Computational protocol: Integrated network reconstruction, visualization and analysis using YANAsquare

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Protocol publication

[…] To increase platform independence and avoid loss of computation time (e.g. by parsing of the Metatool output) we included a Java implementation of the well known Schuster algorithm [] which computes the EMs through a step-wise satisfaction of the steady state condition for each metabolite. The original version of the algorithm has been improved according to Klamt and Gagneur 2004 [] by representing EMs during calculation by bit patterns rather than by their fluxes. This is possible due to the existence of a direct mapping of the set of reactions of an EM to the fluxes of these reactions []. The most frequently called function during the computation, the test for elementarity of an intermediate EM, is then reduced to a mere bit operation which drastically improves the algorithm's runtime behavior. This implementation is also used by a software package destinated at the computation of chemical organizations in chemical reaction networks [].The current version allows computation of the complete set of EMs or only the convex basis, both using either the external Metatool or the newly implemented internal EMA routine. We tested the algorithm thoroughly for consistency with the Metatool results. [...] Successful analysis of a metabolic network which is expected to give biologically meaningful results heavily depends upon its accurate reconstruction. Complete sets of all modeled metabolites and enzymes have to be set up, and system boundaries have to be defined carefully. Every enzyme has to be checked for its absence or presence in the actual organism by investigation of genome annotations, homology searches or literature data mining. This task is hindered additionally by the missing or only partially established standardization of compound and enzyme names. Public databases such as Brenda [], Enzyme [] or the KEGG encyclopedia [] have eased this process considerably by providing algorithms and interfaces to find the correct subset of enzymes to work on, their associated pathway structures and check for their occurrences in the organism under study. They further aim to unify the different available naming conventions, but so far the actual network setup in the modeling software had to be done manually. To further accelerate and automate the initial setup of a metabolic network we implemented the KEGG Browser (KGB) module in YANAsquare. It is capable of connecting directly to the KEGG database to browse pathways, reaction lists, metabolites and pathway maps online. Organisms or reference pathways can be selected to only retrieve enzymes which have been annotated as present in the selected organism or to obtain the complete set of available reactions respectively. All associated metabolites are collected automatically, highly abundant metabolites such as H+, CO2, H2O, ADP or ATP considered to be well buffered can be filtered out to lower the network complexity in the EMA analysis.One problem when dealing directly with the KEGG database entries is that metabolites and reaction names are often long and may contain special characters. For a later visualization of the network, for backwards-compatibility to the Metatool input files and most importantly to satisfy the regular expression facets of the SBML2 standard which serves as our main file format, identifiers for enzymes and metabolites have to be standardized. We implemented an automatic abbreviation routine capable of shortening chemical names in a standardized way using a dictionary of keyword abbreviations. Generated abbreviations are validated so that characters illegal in the context of the SBML2 or Metatool format definitions will be eliminated. As enzyme names from ExPASy are generally even shorter, an additional step for the routine is to adapt shorter alternative names from ExPASy, which can again be further shortened by the abbreviation approach if necessary.When accessing KEGG through a low-bandwidth Internet connection the retrieval of large chunks of reactions can be time consuming. We therefore additionally provide an accelerated query method using a local reaction cache (instead of retrieving all records directly via SOAP). This semi-online mode works significantly faster but the reaction cache has to be updated on a regular basis to assure that the KGB makes use of the latest KEGG definitions and annotations, an operation which can easily be performed through the KEGG Browser's update manager.Once the desired topology has been assembled, the chosen reactions and metabolites can be imported directly into YANAsquare for further network analysis.We used version 1.3 of the Apache Axis library, a Java implementation of the Simple Object Access Protocol (SOAP) and Web Services Description Language (WSDL) on top of the HTTP protocol to connect to the database server. We developed a user-friendly graphical interface using the SWING framework to retrieve the information from KEGG (see Fig. ). Pathways and reactions of interests are specified by the user, all reaction equations are retrieved and listed in a table. A smart editor allows users to edit the results, change metabolite and enzyme names and abbreviations and/or append novel reactions if these are known to the user but not yet reported in KEGG. The whole software can also be easily adopted to extract data from another database, if format requirements are adapted or met, e.g. a private in-house metabolite and enzyme database in the KEGG format. […]

Pipeline specifications

Software tools Metatool, YANAsquare
Databases KEGG
Application 13C-fluxomics
Organisms Epipremnum aureum, Staphylococcus epidermidis, Staphylococcus saprophyticus