Genome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources.Arabidopsis thaliana. A systematic comparison of compounds and reactions between two genome-wide databases allowed us to obtain a high-quality core consensus reconstruction, which was validated for stoichiometric consistency. A lower level of consensus led to a larger reconstruction, which has a lower quality standard but provides a baseline for further manual curation.In this article, we report a semi-automated methodology aimed at streamlining the process of metabolic network reconstruction by enabling the integration of different genome-wide databases of metabolic reactions. We present results obtained by applying this methodology to the metabolic network of the plant This semi-automated methodology may be applied to other organisms and help to streamline the process of genome-scale network reconstruction in order to accelerate the transfer of such models to applications. Metabolism is perhaps the best characterised of all molecular interaction networks in biology. Large amounts of data relating to metabolic reactions are available to date, but despite this wealth of information metabolic phenotypes remain difficult to predict accurately . The recHowever, the process of reconstructing the genome-scale metabolic network of an organism remains very labour-intensive. It has been observed that the number of available metabolic models is smaller than 1% of that of fully sequenced genomes . There iIn this work, we present an original semi-automated methodology aimed at accelerating the process of metabolic network reconstruction, while at the same time avoiding the loss of accuracy consecutive to a fully automated reconstruction. The underlying principle of our approach is to confront and integrate different sources of data. Different databases may contain errors or gaps about enzymes, reactions and metabolites present in an organism, but if data sources are independent, the likelihood that the same error would appear twice would be expected to be of an order of magnitude smaller than the frequency of errors in each data source. By creating an intersection between two databases, differences can be identified and assessed for further curation. The resulting process can be defined as semi-automatic because the processes of extraction and comparison between databases can be largely automated, yet a manual curation remains necessary to analyse and solve discrepancies.Arabidopsis thaliana. Plants offer a wide range of potential applications for metabolic modelling and engineering, which include the generation of pharmaceutical compounds, increasing the production of key secondary metabolites of commercial interest, improving yield and nutritional quality of crops. Despite such promising applications, the challenges of metabolic reconstruction are compounded in plants and only recently were the first genome-scale metabolic models of Arabidopsis reported [As an application of our methodology, we present results of the integration of metabolite and reaction data for the model plant reported ,17, whicreported  and Keggreported .In order to map metabolites between Kegg and AraCyc, several features of the data were taken into account. Two compounds were defined as being identical only if all features were positively matched. These features included compound names, chemical formulae and structures, and enzymes catalysing reactions where the considered metabolites participate.Names of metabolites may differ between databases for several reasons. Many chemical compounds are commonly known under multiple names, and all of their names are not necessarily indicated in all databases. Furthermore, there is no universal and common identifier between Kegg and AraCyc, as compounds are sometimes referred by their ChEBI , CAS .The formula of a metabolite theoretically confines the search for matching metabolites considerably. However it is not usable as a unique identifier. Two metabolites can have the same global formula and be completely different chemically because of the various possible structures of atoms. For example, 2-carboxy-D-arabinitol 1-phosphate, L-galactose-1-phosphate, D-hexose 6-phosphate and alpha-D-mannose 1-phosphate all have the same formula CAll chemical structure processing was implemented in Pipeline Pilot workflows . Input sIn previous metabolic network reconstructions it was observed that equivalent metabolites differed across sources in a number of ways. These included stereochemical differences , tautomeric variants , and charge states. To identify differences of these types SMILES strings were matched after purging stereochemical information from structure, or after calculation of the canonical tautomer, or following recalculation of ionisation at pH 7.4.One further important way in which equivalent metabolites from different sources were noted to differ was by simple structural errors in their representation, such as incorrect bonding or missing functional groups. Providing that the majority of the structure is correctly specified then corrections for errors of this type can be suggested by standard cheminformatics molecular similarity measures   and so require further manual checking. The approximate matching algorithms do, however, significantly reduce the manual checking workload.An iterative approach was adopted to integrate the different features of metabolite identification Figure . The firAt the end of the first iteration step, new compounds were added to the list of matched compounds and a new iteration was started. The whole process was repeated several times until no additional matched compounds could be found. During the process, compounds that gave a positive result in some but not all of the before-mentioned features were copied into a separate list that was manually examined. The positive results of these checks were added to the list of compounds in order to improve the outcome of the next iteration.core (yellow) metabolic network. Compounds and reactions that were not matched in both databases were assigned to the second or third network depending on an additional process. In the intermediate (green) network, we included reactions for which either the full set of substrates or the full set of products belonged to the core network. Such reactions are likely to be valid as they have a strong connection to the core network, but may be insufficiently or inconsistently described resulting in discrepancies between databases or their absence in one of them. Compounds involved in such reactions and not already included in the core model were also added to the intermediate network. All remaining compounds and reactions were included into the complete (blue) network.The final lists of matched compounds and reactions were given new unique identifiers for the new reconstructions. These matched compounds and reactions constitute the first or A general problem in metabolic models is caused by the fact that many compounds have different protonation states depending on the pH. There is no pH consistency in formulae found in databases, and AraCyc does not represent protons in reactions. As a consequence, we neglected differences in proton content between formulae and did not represent protons in reactions either. A strict balance of hydrogen atoms thus cannot be expected in our reconstructions.Incorrect definition of reaction stoichiometries often results in stoichiometric inconsistencies - a common type of modelling error, defined as contradictions between the fundamental physical constraints of mass positivity and mass conservation . An examR1: A ↔ BR2: A ↔ B + CIn this network, the metabolite C cannot be assigned any positive molecular mass without violating the mass balance in the whole system. Stoichiometric inconsistencies are often caused by violations of atomic balance, by ambiguous generic metabolite definitions  and by inclusion of polymers with variable polymerisation degrees and units (e.g. starch and protein).Stoichiometric consistency validation involves inspection of the left null-space (the null-space of the transposed stoichiometry matrix) and may include the following optional steps: Firstly, the stoichiometric consistency of the full network is verified. If the network is inconsistent, the non-conserved metabolites  are deteAn SBML version of the core network was constructed using libSBML . The filA network representation was constructed from the complete (blue) reconstruction for topological analysis and visualisation. In this representation, compounds are nodes and reactions are edges. All substrates of a given reaction were connected to all products of that reaction. Isolated compounds were not included in the network representation. The Cytoscape software was used for network visualisation , and theconnected components indicates how many disjoint subnetworks the network is broken into. A self-loop is a node connected to itself. The number of shared neighbours between two nodes is the number of nodes that are neighbours of both of them. The shortest path length, also called the distance between two nodes is the smallest number of edges that have to be crossed to go from one edge to the other. The characteristic path length is the average distance and the network diameter is the largest distance between two nodes in the network.The number of connectivity of a node is the number of edges connected to it. The network density is a measure of how densely the network is populated with edges. A network that contains only isolated nodes has a density of 0, whereas a clique has a density of 1. The network centralisation is a measure of how strongly a network is focused around central nodes. Networks resembling a star have a centralisation close to 1, whereas decentralised networks have a centralisation close to 0. The network heterogeneity measures the variance of the connectivity and reflects the tendency of a network to contain hub nodes. The clustering coefficient of a given node n is a ratio between the number of edges between the neighbours of n and the maximum number of edges that could possibly exist between them.The bbetweenness centrality C(n) of a node n is computed as follows:The s and t are nodes in the network different from n, st σis the number of shortest paths from s to t, stσ(n) is the number of shortest paths from s to t that n lies on, and N is the total number of nodes in the connected component that n belongs to. The betweenness centrality of each node is a number between 0 and 1. It reflects the amount of control that a node exerts over the interactions between other nodes in the network. A node acting as a bridge between different communities has a high betweenness centrality, while a node that lies inside a community has a low one.where ccloseness centrality C(n) measures how close a node n is to others in the same connected component. It is defined as follows:The L is the length of the shortest path between n and m, and N is the total number of nodes in the connected component that n belongs to. The closeness centrality is a measure of how fast information can spread from a given node to other reachable nodes in the network.where A. thaliana metabolism corresponding to decreasing levels of confidence  network contains only compounds and reactions which have been reliably identified in both databases and whose description is identical in both of them. The intermediate (green) network contains compounds and reactions found in only one database, but with a strong connection to the core network so that the absence of a perfect match is likely to be due to minor inaccuracies. The complete (blue) network contains all remaining compounds and reactions.Another important and often ignored aspect of metabolic network reconstruction is that different sources of data have different levels of certainty. A reaction consistently described by several independent sources should have a higher degree of reliability than a reaction described by a unique source. For this reason, we here present three different networks of e Figure . The corA. thaliana corresponding to different levels of confidence. The core (yellow) metabolic network only contains compounds and reactions that have been unambiguously identified and matched in the two databases  network additionally contains reactions from both databases where either the full set of substrates or the full set of products belongs to the core network  metabolic network, so that a comprehensive data set of all metabolic reactions and compounds described in For a better understanding of the different cases that result in the attribution of compounds and reactions to different reconstruction levels, several examples are shown in Figure We investigated the distributions of enzymes belonging to the three metabolic networks among Kegg pathways  The succinate dehydrogenase reaction between succinate and fumarate was not included into the core network due to an ambiguity between various forms of ubiquinones and ubiquinols acting as cofactors. These compounds are represented by generic classes in Kegg, but by specific ubiquinone-8 and ubiquinol-8 compounds in AraCyc.(ii) The transition between 2-oxoglutarate and succinyl-CoA is represented by a direct alpha-ketoglutarate dehydrogenase reaction in AraCyc. This reaction is not present in Kegg, which instead represents the transition by three different steps involving 3-carboxy-1-hydroxy-propyl-thiamine diphosphate and succinyldihydro-lipoamide-E.(iii) Similarly, the transition between isocitrate and 2-oxoglutarate via oxalosuccinate is represented in Kegg but not in AraCyc. The direct isocitrate dehydrogenase reaction does appear in both databases.The intermediate (green) and complete (blue) metabolic networks unsurprisingly contain many stoichiometric inconsistencies because these reconstructions contain generic metabolite classes . The stoichiometric consistency validation of the core (yellow) network was successful with the exception of molecular hydrogen. This inconsistency inevitably follows from skipping protons from reaction definitions and currently cannot be resolved, given the inaccuracies in the input data. Achieving strict hydrogen and charge balancing would nevertheless be important to obtain a high quality genome-scale metabolic model, particularly for a photosynthetic organism .We analysed the topological properties of the reconstructed metabolic networks in order to verify whether they were compatible with those of previously reported networks of other species Table . DiffereE. coli, 0.23 for S. cerevisiae, and 0.28 for H. pylori based on the same network representation [Distributions of the most important network properties are plotted in Figure entation . The aveentation . It is wentation  or when entation .In Arita's atomistic representation, the substrates and products of a reaction are connected only if carbon atoms can be traced between them. The Kegg RPAIR database provides atomic mappings between Kegg compounds, making it possible to construct an atomistic network for our core model. However, our intermediate and complete models contain some compounds that were not found in Kegg, therefore an atomistic network constructed from these models would be partially biased. For this reason, we provide a comparison between both network representations for the core model . The Poolman model additionally ignored the reactions involving 3-carboxy-1-hydroxypropyl-ThPP and succinyldihydrolipoamide-E, but these were included in the AraGEM model. Other discrepancies can be observed in the use of cofactors: the succinate oxidoreductase is represented with ubiquinone/ubiquinol in our and Poolman's models, but with flavin adenine dinucleotide (FAD/FADH2) in AraGEM; the 2-oxoglutarate oxidoreductase reaction uses NAD/NADH in our and Poolman's models, but ferredoxin in AraGEM; the Poolman model ignores the use of NADP/NADPH by the isocitrate oxidoreductase and malate oxidoreductase reactions; the AraGEM model ignores the use of GTP/GDP by the succinate:CoA ligase reaction; the water molecule is missing in some reactions of the Poolman model. These results illustrate the difficulty to reach a consensus between different models and the amount of manual literature research that would be necessary to solve all discrepancies. Nevertheless, as seen from this example, the quality of our semi-automatic reconstruction compares favourably to manual reconstructions.We have nevertheless carried out such a manual comparison for the pathway shown in Figure es Table . This coLike the Poolman model, our reconstruction does not distinguish between cellular compartments, but the AraGEM model distinguishes between cytosol, mitochondria, plastid, vacuole and peroxisome. Since the aim of this work is to present a methodology for the integration of different databases, our reconstruction is not immediately intended for FBA simulations. There is a trade-off between the search for a consensus and the gap filling required for FBA.E. coli and S. cerevisiae [The number of published genome-scale metabolic network reconstructions has grown rapidly in recent years. After the first reconstructions were published for revisiae ,43 the nrevisiae . These aVery few reconstructions of plant metabolic networks have been undertaken so far, and yet many of the applications mentioned before take even higher relevance in plants. Metabolic engineering is of particular significance in plants and offers promising perspectives to improving production yields, enhancing the nutritional value of crops, and generating valuable molecules for pharmacology and energy production. High-quality and comprehensive models of plant metabolism will be crucial to allow these applications to be developed. The metabolic networks of plants are of a higher complexity than those of most other living species; it is therefore both relevant and timely to start investing efforts in the construction of such models.Arabidopsis thaliana has been widely used as a model plant, its metabolic network has not been studied in great details and at a large scale. There has been renewed interest in A. thaliana metabolism recently. More than 170 secondary metabolites from seven different classes have been identified in A. thaliana [A. thaliana was also used as a model plant to study polyamine metabolism, which plays an essential role in stress tolerance [Although thaliana , whose pthaliana ,46. Biosthaliana . A. thalolerance , and flaolerance .However many issues presently hamper the use of genome-scale metabolic reconstructions by the wider research community and industry. These issues include:Arabidopsis models with each other.(i) A limited usage of standardised formats, nomenclatures and annotations. Many metabolic reconstructions are published in spreadsheet format, using customs identifiers and nomenclature. This is a considerable obstacle to the transfer of these reconstructions to other applications and to comparisons between different networks, making it challenging to compare S. cerevisiae [(ii) Limited coordination between different reconstruction efforts. One such coordination has recently lead to the publication of a consensus metabolic reconstruction of revisiae  and a sirevisiae .(iii) The absence of update mechanisms enabling the integration of the latest scientific discoveries by the wider research community into existing models.(iv) The absence of universal quality and validation standards.A. thaliana  metabolic network of A. thaliana, whose properties are comparable to existing manually reconstructed networks of other species. Such mechanisms could be generalised by the use of common repositories, following the model used for gene sequences or protein structures, allowing users to deposit new reconstructions and enhance existing ones through a seamless integration process.While large international meetings have proven successful to confront and integrate different existing metabolic reconstructions, mechanisms allowing a convenient integration of models as they are developed would be more efficient. We showed that by confronting and integrating two independent sources, we were able to semi-automatically reconstruct a core metabolic network of Arabidopsis metabolism [Last, all applications using genome-scale metabolic reconstructions do not necessarily require the same level of data quality. For network analysis, a relatively straightforward or automatic reconstruction may be sufficient, while for metabolic engineering or experimental design a highly accurate and well-annotated model is generally necessary. It is therefore important to keep track of the sources and validation level of data used in reconstructions, so that users are able to select the data with the appropriate level of confidence for their application. As a first step towards such a process, we here provide three reconstructions with different levels of confidence. The core (yellow) network has the highest confidence level and was proven to be stoichiometrically consistent, but has some gaps. For applications such as Flux Balance Analysis, a more continuous model can be preferable even though some reactions might be of a lower confidence level. The intermediate (green) network attempts to suit such needs by filling gaps through the inclusion of partial information. The complete (blue) network eventually contains the largest amount of available information, but with the restriction that some reactions may be unconfirmed and the risk of duplications. These different levels of network reconstructions provide baselines upon which future improvements can be built by the community to ultimately obtain a high-quality genome-scale of tabolism .The software tools developed for this work are provided in Additional file Arabidopsis thaliana. The integration of different data sources significantly enhances the quality of a reconstruction and leads to quality standards that are comparable to manual reconstructions. A long-term and coordinated international effort will be desirable to provide comprehensive and accurate genome-scale metabolic models of plants, and to provide the infrastructure allowing widespread diffusion, frequent update, unimpeded compatibility and convenience of use of such models by the widest research community and industry.In this work, we presented a methodology allowing an efficient semi-automatic reconstruction of metabolic networks via the integration of different databases, and applied this methodology to the plant KR developed the methodology and implemented algorithms for network reconstruction and database integration. YT, PD, AG, NS implemented algorithms for network reconstruction and validation. GB implemented gene-to-reaction associations. JMS designed and coordinated the study and analysed results. KR, PD, AG, NS, JMS wrote the manuscript. All authors read and approved the final manuscript.Compound and reaction data of the core (yellow) metabolic network. The first sheet contains the list of compounds and the second sheet the list of reactions. Each compound is identified by a local identifier consisting of "Ath_C" followed by a four-digit number, its Kegg identifier and AraCyc name. Each reaction is identified by a local identifier consisting of "Ath_R" followed by a four-digit number, its Kegg identifier and AraCyc name. The stoichiometry column describes the reaction using local compound identifier. Substrates and products are separated by the equal ("=") sign. The stoichiometry is always explicitly written even when it is one. The enzyme column lists the enzymes catalysing each reaction by their EC number.Click here for fileCompound and reaction data of the intermediate (green) metabolic network. The first sheet contains the list of compounds and the second sheet the list of reactions. Each compound is identified by a local identifier consisting of "Ath_C" followed by a four-digit number, its Kegg identifier and AraCyc name. Each reaction is identified by a local identifier consisting of "Ath_R" followed by a four-digit number, its Kegg identifier and AraCyc name. The stoichiometry column describes the reaction using local compound identifier. Substrates and products are separated by the equal ("=") sign. The stoichiometry is always explicitly written even when it is one. The enzyme column lists the enzymes catalysing each reaction by their EC number.Click here for fileCompound and reaction data of the complete (blue) metabolic network. The first sheet contains the list of compounds and the second sheet the list of reactions. Each compound is identified by a local identifier consisting of "Ath_C" followed by a four-digit number, its Kegg identifier and AraCyc name. Each reaction is identified by a local identifier consisting of "Ath_R" followed by a four-digit number, its Kegg identifier and AraCyc name. The stoichiometry column describes the reaction using local compound identifier. Substrates and products are separated by the equal ("=") sign. The stoichiometry is always explicitly written even when it is one. The enzyme column lists the enzymes catalysing each reaction by their EC number. The gene columns list genes associated to each reaction based on EC numbers.Click here for fileDistribution of enzymes in the three metabolic networks for each Kegg pathway. The first two columns give the Kegg identifier and name of each pathway. The yellow columns give the number of enzymes from this pathway attributed to the core metabolic network and its percentage in relation to the total number of enzymes contained in the pathway. The green columns give the number of enzymes attributed to the intermediate metabolic network and its percentage in relation to the total number of enzymes. The blue column gives the number of enzymes contained in the complete network, which is equal to the total number of enzymes contained in the pathway.Click here for fileComparison between topological properties of a classical and atomistic representation for the core (yellow) metabolic network. Red colour is used for the classical network, orange for the atomistic network. (a) Node degree distribution. (b) Average clustering coefficient distribution. (c) Betweenness centrality. (d) Closeness centrality. (e) Shared neighbours distribution. (f) Shortest path length distribution. See methods section for an explanation of network parameters.Click here for fileAnnotated SBML file of the core (yellow) metabolic network.Click here for fileSBML file of the intermediate (green) metabolic network.Click here for fileSBML file of the complete (blue) metabolic network.Click here for fileSoftware and protocol for semi-automatic reconstruction (Java source code).Click here for file


The advent of various high-throughput experimental techniques for measuring molecular interactions has enabled the systematic study of biological interactions on a global scale. Since biological processes are carried out by elaborate collaborations of numerous molecules that give rise to a complex network of molecular interactions, comparative analysis of these biological networks can bring important insights into the functional organization and regulatory mechanisms of biological systems.In this paper, we present an effective framework for identifying common interaction patterns in the biological networks of different organisms based on hidden Markov models (HMMs). Given two or more networks, our method efficiently finds the top Based on several protein-protein interaction (PPI) networks obtained from the Database of Interacting Proteins (DIP) and other public databases, we demonstrate that our method is able to detect biologically significant pathways that are conserved across different organisms. Our algorithm has a polynomial complexity that grows linearly with the size of the aligned paths. This enables the search for very long paths with more than 10 nodes within a few minutes on a desktop computer. The software program that implements this algorithm is available upon request from the authors. Recent advances in high-throughput experimental techniques for measuring molecular interactions global alignment, which tries to find the best coherent mapping between nodes in different networks that covers all nodes; and local alignment, which simply tries to detect significant common substructures in the given networks. Typically, the global network alignment problem is formulated as a graph matching problem whose goal is to find the optimal alignment that maximizes a global objective function that simultaneously measures the similarity between the constituent nodes and also between their interaction patterns. This optimization problem can be solved by a number of techniques, such as integer programming Network alignment can be broadly divided into two categories, namely, querying pathways in a target network, hence it cannot be directly used for local alignment of general networks.There are also many local network alignment algorithms, where examples include PathBLAST In this paper, we extend the HMM-based framework proposed in In this section, we present an algorithm for solving the local network alignment problem based on HMMs. For simplicity, we first focus on the problem of aligning two networks, which can be formally defined as follows: Given two biological networks Let Our goal is to find the best matching pair of paths jointly produce (or “emit”) the virtual path To define the alignment score The described HMM-based network representation allows us to naturally integrate the interaction reliability scores and the node similarity scores into an effective probabilistic framework. We first define two mappings Based on the HMM framework, the problem of finding the best matching pair of paths is transformed into the problem of finding the optimal pair of state sequences in the two HMMs that jointly maximize the observation probability of the virtual path We repeat the above iterations until The computational complexity of the above algorithm is The log-probability As we can see, the concept of the “virtual” path provides an intuitive way of coupling states in two different HMMs. In fact, by taking a closer look at the recursive equation (4), the proposed alignment algorithm can also be viewed as a Markovian walk on a product graph, whose nodes consist of all possible pairs of hidden states in the respective HMMs and the edges between these nodes are determined by the connectivity (or transition probability) between the corresponding states in the HMMs. The algorithm searches for the optimal path  that maximize the pathway alignment score, we can apply the same dynamic programming algorithm described in the previous section. The retrieved paths can contain any of the hidden states It is straightforward to extend the described pairwise network alignment algorithm for aligning multiple networks. Without loss of generality, we only consider the extension to the alignment of three networks. Given three network graphs It should be noted that although we fix the length of the virtual path to In order to obtain more general subnetwork alignments, not just alignments of linear paths, we can combine the overlapping paths among the top The memory complexity of the proposed algorithm is To demonstrate the effectiveness of the HMM-based network alignment algorithm, we carried out the following experiments. First, we used our algorithm to align two pairs of small synthetic networks that were used to validate the network alignment algorithm proposed in http://www.cytoscape.org/plugins1.php) and they were used for the validation of a network alignment algorithm called MNAligner To illustrate the potential capability of aligning different types of molecular networks, we first tested our algorithm using two small synthetic examples, which include a pair of undirected networks and another pair of directed networks. These examples were obtained from the tutorial files in the PathBLAST plugin of software Cytopscape  D. melanogaster network agreed well with the putative D. melanogaster hedgehog signaling pathway reported in the KEGG database http://www.genome.jp/dbget-bin/get_pathway?org_name=dme&mapno=04340) in the KEGG database http://www.genome.jp/dbget-bin/get_pathway?org_name=map&mapno=04010) reported in KEGG. These results compare favorably to the results obtained by one of the state-of-the-art algorithms We first obtained the PPI network of E. coli and the C. crescentus networks as well as a pairwise alignment between the E. coli and the S. typhimurium networks. We assessed the accuracy of our algorithm based on the consistency of the KEGG ortholog (KO) group annotations correct if both proteins have the same KO group annotations, and incorrect if the annotations do not agree. The specificity is defined as the ratio of the number of “correct” protein pairs among all annotated protein pairs.In order to validate the accuracy of our algorithm for predicting functional modules that are conserved in different organisms, we performed additional experiments using three microbial PPI networks obtained from For this experiment, the parameters of the HMMs have been chosen as follows. First, the transition scores E. coli and the C. crescentus networks is shown in E. coli and the S. typhimurium networks is shown in Based on the constructed HMMs, we used our algorithm to find the top-scoring pathway alignment with gaps. At each iteration, we looked for the top aligned pair of paths, stored the alignment, and removed the interactions included in the alignment from the respective networks for the next iteration. By repeating this iteration, we found 200 high-scoring path alignments. This experiment has been repeated with varying virtual path length: E. coli and C. crescentus networks and the alignment of E. coli and S. typhimurium networks both detected conserved DNA replication modules. The module contained components of the primosome , subunits of topoisomerase IV , and a subunit of DNA polymerase III (dnaN). These protein families are all known to be involved in DNA replication. We also found other interesting conserved modules, which include both large and small subunits of ribosomal protein complexes ; DNA-directed RNA polymerase complex containing rpoA, rpoB, rpoC, and other subunits; the citrate cycle (TCA cycle) containing 2-oxoglutarate dehydrogenase E1 component  and succinyl-CoA synthetase ; NADH dehydrogenase I , which is a part of the oxidative phosphorylation pathway; nitrate reductase 1 ; and a portion of the bacterial secretion system .Further analysis of the predicted alignments led to a number of interesting observations. For example, the alignment of In this paper, we proposed an HMM-based network alignment algorithm that can be used for finding conserved pathways in two or more biological networks. The HMM framework and the proposed alignment algorithm has a number of important advantages compared to other existing local network alignment algorithms. First of all, despite its generality, the proposed algorithm is very simple and efficient. In fact, the alignment algorithm based on the proposed HMM framework is a variant of the Viterbi algorithm. As a result, it has a very low polynomial computational complexity, which grows only linearly with respect to the length of the identified pathways and the number of edges in each network. This makes it possible to find conserved pathways with more than 10 nodes in networks with thousands of nodes and tens of thousands of interactions within a few minutes on a personal computer. Furthermore, the HMM-based framework can handle a large class of path isomorphism, which allows us to find pathway alignments with any number of gaps (node insertions and deletions) at arbitrary locations. In addition to this, the proposed framework is very flexible in choosing the scoring scheme for pathway alignments, where different penalties can be used for mismatches, insertions and deletions. We can also assign different penalties for gap opening and gap extension, which can be convenient when comparing networks that are remotely related to each other. Another important advantage of the proposed framework is that it allows us to use an efficient dynamic programming algorithm for finding the mathematically optimal alignment. Considering that many available algorithms rely on heuristics that cannot guarantee the optimality of the obtained solutions, this is certainly a significant merit of the HMM-based approach. Although the mathematical optimality does not guarantee the biological significance of the obtained solution, it can certainly lead to more accurate predictions if combined with a realistic scoring scheme for assessing pathway homology. As demonstrated in our experiments, the proposed algorithm yields accurate and biologically meaningful results both for querying known pathways in the network of another organism and also for finding conserved functional modules in the networks of different organisms. Finally, the HMM-based framework presented in this paper can be extended for aligning multiple networks. While many current multiple network alignment algorithms adopt a progressive approach for comparing multiple networks For future research, we plan to evaluate the performance of our HMM-based algorithm more extensively by investigating the consistency of the predicted alignments based on other available functional annotations, including the gene ontology (GO) annotations Supporting Information S1(0.06 MB PDF)Click here for additional data file.


The crista terminalis is a normal anatomical structure within the right atrium that is not normally visualised in the standard views obtained while performing a transthoracic echocardiogram. In this case report, transthoracic echocardiography suggested the presence of a right atrial mass in a patient with end stage renal disease. However, subsequent transesophageal echocardiography revealed that the right atrial mass was actually a thick muscular bridge in the right atrium consistent with a prominent crista terminalis. An understanding of the anatomy and the echocardiographic appearance of a prominent crista terminalis will minimize the misdiagnosis of this structure avoiding unnecessary expensive additional tests. The crista terminalis is a well-defined fibromuscular ridge formed by the junction of the sinus venosus and primitive right atrium that extends along the posterolateral aspect of the right atrial wall. Occasionally, this structure can be prominent, thus mimicking right atrial mass-like tumour, thrombus, or vegetation -6. We reA 26-year-old lady with end stage renal disease was referred for transthoracic echocardiogram (TTE) as part of the departmental protocol before renal transplant. There was no history of fever, chest pain, dyspnea on exertion or palpitations. During the TTE an echogenic structure was noted in the right atrium mimicking a thrombus or a tumor Figure , Panel AThe crista terminalis is originated from regression of the septum spurium as the sinus venosus is incorporated into the right atrial wall. The regression of the crista terminalis shows wide variations and thus the thickness of the crista terminalis varies widely in adults ranging usually from 3 to 6 mm . HoweverThere are a few case reports of prominent crista terminalis in the literature, evaluated with different diagnostic tools, and interestingly all of them are female . Clockwise rotation of the probe allows the visualization of the structure. This is a ridge extending from the opening of the superior vena cava to the lateral side of the entrance of the inferior vena cava, consistent with a crista terminalis.Click here for file


Different studies have shown that cellular enzymatic activities are able to self-organize spontaneously, forming a metabolic core of reactive processes that remain active under different growth conditions while the rest of the molecular catalytic reactions exhibit structural plasticity. This global cellular metabolic structure appears to be an intrinsic characteristic common to all cellular organisms. Recent work performed with dissipative metabolic networks has shown that the fundamental element for the spontaneous emergence of this global self-organized enzymatic structure could be the number of catalytic elements in the metabolic networks.In order to investigate the factors that may affect the catalytic dynamics under a global metabolic structure characterized by the presence of metabolic cores we have studied different transitions in catalytic patterns belonging to a dissipative metabolic network. The data were analyzed using non-linear dynamics tools: power spectra, reconstructed attractors, long-term correlations, maximum Lyapunov exponent and Approximate Entropy; and we have found the emergence of self-regulation phenomena during the transitions in the metabolic activities.The analysis has also shown that the chaotic numerical series analyzed correspond to the fractional Brownian motion and they exhibit long-term correlations and low Approximate Entropy indicating a high level of predictability and information during the self-regulation of the metabolic transitions. The results illustrate some aspects of the mechanisms behind the emergence of the metabolic self-regulation processes, which may constitute an important property of the global structure of the cellular metabolism. Living cells are essentially dynamic reactive structures formed by complex membranes surrounding a singular fluid mixture where millions of different biochemical elements interact. In this complex mixture most of the molecules are incessantly synthesized and destroyed through a labyrinthine network of biochemical reactions densely integrated forming one of the most complex dynamical systems in the nature The enzymes are the most outstanding molecules of this surprising biochemical reactive machinery. They are responsible for almost all the biomolecular transformations, which considered globally are called cellular metabolism.Saccharomyces cerevisiae has shown that 83% of their proteins form complexes containing from two to eighty-three proteins, and its whole enzymatic structure is formed by a modular network of biochemical interactions between protein complexes In the conditions prevailing inside the cell, the enzymes do not work in an isolated way but forming molecular associations, e.g., the analysis of proteome of Nowadays, there are enough experimental data showing the existence both in prokaryotic and eukaryotic cells of numerous functional enzymatic associations belonging to metabolic pathways like: glycolysis, protein synthesis, lipid synthesis, purine synthesis, Krebs cycle, urea cycle, respiratory chain, fatty acid oxidation, DNA and RNA synthesis, amino acid metabolism, cyclic AMP degradation, etc. In addition, reversible interactions of enzymes with structural proteins and membranes are a common occurrence. This results in the existence of microcompartments within the soluble phases of cells. The microcompartmentation provides, on one hand, biophysical and biochemical mechanisms of physiological importance for the regulation of metabolic pathways, and on the other hand, direct transfers of the intermediate substrates from one enzyme to an adjacent enzyme in a process that is called metabolite channelling Extensive studies of cellular metabolism during the last three decades have shown that the functional enzymatic associations, the microcompartmentation of the metabolic processes and the metabolite channelling are the principal ways of microstructural organization of cell metabolism The cellular organization at the molecular level presents another relevant dynamic characteristic: the emergence of dissipative catalytic patterns.Experimental observations have shown that the enzymes may form functional catalytic associations in which molecular oscillations may spontaneously emerge. When the oscillations in an enzymatic association are periodic This new type of supramolecular self-organization that operates far from equilibrium conditions was called dissipative structures by Prigogine Numerous experimental observations both in prokaryotic and eukaryotic cells have shown the spontaneous emergence of molecular oscillations in most of the fundamental metabolic processes. For instance, there are oscillatory biochemical processes involved in: intracellular free amino acid pools To get a more accurate understanding of the global metabolic phenomena, we have developed a reactive dynamical system called dissipative metabolic networks (DMNs) which is basically formed by functional enzymatic associations that may present both steady states and oscillatory molecular patterns.We define as a metabolic subsystem any group of dissipatively structured functional enzymatic associations that form a catalytic entity as a whole, in which the activity is autonomous with respect to the other enzymatic associations (they carry out their activities relatively independently between them) and molecular oscillations and steady states may emerge spontaneously.The presence of some regulatory enzymes  in each metabolic subsystem makes possible the interconnection among them. Allosteric regulation is the major mechanism by which the enzymatic activities are controlled in cells; they are modulated by means of effectors which are not binding at the active site , but at another locus on the surface of the enzyme  In agreement with all these considerations, a DMN is an open metabolic system formed by a set of discrete modules of functionally associated enzymes (metabolic subsystems) interconnected by substrate fluxes and regulatory signals  in which both steady states and oscillatory catalytic patterns can emerge.on-off changing states  In 1999 we found a singular global metabolic structure able to self-organize spontaneously, characterized by a set of metabolic subsystems always locked into active states (metabolic cores) while the rest of the catalytic elements present dynamics of Escherichia coli, Helicobacter pylori, and Saccharomyces cerevisiae under different growth conditions by means of flux balance analysis applied to metabolic networks and it was also suggested that this self-organized enzymatic configuration appears to be an intrinsic characteristic of metabolism, common to all living cellular organisms In 2004 and 2005 the existence of this global metabolic structure was verified for We have investigated the influence of some molecular processes in the self-organization of the global metabolic configuration by means of numeric simulations in DMNs and we have observed an asymptotic trend approximately 100% of the networks displaying this kind of global configuration when the number of metabolic subsystems is incremented: this suggested that the number of catalytic elements could be the fundamental element for the emergence of this global metabolic behaviour Recently, we have performed extensive DMNs simulations  taking into account: the proportion of the allosterically regulated enzymes and covalent enzymes present in the networks, the variation in the number of substrate fluxes and regulatory signals per catalytic element, the random changes in the topology of all flux and regulatory signal interconnections, as well as the random changes in the values of the outer fluxes. The results show that the fundamental factor for the spontaneous emergence of this global self-organized enzymatic structure is the number of catalytic elements in the metabolic networks Here, our main goal is to get a more accurate understanding on the dynamical features which guarantee that the global functional structure is preserved under different external inputs. For this aim, we have studied by means of non-linear dynamic tools, different transitions in the catalytic patterns belonging to a dissipative metabolic network. Concretely, we have analyzed by means of non-equilibrium statistical physics tools the following: the stability of the catalytic activities by means of the Lyapunov exponent, the type of signal belonging to the complex transitions by calculating the slope of the power spectral, the rate of entropy production by testing the Approximate Entropy and the possible presence of long-term correlations in the enzymatic transitions data by means of the bridge detrended scaled windowed variance analysis.Here we have also studied both simple and complex dynamical transitions of different metabolic subsystems and we have reconstructed the complex attractors responsible for the dynamical regulations during the transitions in the metabolic activities.Our analysis has shown the spontaneous emergence of self-regulation processes in the metabolic patterns. Likewise, we have observed that the complex numerical series analyzed correspond to fractional Brownian motion. They exhibit long-term correlations and low Approximate Entropy indicating a high level of predictability and information during the self-regulations of the metabolic transitions.These results illustrate some aspects in the mechanisms behind the emergence of the metabolic self-regulation processes which may constitute an important property of the global structure of the cellular metabolism.Dissipative metabolic networks (DMNs) are dynamical systems basically formed by a given number of interconnected metabolic subsystems. Each metabolic subsystem represents a group of dissipatively structured functional enzymatic associations . These enzymatic sets are considered as individual catalytic entities and receive both input fluxes (the substrates of the enzymatic reactions) and regulatory signals, which may be of three types: activatory , inhibitory  and all-or-nothing type .Input-output conversion is made in two stages. First, the input fluxes are transformed into an intermediary activity by means of flux integration functions. In a second phase, the “intermediary activity” is modified by means of the “regulatory signals integration”, which depends on the combination of the received regulatory signals. Each regulatory signal has an associated regulatory coefficient which defines the intensity of its influence.In DMNs, when the set of dissipatively structured enzymes shows an activity with a rhythmic behaviour the output activities present nonlinear oscillations with different levels of complexity as could be expected in the cellular conditions “in vivo”.Formally, we assume that the activity of the i-th metabolic subsystem is defined by N is the total number of subsystems.Fix To study the evolution of the whole system, we assume that each subsystem receives two different kinds of inputs:The substrates of the biochemical reactions.Regulatory signals of three types: activatory, inhibitory and total inhibitory.These inputs may produce a change in the activity of the subsystems. Moreover, according to experimental observations, the output activity must be stationary or periodic.Each subsystem processes inputs to produce outputs in two stages:An intermediate activity is obtained using the flux integration functions.The received regulatory signals originate a regulatory signal integration which varies the intermediary activity.Let us suppose that the i-th subsystem receive flux from the j-th, its intermediate values In this second stage, the intermediary values are modified using the signal integration functions, which depend on the combination of the received regulatory signals and their corresponding parameters (regulatory coefficients). In the metabolic subsystems, the existence of some regulatory enzymes  permits the interconnection among them. The allosteric enzymes present different sensitivities to the effectors, which can generate diverse changes on the kinetic parameters and in their molecular structure; likewise, the enzymatic activity of covalent modulation also presents different levels of regulation depending on the sensitivity to other activators or inhibitors.These effects on the catalytic activities are represented in the dynamical system by the regulatory coefficients and consequently each signal has an associated coefficient which defines the intensity of its influence. There exist three kinds of signal integration functions:Activation function AC.Inhibition function IN.Total inhibition function TI.k = 1, 2, 3 and In this way, to compute If the allosteric signal is inhibitoryFirst, we have fixed the following elements with control parameters: 12 subsystems in the DMN, 2 substrate input fluxes for each subsystem , 3 inputHaving fixed these elements, the structure of the network has been randomly configured (following the uniform distribution) including: the topology of flux interconnections and regulatory signals, the bsystems .The values of We have taken the constants T and M we calculate the activity matrices m = 1, …, M using the flux integration functions and the regulatory signals.Finally, given t between The output of our automaton provides a sequence of three positive values p(t) whose graph passes through the points p(t) plus sinusoids is the activity in a bit interval To avoid these discontinuities, per each To calculate the maximal Lyapunov exponent we have used the Wolf algorithm The Maximal Lyapunov exponent is estimated as This method generates an estimation of the Hurst exponent (H) for each series.N, then the following steps are carried out for each one of the window sizes n = 2,4,…,N/2,N :According to this procedure, if the signal is of the form n, where N is not a power of 2 and N is not divisible by n, then the last remaining points are ignored for this value of n. For instance, if N = 31 and n = 4, the first 28 points are partitioned into seven windows.Partition of the data points in Subtraction of the line between the first and last points for the points in the n-th window.For each Evaluation of the average n over which the regression line of Observation of the range of the window sizes H.In this range, the slope of the regression line gives the estimation of the Hurst coefficient The empirical range of windows corresponding to step 5) which we have found to be in accordance with the guidelines appearing in To investigate the elements that may affect the catalytic dynamics under a global metabolic structure characterised by present metabolic cores, we have performed a unique DMN, which is later perturbed, to study its reactive dynamical answer.In this metabolic network, first we have fixed the following parameters:The number of subsystems in the DMN: 12.The number of substrate input fluxes for each subsystem: 2 .The number of input regulatory signals for each metabolic subsystem: 3 ; these rThe same proportion of the allosteric activation signals, allosteric inhibition signals and regulatory signals of covalent modulation present in the network.DMNs are open systems, and certain metabolic subsystems may receive a substrate flux from the exterior. Here, we have arbitrarily fixed the metabolic subsystem number three for this function.After determining these characteristics of the network, its architecture was randomly configured including: the topology of flux interconnections and regulatory signals, the parameters associated to the flux integration functions and regulatory coefficients of the allosteric activities, as well as the values of the initial conditions in the activities of all metabolic subsystems  whereas the subsystem number seven always exhibits a chaotic dynamic on-off .Again, the network spontaneously auto-organizes its activity patterns provoking the emergence of a qualitative change in the whole system and the activities of all subsystems presents simple regular transitions between different periodic oscillations or steady-state patterns . For insThe ranges of amplitudes and periods of these regular patterns vary considerably from a subsystem to another. For instance, subsystem number two, under the new conditions shows very low activity with low frequency values.The subsystem number 7  exhibitsThe dynamical reorganizations of the metabolic network during the changes provoked by the three different external fluxes  originatFormally, if y(t) is the activity of any subsystem in the network, a set A is called an attractor for this subsystem in the following three conditions:It is impossible to go out, in other words, if There exists a neighbourhood of itself B (basin of attraction) such that for any initial condition in B, the system approaches indefinitely A.A is a compact set; this means it is a closed and bounded set.Consequently, the attractor characterizes the asymptotic catalytic behaviour of each subsystem.To investigate the dynamics of DMN, we need to reconstruct the attractor from the time series and compute its maximal Lyapunov exponent which is very useful in testing the existence of chaos.Positive Lyapunov exponent indicates sensitivity to initial conditions, a hallmark of chaos To reconstruct attractors we have used time-delay embedding Given a time series x(t), t = 1,2,…N, the m-dimensional return map is obtained by plotting the vector This converts the one dimensional vector x(t), into the m-dimensional vector X(t). The dimension m is known as the embedding dimension, and the trajectory of X(t) converges to an attractor in the m-dimensional Euclidian space, which is, up to a continuous change of variable, the attractor of the subunit dynamical system. In Once the attractor for each metabolic subsystem has been created, we have computed the maximal Lyapunov exponent with the results of the third column in In order to study the presence of long-term correlations in the numerical chaotic data, we have determined whether the series is a fractional Gaussian noise (fGn) or a fractional Brownian motion (fBm); fGn and fBm can be distinguished by calculating the slope of the power spectral density plot.The signal is said to exhibit power law scaling if the relationship between its Fourier spectrum and the frequency is approximated asymptotically by f), log f), where f is the frequency and S(f) the absolute value of the Fourier transform  for each series. For a random process with independent increments, the expected value of H is 0.5. When H differs from 0.5, it indicates the existence of long-term memory, that is to say, dependence among the values of the process. If H>0.5, it was produced by a biased random process which exhibits persistent behaviour. In this case, for several previous transitions, an increment on the phase-shift average value implies an increasing trend in the future. Conversely, a previously decreasing trend for a sequence of transitions usually implies a decrease for a similar sequence. Antipersistent behaviour is obtained for 0<H<0.5, a previously decreasing trend implies a probable increasing trend in the future and an increase is usually followed by decreases We have used in our work the program bdSWV, available on the web of the Fractal Analysis Programs of the National Simulation Resource In The set formed by the asymptotical behaviours followed by all the metabolic subsystem activities form the global attractor of the metabolic net.Formally, if After some transitory time, the activity subsystem functions In The entropy theory of dynamical systems can be found in many textbooks The entropy is also a useful concept in the study of the attractors which may allow estimating the degree of complexity and information contained in them. Concretely, Kolmogorov–Sinai entropy (K–S entropy) provides a measure of the information and the level of predictability in the attractor A practical solution to this problem has been put forward using a developed family of statistics named Approximate Entropy (ApEn) which is a good approximation of the Kolmogorov-Sinai entropy Formally, given N data points from a time series x(1), x(2),., x(N), two input parameters m and r must be fixed to compute ApEn, denoted precisely by ApEn.To estimate ApEn, first we form the m dimensional vector sequences X(1)….X(N-m+1) such that X(i) = (x(i)…..x(i-m+1)) which represent m consecutive values. Let define the distance between X(i) and X(j) (d) as the maximum absolute difference between their respective scalar components and for each X(i) we count the number of j such that d<r, denoted as The average value of In Finally, we have computed the ApEn entropy of the regular series see  and all In any process, if the series are regular, the KS entropy is very near to zero, when they are chaotic, it grows and it is infinite in numeric series with random noise (82).In an attempt to investigate the dynamical features of the dissipative metabolic networks which guarantee that the global functional structure is preserved under different external inputs we have studied, using non-equilibrium statistical physics tools, catalytic activities belonging to a dissipative metabolic network.The first results show that the chaotic numerical series present power-law scaling with a slope of the power spectral density plot β varying between 1.126 and 2.658, which corresponds in all case to fractional Brownian motion (fBm). This fBm is a generalization of Brownian motion in which the increments are normally distributed but they are no longer independent and therefore the process is correlated in time. This dynamic characteristic is also present in different physiological signals In order to estimate the level of the long-term correlations, the chaotic numerical data were analyzed by means of the bdSWV method finding values for the Hurst exponent (H) in a range of We clearly found that the values of the Hurst exponent were lower than 0.5 in all cases and consequently, the metabolic processes analyzed depend on their history; they are conditioned to some extent on the previous ones.Values of H<0.5 are interpreted as characteristic of ‘trend-reversing’ or long-term antipersistence, i.e., the behaviour of the metabolic series tends to reverse itself, an increasing tendency in the past for a sequence of metabolic transitions is followed on average by decreases in the future, and conversely, a decreasing trend in the past is likely to be followed by an increasing tendency.The chaotic metabolic transitions transmit information and this information presents long-term memory properties. A complex dynamic phenomenon may emerge in the metabolic system during large sequences so that the state of the catalytic activity in each subsystem is affected by a certain number of its previous activity states.In deterministic chaotic processes, the sensitive dependence on initial conditions will lead to large changes in posterior system states with exponential divergence in the orbits of the chaotic attractors. However, these perturbations do not prevent the integral trajectories belong to the attractor. In fact, the chaotic dynamical system has enough information to allow the trajectories to remain quite close to the attractor. This information can be estimated by means of the Kolmogorov-Sinai entropy which indicates the degree of complexity and information contained in the attractors and estimates the rate at which information about the state of a system is lost. Likewise, this information is correlated in time i.e., it exhibits long-term persistence.Both properties, sensitive dependence on initial conditions and persistence, coexist in the chaotic dynamic systems. These deterministic systems contain a certain level of information (level of predictability) and this information presents long-term correlation.Long-term correlations have also been observed in experimental studies, e.g., the quantification of DNA patchiness R/S method, the relationship between chaotic deterministic processes and long-term correlations. For instance, we have investigated persistent properties in the Lorenz attractor R/S method) in numerical series of oscillatory amplitudes belonging to a DMN In previous works, we have also studied, by means of the On another hand, the DMN was perturbed by three different values of the external input flux and as consequence local and global attractors appear spontaneously in the phase spaces belonging to metabolic subsystems and global network.When a metabolic subsystem begins its activity the catalytic patterns fall into one of the possible local attractors and all the dynamical reactive behaviours become trapped inside. Local attractors determine the output activity of the metabolic subsystems. Changes in their parameters see  section Global attractors determine that the metabolic system operates like an individual and complete integral system, and their dynamics are responsible, on one hand, for the self-organization of the metabolic processes  and, on the other hand, for global self-regulations of the metabolic transitions .We have used ApEn to estimate the degree of complexity and of information contained in the local attractors. Approximate Entropy is also a measure of the self-organization of a system and estimates the rate at which information about the state of a system is lost.In this sense, the results have shown that in the numerical series the entropy is low indicating a high level of predictability and information in the local attractors which govern the corresponding transition patterns. The entropy values of the numerical metabolic series exhibit a narrow range of low values  acts like a “dynamic network of functional links” which defines in every moment a set of instructions that makes each subsystem evolve with a particular and precise catalytic pattern. As a consequence of these complex processes the metabolic structure acts as a whole being able to self-regulate.Contrarily to self-organization, the self-regulation does not seem to be a spontaneous process since it depends on local properties of subsystems . Self-organization requires processing the information relative to the different states of all catalytic elements of the network.Metabolic subsystems are enzymatic sets self-organized as dissipative dynamical systems which operate in far from equilibrium conditions forming a catalytic entity as a whole. They carry out their activities relatively independently between them and they play distinctive, systematic and essential roles in the cell.The metabolic network is a dynamic complex super-structure which integrates different dynamic systems (the metabolic subsystems) and it forms a global and a unique, absolutely well defined, deterministic, dynamical system in which self-organization, self-regulation and persistent properties emerge.Finally, we think that the existence of chaotic patterns long-term memory properties in the activity of the metabolic subsystems integrated in a stable global functional structure may constitute a biological advantage.Chaotic patterns exhibit sensitive dependence on initial conditions. Sensitivity means that a small change in the initial state will lead to large changes in posterior system states and the fluctuations of the chaotic patterns are conditioned by the degree of perturbation on the initial conditions. These changes in the system states present exponential divergence, provoking fast separations in the chaotic orbits.For “slow dynamical systems” the typical time scale of the chaotic fluctuations is on the order of 1On the other hand, different studies have shown that chaos permits fast transmission of information and high efficiency The existence of chaos in some metabolic processes may constitute a biological advantage by allowing fast and specific responses during the adaptation of the metabolic system to environmental perturbations.For example, calcium plays an important role in the regulation of cell metabolism, modulating many physiological processes In this sense, numerous works have shown chaotic behaviours at cellular conditions .(0.08 MB DOC)Click here for additional data file.


Postpartum hemorrhage is one of the rare occasions when a general or acute care surgeon may be emergently called to labor and delivery, a situation in which time is limited and the stakes high. Unfortunately, there is generally a paucity of exposure and information available to surgeons regarding this topic: obstetric training is rarely found in contemporary surgical residency curricula and is omitted nearly completely from general and acute care surgery literature and continuing medical education.The purpose of this manuscript is to serve as a topic specific review for surgeons and to present a surgeon oriented management algorithm. Medline and Ovid databases were utilized in a comprehensive literature review regarding the management of postpartum hemorrhage and a management algorithm for surgeons developed based upon a collaborative panel of general, acute care, trauma and obstetrical surgeons' review of the literature and expert opinion.A stepwise approach for surgeons of the medical and surgical interventions utilized to manage and treat postpartum hemorrhage is presented and organized into a basic algorithm.The manuscript should promote and facilitate a more educated, systematic and effective surgeon response and participation in the management of postpartum hemorrhage. Postpartum hemorrhage (PPH) is one of the rare occasions when a general or acute care surgeon may be called to labor and delivery emergently. At the least, this represents entrance into an environment and scenario that for most surgeons is not only foreign but also one in which time is limited and the stakes high. Being prepared to competently participate in the management of severe postpartum hemorrhage necessitates a basic knowledge of pelvic and gynecologic anatomy, the pathophysiology of such hemorrhage and a conceptual algorithm for its management to permit integrated participation with the obstetrical team for efficient and efficacious care of the new mother.Postpartum hemorrhage may occur in 1-5% of deliveries in developed countries , and is It is the goal of this paper to serve as a refresher and basic fund of knowledge for general surgeons with regard to postpartum hemorrhage so that when called upon to assist in such a scenario, prompt and efficacious assistance may be provided in a spontaneous, educated and systematic manner.When a significant postpartum hemorrhage occurs, a call may be placed for assistance from a general or acute care surgeon. This call should be considered and responded to as an emergency, synonymous with a cardiopulmonary arrest or trauma alert or activation. There are 3 common clinical scenarios involving acute postpartum hemorrhage (PPH within the first 24 hours from delivery) when a general surgeon or acute care surgeon may be called upon:1. Most commonly, the patient is in the operating suite in labor and delivery following a cesarean section and a hysterectomy is being considered or performed for PPH that has not responded to the usual medical and surgical measures. These patients likely will be hemodynamically unstable and may be experiencing latent or full-blown disseminated intravascular coagulation (DIC).2. The second most common scenario will be a patient status post a vaginal delivery who is experiencing PPH refractory to medical measures who has been or is being moved to the labor and delivery operating suite for an operative intervention. Similarly, these patients will be in or near significant hemodynamic compromise and DIC.3. Lastly, and probably the least likely scenario, is the previous patient, still in the delivery suite. A good number of these patients will respond to medical interventions to control their PPH. This situation is usually handled by obstetrical practitioners, who would try medical measures on their own, or call another obstetrical practitioner.Once significant postpartum hemorrhage has been recognized, resuscitation is performed in parallel to diagnostic efforts. The initial assessment of the patient should be conducted in much the same manner as per Advanced Trauma Life Support (ATLS) guidelines. Certainly, this should be tailored and should take into account what has been and is already underway; however, "ABCs" must be evaluated with interventions provided as needed. If not already in place, appropriate monitors should be attached and monitoring commenced: Continuous pulse oximetry, heart rate and blood pressure measurements (a minimum of every 5 minutes), and a foley catheter, if not in place, should be placed and utilized to monitor urine output. Initial lab studies should be ordered and repeated as needed and at least every 4 hours, to include type & cross for six units of packed red blood cells (PRBCs), chemistry panel, complete blood count (CBC), coagulation panel, and fibrinogen. Unique to the postpartum patient, D-Dimer studies may be sent; however interpretation must take into account that pregnancy itself results in elevated values, therefore limiting its utility . At a miFluids should be replaced with the goal of matching all previous losses within the first hour. The rate is then titrated to provide maintenance fluids and make up for continued losses so appropriate vital signs can be maintained. It is prudent to limit fluids to no more than 2 L of crystalloids, 1.5 L of colloid or 2 units of type O-negative blood prior to providing cross-matched blood to the patient . A more Establishing a cause of hemorrhage is the first step towards correcting the problem. The most common causes include, in decreasing incidence: uterine atony, retained products of conception, placental abnormalities, uterine inversion, uterine rupture, genital tract trauma and coagulopathies . An initUterine atony refers to a floppy, flaccid uterus, one in which the myometrium is unable to contract effectively after the expulsion of the placenta leading to hemorrhage. Bimanual uterine massage should be performed, with one hand in the vagina, and the other hand placed on the abdomen at the level of the uterine fundus to stimulate uterine contraction.Retained uterine products are the most common cause of delayed (occurring more than 24 hours after birth) post partum hemorrhage . In normIf the placenta has not been delivered within 15-30 minutes of childbirth or in cases suspicious for retained placental fragments, the placenta must be retrieved . Golan aInjuries to the genital tract may produce severe bleeding, a quantity that may be unexpected to the inexperienced. Optimal repair includes correct positioning of the patient to allow for adequate vision and access of surgical instruments. In order to gain effective control of the bleeding, the injured area should be sutured, starting at the apex of the tear. If the apex cannot be reached, the suture should be started as close to the apex as possible, then, once the remainder of the tear has been approximated, place traction to reach the previously hidden apex. If there is extensive trauma to the vaginal wall, with multiple lacerations, bruising and oozing repairs, vaginal packing to provide hemostasis should be placed and maintained for 12-24 hours . VaginalIf postpartum hemorrhage has not been controlled at this point the patient should be emergently moved to the labor and delivery OR suite, notifying the anesthesia provider, the blood bank (of the possibility for massive transfusion protocol) and the following staff, if available: Staff General/Trauma surgeon, senior general surgery residents, the patient's nurse and any available nurse's assistants. Activation of the hospital's massive transfusion protocol specifies a predetermined ratio of PRBCs, thawed plasma, cryoprecipitate, platelets, and recombinant factor VIIa (rFVIIa) in hopes of preventing the early coagulopathy seen in trauma patients receiving 10 or more units of packed red blood cells . Many hoIf initial interventions fail to control postpartum hemorrhage, a stepwise progression of medical therapy is available using uterotonics to facilitate contraction of the uterus.The first agent used is oxytocin. In the United States, oxytocin is typically administered after delivery of the placenta dosed at 10-20 units in 1000 mL of crystalloid solution, given intravenously (IV) and titrated to an in infusion rate that achieves adequate uterine contractions. Less commonly, it can be given intramuscularly (IM) or intrauterine (IU). It is common practice to double the oxytocin in PPH, i.e., 40 units in 1 L, and safety/efficacy has been documented up to 80 units per liter of crystalloid [If there is not adequate uterine tone with oxytocin, the second line agent used will depend on the medications' side effects and contraindications. Two classes of drugs are available: ergot alkaloids (methylergonovine) or prostaglandins . Methylergonovine may be used, dosed as 0.2 mg IM and repeated 2-4 hrs later, as long as the patient does not have hypertension or preeclampsia. If the patient has contraindications to methylergonovine or if the hemorrhage is still non-responsive, 250 μg of 15-methylprostagandin F2α may be injected intramuscularly (IM) up to 3 times at 15-20 minute intervals (maximum dose 2 mg) . ApproprAlthough not commonly described in discussions of post-partum hemorrhage management, Lurie and colleagues, 1997 , describPressure and tamponade are commonly used methods to control bleeding. Uterine packing applies these principles, making it a popular technique for over a century, whereas balloon tamponade is a more recent development.Uterine packing is a quick, viable option to create hemostasis. Critics' concerns address the large quantities of blood that may be absorbed by the pack or hidden behind the pack before hospital staff can recognize that bleeding has continued. It may be performed in one of two acceptable transvaginal methods; both using non-medicated, dry gauze. The first technique of uterine packing employs a tubular packer, such as the Holmes or Torpin packer. The cervix is exposed, then grasped securely with a sponge forceps or a tenaculum. The stylet or plunger of the packer is used to insert the gauze into the uterus until it is packed tightly all the way to the introitus. In the second technique, a packing or dressing forceps is used to introduce the gauze into the uterus, using short strokes and taking care not to remove the tips of the forceps until the uterus and vagina are tightly packed. Broad-spectrum antibiotics should always be used prophylactically to prevent complications from sepsis. The pack can be left in place and managed in the same fashion as intraabdominal packing for abdominal damage control. To remove the pack, the patient should first receive an anxiolytic, such as 10 mg of IV diazepam before slowly pulling the gauze out. Oxytocin, administered for 12-24 hours after the pack is removed, provides adequate muscle contraction, thus preventing the re-initiation of bleeding .An alternate method is balloon tamponade. Balloon tamponade has been used in different scenarios of uncontrolled bleeding, including esophageal varices, massive bladder hemorrhage and bleeding associated with prostatectomy. The general idea is to insert a sterilized balloon into the uterine cavity, then fill the balloon with warm water to see if additional pressure can control the patient's hemorrhage. Four methods have been described in the literature.In the original description of the 'tamponade test', a Sengstaken-Blakemore tube is used, prepared by cutting off the portion of the tube distal to the stomach balloon. Two pair of sponge forceps are needed: the first, used to grasp the anterior lip of the cervix and facilitates the placement of the balloon into the uterine cavity, held by the second pair of forceps. Warm saline was used to fill the balloon until it was visible at the cervical canal - using approximately 50-300 mL of fluid -29.Johanson, et al, 2001 , describBakri, et al., 2001 , developA successful tamponade demonstrates decreased or minimal bleeding after balloon inflation, thus terminating the need for surgical treatment. To help maintain the placement of the balloon, the upper vagina is packed with roller gauze. The previously placed Foley catheter should be kept in place to facilitate bladder drainage. Additionally, the previously started oxytocin infusion should be maintained for 12-24 hours and broad spectrum antibiotics are continued for three days to decrease the patient's risk for sepsis. After 24 hours of monitoring without subsequent bleeding, hemostatic interventions are removed in a step-wise manner. First the balloon is deflated but left in place. If no bleeding is seen after 30 minutes of observation, the oxytocin infusion is stopped and the patient is again monitored for 30 minutes. The balloon and vaginal packing may be removed if all bleeding has ceased .For surgical management, the patient is generally placed under general anesthesia in the Lloyd Davies position (lithotomy position with trendelenberg) ; althougOne of the simplest surgical solutions to stop post-partum hemorrhage is the undersuture. The thinness of the tissue in the lower uterine segment and the narrowed section of the cervical canal often causes difficulty, due to the friability of the area. Because of this, full-thickness sutures work best. Horizontal sutures are placed across and below the bleeding points. It is important not to obliterate the OS or the cervical canal to allow residual blood to drain through the vagina .Compression sutures are a recent innovation used to address post-partum hemorrhage. The original technique was the B-Lynch suture, created by Dr. B-Lynch, a British Obstetrician/Gynecologist . Adaptathttp://www.cblynch.com/video.html. 33 the stitch is depicted in Figure The B-Lynch suture technique was introduced in 1997 as a type of vertical brace suture used for diffuse uterine bleeding. It works by opposing the anterior and posterior walls of the uterus . The utiA No. 2 chromic catgut suture is used to enter the uterus 3 cm from the right lateral border and 3 cm below the right lower edge of the uterine incision. The suture is passed through the uterine cavity, exiting 3 cm above and 4 cm medial to the lateral border at the upper margin of the uterus. The suture is run externally over the anterior, fundal, and then posterior surfaces of the uterus in a plane 3-4 cm medial to the right cornual border before the needle is reinserted at a point in the posterior wall that corresponds to the anterior uterine incision. A surgical assistant may apply bimanual uterine compression to aid in pulling the suture under moderate tension.Once the right side of the uterus has been compressed by the first half of the B-Lynch suture, the needle is passed laterally to the left side of the cavity, exiting the posterior wall of the uterus in a horizontal plane to the posterior wall entry point. The suture is threaded over the posterior, fundal and anterior surfaces in a plane 3-4 cm medial to the left cornual border before re-entering the uterine cavity anteriorly at a point 3 cm above the uterine incision and 4 cm from the lateral border; effectively completing the first half of the stitch in the opposite direction. Again, it is useful to have an assistant present to apply bimanual uterine compression while the stitch is pulled under moderate tension. The suture is passed inferior to the uterine incision, and then emerges through the anterior uterine wall at a point 3 cm below the uterine incision and 3 cm medial to the lateral border of the uterine wall. The stitch is completed by tying the right and left sides of the suture on the anterior surface of the uterus inferior to the uterine incision. The uterine incision, followed by the abdominal wall is then closed similar to the closure of a cesarean section.Cho and colleagues, 2000 , describHayman, et al., 2002 , describTo perform this stitch, a straight needle with a 2-Dexon suture is inserted into the uterus above the bladder reflection 2 cm medial to the lateral border of the lower uterine segment and 3 cm below the left lower edge of the uterine incision. The needle is then threaded through to the posterior wall of uterus, then returned from posterior to anterior wall at a point 1-2 cm medial to the first pass of the suture and both ends were tied on the anterior aspect of the anterior wall. The stitch is then repeated on the same horizontal plane on the right side of the lower uterine segment .To control bleeding in the body of the fundus, the modified brace suture is added. A No. 2 chromic cat gut suture is placed in the anterior wall of the uterus and passed through the posterior wall of the uterus, just superior to the isthmic-cervical apposition suture. The ends of the suture are tied using a three-knot technique at the fundus, 3-4 cm medial to the cornua while external compression is performed by an assistant. An identical stitch is performed on the contralateral side. If this doesn't control the bleeding, horizontal compression sutures may be added to the modified B-Lynch sutures .Throughout this technique, it is important to keep the lower cervical segment and the OS patent in order to allow drainage of blood from the cavity. Afterwards, the bladders, ureters and bowel must be inspected to exclude trauma .Uterine artery ligation is one of the easiest and most effective surgical measures to control PPH. It is relatively safe, can be performed easily, and allows for future childbearing. The uterine arteries supply 90% of the blood to the uterus; therefore, ligation drastically decreases blood flow and subsequent blood loss . DespiteThis procedure is performed as follows. First the vesicouterine fold of peritoneum is identified and incised transversely in order to mobilize the bladder inferiorly. Next, the uterus is externalized for full exposure in order to identify an avascular window in the broad ligament. If an avascular area is not readily apparent, the surgeon may use the lateral border of the uterus. A No. 1 chromic catgut or polyglycolic suture should be used to make a posterior to anterior stitch through the myometrium at a site 2-3 cm medial to the uterine artery. The needle is returned anterior to posterior through the avascular window at a site just below the level of the utero-vesical peritoneal reflection. The two ends are tied securely, completing the ligation. The ureters, bladder and bowel should all be inspected for inadvertent trauma before repeating the procedure on the contralateral uterine artery .Ligation of the utero-ovarian artery anastomosis is similar to the uterine artery ligation. An avascular area is identified in the meso-ovarium, just inferior to the utero-ovarian ligament. Using this site as a securing point, a ligature is placed around the utero-ovarian anastomosis. The ovaries should be checked to ensure ovarian blood supply has not been compromised . Please Internal Iliac artery ligation is the next step in treatment. Bilateral ligation of the vaginal branch decreases pulse pressure in the distal arteries by 85%, improving. Unfortunately this procedure has a low success rate, estimated at 40%, mostly attributed to the late stage at which the ligation is attempted and that it is frequently complicated by hematoma formation and tissue edema that obscure the anatomy .The steps to perform the internal iliac artery ligation are as follows. An 8-10 cm incision is made in the peritoneum parallel and lateral to the ureter which opens the retroperitoneal space. The peritoneal flap with the ureter is retracted medially so that the internal iliac artery may be dissected out and the posterior branch can be identified. Next, the posterior branch of the internal iliac artery is separated from the internal iliac vein and a right-angled clamp is used to place two ligatures around each of the vessels. It is important to check the external iliac artery to confirm that adequate pulse pressure is present for perfusion of distal branches. It is also important to inspect the ureters for signs of trauma. Once these are completed, the steps are repeated on the contralateral side . Please Hysterectomy is the last line of treatment available for treating post-partum hemorrhage attributed to uterine bleeding. It is only used for hemorrhage unresponsive to other management attempts, as it removes the patient's option to bear additional children . RecentlA midline or transverse incision is used to open the abdomen. The bowels are packed out of the operating field to protect them from injury. The round ligaments are identified bilaterally, then clamped, divided and ligated. Next, the posterior leaf of the broad ligament is identified. It is perforated just inferior to the Fallopian tubes so that the utero-ovarian ligament and ovarian vessel can be clamped, divided and ligated. This step is repeated on the opposite side. Now, the broad ligament is detached: the posterior leaf is divided up to the uterosacral ligaments, and the anterior leaf is divided down to the superior margin of the bladder. The bladder is mobilized by making an incision in the vesicouterine fold of the peritoneum then bluntly dissecting the fascia away. By dissecting with a downward placement of tissue, the ureters should be pushed out of the operating field and out of harm's way. Next, the uterine vessels are identified bilaterally. Each is clamped close to the uterus so they may be divided and ligated. If a subtotal hysterectomy is adequate, the procedure is completed by transecting the cervix and closing the residual stump with interrupted stitches. If a total hysterectomy is necessary, the bladder is dissected away from the cervix until the superior portion of the vagina can be identified. The cardinal ligaments are located, again clamping each before their division and ligation. Finally, the cervix is detached from the vagina by cutting as close to the cervical margin as possible. The cardinal ligaments are used to secure the lateral vaginal fornices prior to suturing the vaginal vault closed .Selective arterial embolization has been well documented throughout the literature as a means of controlling post-partum hemorrhage. It is recommended as an alternative to surgical therapy with success rates of 85-100%. The uterine artery is the most commonly embolized vessel, followed by the pudendal, hypogastric, obturator, vaginal and cervical arteries . UnfortuLocal anesthesia or an epidural should be administered prior to the initiation of embolization by cannulization of the femoral artery . The catThe surgeon may change to a consultant role once control of the bleeding has occurred and the patient has been stabilized. The patient should be admitted to an intensive care unit for close monitoring until stability has been assured.General and acute care surgeons likely will be called emergently to labor and delivery to render assistance for PPH at some point in their careers. The point, at which a surgeon is called, can be anticipated to be later rather than earlier, at a point where operative intervention is being initialized or already underway. Most likely the medical management and non-operative measures presented will not be administered by a surgeon; however, practice and event dynamics will ultimately determine the situation encountered and therefore the knowledge of this information prudent. Though the specific management of severe postpartum hemorrhage is seldom addressed in surgical education and literature, the application of commonly practiced surgical strategies in combination with a basic knowledge PPH specific etiologies, physiology and interventions permits surgeons to efficiently and efficaciously participate in the care of these patients. For our colleagues to have a quick reference guide a flow sheet is available in Figure The authors declare that they have no competing interests.AW collected data, drafted the manuscript and developed the illustrations and figures. FS conceived the initial idea and design of the study, and drafted the manuscript. MC reviewed and assisted with the critical revisions. CB conceived the initial idea and design of the study, reviewed and assisted with the critical revisions. FG assisted with data collection and final edits to manuscript. GW reviewed and assisted with the critical revisions. EE reviewed and assisted with the critical revisions. WL conceived the initial idea, reviewed and assisted with the critical revisions and oversaw project to completion. All authors have read and approved the final manuscript.