemisphere opposite the more clinically affected body side. The presence of this corticobasal degeneration-related metabolic topography was confirmed in two independent testing sets of patient and control scans, with elevated pattern expression (P < 0.001) in both disease groups relative to corresponding normal values. We next determined whether prospectively computed expression values for this pattern accurately discriminated corticobasal degeneration from multiple system atrophy and progressive supranuclear palsy (the two most common atypical parkinsonian syndromes) on a single case basis. Based upon this measure, corticobasal degeneration was successfully distinguished from

  15. 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.

  16. 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.

  17. Two-stage flux balance analysis of metabolic networks for drug target identification

    PubMed Central

    2011-01-01

    Background Efficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and in vitro binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective alternative. Wealth of omics data generated from genomic, proteomic and metabolomic techniques changes the way researchers view drug targets and provides unprecedent opportunities for drug target identification. Results In this paper, we develop a method based on flux balance analysis (FBA) of metabolic networks to identify potential drug targets. This method consists of two linear programming (LP) models, which first finds the steady optimal fluxes of reactions and the mass flows of metabolites in the pathologic state and then determines the fluxes and mass flows in the medication state with the minimal side effect caused by the medication. Drug targets are identified by comparing the fluxes of reactions in both states and examining the change of reaction fluxes. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method, then apply it to a hyperuricemia-related purine metabolic pathway. Known drug targets for hyperuricemia are correctly identified by our two-stage FBA method, and the side effects of these targets are also taken into account. A number of other promising drug targets are found to be both effective and safe. Conclusions Our method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks. It can generate testable predictions, provide insights into drug action mechanisms and guide experimental design of drug discovery. PMID:21689470

  18. Endothelial-dependent vasodilators preferentially increase subendocardial blood flow

    SciTech Connect

    Pelc, L.R.; Gross, G.J.; Warltier, D.C.

    1986-03-05

    Interference with arachidonic acid metabolism on the effect of acetylcholine (Ach) or arachidonic acid (AA) to preferentially increase subendocardial perfusion was investigated in anesthetized dogs. Hemodynamics, regional myocardial blood flow (MBF (ml/min/g):radioactive microspheres) and the left ventricular transmural distribution of flow (endo/epi) were measured. Intracoronary infusion of Ach (10 ..mu..g/min) and AA (585 ..mu..g/min) significantly (P < .05*) increased myocardial perfusion and selectively redistributed flow to the subendocardium (increased endo/epi) without changes in systemic hemodynamics. Inhibition of phospholipase A/sub 2/ by quinacrine (Q; 600 ..mu..g/min, ic) attenuated the increase in myocardial perfusion produced by Ach but not by AA and inhibited the redistribution of flow to the subendocardium. The present results suggest that endothelium-dependent vasodilators produce a preferential increase in subendocardial perfusion via a product of AA metabolism.

  19. Preferential Amplification of Pathogenic Sequences.

    PubMed

    Ge, Fang; Parker, Jayme; Chul Choi, Sang; Layer, Mark; Ross, Katherine; Jilly, Bernard; Chen, Jack

    2015-06-11

    The application of next generation sequencing (NGS) technology in the diagnosis of human pathogens is hindered by the fact that pathogenic sequences, especially viral, are often scarce in human clinical specimens. This known disproportion leads to the requirement of subsequent deep sequencing and extensive bioinformatics analysis. Here we report a method we called "Preferential Amplification of Pathogenic Sequences (PATHseq)" that can be used to greatly enrich pathogenic sequences. Using a computer program, we developed 8-, 9-, and 10-mer oligonucleotides called "non-human primers" that do not match the most abundant human transcripts, but instead selectively match transcripts of human pathogens. Instead of using random primers in the construction of cDNA libraries, the PATHseq method recruits these short non-human primers, which in turn, preferentially amplifies non-human, presumably pathogenic sequences. Using this method, we were able to enrich pathogenic sequences up to 200-fold in the final sequencing library. This method does not require prior knowledge of the pathogen or assumption of the infection; therefore, it provides a fast and sequence-independent approach for detection and identification of human viruses and other pathogens. The PATHseq method, coupled with NGS technology, can be broadly used in identification of known human pathogens and discovery of new pathogens.

  20. Preferential nucleation during polymorphic transformations

    DOE PAGES

    Sharma, H.; Sietsma, J.; Offerman, S. E.

    2016-08-03

    Polymorphism is the ability of a solid material to exist in more than one phase or crystal structure. Polymorphism may occur in metals, alloys, ceramics, minerals, polymers, and pharmaceutical substances. Unresolved are the conditions for preferential nucleation during polymorphic transformations in which structural relationships or special crystallographic orientation relationships (OR’s) form between the nucleus and surrounding matrix grains. We measured in-situ and simultaneously the nucleation rates of grains that have zero, one, two, three and four special OR’s with the surrounding parent grains. These experiments show a trend in which the activation energy for nucleation becomes smaller – and thereforemore » nucleation more probable - with increasing number of special OR’s. As a result, these insights contribute to steering the processing of polymorphic materials with tailored properties, since preferential nucleation affects which crystal structure forms, the average grain size and texture of the material, and thereby - to a large extent - the final properties of the material.« less

  1. Preferential nucleation during polymorphic transformations

    SciTech Connect

    Sharma, H.; Sietsma, J.; Offerman, S. E.

    2016-08-03

    Polymorphism is the ability of a solid material to exist in more than one phase or crystal structure. Polymorphism may occur in metals, alloys, ceramics, minerals, polymers, and pharmaceutical substances. Unresolved are the conditions for preferential nucleation during polymorphic transformations in which structural relationships or special crystallographic orientation relationships (OR’s) form between the nucleus and surrounding matrix grains. We measured in-situ and simultaneously the nucleation rates of grains that have zero, one, two, three and four special OR’s with the surrounding parent grains. These experiments show a trend in which the activation energy for nucleation becomes smaller – and therefore nucleation more probable - with increasing number of special OR’s. As a result, these insights contribute to steering the processing of polymorphic materials with tailored properties, since preferential nucleation affects which crystal structure forms, the average grain size and texture of the material, and thereby - to a large extent - the final properties of the material.

  2. Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.

    PubMed

    Puchałka, Jacek; Oberhardt, Matthew A; Godinho, Miguel; Bielecka, Agata; Regenhardt, Daniela; Timmis, Kenneth N; Papin, Jason A; Martins dos Santos, Vítor A P

    2008-10-01

    A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13)C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile bacterium and to

  3. Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803.

    PubMed

    Montagud, Arnau; Zelezniak, Aleksej; Navarro, Emilio; de Córdoba, Pedro Fernández; Urchueguía, Javier F; Patil, Kiran Raosaheb

    2011-03-01

    Synechocystis sp. PCC6803 is a model cyanobacterium capable of producing biofuels with CO(2) as carbon source and with its metabolism fueled by light, for which it stands as a potential production platform of socio-economic importance. Compilation and characterization of Synechocystis genome-scale metabolic model is a pre-requisite toward achieving a proficient photosynthetic cell factory. To this end, we report iSyn811, an upgraded genome-scale metabolic model of Synechocystis sp. PCC6803 consisting of 956 reactions and accounting for 811 genes. To gain insights into the interplay between flux activities and metabolic physiology, flux coupling analysis was performed for iSyn811 under four different growth conditions, viz., autotrophy, mixotrophy, heterotrophy, and light-activated heterotrophy (LH). Initial steps of carbon acquisition and catabolism formed the versatile center of the flux coupling networks, surrounded by a stable core of pathways leading to biomass building blocks. This analysis identified potential bottlenecks for hydrogen and ethanol production. Integration of transcriptomic data with the Synechocystis flux coupling networks lead to identification of reporter flux coupling pairs and reporter flux coupling groups - regulatory hot spots during metabolic shifts triggered by the availability of light. Overall, flux coupling analysis provided insight into the structural organization of Synechocystis sp. PCC6803 metabolic network toward designing of a photosynthesis-based production platform.

  4. Glucose Metabolism during Resting State Reveals Abnormal Brain Networks Organization in the Alzheimer’s Disease and Mild Cognitive Impairment

    PubMed Central

    Martínez-Montes, Eduardo

    2013-01-01

    This paper aims to study the abnormal patterns of brain glucose metabolism co-variations in Alzheimer disease (AD) and Mild Cognitive Impairment (MCI) patients compared to Normal healthy controls (NC) using the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The local cerebral metabolic rate for glucose (CMRgl) in a set of 90 structures belonging to the AAL atlas was obtained from Fluro-Deoxyglucose Positron Emission Tomography data in resting state. It is assumed that brain regions whose CMRgl values are significantly correlated are functionally associated; therefore, when metabolism is altered in a single region, the alteration will affect the metabolism of other brain areas with which it interrelates. The glucose metabolism network (represented by the matrix of the CMRgl co-variations among all pairs of structures) was studied using the graph theory framework. The highest concurrent fluctuations in CMRgl were basically identified between homologous cortical regions in all groups. Significant differences in CMRgl co-variations in AD and MCI groups as compared to NC were found. The AD and MCI patients showed aberrant patterns in comparison to NC subjects, as detected by global and local network properties (global and local efficiency, clustering index, and others). MCI network’s attributes showed an intermediate position between NC and AD, corroborating it as a transitional stage from normal aging to Alzheimer disease. Our study is an attempt at exploring the complex association between glucose metabolism, CMRgl covariations and the attributes of the brain network organization in AD and MCI. PMID:23894356

  5. Metabolic network reconstruction and flux variability analysis of storage synthesis in developing oilseed rape (Brassica napus L.) embryos

    SciTech Connect

    Hay, J.; Schwender, J.

    2011-08-01

    Computational simulation of large-scale biochemical networks can be used to analyze and predict the metabolic behavior of an organism, such as a developing seed. Based on the biochemical literature, pathways databases and decision rules defining reaction directionality we reconstructed bna572, a stoichiometric metabolic network model representing Brassica napus seed storage metabolism. In the highly compartmentalized network about 25% of the 572 reactions are transport reactions interconnecting nine subcellular compartments and the environment. According to known physiological capabilities of developing B. napus embryos, four nutritional conditions were defined to simulate heterotrophy or photoheterotrophy, each in combination with the availability of inorganic nitrogen (ammonia, nitrate) or amino acids as nitrogen sources. Based on mathematical linear optimization the optimal solution space was comprehensively explored by flux variability analysis, thereby identifying for each reaction the range of flux values allowable under optimality. The range and variability of flux values was then categorized into flux variability types. Across the four nutritional conditions, approximately 13% of the reactions have variable flux values and 10-11% are substitutable (can be inactive), both indicating metabolic redundancy given, for example, by isoenzymes, subcellular compartmentalization or the presence of alternative pathways. About one-third of the reactions are never used and are associated with pathways that are suboptimal for storage synthesis. Fifty-seven reactions change flux variability type among the different nutritional conditions, indicating their function in metabolic adjustments. This predictive modeling framework allows analysis and quantitative exploration of storage metabolism of a developing B. napus oilseed.

  6. On correlated reaction sets and coupled reaction sets in metabolic networks.

    PubMed

    Marashi, Sayed-Amir; Hosseini, Zhaleh

    2015-08-01

    Two reactions are in the same "correlated reaction set" (or "Co-Set") if their fluxes are linearly correlated. On the other hand, two reactions are "coupled" if nonzero flux through one reaction implies nonzero flux through the other reaction. Flux correlation analysis has been previously used in the analysis of enzyme dysregulation and enzymopathy, while flux coupling analysis has been used to predict co-expression of genes and to model network evolution. The goal of this paper is to emphasize, through a few examples, that these two concepts are inherently different. In other words, except for the case of full coupling, which implies perfect correlation between two fluxes (R(2) = 1), there are no constraints on Pearson correlation coefficients (CC) in case of any other type of (un)coupling relations. In other words, Pearson CC can take any value between 0 and 1 in other cases. Furthermore, by analyzing genome-scale metabolic networks, we confirm that there are some examples in real networks of bacteria, yeast and human, which approve that flux coupling and flux correlation cannot be used interchangeably.

  7. Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models

    PubMed Central

    Yuan, Qianqian; Li, Peishun; Hao, Tong; Li, Feiran; Ma, Hongwu; Wang, Zhiwen; Zhao, Xueming; Chen, Tao; Goryanin, Igor

    2017-01-01

    Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design. PMID:28085902

  8. Perspectives for a better understanding of the metabolic integration of photorespiration within a complex plant primary metabolism network

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Photorespiration is an important high flux metabolic pathway that is found in all oxygen-producing photosynthetic organisms. It is often viewed as a closed loop that recycles carbon to fuel the Calvin cycle. However, the photorespiratory cycle is known to interact with several primary metabolic path...

  9. Integer programming-based method for designing synthetic metabolic networks by Minimum Reaction Insertion in a Boolean model.

    PubMed

    Lu, Wei; Tamura, Takeyuki; Song, Jiangning; Akutsu, Tatsuya

    2014-01-01

    In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."

  10. Integer Programming-Based Method for Designing Synthetic Metabolic Networks by Minimum Reaction Insertion in a Boolean Model

    PubMed Central

    Song, Jiangning; Akutsu, Tatsuya

    2014-01-01

    In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at “http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html.” PMID:24651476

  11. Analysis of preferential particle concentration

    NASA Astrophysics Data System (ADS)

    Shariff, Karim

    2008-11-01

    It is known from simulations of particle laden turbulent flows (Squires and Eaton 1991; Wang and Maxey 1993) that particles having a relaxation time nearly equal to the Kolmogorov time preferentially concentrate in regions of weak vorticity. Here we consider the set of equations for particle dilatation, strain, and rotation which provides an understanding of this behavior. This set is derived from the two-fluid equations for the coupled fluid and particle phases. Fluid strain induces particle strain, which causes particle dilatation to always decrease. Fluid rotation, on the other hand, induces particle rotation, which causes particle dilatation to always increase. Illustrative solutions are provided for spatially linear flows and the case of pure strain nicely illustrates how particles concentrate. The analysis also suggests devices and flows that would be particularly good at concentrating particles.

  12. 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

  13. Monitoring Preferential Flow During Infiltration Experiments

    NASA Astrophysics Data System (ADS)

    Jelinkova, V.; Votrubova, J.; Sanda, M.; Cislerova, M.

    2006-12-01

    Field ponded infiltration experiments monitored by electrical resistivity tomography (ERT) were conducted at the experimental site Liz, Sumava Mts., Southern Bohemia. Single-ring ponded infiltration experiments were carried out repeatedly using a set of permanently installed plastic infiltration rings and additional ring made of concrete. The preferential flow occurring during the infiltration experiment was monitored by means of invasive and non-invasive visualization of flow paths. For the noninvasive visualization two geophysical methods were tested, namely TDR and ERT. Geophysical measurements were taken before, during and after the infiltration. TDR measurements were conducted using Tektronix 1502C cable tester connected to three steel electrodes 1- m long installed vertically in the centre of the infiltration ring. ERT was done using the ARES device (the multi electrode VES employing the Wenner-Schlumberger method). After the initial period of clean water infiltration 10g/l NaCl solute was used to improve the ERT signal. The electrical resistivity images were reconstructed using RES2DINV (Geotomo software). In ERT images the evolution of infiltration processes is clearly visible however the preferential character of the flow is completely smeared. This is clear from the comparison of ERT images with the images of Brilliant Blue dye distribution taken from the dug out horizons at the end of infiltration. In addition, the ERT results show some inconsistencies, which are most probably related to the design and the scale of the ERT network which is not fully consistent with the assumption of the methods employed. The research has been performed in the frame of research projects VaV/650/5/03 and GACR 103/04/0663.

  14. 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.

  15. The fractal architecture of cytoplasmic organization: scaling, kinetics and emergence in metabolic networks.

    PubMed

    Aon, Miguel Antonio; O'Rourke, Brian; Cortassa, Sonia

    2004-01-01

    In this work, we highlight the links between fractals and scaling in cells and explore the kinetic consequences for biochemical reactions operating in fractal media. Based on the proposal that the cytoskeletal architecture is organized as a percolation lattice, with clusters emerging as fractal forms, the analysis of kinetics in percolation clusters is especially emphasized. A key consequence of this spatiotemporal cytoplasmic organization is that enzyme reactions following Michaelis-Menten or allosteric type kinetics exhibit higher rates in fractal media (for short times and at lower substrate concentrations) at the percolation threshold than in Euclidean media. As a result, considerably faster and higher amplification of enzymatic activity is obtained. Finally, we describe some of the properties bestowed by cytoskeletal organization and dynamics on metabolic networks.

  16. Regulatory network of secondary metabolism in Brassica rapa: insight into the glucosinolate pathway.

    PubMed

    Pino Del Carpio, Dunia; Basnet, Ram Kumar; Arends, Danny; Lin, Ke; De Vos, Ric C H; Muth, Dorota; Kodde, Jan; Boutilier, Kim; Bucher, Johan; Wang, Xiaowu; Jansen, Ritsert; Bonnema, Guusje

    2014-01-01

    Brassica rapa studies towards metabolic variation have largely been focused on the profiling of the diversity of metabolic compounds in specific crop types or regional varieties, but none aimed to identify genes with regulatory function in metabolite composition. Here we followed a genetical genomics approach to identify regulatory genes for six biosynthetic pathways of health-related phytochemicals, i.e carotenoids, tocopherols, folates, glucosinolates, flavonoids and phenylpropanoids. Leaves from six weeks-old plants of a Brassica rapa doubled haploid population, consisting of 92 genotypes, were profiled for their secondary metabolite composition, using both targeted and LC-MS-based untargeted metabolomics approaches. Furthermore, the same population was profiled for transcript variation using a microarray containing EST sequences mainly derived from three Brassica species: B. napus, B. rapa and B. oleracea. The biochemical pathway analysis was based on the network analyses of both metabolite QTLs (mQTLs) and transcript QTLs (eQTLs). Co-localization of mQTLs and eQTLs lead to the identification of candidate regulatory genes involved in the biosynthesis of carotenoids, tocopherols and glucosinolates. We subsequently focused on the well-characterized glucosinolate pathway and revealed two hotspots of co-localization of eQTLs with mQTLs in linkage groups A03 and A09. Our results indicate that such a large-scale genetical genomics approach combining transcriptomics and metabolomics data can provide new insights into the genetic regulation of metabolite composition of Brassica vegetables.

  17. Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis

    PubMed Central

    Bartell, Jennifer A.; Blazier, Anna S.; Yen, Phillip; Thøgersen, Juliane C.; Jelsbak, Lars; Goldberg, Joanna B.; Papin, Jason A.

    2017-01-01

    Virulence-linked pathways in opportunistic pathogens are putative therapeutic targets that may be associated with less potential for resistance than targets in growth-essential pathways. However, efficacy of virulence-linked targets may be affected by the contribution of virulence-related genes to metabolism. We evaluate the complex interrelationships between growth and virulence-linked pathways using a genome-scale metabolic network reconstruction of Pseudomonas aeruginosa strain PA14 and an updated, expanded reconstruction of P. aeruginosa strain PAO1. The PA14 reconstruction accounts for the activity of 112 virulence-linked genes and virulence factor synthesis pathways that produce 17 unique compounds. We integrate eight published genome-scale mutant screens to validate gene essentiality predictions in rich media, contextualize intra-screen discrepancies and evaluate virulence-linked gene distribution across essentiality datasets. Computational screening further elucidates interconnectivity between inhibition of virulence factor synthesis and growth. Successful validation of selected gene perturbations using PA14 transposon mutants demonstrates the utility of model-driven screening of therapeutic targets. PMID:28266498

  18. Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis

    NASA Astrophysics Data System (ADS)

    Bartell, Jennifer A.; Blazier, Anna S.; Yen, Phillip; Thøgersen, Juliane C.; Jelsbak, Lars; Goldberg, Joanna B.; Papin, Jason A.

    2017-03-01

    Virulence-linked pathways in opportunistic pathogens are putative therapeutic targets that may be associated with less potential for resistance than targets in growth-essential pathways. However, efficacy of virulence-linked targets may be affected by the contribution of virulence-related genes to metabolism. We evaluate the complex interrelationships between growth and virulence-linked pathways using a genome-scale metabolic network reconstruction of Pseudomonas aeruginosa strain PA14 and an updated, expanded reconstruction of P. aeruginosa strain PAO1. The PA14 reconstruction accounts for the activity of 112 virulence-linked genes and virulence factor synthesis pathways that produce 17 unique compounds. We integrate eight published genome-scale mutant screens to validate gene essentiality predictions in rich media, contextualize intra-screen discrepancies and evaluate virulence-linked gene distribution across essentiality datasets. Computational screening further elucidates interconnectivity between inhibition of virulence factor synthesis and growth. Successful validation of selected gene perturbations using PA14 transposon mutants demonstrates the utility of model-driven screening of therapeutic targets.

  19. Bistability in a Metabolic Network Underpins the De Novo Evolution of Colony Switching in Pseudomonas fluorescens

    PubMed Central

    Gallie, Jenna; Libby, Eric; Bertels, Frederic; Remigi, Philippe; Jendresen, Christian B.; Ferguson, Gayle C.; Desprat, Nicolas; Buffing, Marieke F.; Sauer, Uwe; Beaumont, Hubertus J. E.; Martinussen, Jan; Kilstrup, Mogens; Rainey, Paul B.

    2015-01-01

    Phenotype switching is commonly observed in nature. This prevalence has allowed the elucidation of a number of underlying molecular mechanisms. However, little is known about how phenotypic switches arise and function in their early evolutionary stages. The first opportunity to provide empirical insight was delivered by an experiment in which populations of the bacterium Pseudomonas fluorescens SBW25 evolved, de novo, the ability to switch between two colony phenotypes. Here we unravel the molecular mechanism behind colony switching, revealing how a single nucleotide change in a gene enmeshed in central metabolism (carB) generates such a striking phenotype. We show that colony switching is underpinned by ON/OFF expression of capsules consisting of a colanic acid-like polymer. We use molecular genetics, biochemical analyses, and experimental evolution to establish that capsule switching results from perturbation of the pyrimidine biosynthetic pathway. Of central importance is a bifurcation point at which uracil triphosphate is partitioned towards either nucleotide metabolism or polymer production. This bifurcation marks a cell-fate decision point whereby cells with relatively high pyrimidine levels favour nucleotide metabolism (capsule OFF), while cells with lower pyrimidine levels divert resources towards polymer biosynthesis (capsule ON). This decision point is present and functional in the wild-type strain. Finally, we present a simple mathematical model demonstrating that the molecular components of the decision point are capable of producing switching. Despite its simple mutational cause, the connection between genotype and phenotype is complex and multidimensional, offering a rare glimpse of how noise in regulatory networks can provide opportunity for evolution. PMID:25763575

  20. Genome-scale metabolic network guided engineering of Streptomyces tsukubaensis for FK506 production improvement

    PubMed Central

    2013-01-01

    Background FK506 is an important immunosuppressant, which can be produced by Streptomyces tsukubaensis. However, the production capacity of the strain is very low. Hereby, a computational guided engineering approach was proposed in order to improve the intracellular precursor and cofactor availability of FK506 in S. tsukubaensis. Results First, a genome-scale metabolic model of S. tsukubaensis was constructed based on its annotated genome and biochemical information. Subsequently, several potential genetic targets (knockout or overexpression) that guaranteed an improved yield of FK506 were identified by the recently developed methodology. To validate the model predictions, each target gene was manipulated in the parent strain D852, respectively. All the engineered strains showed a higher FK506 production, compared with D852. Furthermore, the combined effect of the genetic modifications was evaluated. Results showed that the strain HT-ΔGDH-DAZ with gdhA-deletion and dahp-, accA2-, zwf2-overexpression enhanced FK506 concentration up to 398.9 mg/L, compared with 143.5 mg/L of the parent strain D852. Finally, fed-batch fermentations of HT-ΔGDH-DAZ were carried out, which led to the FK506 production of 435.9 mg/L, 1.47-fold higher than the parent strain D852 (158.7 mg/L). Conclusions Results confirmed that the promising targets led to an increase in FK506 titer. The present work is the first attempt to engineer the primary precursor pathways to improve FK506 production in S. tsukubaensis with genome-scale metabolic network guided metabolic engineering. The relationship between model prediction and experimental results demonstrates the rationality and validity of this approach for target identification. This strategy can also be applied to the improvement of other important secondary metabolites. PMID:23705993

  1. Insights on the evolution of metabolic networks of unicellular translationally biased organisms from transcriptomic data and sequence analysis.

    PubMed

    Carbone, Alessandra; Madden, Richard

    2005-10-01

    Codon bias is related to metabolic functions in translationally biased organisms, and two facts are argued about. First, genes with high codon bias describe in meaningful ways the metabolic characteristics of the organism; important metabolic pathways corresponding to crucial characteristics of the lifestyle of an organism, such as photosynthesis, nitrification, anaerobic versus aerobic respiration, sulfate reduction, methanogenesis, and others, happen to involve especially biased genes. Second, gene transcriptional levels of sets of experiments representing a significant variation of biological conditions strikingly confirm, in the case of Saccharomyces cerevisiae, that metabolic preferences are detectable by purely statistical analysis: the high metabolic activity of yeast during fermentation is encoded in the high bias of enzymes involved in the associated pathways, suggesting that this genome was affected by a strong evolutionary pressure that favored a predominantly fermentative metabolism of yeast in the wild. The ensemble of metabolic pathways involving enzymes with high codon bias is rather well defined and remains consistent across many species, even those that have not been considered as translationally biased, such as Helicobacter pylori, for instance, reveal some weak form of translational bias for this genome. We provide numerical evidence, supported by experimental data, of these facts and conclude that the metabolic networks of translationally biased genomes, observable today as projections of eons of evolutionary pressure, can be analyzed numerically and predictions of the role of specific pathways during evolution can be derived. The new concepts of Comparative Pathway Index, used to compare organisms with respect to their metabolic networks, and Evolutionary Pathway Index, used to detect evolutionarily meaningful bias in the genetic code from transcriptional data, are introduced.

  2. Gene network analysis identifies rumen epithelial cell proliferation, differentiation and metabolic pathways perturbed by diet and correlated with methane production

    PubMed Central

    Xiang, Ruidong; McNally, Jody; Rowe, Suzanne; Jonker, Arjan; Pinares-Patino, Cesar S.; Oddy, V. Hutton; Vercoe, Phil E.; McEwan, John C.; Dalrymple, Brian P.

    2016-01-01

    Ruminants obtain nutrients from microbial fermentation of plant material, primarily in their rumen, a multilayered forestomach. How the different layers of the rumen wall respond to diet and influence microbial fermentation, and how these process are regulated, is not well understood. Gene expression correlation networks were constructed from full thickness rumen wall transcriptomes of 24 sheep fed two different amounts and qualities of a forage and measured for methane production. The network contained two major negatively correlated gene sub-networks predominantly representing the epithelial and muscle layers of the rumen wall. Within the epithelium sub-network gene clusters representing lipid/oxo-acid metabolism, general metabolism and proliferating and differentiating cells were identified. The expression of cell cycle and metabolic genes was positively correlated with dry matter intake, ruminal short chain fatty acid concentrations and methane production. A weak correlation between lipid/oxo-acid metabolism genes and methane yield was observed. Feed consumption level explained the majority of gene expression variation, particularly for the cell cycle genes. Many known stratified epithelium transcription factors had significantly enriched targets in the epithelial gene clusters. The expression patterns of the transcription factors and their targets in proliferating and differentiating skin is mirrored in the rumen, suggesting conservation of regulatory systems. PMID:27966600

  3. A genome scale metabolic network for rice and accompanying analysis of tryptophan, auxin and serotonin biosynthesis regulation under biotic stress

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Functional annotations of large plant genome projects mostly provide information on gene function and gene families based on the presence of protein domains and gene homology, but not necessarily in association with gene expression or metabolic and regulatory networks. These additional annotations a...

  4. Impulsivity is Associated with Increased Metabolism in the Fronto-Insular Network in Parkinson’s Disease

    PubMed Central

    Tahmasian, Masoud; Rochhausen, Luisa; Maier, Franziska; Williamson, Kim L.; Drzezga, Alexander; Timmermann, Lars; Van Eimeren, Thilo; Eggers, Carsten

    2015-01-01

    Various neuroimaging studies demonstrated that the fronto-insular network is implicated in impulsive behavior. We compared glucose metabolism (as a proxy measure of neural activity) among 24 patients with Parkinson’s disease (PD) who presented with low or high levels of impulsivity based on the Barratt Impulsiveness Scale 11 (BIS) scores. Subjects underwent 18-fluorodeoxyglucose positron emission tomography (FDG-PET) and the voxel-wise group difference of FDG-metabolism was analyzed in Statistical Parametric Mapping (SPM8). Subsequently, we performed a partial correlation analysis between the FDG-metabolism and BIS scores, controlling for covariates (i.e., age, sex, severity of disease and levodopa equivalent daily doses). Voxel-wise group comparison revealed higher FDG-metabolism in the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and right insula in patients with higher impulsivity scores. Moreover, there was a positive correlation between the FDG-metabolism and BIS scores. Our findings provide evidence that high impulsivity is associated with increased FDG-metabolism within the fronto-insular network in PD. PMID:26648853

  5. Metabolic network analysis revealed distinct routes of deletion effects between essential and non-essential genes.

    PubMed

    Ma, Jing; Zhang, Xun; Ung, Choong Yong; Chen, Yu Zong; Li, Baowen

    2012-04-01

    Interest in essential genes has arisen recently given their importance in antimicrobial drug development. Although knockouts of essential genes are commonly known to cause lethal phenotypes, there is insufficient understanding on the intermediate changes followed by genetic perturbation and to what extent essential genes correlate to other genes. Here, we characterized the gene knockout effects by using a list of affected genes, termed as 'damage lists'. These damage lists were identified through a refined cascading failure approach that was based on a previous topological flux balance analysis. Using an Escherichia coli metabolic network, we incorporated essentiality information into damage lists and revealed that the knockout of an essential gene mainly affects a large range of other essential genes whereas knockout of a non-essential gene only interrupts other non-essential genes. Also, genes sharing common damage lists tend to have the same essentiality. We extracted 72 core functional modules from the common damage lists of essential genes and demonstrated their ability to halt essential metabolites production. Overall, our network analysis revealed that essential and non-essential genes propagated their deletion effects via distinct routes, conferring mechanistic explanation to the observed lethality phenotypes of essential genes.

  6. Link prediction based on local information considering preferential attachment

    NASA Astrophysics Data System (ADS)

    Zeng, Shan

    2016-02-01

    Link prediction in complex networks has attracted much attention in many fields. In this paper, a common neighbors plus preferential attachment index is presented to estimate the likelihood of the existence of a link between two nodes based on local information of the nearest neighbors. Numerical experiments on six real networks demonstrated the high effectiveness and efficiency of the new index compared with five well-known and widely accepted indices: the common neighbors, resource allocation index, preferential attachment index, local path index and Katz index. The new index provides competitively accurate prediction with local path index and Katz index while has less computational complexity and is more accurate than the other two indices.

  7. Metabolic and protein interaction sub-networks controlling the proliferation rate of cancer cells and their impact on patient survival.

    PubMed

    Feizi, Amir; Bordel, Sergio

    2013-10-24

    Cancer cells can have a broad scope of proliferation rates. Here we aim to identify the molecular mechanisms that allow some cancer cell lines to grow up to 4 times faster than other cell lines. The correlation of gene expression profiles with the growth rate in 60 different cell lines has been analyzed using several genome-scale biological networks and new algorithms. New possible regulatory feedback loops have been suggested and the known roles of several cell cycle related transcription factors have been confirmed. Over 100 growth-correlated metabolic sub-networks have been identified, suggesting a key role of simultaneous lipid synthesis and degradation in the energy supply of the cancer cells growth. Many metabolic sub-networks involved in cell line proliferation appeared also to correlate negatively with the survival expectancy of colon cancer patients.

  8. Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation.

    PubMed

    Meng, Hu; Liu, Ying; Lai, Luhua

    2015-08-18

    Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low

  9. Linkage of organic anion transporter-1 to metabolic pathways through integrated "omics"-driven network and functional analysis.

    PubMed

    Ahn, Sun-Young; Jamshidi, Neema; Mo, Monica L; Wu, Wei; Eraly, Satish A; Dnyanmote, Ankur; Bush, Kevin T; Gallegos, Tom F; Sweet, Douglas H; Palsson, Bernhard Ø; Nigam, Sanjay K

    2011-09-09

    The main kidney transporter of many commonly prescribed drugs (e.g. penicillins, diuretics, antivirals, methotrexate, and non-steroidal anti-inflammatory drugs) is organic anion transporter-1 (OAT1), originally identified as NKT (Lopez-Nieto, C. E., You, G., Bush, K. T., Barros, E. J., Beier, D. R., and Nigam, S. K. (1997) J. Biol. Chem. 272, 6471-6478). Targeted metabolomics in knockouts have shown that OAT1 mediates the secretion or reabsorption of many important metabolites, including intermediates in carbohydrate, fatty acid, and amino acid metabolism. This observation raises the possibility that OAT1 helps regulate broader metabolic activities. We therefore examined the potential roles of OAT1 in metabolic pathways using Recon 1, a functionally tested genome-scale reconstruction of human metabolism. A computational approach was used to analyze in vivo metabolomic as well as transcriptomic data from wild-type and OAT1 knock-out animals, resulting in the implication of several metabolic pathways, including the citric acid cycle, polyamine, and fatty acid metabolism. Validation by in vitro and ex vivo analysis using Xenopus oocyte, cell culture, and kidney tissue assays demonstrated interactions between OAT1 and key intermediates in these metabolic pathways, including previously unknown substrates, such as polyamines (e.g. spermine and spermidine). A genome-scale metabolic network reconstruction generated some experimentally supported predictions for metabolic pathways linked to OAT1-related transport. The data support the possibility that the SLC22 and other families of transporters, known to be expressed in many tissues and primarily known for drug and toxin clearance, are integral to a number of endogenous pathways and may be involved in a larger remote sensing and signaling system (Ahn, S. Y., and Nigam, S. K. (2009) Mol. Pharmacol. 76, 481-490, and Wu, W., Dnyanmote, A. V., and Nigam, S. K. (2011) Mol. Pharmacol. 79, 795-805). Drugs may alter metabolism by

  10. SS-mPMG and SS-GA: tools for finding pathways and dynamic simulation of metabolic networks.

    PubMed

    Katsuragi, Tetsuo; Ono, Naoaki; Yasumoto, Keiichi; Altaf-Ul-Amin, Md; Hirai, Masami Y; Sriyudthsak, Kansuporn; Sawada, Yuji; Yamashita, Yui; Chiba, Yukako; Onouchi, Hitoshi; Fujiwara, Toru; Naito, Satoshi; Shiraishi, Fumihide; Kanaya, Shigehiko

    2013-05-01

    Metabolomics analysis tools can provide quantitative information on the concentration of metabolites in an organism. In this paper, we propose the minimum pathway model generator tool for simulating the dynamics of metabolite concentrations (SS-mPMG) and a tool for parameter estimation by genetic algorithm (SS-GA). SS-mPMG can extract a subsystem of the metabolic network from the genome-scale pathway maps to reduce the complexity of the simulation model and automatically construct a dynamic simulator to evaluate the experimentally observed behavior of metabolites. Using this tool, we show that stochastic simulation can reproduce experimentally observed dynamics of amino acid biosynthesis in Arabidopsis thaliana. In this simulation, SS-mPMG extracts the metabolic network subsystem from published databases. The parameters needed for the simulation are determined using a genetic algorithm to fit the simulation results to the experimental data. We expect that SS-mPMG and SS-GA will help researchers to create relevant metabolic networks and carry out simulations of metabolic reactions derived from metabolomics data.

  11. Complex metabolic network of 1,3-propanediol transport mechanisms and its system identification via biological robustness.

    PubMed

    Guo, Yanjie; Feng, Enmin; Wang, Lei; Xiu, Zhilong

    2014-04-01

    The bioconversion of glycerol to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae (K. pneumoniae) can be characterized by an intricate metabolic network of interactions among biochemical fluxes, metabolic compounds, key enzymes and genetic regulation. Since there are some uncertain factors in the fermentation, especially the transport mechanisms of 1,3-PD across cell membrane, the metabolic network contains multiple possible metabolic systems. Considering the genetic regulation of dha regulon and inhibition of 3-hydroxypropionaldehyde to the growth of cells, we establish a 14-dimensional nonlinear hybrid dynamical system aiming to determine the most possible metabolic system and the corresponding optimal parameter. The existence, uniqueness and continuity of solutions are discussed. Taking the robustness index of the intracellular substances together as a performance index, a system identification model is proposed, in which 1,395 continuous variables and 90 discrete variables are involved. The identification problem is decomposed into two subproblems and a parallel particle swarm optimization procedure is constructed to solve them. Numerical results show that it is most possible that 1,3-PD passes the cell membrane by active transport coupled with passive diffusion.

  12. Metabolic network reconstruction and flux variability analysis of storage synthesis in developing oilseed rape (Brassica napus L.) embryos.

    PubMed

    Hay, Jordan; Schwender, Jörg

    2011-08-01

    Computational simulation of large-scale biochemical networks can be used to analyze and predict the metabolic behavior of an organism, such as a developing seed. Based on the biochemical literature, pathways databases and decision rules defining reaction directionality we reconstructed bna572, a stoichiometric metabolic network model representing Brassica napus seed storage metabolism. In the highly compartmentalized network about 25% of the 572 reactions are transport reactions interconnecting nine subcellular compartments and the environment. According to known physiological capabilities of developing B. napus embryos, four nutritional conditions were defined to simulate heterotrophy or photoheterotrophy, each in combination with the availability of inorganic nitrogen (ammonia, nitrate) or amino acids as nitrogen sources. Based on mathematical linear optimization the optimal solution space was comprehensively explored by flux variability analysis, thereby identifying for each reaction the range of flux values allowable under optimality. The range and variability of flux values was then categorized into flux variability types. Across the four nutritional conditions, approximately 13% of the reactions have variable flux values and 10-11% are substitutable (can be inactive), both indicating metabolic redundancy given, for example, by isoenzymes, subcellular compartmentalization or the presence of alternative pathways. About one-third of the reactions are never used and are associated with pathways that are suboptimal for storage synthesis. Fifty-seven reactions change flux variability type among the different nutritional conditions, indicating their function in metabolic adjustments. This predictive modeling framework allows analysis and quantitative exploration of storage metabolism of a developing B. napus oilseed.

  13. Metabolic network analysis of perfused livers under fed and fasted states: incorporating thermodynamic and futile-cycle-associated regulatory constraints.

    PubMed

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

    2012-01-21

    Isolated liver perfusion systems have been extensively used to characterize intrinsic metabolic changes in liver under various conditions, including systemic injury, hepatotoxin exposure, and warm ischemia. Most of these studies were performed utilizing fasted animals prior to perfusion so that a simplified metabolic network could be used in order to determine intracellular fluxes. However, fasting induced metabolic alterations might interfere with disease related changes. Therefore, there is a need to develop a "unified" metabolic flux analysis approach that could be similarly applied to both fed and fasted states. In this study we explored a methodology based on elementary mode analysis in order to determine intracellular fluxes and active pathways simultaneously. In order to decrease the solution space, thermodynamic constraints, and enzymatic regulatory properties for the formation of futile cycles were further considered in the model, resulting in a mixed integer quadratic programming problem. Given the published experimental observations describing the perfused livers under fed and fasted states, the proposed approach successfully determined that gluconeogenesis, glycogenolysis and fatty acid oxidation were active in both states. However, fasting increased the fluxes in gluconeogenic reactions whereas it decreased fluxes associated with glycogenolysis, TCA cycle, fatty acid oxidation and electron transport reactions. This analysis further identified that more pathways were found to be active in fed state while their weight values were relatively lower compared to fasted state. Glucose, lactate, glutamine, glutamate and ketone bodies were also found to be important external metabolites whose extracellular fluxes should be used in the hepatic metabolic network analysis. In conclusion, the mathematical formulation explored in this study is an attractive tool to analyze the metabolic network of perfused livers under various disease conditions. This approach could

  14. Continuous modeling of metabolic networks with gene regulation in yeast and in vivo determination of rate parameters.

    PubMed

    Moisset, P; Vaisman, D; Cintolesi, A; Urrutia, J; Rapaport, I; Andrews, B A; Asenjo, J A

    2012-09-01

    A continuous model of a metabolic network including gene regulation to simulate metabolic fluxes during batch cultivation of yeast Saccharomyces cerevisiae was developed. The metabolic network includes reactions of glycolysis, gluconeogenesis, glycerol and ethanol synthesis and consumption, the tricarboxylic acid cycle, and protein synthesis. Carbon sources considered were glucose and then ethanol synthesized during growth on glucose. The metabolic network has 39 fluxes, which represent the action of 50 enzymes and 64 genes and it is coupled with a gene regulation network which defines enzyme synthesis (activities) and incorporates regulation by glucose (enzyme induction and repression), modeled using ordinary differential equations. The model includes enzyme kinetics, equations that follow both mass-action law and transport as well as inducible, repressible, and constitutive enzymes of metabolism. The model was able to simulate a fermentation of S. cerevisiae during the exponential growth phase on glucose and the exponential growth phase on ethanol using only one set of kinetic parameters. All fluxes in the continuous model followed the behavior shown by the metabolic flux analysis (MFA) obtained from experimental results. The differences obtained between the fluxes given by the model and the fluxes determined by the MFA do not exceed 25% in 75% of the cases during exponential growth on glucose, and 20% in 90% of the cases during exponential growth on ethanol. Furthermore, the adjustment of the fermentation profiles of biomass, glucose, and ethanol were 95%, 95%, and 79%, respectively. With these results the simulation was considered successful. A comparison between the simulation of the continuous model and the experimental data of the diauxic yeast fermentation for glucose, biomass, and ethanol, shows an extremely good match using the parameters found. The small discrepancies between the fluxes obtained through MFA and those predicted by the differential

  15. Combined Use of Genome-Wide Association Data and Correlation Networks Unravels Key Regulators of Primary Metabolism in Arabidopsis thaliana

    PubMed Central

    Wu, Si; Alseekh, Saleh; Cuadros-Inostroza, Álvaro; Mutwil, Marek; Fernie, Alisdair R.; Brotman, Yariv

    2016-01-01

    Plant primary metabolism is a highly coordinated, central, and complex network of biochemical processes regulated at both the genetic and post-translational levels. The genetic basis of this network can be explored by analyzing the metabolic composition of genetically diverse genotypes in a given plant species. Here, we report an integrative strategy combining quantitative genetic mapping and metabolite‒transcript correlation networks to identify functional associations between genes and primary metabolites in Arabidopsis thaliana. Genome-wide association study (GWAS) was used to identify metabolic quantitative trait loci (mQTL). Correlation networks built using metabolite and transcript data derived from a previously published time-course stress study yielded metabolite‒transcript correlations identified by covariation. Finally, results obtained in this study were compared with mQTL previously described. We applied a statistical framework to test and compare the performance of different single methods (network approach and quantitative genetics methods, representing the two orthogonal approaches combined in our strategy) with that of the combined strategy. We show that the combined strategy has improved performance manifested by increased sensitivity and accuracy. This combined strategy allowed the identification of 92 candidate associations between structural genes and primary metabolites, which not only included previously well-characterized gene‒metabolite associations, but also revealed novel associations. Using loss-of-function mutants, we validated two of the novel associations with genes involved in tyrosine degradation and in β-alanine metabolism. In conclusion, we demonstrate that applying our integrative strategy to the largely untapped resource of metabolite–transcript associations can facilitate the discovery of novel metabolite-related genes. This integrative strategy is not limited to A. thaliana, but generally applicable to other plant

  16. A Network-Based Approach to Visualize Prevalence and Progression of Metabolic Syndrome Components

    PubMed Central

    Völker, Uwe; Völzke, Henry; Kroemer, Heyo; Nauck, Matthias; Wallaschofski, Henri

    2012-01-01

    Background The additional clinical value of clustering cardiovascular risk factors to define the metabolic syndrome (MetS) is still under debate. However, it is unclear which cardiovascular risk factors tend to cluster predominately and how individual risk factor states change over time. Methods & Results We used data from 3,187 individuals aged 20–79 years from the population-based Study of Health in Pomerania for a network-based approach to visualize clustered MetS risk factor states and their change over a five-year follow-up period. MetS was defined by harmonized Adult Treatment Panel III criteria, and each individual's risk factor burden was classified according to the five MetS components at baseline and follow-up. We used the map generator to depict 32 (25) different states and highlight the most important transitions between the 1,024 (322) possible states in the weighted directed network. At baseline, we found the largest fraction (19.3%) of all individuals free of any MetS risk factors and identified hypertension (15.4%) and central obesity (6.3%), as well as their combination (19.0%), as the most common MetS risk factors. Analyzing risk factor flow over the five-year follow-up, we found that most individuals remained in their risk factor state and that low high-density lipoprotein cholesterol (HDL) (6.3%) was the most prominent additional risk factor beyond hypertension and central obesity. Also among individuals without any MetS risk factor at baseline, low HDL (3.5%), hypertension (2.1%), and central obesity (1.6%) were the first risk factors to manifest during follow-up. Conclusions We identified hypertension and central obesity as the predominant MetS risk factor cluster and low HDL concentrations as the most prominent new onset risk factor. PMID:22724019

  17. Metabolic network analysis of DB1 melanoma cells: how much energy is derived from aerobic glycolysis?

    PubMed

    Shestov, A A; Mancuso, A; Leeper, D B; Glickson, J D

    2013-01-01

    A network model has been developed for analysis of tumor glucose metabolism from (13)C MRS isotope exchange kinetic data. Data were obtained from DB1 melanoma cells grown on polystyrene microcarrier beads contained in a 20-mm diameter perfusion chamber in a 9.4 T Varian NMR spectrometer; the cells were perfused with 26 mM [1,6-(13)C(2)]glucose under normoxic conditions and 37°C and monitored by (13)C NMR spectroscopy for 6 h. The model consists of ∼150 differential equations in the cumomer formalism describing glucose and lactate transport, glycolysis, TCA cycle, pyruvate cycling, the pentose shunt, lactate dehydrogenase, the malate-aspartate and glycerophosphate shuttles, and various anaplerotic pathways. The rate of oxygen consumption (CMRO(2)) was measured polarographically by monitoring differences in pO(2). The model was validated by excellent agreement between model predicted and experimentally measured values of CMRO(2) and glutamate pool size. Assuming a P/O ratio of 2.5 for NADH and 1.5 for FADH2, ATP production was estimated as 46% glycolytic and 54% mitochondrial based on average values of CMRO(2) and glycolytic flux (two experiments).

  18. Dissecting and engineering metabolic and regulatory networks of thermophilic bacteria for biofuel production.

    PubMed

    Lin, Lu; Xu, Jian

    2013-11-01

    Interest in thermophilic bacteria as live-cell catalysts in biofuel and biochemical industry has surged in recent years, due to their tolerance of high temperature and wide spectrum of carbon-sources that include cellulose. However their direct employment as microbial cellular factories in the highly demanding industrial conditions has been hindered by uncompetitive biofuel productivity, relatively low tolerance to solvent and osmic stresses, and limitation in genome engineering tools. In this work we review recent advances in dissecting and engineering the metabolic and regulatory networks of thermophilic bacteria for improving the traits of key interest in biofuel industry: cellulose degradation, pentose-hexose co-utilization, and tolerance of thermal, osmotic, and solvent stresses. Moreover, new technologies enabling more efficient genetic engineering of thermophiles were discussed, such as improved electroporation, ultrasound-mediated DNA delivery, as well as thermo-stable plasmids and functional selection systems. Expanded applications of such technological advancements in thermophilic microbes promise to substantiate a synthetic biology perspective, where functional parts, module, chassis, cells and consortia were modularly designed and rationally assembled for the many missions at industry and nature that demand the extraordinary talents of these extremophiles.

  19. LmSmdB: an integrated database for metabolic and gene regulatory network in Leishmania major and Schistosoma mansoni.

    PubMed

    Patel, Priyanka; Mandlik, Vineetha; Singh, Shailza

    2016-03-01

    A database that integrates all the information required for biological processing is essential to be stored in one platform. We have attempted to create one such integrated database that can be a one stop shop for the essential features required to fetch valuable result. LmSmdB (L. major and S. mansoni database) is an integrated database that accounts for the biological networks and regulatory pathways computationally determined by integrating the knowledge of the genome sequences of the mentioned organisms. It is the first database of its kind that has together with the network designing showed the simulation pattern of the product. This database intends to create a comprehensive canopy for the regulation of lipid metabolism reaction in the parasite by integrating the transcription factors, regulatory genes and the protein products controlled by the transcription factors and hence operating the metabolism at genetic level.

  20. LmSmdB: an integrated database for metabolic and gene regulatory network in Leishmania major and Schistosoma mansoni

    PubMed Central

    Patel, Priyanka; Mandlik, Vineetha; Singh, Shailza

    2015-01-01

    A database that integrates all the information required for biological processing is essential to be stored in one platform. We have attempted to create one such integrated database that can be a one stop shop for the essential features required to fetch valuable result. LmSmdB (L. major and S. mansoni database) is an integrated database that accounts for the biological networks and regulatory pathways computationally determined by integrating the knowledge of the genome sequences of the mentioned organisms. It is the first database of its kind that has together with the network designing showed the simulation pattern of the product. This database intends to create a comprehensive canopy for the regulation of lipid metabolism reaction in the parasite by integrating the transcription factors, regulatory genes and the protein products controlled by the transcription factors and hence operating the metabolism at genetic level. PMID:26981382

  1. Can the whole be less than the sum of its parts? Pathway analysis in genome-scale metabolic networks using elementary flux patterns

    PubMed Central

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

    2009-01-01

    Elementary modes represent a valuable concept in the analysis of metabolic reaction networks. However, they can only be computed in medium-size systems, preventing application to genome-scale metabolic models. In consequence, the analysis is usually constrained to a specific part of the known metabolism, and the remaining system is modeled using abstractions like exchange fluxes and external species. As we show by the analysis of a model of the central metabolism of Escherichia coli that has been previously analyzed using elementary modes, the choice of these abstractions heavily impacts the pathways that are detected, and the results are biased by the knowledge of the metabolic capabilities of the network by the user. In order to circumvent these problems, we introduce the concept of elementary flux patterns, which explicitly takes into account possible steady-state fluxes through a genome-scale metabolic network when analyzing pathways through a subsystem. By being similar to elementary mode analysis, our concept now allows for the application of many elementary-mode-based tools to genome-scale metabolic networks. We present an algorithm to compute elementary flux patterns and analyze a model of the tricarboxylic acid cycle and adjacent reactions in E. coli. Thus, we detect several pathways that can be used as alternative routes to some central metabolic pathways. Finally, we give an outlook on further applications like the computation of minimal media, the development of knockout strategies, and the analysis of combined genome-scale networks. PMID:19541909

  2. Metabolic network analysis of Bacillus clausii on minimal and semirich medium using (13)C-labeled glucose.

    PubMed

    Christiansen, Torben; Christensen, Bjarke; Nielsen, Jens

    2002-04-01

    Using (13)C-labeled glucose fed to the facultative alkalophilic Bacillus clausii producing the alkaline serine protease Savinase, the intracellular fluxes were quantified in continuous cultivation and in batch cultivation on a minimal medium. The flux through the pentose phosphate pathway was found to increase with increasing specific growth rate but at a much lower level than previously reported for Bacillus subtilis. Two futile cycles in the pyruvate metabolism were included in the metabolic network. A substantial flux in the futile cycle involving malic enzyme was estimated, whereas only a very small or zero flux through PEP carboxykinase was estimated, indicating that the latter enzyme was not active during growth on glucose. The uptake of the amino acids in a semirich medium containing 15 of the 20 amino acids normally present in proteins was estimated using fully labeled glucose in batch cultivations. It was found that leucine, isoleucine, and phenylalanine were taken up from the medium and not synthesized de novo from glucose. In contrast, serine and threonine were completely synthesized from other metabolites and not taken up from the medium. Valine, proline, and lysine were partly taken up from the medium and partly synthesized from glucose. The metabolic network analysis was extended to include analysis of growth on the semirich medium containing amino acids, and the metabolic flux distribution on this medium was estimated and compared with growth on minimal medium.

  3. Increased interictal cerebral glucose metabolism in a cortical-subcortical network in drug naive patients with cryptogenic temporal lobe epilepsy.

    PubMed Central

    Franceschi, M; Lucignani, G; Del Sole, A; Grana, C; Bressi, S; Minicucci, F; Messa, C; Canevini, M P; Fazio, F

    1995-01-01

    Positron emission tomography with [18F]-2-fluoro-2-deoxy-D-glucose ([18F]FDG) has been used to assess the pattern of cerebral metabolism in different types of epilepsies. However, PET with [18F]FDG has never been used to evaluate drug naive patients with cryptogenic temporal lobe epilepsy, in whom the mechanism of origin and diffusion of the epileptic discharge may differ from that underlying other epilepsies. In a group of patients with cryptogenic temporal lobe epilepsy, never treated with antiepileptic drugs, evidence has been found of significant interictal glucose hypermetabolism in a bilateral neural network including the temporal lobes, thalami, basal ganglia, and cingular cortices. The metabolism in these areas and frontal lateral cortex enables the correct classification of all patients with temporal lobe epilepsy and controls by discriminant function analysis. Other cortical areas--namely, frontal basal and lateral, temporal mesial, and cerebellar cortices--had bilateral increases of glucose metabolism ranging from 10 to 15% of normal controls, although lacking stringent statistical significance. This metabolic pattern could represent a pathophysiological state of hyperactivity predisposing to epileptic discharge generation or diffusion, or else a network of inhibitory circuits activated to prevent the diffusion of the epileptic discharge. PMID:7561924

  4. Dissimilatory Metabolism of Nitrogen Oxides in Bacteria:Comparative Reconstruction of Transcriptional Networks

    SciTech Connect

    Rodionov, Dmitry A.; Dubchak, Inna L.; Arkin, Adam P.; Alm, EricJ.; Gelfand, Mikhail S.

    2005-09-01

    Bacterial response to nitric oxide (NO) is of major importance since NO is an obligatory intermediate of the nitrogen cycle. Transcriptional regulation of the dissimilatory nitric oxides metabolism in bacteria is diverse and involves FNR-like transcription factors HcpR, DNR and NnrR, two-component systems NarXL and NarQP, NO-responsive activator NorR, and nitrite sensitive repressor NsrR. Using comparative genomics approaches we predict DNA-binding signals for these transcriptional factors and describe corresponding regulons in available bacterial genomes. Within the FNR family of regulators, we observed a correlation of two specificity-determining amino acids and contacting bases in corresponding DNA signal. Highly conserved regulon HcpR for the hybrid cluster protein and some other redox enzymes is present in diverse anaerobic bacteria including Clostridia, Thermotogales and delta-proteobacteria. NnrR and DNR control denitrification in alpha- and beta-proteobacteria, respectively. Sigma-54-dependent NorR regulon found in some gamma- and beta-proteobacteria contains various enzymes involved in the NO detoxification. Repressor NsrR, which was previously known to control only nitrite reductase operon in Nitrosomonas spp., appears to be the master regulator of the nitric oxides metabolism not only in most gamma- and beta-proteobacteria (including well-studied species like Escherichia coli), but also in Gram-positive Bacillus and Streptomyces species. Positional analysis and comparison of regulatory regions of NO detoxification genes allows us to propose the candidate NsrR-binding signal. The most conserved member of the predicted NsrR regulon is the NO-detoxifying flavohemoglobin Hmp. In enterobacteria, the regulon includes also two nitrite-responsive loci, nipAB (hcp-hcr) and nipC(dnrN), thus confirming the identity of the effector, i.e., nitrite. The proposed NsrR regulons in Neisseria and some other species are extended to include denitrification genes. As the

  5. Modulation of Abnormal Metabolic Brain Networks by Experimental Therapies in a Nonhuman Primate Model of Parkinson Disease: An Application to Human Retinal Pigment Epithelial Cell Implantation.

    PubMed

    Peng, Shichun; Ma, Yilong; Flores, Joseph; Cornfeldt, Michael; Mitrovic, Branka; Eidelberg, David; Doudet, Doris J

    2016-10-01

    Abnormal covariance pattern of regional metabolism associated with Parkinson disease (PD) is modulated by dopaminergic pharmacotherapy. Using high-resolution (18)F-FDG PET and network analysis, we previously derived and validated a parkinsonism-related metabolic pattern (PRP) in nonhuman primate models of PD. It is currently not known whether this network is modulated by experimental therapeutics. In this study, we examined changes in network activity by striatal implantation of human levodopa-producing retinal pigment epithelial (hRPE) cells in parkinsonian macaques and evaluated the reproducibility of network activity in a small test-retest study.

  6. Circadian rhythms, metabolism, and insulin sensitivity: transcriptional networks in animal models.

    PubMed

    Kitazawa, Masashi

    2013-04-01

    Homeostatic systems have adapted to respond to the diurnal light/dark cycle. Numerous physiological pathways, including metabolism, are coordinated by this 24-h cycle. Animals with mutations in clock genes show abnormal glucose and lipid metabolism, indicating a critical relationship between the circadian clock and metabolism. Energy homeostasis is achieved through circadian regulation of the expression and activity of several key metabolic enzymes. Temporal organization of tissue metabolism is coordinated by reciprocal cross-talk between the core clock mechanism and key metabolic enzymes and transcriptional activators. The aim of this review is to define the role of the circadian clock in the regulation of insulin sensitivity by describing the interconnection between the circadian clock and metabolic pathways.

  7. 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.

  8. Transient exposure to low levels of insecticide affects metabolic networks of honeybee larvae.

    PubMed

    Derecka, Kamila; Blythe, Martin J; Malla, Sunir; Genereux, Diane P; Guffanti, Alessandro; Pavan, Paolo; Moles, Anna; Snart, Charles; Ryder, Thomas; Ortori, Catharine A; Barrett, David A; Schuster, Eugene; Stöger, Reinhard

    2013-01-01

    The survival of a species depends on its capacity to adjust to changing environmental conditions, and new stressors. Such new, anthropogenic stressors include the neonicotinoid class of crop-protecting agents, which have been implicated in the population declines of pollinating insects, including honeybees (Apis mellifera). The low-dose effects of these compounds on larval development and physiological responses have remained largely unknown. Over a period of 15 days, we provided syrup tainted with low levels (2 µg/L(-1)) of the neonicotinoid insecticide imidacloprid to beehives located in the field. We measured transcript levels by RNA sequencing and established lipid profiles using liquid chromatography coupled with mass spectrometry from worker-bee larvae of imidacloprid-exposed (IE) and unexposed, control (C) hives. Within a catalogue of 300 differentially expressed transcripts in larvae from IE hives, we detect significant enrichment of genes functioning in lipid-carbohydrate-mitochondrial metabolic networks. Myc-involved transcriptional response to exposure of this neonicotinoid is indicated by overrepresentation of E-box elements in the promoter regions of genes with altered expression. RNA levels for a cluster of genes encoding detoxifying P450 enzymes are elevated, with coordinated downregulation of genes in glycolytic and sugar-metabolising pathways. Expression of the environmentally responsive Hsp90 gene is also reduced, suggesting diminished buffering and stability of the developmental program. The multifaceted, physiological response described here may be of importance to our general understanding of pollinator health. Muscles, for instance, work at high glycolytic rates and flight performance could be impacted should low levels of this evolutionarily novel stressor likewise induce downregulation of energy metabolising genes in adult pollinators.

  9. Transient Exposure to Low Levels of Insecticide Affects Metabolic Networks of Honeybee Larvae

    PubMed Central

    Derecka, Kamila; Blythe, Martin J.; Malla, Sunir; Genereux, Diane P.; Guffanti, Alessandro; Pavan, Paolo; Moles, Anna; Snart, Charles; Ryder, Thomas; Ortori, Catharine A.; Barrett, David A.; Schuster, Eugene; Stöger, Reinhard

    2013-01-01

    The survival of a species depends on its capacity to adjust to changing environmental conditions, and new stressors. Such new, anthropogenic stressors include the neonicotinoid class of crop-protecting agents, which have been implicated in the population declines of pollinating insects, including honeybees (Apis mellifera). The low-dose effects of these compounds on larval development and physiological responses have remained largely unknown. Over a period of 15 days, we provided syrup tainted with low levels (2 µg/L−1) of the neonicotinoid insecticide imidacloprid to beehives located in the field. We measured transcript levels by RNA sequencing and established lipid profiles using liquid chromatography coupled with mass spectrometry from worker-bee larvae of imidacloprid-exposed (IE) and unexposed, control (C) hives. Within a catalogue of 300 differentially expressed transcripts in larvae from IE hives, we detect significant enrichment of genes functioning in lipid-carbohydrate-mitochondrial metabolic networks. Myc-involved transcriptional response to exposure of this neonicotinoid is indicated by overrepresentation of E-box elements in the promoter regions of genes with altered expression. RNA levels for a cluster of genes encoding detoxifying P450 enzymes are elevated, with coordinated downregulation of genes in glycolytic and sugar-metabolising pathways. Expression of the environmentally responsive Hsp90 gene is also reduced, suggesting diminished buffering and stability of the developmental program. The multifaceted, physiological response described here may be of importance to our general understanding of pollinator health. Muscles, for instance, work at high glycolytic rates and flight performance could be impacted should low levels of this evolutionarily novel stressor likewise induce downregulation of energy metabolising genes in adult pollinators. PMID:23844170

  10. Kinetic hybrid models composed of mechanistic and simplified enzymatic rate laws--a promising method for speeding up the kinetic modelling of complex metabolic networks.

    PubMed

    Bulik, Sascha; Grimbs, Sergio; Huthmacher, Carola; Selbig, Joachim; Holzhütter, Hermann G

    2009-01-01

    Kinetic modelling of complex metabolic networks - a central goal of computational systems biology - is currently hampered by the lack of reliable rate equations for the majority of the underlying biochemical reactions and membrane transporters. On the basis of biochemically substantiated evidence that metabolic control is exerted by a narrow set of key regulatory enzymes, we propose here a hybrid modelling approach in which only the central regulatory enzymes are described by detailed mechanistic rate equations, and the majority of enzymes are approximated by simplified(non mechanistic) rate equations (e.g. mass action, LinLog, Michaelis-Menten and power law) capturing only a few basic kinetic features and hence containing only a small number of parameters to be experimentally determined. To check the reliability of this approach, we have applied it to two different metabolic networks, the energy and redox metabolism of red blood cells, and the purine metabolism of hepatocytes, using in both cases available comprehensive mechanistic models as reference standards. Identification of the central regulatory enzymes was performed by employing only information on network topology and the metabolic data for a single reference state of the network [Grimbs S, Selbig J, Bulik S, Holzhutter HG & Steuer R (2007) Mol Syst Biol 3, 146, doi:10.1038/msb4100186].Calculations of stationary and temporary states under various physiological challenges demonstrate the good performance of the hybrid models. We propose the hybrid modelling approach as a means to speed up the development of reliable kinetic models for complex metabolic networks.

  11. Untangling the role of one-carbon metabolism in colorectal cancer risk: a comprehensive Bayesian network analysis

    PubMed Central

    Myte, Robin; Gylling, Björn; Häggström, Jenny; Schneede, Jörn; Magne Ueland, Per; Hallmans, Göran; Johansson, Ingegerd; Palmqvist, Richard; Van Guelpen, Bethany

    2017-01-01

    The role of one-carbon metabolism (1CM), particularly folate, in colorectal cancer (CRC) development has been extensively studied, but with inconclusive results. Given the complexity of 1CM, the conventional approach, investigating components individually, may be insufficient. We used a machine learning-based Bayesian network approach to study, simultaneously, 14 circulating one-carbon metabolites, 17 related single nucleotide polymorphisms (SNPs), and several environmental factors in relation to CRC risk in 613 cases and 1190 controls from the prospective Northern Sweden Health and Disease Study. The estimated networks corresponded largely to known biochemical relationships. Plasma concentrations of folate (direct), vitamin B6 (pyridoxal 5-phosphate) (inverse), and vitamin B2 (riboflavin) (inverse) had the strongest independent associations with CRC risk. Our study demonstrates the importance of incorporating B-vitamins in future studies of 1CM and CRC development, and the usefulness of Bayesian network learning for investigating complex biological systems in relation to disease. PMID:28233834

  12. The origin of modern metabolic networks inferred from phylogenomic analysis of protein architecture.

    PubMed

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

    2007-05-29

    Metabolism represents a complex collection of enzymatic reactions and transport processes that convert metabolites into molecules capable of supporting cellular life. Here we explore the origins and evolution of modern metabolism. Using phylogenomic information linked to the structure of metabolic enzymes, we sort out recruitment processes and discover that most enzymatic activities were associated with the nine most ancient and widely distributed protein fold architectures. An analysis of newly discovered functions showed enzymatic diversification occurred early, during the onset of the modern protein world. Most importantly, phylogenetic reconstruction exercises and other evidence suggest strongly that metabolism originated in enzymes with the P-loop hydrolase fold in nucleotide metabolism, probably in pathways linked to the purine metabolic subnetwork. Consequently, the first enzymatic takeover of an ancient biochemistry or prebiotic chemistry was related to the synthesis of nucleotides for the RNA world.

  13. The origin of modern metabolic networks inferred from phylogenomic analysis of protein architecture

    PubMed Central

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

    2007-01-01

    Metabolism represents a complex collection of enzymatic reactions and transport processes that convert metabolites into molecules capable of supporting cellular life. Here we explore the origins and evolution of modern metabolism. Using phylogenomic information linked to the structure of metabolic enzymes, we sort out recruitment processes and discover that most enzymatic activities were associated with the nine most ancient and widely distributed protein fold architectures. An analysis of newly discovered functions showed enzymatic diversification occurred early, during the onset of the modern protein world. Most importantly, phylogenetic reconstruction exercises and other evidence suggest strongly that metabolism originated in enzymes with the P-loop hydrolase fold in nucleotide metabolism, probably in pathways linked to the purine metabolic subnetwork. Consequently, the first enzymatic takeover of an ancient biochemistry or prebiotic chemistry was related to the synthesis of nucleotides for the RNA world. PMID:17517598

  14. Stealing the Keys to the Kitchen: Viral Manipulation of the Host Cell Metabolic Network.

    PubMed

    Goodwin, Christopher M; Xu, Shihao; Munger, Joshua

    2015-12-01

    Host cells possess the metabolic assets required for viral infection. Recent studies indicate that control of the host's metabolic resources is a core host-pathogen interaction. Viruses have evolved mechanisms to usurp the host's metabolic resources, funneling them towards the production of virion components as well as the organization of specialized compartments for replication, maturation, and dissemination. Consequently, hosts have developed a variety of metabolic countermeasures to sense and resist these viral changes. The complex interplay between virus and host over metabolic control has only just begun to be deconvoluted. However, it is clear that virally induced metabolic reprogramming can substantially impact infectious outcomes, highlighting the promise of targeting these processes for antiviral therapeutic development.

  15. Correlation network analysis reveals relationships between diet-induced changes in human gut microbiota and metabolic health

    PubMed Central

    Kelder, T; Stroeve, J H M; Bijlsma, S; Radonjic, M; Roeselers, G

    2014-01-01

    Background: Recent evidence suggests that the gut microbiota plays an important role in human metabolism and energy homeostasis and is therefore a relevant factor in the assessment of metabolic health and flexibility. Understanding of these host–microbiome interactions aids the design of nutritional strategies that act via modulation of the microbiota. Nevertheless, relating gut microbiota composition to host health states remains challenging because of the sheer complexity of these ecosystems and the large degrees of interindividual variation in human microbiota composition. Methods: We assessed fecal microbiota composition and host response patterns of metabolic and inflammatory markers in 10 apparently healthy men subjected to a high-fat high-caloric diet (HFHC, 1300 kcal/day extra) for 4 weeks. DNA was isolated from stool and barcoded 16S rRNA gene amplicons were sequenced. Metabolic health parameters, including anthropomorphic and blood parameters, where determined at t=0 and t=4 weeks. Results: A correlation network approach revealed diet-induced changes in Bacteroides levels related to changes in carbohydrate oxidation rates, whereas the change in Firmicutes correlates with changes in fat oxidation. These results were confirmed by multivariate models. We identified correlations between microbial diversity indices and several inflammation-related host parameters that suggest a relation between diet-induced changes in gut microbiota diversity and inflammatory processes. Conclusions: This approach allowed us to identify significant correlations between abundances of microbial taxa and diet-induced shifts in several metabolic health parameters. Constructed correlation networks provide an overview of these relations, revealing groups of correlations that are of particular interest for explaining host health aspects through changes in the gut microbiota. PMID:24979151

  16. 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.

  17. Addressing unknown constants and metabolic network behaviors through petascale computing: understanding H2 production in green algae

    NASA Astrophysics Data System (ADS)

    Chang, Christopher; Alber, David; Graf, Peter; Kim, Kwiseon; Seibert, Michael

    2007-07-01

    The Genomics Revolution has resulted in a massive and growing quantity of whole-genome DNA sequences, which encode the metabolic catalysts necessary for life. However, gene annotations can rarely be complete, and measurement of the kinetic constants associated with the encoded enzymes can not possibly keep pace, necessitating the use of careful modeling to explore plausible network behaviors. Key challenges are (1) quantitatively formulating kinetic laws governing each transformation in a fixed model network; (2) characterizing the stable solution (if any) of the associated ordinary differential equations (ODEs); (3) fitting the latter to metabolomics data as it becomes available; and, (4) optimizing a model output against the possible space of kinetic parameters, with respect to properties such as robustness of network response, or maximum consumption/production. This SciDAC-2 project addresses this large-scale uncertainty in the genome-scale metabolic network of the water-splitting, H2-producing green alga Chlamydomonas reinhardtii. Each metabolic transformation is formulated as an irreversible steady-state process, such that the vast literature on known enzyme mechanisms may be incorporated directly. To start, glycolysis, the tricarboxylic acid cycle, and basic fermentation pathways have been encoded in Systems Biology Markup Language (SBML) with careful annotation and consistency with the KEGG database, yielding a model with 3 compartments, 95 species, 38 reactions, and 109 kinetic constants. To study and optimize such models with a view toward larger models, we have developed a system which takes as input an SBML model, and automatically produces C code that when compiled and executed optimizes the model's kinetic parameters according to test criteria. We describe the system and present numerical results. Further development, including overlaying of a parallel multistart algorithm, will allow optimization of thousands of parameters on high-performance systems

  18. Loss of variation of state detected in soybean metabolic and human myelomonocytic leukaemia cell transcriptional networks under external stimuli.

    PubMed

    Sakata, Katsumi; Saito, Toshiyuki; Ohyanagi, Hajime; Okumura, Jun; Ishige, Kentaro; Suzuki, Harukazu; Nakamura, Takuji; Komatsu, Setsuko

    2016-10-24

    Soybean (Glycine max) is sensitive to flooding stress, and flood damage at the seedling stage is a barrier to growth. We constructed two mathematical models of the soybean metabolic network, a control model and a flooded model, from metabolic profiles in soybean plants. We simulated the metabolic profiles with perturbations before and after the flooding stimulus using the two models. We measured the variation of state that the system could maintain from a state-space description of the simulated profiles. The results showed a loss of variation of state during the flooding response in the soybean plants. Loss of variation of state was also observed in a human myelomonocytic leukaemia cell transcriptional network in response to a phorbol-ester stimulus. Thus, we detected a loss of variation of state under external stimuli in two biological systems, regardless of the regulation and stimulus types. Our results suggest that a loss of robustness may occur concurrently with the loss of variation of state in biological systems. We describe the possible applications of the quantity of variation of state in plant genetic engineering and cell biology. Finally, we present a hypothetical "external stimulus-induced information loss" model of biological systems.

  19. Loss of variation of state detected in soybean metabolic and human myelomonocytic leukaemia cell transcriptional networks under external stimuli

    PubMed Central

    Sakata, Katsumi; Saito, Toshiyuki; Ohyanagi, Hajime; Okumura, Jun; Ishige, Kentaro; Suzuki, Harukazu; Nakamura, Takuji; Komatsu, Setsuko

    2016-01-01

    Soybean (Glycine max) is sensitive to flooding stress, and flood damage at the seedling stage is a barrier to growth. We constructed two mathematical models of the soybean metabolic network, a control model and a flooded model, from metabolic profiles in soybean plants. We simulated the metabolic profiles with perturbations before and after the flooding stimulus using the two models. We measured the variation of state that the system could maintain from a state–space description of the simulated profiles. The results showed a loss of variation of state during the flooding response in the soybean plants. Loss of variation of state was also observed in a human myelomonocytic leukaemia cell transcriptional network in response to a phorbol-ester stimulus. Thus, we detected a loss of variation of state under external stimuli in two biological systems, regardless of the regulation and stimulus types. Our results suggest that a loss of robustness may occur concurrently with the loss of variation of state in biological systems. We describe the possible applications of the quantity of variation of state in plant genetic engineering and cell biology. Finally, we present a hypothetical “external stimulus-induced information loss” model of biological systems. PMID:27775018

  20. Designer labels for plant metabolism: statistical design of isotope labeling experiments for improved quantification of flux in complex plant metabolic networks.

    PubMed

    Nargund, Shilpa; Sriram, Ganesh

    2013-01-27

    Metabolic fluxes are powerful indicators of cell physiology and can be estimated by isotope-assisted metabolic flux analysis (MFA). The complexity of the compartmented metabolic networks of plants has constrained the application of isotope-assisted MFA to them, principally because of poor identifiability of fluxes from the measured isotope labeling patterns. However, flux identifiability can be significantly improved by a priori design of isotope labeling experiments (ILEs). This computational design involves evaluating the effect of different isotope label and isotopomer measurement combinations on flux identifiability, and thereby identifying optimal labels and measurements toward evaluating the fluxes of interest with the highest confidence. This article reports ILE designs for two major, compartmented plant metabolic pathways - the pentose phosphate pathway (PPP) and γ-aminobutyric acid (GABA) shunt. Together, these pathways represent common motifs in plant metabolism including duplication of pathways in different subcellular compartments, reversible reactions and cyclic carbon flow. To compare various ILE designs, we employed statistical A- and D-optimality criteria. Our computations showed that 1,2-(13)C Glc is a powerful and robust label for the plant PPPs, given currently popular isotopomer measurement techniques (single quadrupole mass spectrometry [MS] and 2-D nuclear magnetic resonance [NMR]). Further analysis revealed that this label can estimate several PPP fluxes better than the popular label 1-(13)C Glc. Furthermore, the concurrent measurement of the isotopomers of hexose and pentose moieties synthesized exclusively in the cytosol or the plastid compartments (measurable through intracellular glucose or sucrose, starch, RNA ribose and histidine) considerably improves the identifiability of PPP fluxes in the individual compartments. Additionally, MS-derived isotopomer measurements outperform NMR-derived measurements in identifying PPP fluxes. The

  1. Using isotopic tracers to assess the impact of tillage and straw management on the microbial metabolic network in soil

    NASA Astrophysics Data System (ADS)

    Van Groenigen, K.; Forristal, D.; Jones, M. B.; Schwartz, E.; Hungate, B. A.; Dijkstra, P.

    2013-12-01

    By decomposing soil organic matter, microbes gain energy and building blocks for biosynthesis and release CO2 to the atmosphere. Therefore, insight into the effect of management practices on microbial metabolic pathways and C use efficiency (CUE; microbial C produced per substrate C utilized) may help to predict long term changes in soil C stocks. We studied the effects of reduced (RT) and conventional tillage (CT) on the microbial central C metabolic network, using soil samples from a 12-year-old field experiment in an Irish winter wheat cropping system. Each year after harvest, straw was removed from half of the RT and CT plots or incorporated into the soil in the other half, resulting in four treatment combinations. We added 1-13C and 2,3-13C pyruvate and 1-13C and U-13C glucose as metabolic tracer isotopomers to composite soil samples taken at two depths (0-15 cm and 15-30 cm) from each treatment and used the rate of position-specific respired 13CO2 to parameterize a metabolic model. Model outcomes were then used to calculate CUE of the microbial community. We found that the composite samples differed in CUE, but the changes were small, with values ranging between 0.757-0.783 across treatments and soil depth. Increases in CUE were associated with a decrease in tricarboxylic acid cycle and reductive pentose phosphate pathway activity and increased consumption of metabolic intermediates for biosynthesis. Our results indicate that RT and straw incorporation promote soil C storage without substantially changing CUE or any of the microbial metabolic pathways. This suggests that at our site, RT and straw incorporation promote soil C storage mostly through direct effects such as increased soil C input and physical protection from decomposition, rather than by feedback responses of the microbial community.

  2. Global metabolic network reorganization by adaptive mutations allows fast growth of Escherichia coli on glycerol.

    PubMed

    Cheng, Kian-Kai; Lee, Baek-Seok; Masuda, Takeshi; Ito, Takuro; Ikeda, Kazutaka; Hirayama, Akiyoshi; Deng, Lingli; Dong, Jiyang; Shimizu, Kazuyuki; Soga, Tomoyoshi; Tomita, Masaru; Palsson, Bernhard O; Robert, Martin

    2014-01-01

    Comparative whole-genome sequencing enables the identification of specific mutations during adaptation of bacteria to new environments and allelic replacement can establish their causality. However, the mechanisms of action are hard to decipher and little has been achieved for epistatic mutations, especially at the metabolic level. Here we show that a strain of Escherichia coli carrying mutations in the rpoC and glpK genes, derived from adaptation in glycerol, uses two distinct metabolic strategies to gain growth advantage. A 27-bp deletion in the rpoC gene first increases metabolic efficiency. Then, a point mutation in the glpK gene promotes growth by improving glycerol utilization but results in increased carbon wasting as overflow metabolism. In a strain carrying both mutations, these contrasting carbon/energy saving and wasting mechanisms work together to give an 89% increase in growth rate. This study provides insight into metabolic reprogramming during adaptive laboratory evolution for fast cellular growth.

  3. Ecological network analysis of an urban water metabolic system: model development, and a case study for Beijing.

    PubMed

    Zhang, Yan; Yang, Zhifeng; Fath, Brian D

    2010-09-15

    Using ecological network analysis, we analyzed the network structure and ecological relationships in an urban water metabolic system. We developed an ecological network model for the system, and used Beijing as an example of analysis based on the model. We used network throughflow analysis to determine the flows among components, and measured both indirect and direct flows. Using a network utility matrix, we determined the relationships and degrees of mutualism among six compartments--1) local environment, 2) rainwater collection, 3) industry, 4) agriculture, 5) domestic sector, and 6) wastewater recycling--which represent producer, consumer, and reducer trophic levels. The capacity of producers to provide water for Beijing decreased from 2003 to 2007, and consumer demand for water decreased due to decreasing industrial and agricultural demand; the recycling capacity of reducers also improved, decreasing the discharge pressure on the environment. The ecological relationships associated with the local environment or the wastewater recycling sector changed little from 2003 to 2007. From 2003 to 2005, the main changes in the ecological relationships among components of Beijing's water metabolic system mostly occurred between the local environment, the industrial and agricultural sectors, and the domestic sector, but by 2006 and 2007, the major change was between the local environment, the agricultural sector, and the industrial sector. The other ecological relationships did not change during the study period. Although Beijing's mutualism indices remained generally stable, the ecological relationships among compartments changed greatly. Our analysis revealed ways to further optimize this system and the relationships among compartments, thereby optimizing future urban water resources development.

  4. HRGRN: A Graph Search-Empowered Integrative Database of Arabidopsis Signaling Transduction, Metabolism and Gene Regulation Networks

    PubMed Central

    Dai, Xinbin; Li, Jun; Liu, Tingsong; Zhao, Patrick Xuechun

    2016-01-01

    The biological networks controlling plant signal transduction, metabolism and gene regulation are composed of not only tens of thousands of genes, compounds, proteins and RNAs but also the complicated interactions and co-ordination among them. These networks play critical roles in many fundamental mechanisms, such as plant growth, development and environmental response. Although much is known about these complex interactions, the knowledge and data are currently scattered throughout the published literature, publicly available high-throughput data sets and third-party databases. Many ‘unknown’ yet important interactions among genes need to be mined and established through extensive computational analysis. However, exploring these complex biological interactions at the network level from existing heterogeneous resources remains challenging and time-consuming for biologists. Here, we introduce HRGRN, a graph search-empowered integrative database of Arabidopsis signal transduction, metabolism and gene regulatory networks. HRGRN utilizes Neo4j, which is a highly scalable graph database management system, to host large-scale biological interactions among genes, proteins, compounds and small RNAs that were either validated experimentally or predicted computationally. The associated biological pathway information was also specially marked for the interactions that are involved in the pathway to facilitate the investigation of cross-talk between pathways. Furthermore, HRGRN integrates a series of graph path search algorithms to discover novel relationships among genes, compounds, RNAs and even pathways from heterogeneous biological interaction data that could be missed by traditional SQL database search methods. Users can also build subnetworks based on known interactions. The outcomes are visualized with rich text, figures and interactive network graphs on web pages. The HRGRN database is freely available at http://plantgrn.noble.org/hrgrn/. PMID:26657893

  5. Low contribution of internal metabolism to carbon dioxide emissions along lotic and lentic environments of a Mediterranean fluvial network

    NASA Astrophysics Data System (ADS)

    Gómez-Gener, Lluís.; Schiller, Daniel; Marcé, Rafael; Arroita, Maite; Casas-Ruiz, Joan Pere; Staehr, Peter Anton; Acuña, Vicenç; Sabater, Sergi; Obrador, Biel

    2016-12-01

    Inland waters are significant sources of carbon dioxide (CO2) to the atmosphere. CO2 supersaturation and subsequent CO2 emissions from inland waters can be driven by internal metabolism, external inputs of dissolved inorganic carbon (DIC) derived from the catchment, and other processes (e.g., internal geochemical reactions of calcite precipitation or photochemical mineralization of organic solutes). However, the sensitivity of the magnitude and sources of CO2 emissions to fluvial network hydromorphological alterations is still poorly understood. Here we investigated both the magnitude and sources of CO2 emissions from lotic (i.e., running waters) and lentic (i.e., stagnant waters associated to small dams) waterbodies of a Mediterranean fluvial network by computing segment-scale mass balances of CO2. Our results showed that sources other than internal metabolism sustained most (82%) of the CO2 emissions from the studied fluvial network. The magnitude and sources of CO2 emissions in lotic waterbodies were highly dependent on hydrology, with higher emissions dominated by DIC inputs derived from the catchment during high flows and lower emissions partially fueled by CO2 produced biologically within the river during low flows. In contrast, CO2 emissions in lentic waterbodies were low, relatively stable over the time and the space, and dominated by DIC inputs from the catchment regardless of the different hydrological situations. Overall, our results stress the sensitivity of fluvial networks to human activities and climate change and particularly highlight the role of hydromorphological conditions on modulating the magnitude and sources of CO2 emissions from fluvial networks.

  6. Principal transcriptional regulation and genome-wide system interactions of the Asp-family and aromatic amino acid networks of amino acid metabolism in plants.

    PubMed

    Less, Hadar; Angelovici, Ruthie; Tzin, Vered; Galili, Gad

    2010-10-01

    Amino acid metabolism is among the most important and best recognized networks within biological systems. In plants, amino acids serve multiple functions associated with growth. Besides their function in protein synthesis, the amino acids are also catabolized into energy-associated metabolites as well we into numerous secondary metabolites, which are essential for plant growth and response to various stresses. Despite the central importance of amino acids in plants growth, elucidation of the regulation of amino acid metabolism within the context of the entire system, particularly transcriptional regulation, is still in its infancy. The different amino acids are synthesized by a number of distinct metabolic networks, which are expected to possess regulatory cross interactions between them for proper coordination of their interactive functions, such as incorporation into proteins. Yet, individual amino acid metabolic networks are also expected to differentially cross interact with various genome-wide gene expression programs and metabolic networks, in respect to their functions as precursors for various metabolites with distinct functions. In the present review, we discuss our recent genomics, metabolic and bioinformatics studies, which were aimed at addressing these questions, focusing mainly on the Asp-family metabolic network as the main example and also comparing it to the aromatic amino acids metabolic network as a second example (Angelovici et al. in Plant Physiol 151:2058-2072, 2009; Less and Galili in BMC Syst Biol 3:14, 2009; Tzin et al. in Plant J 60:156-167, 2009). Our focus on these two networks is because of the followings: (i) both networks are central to plant metabolism and growth and are also precursors for a wide range of primary and secondary metabolites that are indispensable to plant growth; (ii) the amino acids produced by these two networks are also essential to the nutrition and health of human and farm animals; and (iii) both networks contain

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

    SciTech Connect

    Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; Overall, Christopher C.; Hill, Eric A.; Beliaev, Alex S.

    2015-03-27

    Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.

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

    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

  9. Candidate States of Helicobacter pylori's Genome-Scale Metabolic Network upon Application of “Loop Law” Thermodynamic Constraints

    PubMed Central

    Price, Nathan D.; Thiele, Ines; Palsson, Bernhard Ø.

    2006-01-01

    Constraint-based modeling has proven to be a useful tool in the analysis of biochemical networks. To date, most studies in this field have focused on the use of linear constraints, resulting from mass balance and capacity constraints, which lead to the definition of convex solution spaces. One additional constraint arising out of thermodynamics is known as the “loop law” for reaction fluxes, which states that the net flux around a closed biochemical loop must be zero because no net thermodynamic driving force exists. The imposition of the loop-law can lead to nonconvex solution spaces making the analysis of the consequences of its imposition challenging. A four-step approach is developed here to apply the loop-law to study metabolic network properties: 1), determine linear equality constraints that are necessary (but not necessarily sufficient) for thermodynamic feasibility; 2), tighten Vmax and Vmin constraints to enclose the remaining nonconvex space; 3), uniformly sample the convex space that encloses the nonconvex space using standard Monte Carlo techniques; and 4), eliminate from the resulting set all solutions that violate the loop-law, leaving a subset of steady-state solutions. This subset of solutions represents a uniform random sample of the space that is defined by the additional imposition of the loop-law. This approach is used to evaluate the effect of imposing the loop-law on predicted candidate states of the genome-scale metabolic network of Helicobacter pylori. PMID:16533855

  10. Candidate states of Helicobacter pylori's genome-scale metabolic network upon application of "loop law" thermodynamic constraints.

    PubMed

    Price, Nathan D; Thiele, Ines; Palsson, Bernhard Ø

    2006-06-01

    Constraint-based modeling has proven to be a useful tool in the analysis of biochemical networks. To date, most studies in this field have focused on the use of linear constraints, resulting from mass balance and capacity constraints, which lead to the definition of convex solution spaces. One additional constraint arising out of thermodynamics is known as the "loop law" for reaction fluxes, which states that the net flux around a closed biochemical loop must be zero because no net thermodynamic driving force exists. The imposition of the loop-law can lead to nonconvex solution spaces making the analysis of the consequences of its imposition challenging. A four-step approach is developed here to apply the loop-law to study metabolic network properties: 1), determine linear equality constraints that are necessary (but not necessarily sufficient) for thermodynamic feasibility; 2), tighten V(max) and V(min) constraints to enclose the remaining nonconvex space; 3), uniformly sample the convex space that encloses the nonconvex space using standard Monte Carlo techniques; and 4), eliminate from the resulting set all solutions that violate the loop-law, leaving a subset of steady-state solutions. This subset of solutions represents a uniform random sample of the space that is defined by the additional imposition of the loop-law. This approach is used to evaluate the effect of imposing the loop-law on predicted candidate states of the genome-scale metabolic network of Helicobacter pylori.

  11. The Carbon Assimilation Network in Escherichia coli Is Densely Connected and Largely Sign-Determined by Directions of Metabolic Fluxes

    PubMed Central

    Baldazzi, Valentina; Ropers, Delphine; Markowicz, Yves; Kahn, Daniel; Geiselmann, Johannes; de Jong, Hidde

    2010-01-01

    Gene regulatory networks consist of direct interactions but also include indirect interactions mediated by metabolites and signaling molecules. We describe how these indirect interactions can be derived from a model of the underlying biochemical reaction network, using weak time-scale assumptions in combination with sensitivity criteria from metabolic control analysis. We apply this approach to a model of the carbon assimilation network in Escherichia coli. Our results show that the derived gene regulatory network is densely connected, contrary to what is usually assumed. Moreover, the network is largely sign-determined, meaning that the signs of the indirect interactions are fixed by the flux directions of biochemical reactions, independently of specific parameter values and rate laws. An inversion of the fluxes following a change in growth conditions may affect the signs of the indirect interactions though. This leads to a feedback structure that is at the same time robust to changes in the kinetic properties of enzymes and that has the flexibility to accommodate radical changes in the environment. PMID:20548959

  12. Preferential sampling in veterinary parasitological surveillance.

    PubMed

    Cecconi, Lorenzo; Biggeri, Annibale; Grisotto, Laura; Berrocal, Veronica; Rinaldi, Laura; Musella, Vincenzo; Cringoli, Giuseppe; Catelan, Dolores

    2016-04-18

    In parasitological surveillance of livestock, prevalence surveys are conducted on a sample of farms using several sampling designs. For example, opportunistic surveys or informative sampling designs are very common. Preferential sampling refers to any situation in which the spatial process and the sampling locations are not independent. Most examples of preferential sampling in the spatial statistics literature are in environmental statistics with focus on pollutant monitors, and it has been shown that, if preferential sampling is present and is not accounted for in the statistical modelling and data analysis, statistical inference can be misleading. In this paper, working in the context of veterinary parasitology, we propose and use geostatistical models to predict the continuous and spatially-varying risk of a parasite infection. Specifically, breaking with the common practice in veterinary parasitological surveillance to ignore preferential sampling even though informative or opportunistic samples are very common, we specify a two-stage hierarchical Bayesian model that adjusts for preferential sampling and we apply it to data on Fasciola hepatica infection in sheep farms in Campania region (Southern Italy) in the years 2013-2014.

  13. Identification of altered metabolic pathways of γ-irradiated rice mutant via network-based transcriptome analysis.

    PubMed

    Hwang, Sun-Goo; Kim, Dong Sub; Hwang, Jung Eun; Park, Hyeon Mi; Jang, Cheol Seong

    2015-12-01

    In order to develop rice mutants for crop improvement, we applied γ-irradiation mutagenesis and selected a rice seed color mutant (MT) in the M14 targeting-induced local lesions in genome lines. This mutant exhibited differences in germination rate, plant height, and root length in seedlings compared to the wild-type plants. We found 1645 different expressed probes of MT by microarray hybridization. To identify the modified metabolic pathways, we conducted integrated genomic analysis such as weighted correlation network analysis with a module detection method of differentially expressed genes (DEGs) in MT on the basis of large-scale microarray transcriptional profiling. These modules are largely divided into three subnetworks and mainly exhibit overrepresented gene ontology functions such as oxidation-related function, ion-binding, and kinase activity (phosphorylation), and the expressional coherences of module genes mainly exhibited in vegetative and maturation stages. Through a metabolic pathway analysis, we detected the significant DEGs involved in the major carbohydrate metabolism (starch degradation), protein degradation (aspartate protease), and signaling in sugars and nutrients. Furthermore, the accumulation of amino acids (asparagine and glutamic acid), sucrose, and starch in MT were affected by gamma rays. Our results provide an effective approach for identification of metabolic pathways associated with useful agronomic traits in mutation breeding.

  14. Pyruvate decarboxylase and alcohol dehydrogenase overexpression in Escherichia coli resulted in high ethanol production and rewired metabolic enzyme networks.

    PubMed

    Yang, Mingfeng; Li, Xuefeng; Bu, Chunya; Wang, Hui; Shi, Guanglu; Yang, Xiushan; Hu, Yong; Wang, Xiaoqin

    2014-11-01

    Pyruvate decarboxylase and alcohol dehydrogenase are efficient enzymes for ethanol production in Zymomonas mobilis. These two enzymes were over-expressed in Escherichia coli, a promising candidate for industrial ethanol production, resulting in high ethanol production in the engineered E. coli. To investigate the intracellular changes to the enzyme overexpression for homoethanol production, 2-DE and LC-MS/MS were performed. More than 1,000 protein spots were reproducibly detected in the gel by image analysis. Compared to the wild-type, 99 protein spots showed significant changes in abundance in the recombinant E. coli, in which 46 were down-regulated and 53 were up-regulated. Most proteins related to tricarboxylic acid cycle, glycerol metabolism and other energy metabolism were up-regulated, whereas proteins involved in glycolysis and glyoxylate pathway were down-regulated, indicating the rewired metabolism in the engineered E. coli. As glycolysis is the main pathway for ethanol production, and it was inhibited significantly in engineered E. coli, further efforts should be directed at minimizing the repression of glycolysis to optimize metabolism network for higher yields of ethanol production.

  15. Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks.

    PubMed

    Zomorrodi, Ali R; Lafontaine Rivera, Jimmy G; Liao, James C; Maranas, Costas D

    2013-09-01

    The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.

  16. Comparative analysis of false discovery rate methods in constructing metabolic association networks.

    PubMed

    Koo, Imhoi; Yao, Sen; Zhang, Xiang; Kim, Seongho

    2014-08-01

    Gaussian graphical model (GGM)-based method, a key approach to reverse engineering biological networks, uses partial correlation to measure conditional dependence between two variables by controlling the contribution from other variables. After estimating partial correlation coefficients, one of the most critical processes in network construction is to control the false discovery rate (FDR) to assess the significant associations among variables. Various FDR methods have been proposed mainly for biomarker discovery, but it still remains unclear which FDR method performs better for network construction. Furthermore, there is no study to see the effect of the network structure on network construction. We selected the six FDR methods, the linear step-up procedure (BH95), the adaptive linear step-up procedure (BH00), Efron's local FDR (LFDR), Benjamini-Yekutieli's step-up procedure (BY01), Storey's q-value procedure (Storey01), and Storey-Taylor-Siegmund's adaptive step-up procedure (STS04), to evaluate their performances on network construction. We further considered two network structures, random and scale-free networks, to investigate their influence on network construction. Both simulated data and real experimental data suggest that STS04 provides the highest true positive rate (TPR) or F1 score, while BY01 has the highest positive predictive value (PPV) in network construction. In addition, no significant effect of the network structure is found on FDR methods.

  17. Amino acid composition and amino acid-metabolic network in supragingival plaque.

    PubMed

    Washio, Jumpei; Ogawa, Tamaki; Suzuki, Keisuke; Tsukiboshi, Yosuke; Watanabe, Motohiro; Takahashi, Nobuhiro

    2016-01-01

    Dental plaque metabolizes both carbohydrates and amino acids. The former can be degraded to acids mainly, while the latter can be degraded to various metabolites, including ammonia, acids and amines, and associated with acid-neutralization, oral malodor and tissue inflammation. However, amino acid metabolism in dental plaque is still unclear. This study aimed to elucidate what kinds of amino acids are available as metabolic substrates and how the amino acids are metabolized in supragingival plaque, by a metabolome analysis. Amino acids and the related metabolites in supragingival plaque were extracted and quantified comprehensively by CE-TOFMS. Plaque samples were also incubated with amino acids, and the amounts of ammonia and amino acid-related metabolites were measured. The concentration of glutamate was the highest in supragingival plaque, while the ammonia-production was the highest from glutamine. The obtained metabolome profile revealed that amino acids are degraded through various metabolic pathways, including deamination, decarboxylation and transamination and that these metabolic systems may link each other, as well as with carbohydrate metabolic pathways in dental plaque ecosystem. Moreover, glutamine and glutamate might be the main source of ammonia production, as well as arginine, and contribute to pH-homeostasis and counteraction to acid-induced demineralization in supragingival plaque.

  18. Multiple horizontal gene transfer events and domain fusions have created novel regulatory and metabolic networks in the oomycete genome.

    PubMed

    Morris, Paul Francis; Schlosser, Laura Rose; Onasch, Katherine Diane; Wittenschlaeger, Tom; Austin, Ryan; Provart, Nicholas

    2009-07-02

    Complex enzymes with multiple catalytic activities are hypothesized to have evolved from more primitive precursors. Global analysis of the Phytophthora sojae genome using conservative criteria for evaluation of complex proteins identified 273 novel multifunctional proteins that were also conserved in P. ramorum. Each of these proteins contains combinations of protein motifs that are not present in bacterial, plant, animal, or fungal genomes. A subset of these proteins were also identified in the two diatom genomes, but the majority of these proteins have formed after the split between diatoms and oomycetes. Documentation of multiple cases of domain fusions that are common to both oomycetes and diatom genomes lends additional support for the hypothesis that oomycetes and diatoms are monophyletic. Bifunctional proteins that catalyze two steps in a metabolic pathway can be used to infer the interaction of orthologous proteins that exist as separate entities in other genomes. We postulated that the novel multifunctional proteins of oomycetes could function as potential Rosetta Stones to identify interacting proteins of conserved metabolic and regulatory networks in other eukaryotic genomes. However ortholog analysis of each domain within our set of 273 multifunctional proteins against 39 sequenced bacterial and eukaryotic genomes, identified only 18 candidate Rosetta Stone proteins. Thus the majority of multifunctional proteins are not Rosetta Stones, but they may nonetheless be useful in identifying novel metabolic and regulatory networks in oomycetes. Phylogenetic analysis of all the enzymes in three pathways with one or more novel multifunctional proteins was conducted to determine the probable origins of individual enzymes. These analyses revealed multiple examples of horizontal transfer from both bacterial genomes and the photosynthetic endosymbiont in the ancestral genome of Stramenopiles. The complexity of the phylogenetic origins of these metabolic pathways and

  19. Multiple Horizontal Gene Transfer Events and Domain Fusions Have Created Novel Regulatory and Metabolic Networks in the Oomycete Genome

    PubMed Central

    Morris, Paul Francis; Schlosser, Laura Rose; Onasch, Katherine Diane; Wittenschlaeger, Tom; Austin, Ryan; Provart, Nicholas

    2009-01-01

    Complex enzymes with multiple catalytic activities are hypothesized to have evolved from more primitive precursors. Global analysis of the Phytophthora sojae genome using conservative criteria for evaluation of complex proteins identified 273 novel multifunctional proteins that were also conserved in P. ramorum. Each of these proteins contains combinations of protein motifs that are not present in bacterial, plant, animal, or fungal genomes. A subset of these proteins were also identified in the two diatom genomes, but the majority of these proteins have formed after the split between diatoms and oomycetes. Documentation of multiple cases of domain fusions that are common to both oomycetes and diatom genomes lends additional support for the hypothesis that oomycetes and diatoms are monophyletic. Bifunctional proteins that catalyze two steps in a metabolic pathway can be used to infer the interaction of orthologous proteins that exist as separate entities in other genomes. We postulated that the novel multifunctional proteins of oomycetes could function as potential Rosetta Stones to identify interacting proteins of conserved metabolic and regulatory networks in other eukaryotic genomes. However ortholog analysis of each domain within our set of 273 multifunctional proteins against 39 sequenced bacterial and eukaryotic genomes, identified only 18 candidate Rosetta Stone proteins. Thus the majority of multifunctional proteins are not Rosetta Stones, but they may nonetheless be useful in identifying novel metabolic and regulatory networks in oomycetes. Phylogenetic analysis of all the enzymes in three pathways with one or more novel multifunctional proteins was conducted to determine the probable origins of individual enzymes. These analyses revealed multiple examples of horizontal transfer from both bacterial genomes and the photosynthetic endosymbiont in the ancestral genome of Stramenopiles. The complexity of the phylogenetic origins of these metabolic pathways and

  20. GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network

    PubMed Central

    2012-01-01

    Background Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets. Results The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network. The semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed (i.e. false negatives) and addresses the false positive rates found with the use of simple pathway-based sets. Conclusions The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis. As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods. PMID:22876834

  1. Drought, salt, and temperature stress-induced metabolic rearrangements and regulatory networks.

    PubMed

    Krasensky, Julia; Jonak, Claudia

    2012-02-01

    Plants regularly face adverse growth conditions, such as drought, salinity, chilling, freezing, and high temperatures. These stresses can delay growth and development, reduce productivity, and, in extreme cases, cause plant death. Plant stress responses are dynamic and involve complex cross-talk between different regulatory levels, including adjustment of metabolism and gene expression for physiological and morphological adaptation. In this review, information about metabolic regulation in response to drought, extreme temperature, and salinity stress is summarized and the signalling events involved in mediating stress-induced metabolic changes are presented.

  2. Drought, salt, and temperature stress-induced metabolic rearrangements and regulatory networks

    PubMed Central

    Krasensky, Julia; Jonak, Claudia

    2015-01-01

    Plants regularly face adverse growth conditions, such as drought, salinity, chilling, freezing, and high temperatures. These stresses can delay growth and development, reduce productivity, and, in extreme cases, cause plant death. Plant stress responses are dynamic and involve complex cross-talk between different regulatory levels, including adjustment of metabolism and gene expression for physiological and morphological adaptation. In this review, information about metabolic regulation in response to drought, extreme temperature, and salinity stress is summarized and the signalling events involved in mediating stress-induced metabolic changes are presented. PMID:22291134

  3. Preferential flow occurs in unsaturated conditions

    USGS Publications Warehouse

    Nimmo, John R.

    2012-01-01

    Because it commonly generates high-speed, high-volume flow with minimal exposure to solid earth materials, preferential flow in the unsaturated zone is a dominant influence in many problems of infiltration, recharge, contaminant transport, and ecohydrology. By definition, preferential flow occurs in a portion of a medium – that is, a preferred part, whether a pathway, pore, or macroscopic subvolume. There are many possible classification schemes, but usual consideration of preferential flow includes macropore or fracture flow, funneled flow determined by macroscale heterogeneities, and fingered flow determined by hydraulic instability rather than intrinsic heterogeneity. That preferential flow is spatially concentrated associates it with other characteristics that are typical, although not defining: it tends to be unusually fast, to transport high fluxes, and to occur with hydraulic disequilibrium within the medium. It also has a tendency to occur in association with large conduits and high water content, although these are less universal than is commonly assumed. Predictive unsaturated-zone flow models in common use employ several different criteria for when and where preferential flow occurs, almost always requiring a nearly saturated medium. A threshold to be exceeded may be specified in terms of the following (i) water content; (ii) matric potential, typically a value high enough to cause capillary filling in a macropore of minimum size; (iii) infiltration capacity or other indication of incipient surface ponding; or (iv) other conditions related to total filling of certain pores. Yet preferential flow does occur without meeting these criteria. My purpose in this commentary is to point out important exceptions and implications of ignoring them. Some of these pertain mainly to macropore flow, others to fingered or funneled flow, and others to combined or undifferentiated flow modes.

  4. Challenges and Opportunities of Long-Term Continuous Stream Metabolism Measurements at the National Ecological Observatory Network

    NASA Astrophysics Data System (ADS)

    Goodman, K. J.; Lunch, C. K.; Baxter, C.; Hall, R.; Holtgrieve, G. W.; Roberts, B. J.; Marcarelli, A. M.; Tank, J. L.

    2013-12-01

    Recent advances in dissolved oxygen sensing and modeling have made continuous measurements of whole-stream metabolism relatively easy to make, allowing ecologists to quantify and evaluate stream ecosystem health at expanded temporal and spatial scales. Long-term monitoring of continuous stream metabolism will enable a better understanding of the integrated and complex effects of anthropogenic change (e.g., land-use, climate, atmospheric deposition, invasive species, etc.) on stream ecosystem function. In addition to their value in the particular streams measured, information derived from long-term data will improve the ability to extrapolate from shorter-term data. With the need to better understand drivers and responses of whole-stream metabolism come difficulties in interpreting the results. Long-term trends will encompass physical changes in stream morphology and flow regime (e.g., variable flow conditions and changes in channel structure) combined with changes in biota. Additionally long-term data sets will require an organized database structure, careful quantification of errors and uncertainties, as well as propagation of error as a result of the calculation of metabolism metrics. Parsing of continuous data and the choice of modeling approaches can also have a large influence on results and on error estimation. The two main modeling challenges include 1) obtaining unbiased, low-error daily estimates of gross primary production (GPP) and ecosystem respiration (ER), and 2) interpreting GPP and ER measurements over extended time periods. The National Ecological Observatory Network (NEON), in partnership with academic and government scientists, has begun to tackle several of these challenges as it prepares for the collection and calculation of 30 years of continuous whole-stream metabolism data. NEON is a national-scale research platform that will use consistent procedures and protocols to standardize measurements across the United States, providing long

  5. Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942.

    PubMed

    Triana, Julián; Montagud, Arnau; Siurana, Maria; Fuente, David; Urchueguía, Arantxa; Gamermann, Daniel; Torres, Javier; Tena, Jose; de Córdoba, Pedro Fernández; Urchueguía, Javier F

    2014-08-20

    The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942.

  6. Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

    PubMed Central

    Triana, Julián; Montagud†, Arnau; Siurana, Maria; Fuente, David; Urchueguía, Arantxa; Gamermann, Daniel; Torres, Javier; Tena, Jose; de Córdoba, Pedro Fernández; Urchueguía, Javier F.

    2014-01-01

    The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942. PMID:25141288

  7. Lactate rescues neuronal sodium homeostasis during impaired energy metabolism

    PubMed Central

    Karus, Claudia; Ziemens, Daniel; Rose, Christine R

    2015-01-01

    Recently, we established that recurrent activity evokes network sodium oscillations in neurons and astrocytes in hippocampal tissue slices. Interestingly, metabolic integrity of astrocytes was essential for the neurons' capacity to maintain low sodium and to recover from sodium loads, indicating an intimate metabolic coupling between the 2 cell types. Here, we studied if lactate can support neuronal sodium homeostasis during impaired energy metabolism by analyzing whether glucose removal, pharmacological inhibition of glycolysis and/or addition of lactate affect cellular sodium regulation. Furthermore, we studied the effect of lactate on sodium regulation during recurrent network activity and upon inhibition of the glial Krebs cycle by sodium-fluoroacetate. Our results indicate that lactate is preferentially used by neurons. They demonstrate that lactate supports neuronal sodium homeostasis and rescues the effects of glial poisoning by sodium-fluoroacetate. Altogether, they are in line with the proposed transfer of lactate from astrocytes to neurons, the so-called astrocyte-neuron-lactate shuttle. PMID:26039160

  8. Integrating large-scale functional genomics data to dissect metabolic networks for hydrogen production

    SciTech Connect

    Harwood, Caroline S

    2012-12-17

    The goal of this project is to identify gene networks that are critical for efficient biohydrogen production by leveraging variation in gene content and gene expression in independently isolated Rhodopseudomonas palustris strains. Coexpression methods were applied to large data sets that we have collected to define probabilistic causal gene networks. To our knowledge this a first systems level approach that takes advantage of strain-to strain variability to computationally define networks critical for a particular bacterial phenotypic trait.

  9. PPARγ isoforms differentially regulate metabolic networks to mediate mouse prostatic epithelial differentiation.

    PubMed

    Strand, D W; Jiang, M; Murphy, T A; Yi, Y; Konvinse, K C; Franco, O E; Wang, Y; Young, J D; Hayward, S W

    2012-08-09

    Recent observations indicate prostatic diseases are comorbidities of systemic metabolic dysfunction. These discoveries revealed fundamental questions regarding the nature of prostate metabolism. We previously showed that prostate-specific ablation of PPARγ in mice resulted in tumorigenesis and active autophagy. Here, we demonstrate control of overlapping and distinct aspects of prostate epithelial metabolism by ectopic expression of individual PPARγ isoforms in PPARγ knockout prostate epithelial cells. Expression and activation of either PPARγ 1 or 2 reduced de novo lipogenesis and oxidative stress and mediated a switch from glucose to fatty acid oxidation through regulation of genes including Pdk4, Fabp4, Lpl, Acot1 and Cd36. Differential effects of PPARγ isoforms included decreased basal cell differentiation, Scd1 expression and triglyceride fatty acid desaturation and increased tumorigenicity by PPARγ1. In contrast, PPARγ2 expression significantly increased basal cell differentiation, Scd1 expression and AR expression and responsiveness. Finally, in confirmation of in vitro data, a PPARγ agonist versus high-fat diet (HFD) regimen in vivo confirmed that PPARγ agonization increased prostatic differentiation markers, whereas HFD downregulated PPARγ-regulated genes and decreased prostate differentiation. These data provide a rationale for pursuing a fundamental metabolic understanding of changes to glucose and fatty acid metabolism in benign and malignant prostatic diseases associated with systemic metabolic stress.

  10. Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity.

    PubMed

    Mintz-Oron, Shira; Meir, Sagit; Malitsky, Sergey; Ruppin, Eytan; Aharoni, Asaph; Shlomi, Tomer

    2012-01-03

    Plant metabolic engineering is commonly used in the production of functional foods and quality trait improvement. However, to date, computational model-based approaches have only been scarcely used in this important endeavor, in marked contrast to their prominent success in microbial metabolic engineering. In this study we present a computational pipeline for the reconstruction of fully compartmentalized tissue-specific models of Arabidopsis thaliana on a genome scale. This reconstruction involves automatic extraction of known biochemical reactions in Arabidopsis for both primary and secondary metabolism, automatic gap-filling, and the implementation of methods for determining subcellular localization and tissue assignment of enzymes. The reconstructed tissue models are amenable for constraint-based modeling analysis, and significantly extend upon previous model reconstructions. A set of computational validations (i.e., cross-validation tests, simulations of known metabolic functionalities) and experimental validations (comparison with experimental metabolomics datasets under various compartments and tissues) strongly testify to the predictive ability of the models. The utility of the derived models was demonstrated in the prediction of measured fluxes in metabolically engineered seed strains and the design of genetic manipulations that are expected to increase vitamin E content, a significant nutrient for human health. Overall, the reconstructed tissue models are expected to lay down the foundations for computational-based rational design of plant metabolic engineering. The reconstructed compartmentalized Arabidopsis tissue models are MIRIAM-compliant and are available upon request.

  11. Comparing Metabolic Energy Expenditure Estimation Using Wearable Multi-Sensor Network and Single Accelerometer

    PubMed Central

    Dong, Bo; Biswas, Subir; Montoye, Alexander; Pfeiffer, Karin

    2014-01-01

    This paper presents the implementation details, system architecture and performance of a wearable sensor network that was designed for human activity recognition and energy expenditure estimation. We also included ActiGraph GT3X+ as a popular single sensor solution for detailed comparison with the proposed wearable sensor network. Linear regression and Artificial Neural Network are implemented and tested. Through a rigorous system study and experiment, it is shown that the wearable multi-sensor network outperforms the single sensor solution in terms of energy expenditure estimation. PMID:24110325

  12. Application of Artificial Neural Networks to Investigate One-Carbon Metabolism in Alzheimer’s Disease and Healthy Matched Individuals

    PubMed Central

    Coppedè, Fabio; Grossi, Enzo; Buscema, Massimo; Migliore, Lucia

    2013-01-01

    Folate metabolism, also known as one-carbon metabolism, is required for several cellular processes including DNA synthesis, repair and methylation. Impairments of this pathway have been often linked to Alzheimer’s disease (AD). In addition, increasing evidence from large scale case-control studies, genome-wide association studies, and meta-analyses of the literature suggest that polymorphisms of genes involved in one-carbon metabolism influence the levels of folate, homocysteine and vitamin B12, and might be among AD risk factors. We analyzed a dataset of 30 genetic and biochemical variables (folate, homocysteine, vitamin B12, and 27 genotypes generated by nine common biallelic polymorphisms of genes involved in folate metabolism) obtained from 40 late-onset AD patients and 40 matched controls to assess the predictive capacity of Artificial Neural Networks (ANNs) in distinguish consistently these two different conditions and to identify the variables expressing the maximal amount of relevant information to the condition of being affected by dementia of Alzheimer’s type. Moreover, we constructed a semantic connectivity map to offer some insight regarding the complex biological connections among the studied variables and the two conditions (being AD or control). TWIST system, an evolutionary algorithm able to remove redundant and noisy information from complex data sets, selected 16 variables that allowed specialized ANNs to discriminate between AD and control subjects with over 90% accuracy. The semantic connectivity map provided important information on the complex biological connections among one-carbon metabolic variables highlighting those most closely linked to the AD condition. PMID:23951366

  13. Ecological network analysis of an urban metabolic system based on input-output tables: model development and case study for Beijing.

    PubMed

    Zhang, Yan; Zheng, Hongmei; Fath, Brian D; Liu, Hong; Yang, Zhifeng; Liu, Gengyuan; Su, Meirong

    2014-01-15

    If cities are considered as "superorganisms", then disorders of their metabolic processes cause something analogous to an "urban disease". It is therefore helpful to identify the causes of such disorders by analyzing the inner mechanisms that control urban metabolic processes. Combining input-output analysis with ecological network analysis lets researchers study the functional relationships and hierarchy of the urban metabolic processes, thereby providing direct support for the analysis of urban disease. In this paper, using Beijing as an example, we develop a model of an urban metabolic system that accounts for the intensity of the embodied ecological elements using monetary input-output tables from 1997, 2000, 2002, 2005, and 2007, and use this data to compile the corresponding physical input-output tables. This approach described the various flows of ecological elements through urban metabolic processes and let us build an ecological network model with 32 components. Then, using two methods from ecological network analysis (flow analysis and utility analysis), we quantitatively analyzed the physical input-output relationships among urban components, determined the ecological hierarchy of the components of the metabolic system, and determined the distribution of advantage-dominated and disadvantage-dominated relationships, thereby providing scientific support to guide restructuring of the urban metabolic system in an effort to prevent or cure urban "diseases".

  14. EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model.

    PubMed

    Hädicke, Oliver; Klamt, Steffen

    2017-01-03

    Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli's central metabolism.

  15. EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

    PubMed Central

    Hädicke, Oliver; Klamt, Steffen

    2017-01-01

    Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli’s central metabolism. PMID:28045126

  16. The Probabilistic Nature of Preferential Choice

    ERIC Educational Resources Information Center

    Rieskamp, Jorg

    2008-01-01

    Previous research has developed a variety of theories explaining when and why people's decisions under risk deviate from the standard economic view of expected utility maximization. These theories are limited in their predictive accuracy in that they do not explain the probabilistic nature of preferential choice, that is, why an individual makes…

  17. A novel proposal of a simplified bacterial gene set and the neo-construction of a general minimized metabolic network

    PubMed Central

    Ye, Yuan-Nong; Ma, Bin-Guang; Dong, Chuan; Zhang, Hong; Chen, Ling-Ling; Guo, Feng-Biao

    2016-01-01

    A minimal gene set (MGS) is critical for the assembly of a minimal artificial cell. We have developed a proposal of simplifying bacterial gene set to approximate a bacterial MGS by the following procedure. First, we base our simplified bacterial gene set (SBGS) on experimentally determined essential genes to ensure that the genes included in the SBGS are critical. Second, we introduced a half-retaining strategy to extract persistent essential genes to ensure stability. Third, we constructed a viable metabolic network to supplement SBGS. The proposed SBGS includes 327 genes and required 431 reactions. This report describes an SBGS that preserves both self-replication and self-maintenance systems. In the minimized metabolic network, we identified five novel hub metabolites and confirmed 20 known hubs. Highly essential genes were found to distribute the connecting metabolites into more reactions. Based on our SBGS, we expanded the pool of targets for designing broad-spectrum antibacterial drugs to reduce pathogen resistance. We also suggested a rough semi-de novo strategy to synthesize an artificial cell, with potential applications in industry. PMID:27713529

  18. Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis-Menten and approximate kinetic equations.

    PubMed

    Costa, Rafael S; Machado, Daniel; Rocha, Isabel; Ferreira, Eugénio C

    2010-05-01

    The construction of dynamic metabolic models at reaction network level requires the use of mechanistic enzymatic rate equations that comprise a large number of parameters. The lack of knowledge on these equations and the difficulty in the experimental identification of their associated parameters, represent nowadays the limiting factor in the construction of such models. In this study, we compare four alternative modeling approaches based on Michaelis-Menten kinetics for the bi-molecular reactions and different types of simplified rate equations for the remaining reactions (generalized mass action, convenience kinetics, lin-log and power-law). Using the mechanistic model for Escherichia coli central carbon metabolism as a benchmark, we investigate the alternative modeling approaches through comparative simulations analyses. The good dynamic behavior and the powerful predictive capabilities obtained using the hybrid model composed of Michaelis-Menten and the approximate lin-log kinetics indicate that this is a possible suitable approach to model complex large-scale networks where the exact rate laws are unknown.

  19. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics.

    PubMed

    Bordbar, Aarash; Yurkovich, James T; Paglia, Giuseppe; Rolfsson, Ottar; Sigurjónsson, Ólafur E; Palsson, Bernhard O

    2017-04-07

    The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed "unsteady-state flux balance analysis" (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through (13)C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.

  20. GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism

    PubMed Central

    Beste, Dany JV; Hooper, Tracy; Stewart, Graham; Bonde, Bhushan; Avignone-Rossa, Claudio; Bushell, Michael E; Wheeler, Paul; Klamt, Steffen; Kierzek, Andrzej M; McFadden, Johnjoe

    2007-01-01

    Background An impediment to the rational development of novel drugs against tuberculosis (TB) is a general paucity of knowledge concerning the metabolism of Mycobacterium tuberculosis, particularly during infection. Constraint-based modeling provides a novel approach to investigating microbial metabolism but has not yet been applied to genome-scale modeling of M. tuberculosis. Results GSMN-TB, a genome-scale metabolic model of M. tuberculosis, was constructed, consisting of 849 unique reactions and 739 metabolites, and involving 726 genes. The model was calibrated by growing Mycobacterium bovis bacille Calmette Guérin in continuous culture and steady-state growth parameters were measured. Flux balance analysis was used to calculate substrate consumption rates, which were shown to correspond closely to experimentally determined values. Predictions of gene essentiality were also made by flux balance analysis simulation and were compared with global mutagenesis data for M. tuberculosis grown in vitro. A prediction accuracy of 78% was achieved. Known drug targets were predicted to be essential by the model. The model demonstrated a potential role for the enzyme isocitrate lyase during the slow growth of mycobacteria, and this hypothesis was experimentally verified. An interactive web-based version of the model is available. Conclusion The GSMN-TB model successfully simulated many of the growth properties of M. tuberculosis. The model provides a means to examine the metabolic flexibility of bacteria and predict the phenotype of mutants, and it highlights previously unexplored features of M. tuberculosis metabolism. PMID:17521419