Guide to The BioCyc Database CollectionThis document provides an overview of the BioCyc collection of Pathway/Genome Databases.
Although its content is limited at the current time, it will expand over time to cover additional aspects of BioCyc.
The information in this document pertains to all BioCyc databases (DBs), and to most other DBs created using the Pathway Tools software. More detailed information about specific members of the BioCyc family is available as follows:
The BioCyc Databases
The BioCyc collection of Pathway/Genome Databases (PGDBs) provides electronic reference sources on the pathways and genomes of different organisms. The databases (DBs) within the BioCyc collection are organized into tiers according to the amount of manual review and updating they have received.
- Tier 1 PGDBs have been created through intensive manual efforts, and receive continuous updating
[details of Tier 1]. The BioCyc Tier 1 DBs are:
- EcoCyc, which describes Escherichia coli K-12.
- MetaCyc, which describes experimentally elucidated enzymes and metabolic pathways from more than 1,900 organisms.
MetaCyc does not seek to model the complete metabolic network of any one organism,
but to provide a comprehensive collection of experimental pathways.
- Tier 2 PGDBs were computationally generated by the PathoLogic program,
and have undergone moderate amounts of review and updating. There are 30 DBs in Tier 2.
[details of Tier 2]
- Tier 3 PGDBs were computationally generated by the PathoLogic program,
and have undergone no review and updating. There are 643 DBs in Tier 3.
[details of Tier 3]
BioCyc databases describe organisms with completely sequenced genomes (not all genomes are closed). Most BioCyc databases are for microbes. In addition, BioCyc contains databases for humans; for important model organisms such as yeast, fly, and mouse; and for other organisms whose PGDBs have been developed by groups outside SRI. One reason for collecting all these PGDBs together within BioCyc is to enable the comparative analyses that become possible when multiple PGDBs are available within one site (see Tools → Comparative Analysis). Most microbial PGDBs within BioCyc have been generated computationally by SRI and are regenerated every 6-12 months to take advantage of improvements in our pathway prediction algorithms and in the MetaCyc pathway database. The PGDBs within BioCyc that have been provided by outside groups are updated with variable frequencies. Usually the date on which a PGDB was generated or last updated can be determined by selecting that PGDB as the current PGDB and then viewing the page at Tools → Reports → Summary Statistics or Tools → Reports → History of Updates.
Looking for pathway databases for other organisms? PGDBs have been created outside SRI for many organisms, including microbes, fungi, plants and animals [details].
What Mechanisms Exist for Accessing BioCyc Data?BioCyc data is accessible in several ways, which are described in more detail on the downloads page.
- Query and visualization access is available through this BioCyc Web site
- Data files for the BioCyc databases are available for download in multiple formats
- The preceding databases can be loaded into SRI's BioWarehouse relational database system (Oracle or MySQL based) for querying.
- A downloadable "software/database bundle" is available that supports querying,
visualization, and analysis of BioCyc data. It also allows users to create their
own Pathway/Genome Databases. The software/database bundle includes functionality
not available through the Web site, and also executes faster than the Web version.
- The software/database bundle also allows users to query BioCyc data via the Java, Perl, and Common Lisp languages.
Important ConceptsThis section introduces a number of concepts that are important to understanding PGDBs.
How are Pathway Boundaries Defined?Pathway boundaries are defined heuristically, using the judgement of expert curators. Curators consider the following aspects of a pathway when defining its boundaries.
- What boundaries were defined historically for pathway?
- When possible, we prefer to define boundaries at the 13 common currency metabolites:
- Coincidence with regulatory units
- Coincidence with metabolic units that are evolutionarily conserved
The preceding philosophy toward pathway boundary definition contrasts sharply with KEGG maps. KEGG maps are on average 4.2 times larger than BioCyc pathways because KEGG tends to group into a single map multiple biological pathways that converge on a single metabolite [Pathway05].
Super Pathways and Base PathwaysWe define a super-pathway as a cluster of related pathways. Typically, a super-pathway consists of a linked set of smaller pathways that share a common metabolite. For example, the super pathway superpathway of phenylalanine, tyrosine, and tryptophan biosynthesis consists of several pathways that converge at the metabolite chorismate.
The components of super-pathways include base pathways (pathways that are not themselves super-pathways), other super-pathways, and individual reactions that have not necessarily been assigned to base pathways. Those reactions typically serve to connect together the component pathways within a super-pathway.
Super-pathways are stored within each BioCyc PGDB -- they are not computed dynamically.
Do We Force a Pathway View of the Metabolic Network?No. Pathways comprise a level defined on top of the metabolic network. Users can choose to compute with the metabolic (reaction) network directly, ignoring the pathway layer, if they so choose. Note also that some metabolic reactions in most PGDBs are not assigned to any metabolic pathway.
Reaction DirectionHow do PGDBs handle reaction direction?
The direction in which a reaction is stored in a PGDB has no implication for the physiological directionality of that reaction. Each reaction is stored as an instance of the Reactions class that includes two slots, Left and Right. It is possible that the reaction is bidirectional; it is possible that the reaction proceeds physiologically in the left-to-right direction, and it is possible that the reaction proceeds in the right-to-left direction.
The equilibrium constraint and change in Gibbs free energy stored for the reaction (if any) refer to the direction of the reaction as stored.
Currently, the best way to query the direction of a reaction is via an internal Pathway Tools Lisp function called get-rxn-direction. In the future, a field will be added to the Pathway Tools schema to record this information.
Background and MotivationsThis section addresses the state of reaction mass balance and protonation state of chemical compounds in the BioCyc databases. Because these issues are still evolving and are influenced to a large degree by history, we include a historical discussion of these issues.
Our long-term goal is for all reactions in BioCyc to be fully mass balanced and charge balanced, and for all chemical compounds to be properly protonated at cellular pH. Although in some cases such a treatment may yield reactions or chemical structures that look non-traditional to biochemists, we believe this approach provides the most consistent and correct treatment. In addition, it provides a treatment that will facilitate automatic generation of flux-balance models from PGDBs.
Historically, the chemical structure data within BioCyc databases has been obtained from many different sources, including textbooks, articles from the primary research literature, and downloading from certain open databases. In the early years of the project we developed programs to check the mass balance and element balance of reactions within BioCyc databases. We found that these programs were extremely valuable because identification of unbalanced reactions allowed us to identify errors in both the reaction equations, and in the chemical structures. However, we also found that, because of the diverse sources from which we obtained chemical structure data, the structures were protonated inconsistently. Therefore, for many years we ignored element imbalances due to hydrogen only, while correcting imbalances due to other elements.
In 2008, we began to address the problem of inconsistent protonation to facilitate automatic generation of flux-balance models. Work was completed on ensuring that reactions in the MetaCyc and EcoCyc PGDBs are completely mass-balanced. The first releases of those fully mass-balanced MetaCyc and EcoCyc DBs were version 13.0 in early 2009. In time, other BioCyc PGDBs will become mass balanced as well. For example, because we periodically regenerate the Tier 3 BioCyc PGDBs, the next time these PGDBs are generated from version 13.0 or higher of MetaCyc, they will be based on the consistently protonated compounds, and the fully mass-balanced reactions.
The following sections describe the methodology by which the protonation-state normalization and reaction mass balancing were achieved.
Protonation State Normalization
For a given chemical compound, there can be atoms that will bind a variable number of hydrogen atoms, depending on their chemical structure and the pH of their environment. A term for the isomers of a compound that differ in the number of hydrogens bound to these atoms is proto-isomer. A term for the atoms with variable numbers of bonded hydrogens is the proto-isomerization centers of a compound. Oxygen, sulfur, phosphorus, and nitrogen are examples of typical proto-isomerization centers.
In order to bring a greater degree of consistency to our PGDBs, we protonated (i.e., assigned the correct number of bound hydrogens to the proto-isomerization centers of a compound) the compounds of EcoCyc with a reference pH value of 7.3, using the Marvin (version 5.1.02) computational chemistry software available from ChemAxon, Ltd . The pH value of 7.3 was selected based on a paper on the measurement of cytoplasmic pH of E. coli . In order to easily exchange compound data between MetaCyc and EcoCyc, MetaCyc was also protonated with a reference pH value of 7.3. This step is an approximation since MetaCyc contains reactions and compounds from many organisms and many cellular compartments.
The Marvin software calculates the protonation state of a compound's proto-isomerization centers by first determining their pKa. The pKa of the proto-isomerization centers of a compound were obtained by computing the partial charge distribution. This, in turn, is calculated using a numerical partial differential equation solver, which computes the distribution by means of the structure of the compound, and the known electronegativities of the constituent atoms. Although we have worked with ChemAxon to improve the accuracy of their calculations to match that of experimentally-verified pKa's of many biochemically-relevant compounds, this calculation is still based on an approximation technique, and will not necessarily yield fully correct pKa's for every substance.
Some caveats about our protonation of compounds:
- Some compounds are present in multiple reactions that take place in various different compartments in a cell, or across membranes, where the pH might vary from our stated value of 7.3.
- For any given compound, only one proto-isomer is present in our PGDBs. We do not represent the other proto-isomers, nor do we represent the proto-isomerization reactions that inter-convert the various proto-isomers of a compound.
- Sometimes a pKa value for a proto-isomerization center is very close to the pH of the solution, and therefore there is approximately a 50 / 50 split between the relative abundance of the two proto-isomers of that compound in solution. The Marvin software will select the most likely proto-isomer based on a comparison of the floating point value of the relative abundance in such situations.
- Our compounds might have a slightly different structure than what you will find for the same compound in an alternate chemical compound database. Please ensure that you are comparing the two compounds for the correct protonation state at a reference pH value of 7.3.
Computational Reaction Balancing for Hydrogen
Once the compounds of EcoCyc and MetaCyc were protonated, all reactions that had a mass-imbalance due only to hydrogen atoms were computationally balanced. This balancing procedure added or removed instances of the proton from the appropriate side of a reaction to achieve mass-balance.
Some caveats about our computational reaction balancing:
- One might notice some reactions that have more or less protons participating than what you would typically see depicted. This might be most evident in our EC reactions. One reason for this, beyond our computational reaction balancing, is that traditionally protons and other small, ubiquitous chemical moieties were considered auxiliary to the main function of a reaction and thus not depicted. In general, our EC reactions may vary from the IUBMB reactions by including more or fewer protons than the original reaction.
- For use in FBA models, one must be aware that we are only representing one of the possibly many proto-isomers of a compound. We also do not represent the fast protonation reactions that inter-convert the proto-isomers. Thus, a FBA model that is attempting to simulate the flux of hydrogen in a PGDB may be inaccurate.
- As of 2009, reactions that were computationally balanced for mass are not necessarily balanced for charge.
Statistics on Reaction Balance and Protonation circa 2009
This table provides information on the small-molecule reaction balance state for both EcoCyc and MetaCyc as of early 2009. The categories below represent reactions that are balanced, unbalanced, and those for which it is not possible to determine the balance state.
Reactions that remain unbalanced are due to non-trivial imbalances (i.e., imbalances not due solely to hydrogens or protons). These imbalances are usually due to omissions or errors in the structures and/or reaction composition obtained from the literature. Our curation staff are actively researching such compounds and reactions and correct the data whenever possible.
For the category of reactions where it is not possible to determine the balance state, these are mainly due to:
- Reactions that have compound classes as substrates
- Polymerization reactions. As of the beginning of 2009, BioCyc.org is working to extend our representation of polymerization reactions to allow for mass and charge balance.
- Reactions with substrates that lack a chemical structure
- Reactions with substrates that include R-groups
|Number of Reactions|
|EcoCyc: Balanced Reactions||801|
|EcoCyc: Unbalanced Reactions||3|
|EcoCyc: Reactions that cannot be balanced||160|
|MetaCyc: Balanced Reactions||5,098|
|MetaCyc: Unbalanced Reactions||317|
|MetaCyc: Reactions that cannot be balanced||1,143|
Comparison of BioCyc to Other Pathway DatabasesPlease see the comparison section of the MetaCyc Guide.
How Do I Learn More About PGDBs and BioCyc?The following information resources are available.
- Read the BioCyc Search Help
- Read the BioCyc Glossary
- Read publications on BioCyc, EcoCyc, MetaCyc, and Pathway Tools
- Watch BioCyc instructional videos ('webinars')
- Take the BioCyc guided tour
- The Pathway Tools software [download] contains the Pathway Tools User's Guide, a document that provides extensive coverage of all aspects of the software, including an extensive description of the database schema that underlies PGDBs.
AcknowledgementsBioCyc is grateful for the following groups:
- ChemAxon's Marvin software for computational chemistry
Wilks, J.C., Slonczewski, J.L.
pH of the cytoplasm and periplasm of Escherichia coli: rapid measurement by green fluorescent protein fluorimetry.
J Bacteriol. 2007 Aug;189(15):5601-7. Epub 2007 Jun 1.