Metabolic networks have become one of the centers of attention in life sciences research with the advancements in the metabolomics field. A vast array of studies analyzes metabolites and their interrelations to seek explanations for various biological questions, and numerous genome-scale metabolic networks have been assembled to serve for this purpose. The increasing focus on this topic comes with the need for software systems that store, query, browse, analyze and visualize metabolic networks. PathCase Metabolomics Analysis Workbench (PathCaseMAW) is built, released and runs on a manually created generic mammalian metabolic network. The PathCaseMAW system provides a database-enabled framework and Web-based computational tools for browsing, querying, analyzing and visualizing stored metabolic networks. PathCaseMAW editor, with its user-friendly interface, can be used to create a new metabolic network and/or update an existing metabolic network. The network can also be created from an existing genome-scale reconstructed network using the PathCaseMAW SBML parser. The metabolic network can be accessed through a Web interface or an iPad application. For metabolomics analysis, steady-state metabolic network dynamics analysis (SMDA) algorithm is implemented and integrated with the system. SMDA tool is accessible through both the Web-based interface and the iPad application for metabolomics analysis based on a metabolic profile. PathCaseMAW is a comprehensive system with various data input and data access subsystems. It is easy to work with by design, and is a promising tool for metabolomics research and for educational purposes.Database URL: http://nashua.case.edu/PathwaysMAW/Web Metabolomics is a relatively new ‘omics’ platform in life sciences research. The advancements in analytical methodology and high-throughput rates have led to the collection of large metabolic data sets. Metabolic profiles and genome-scale metabolic networks  are usedMAW  is to provide a metabolic network database and a Web- or tablet-based system that enables users to interact with the underlying metabolic network. PathCaseMAW provides the following functionalities:A metabolic network database that captures the metabolic network with a compartment hierarchy and metabolic regulation relationships.MAW database as input. Then, it produces all possible steady-state flow scenarios  for the selected subnetwork as output (that are consistent with the observed metabolite measurements and the underlying biochemistry) (available at http://nashua.case.edu/PathwaysMAW/web/).A Web site that (i) enables users to browse pathways, reactions, metabolites/metabolite pools and compartments stored in the database, (ii) provides several built-in queries and interactive visualization and (iii) has the integrated steady-state metabolic dynamics analysis (SMDA) tool. SMDA tool takes a set of metabolite measurements and a metabolic subnetwork stored in the PathCaseMAW system with the exception of browsing/querying (available at Apple AppStore).An iPad application that has all capabilities of the Web-based PathCaseAn offline metabolic network editor with visualization capabilities that enables users to create their own network in a user-friendly way.MAW database.An SBML Parser to parse and store genome-scale reconstructed metabolic networks [e.g. Recon 1 of humans ] into thThe goal of PathCaseMAW system works on a manually created (and generic) mammalian metabolic network, which is obtained from the metabolic atlas by Selway et al. (RCMN (PathCase Reconstructed Metabolic Networks) Web site . Users are able to use iPad’s multi-touch screen to visualize pathways and to run the SMDA tool in a mobile tablet environment.MAW editorPathCase. This application is used by data owners to create/update metabolic networks in the PathCaseMAW database. This is an offline tool and is implemented in JAVA to provide platform independence.MAW SBML parserPathCase. This is a console application that takes as input an SBML file for a metabolic network, and parses it into the PathCaseMAW database as a separate metabolic network. It is written in C# using .NET framework.On the server side, we have the following components.MAW databasePathCase. This application is stored in Microsoft SqlServer 2008 and is accessed through the Data Access Library.The data access library. This contains (i) database wrappers that abstract the database schema, (ii) built-in queries and (iii) an XML generator that generates a metabolic network/subnetwork/pathway visualization file, which is then passed to either (a) JAVA applet of the PathCaseMAW client-side browser, (b) PathCaseMAW iPad application or (c) the PathCaseMAW editor.The web content UI manager. This handles synchronous or asynchronous requests sent from the client browser, and responds in HTML format.The SMDA tool. This tool implements the SMDA algorithm , and shows the software architecture of the system (bottom). Details regarding the system are given in individual subsystem sections.MAW system to browse, query, visualize and analyze the data. There are two main ways to access the data: (i) a Web browser (e.g. Google Chrome) and (ii) iPad App of PathCaseMAW. Next, we explain the capabilities provided in each type of interface.In this section, we present the interfaces provided by PathCaseMAW is an online system that can be accessed through an Internet browser. The backend is implemented in C#, using .NET framework. The Web site is hosted on a machine running Windows server 2008. We use Internet Information Services (IIS) 7.0 as our Web server. It has been tested on four major Web browsers with the recent versions of Microsoft Internet Explorer, Mozilla Firefox, Google Chrome and Apple Safari. The Web interface includes four applications, namely, (i) browser, (ii) visualization interface, (iii) querying interface and (iv) SMDA tool.PathCaseThis interface lets the user to view the data stored in the database from various different starting points. For instance, the user can start from a metabolite to list related reactions, to find the pathways this metabolite plays a role in or one can start from a pathway to find related reactions and the compartments where each of these reactions takes place.The browser interface has two parts: the navigation bar on the left hand side, and the main frame on the right hand side that shows details of the selected browser item. The left hand side is called the ‘Browser’ page, and the right hand page is called the ‘Details’ page.MAW. The main browser page has three subsections. Part A redirects the user to browse the data starting from (i) pathways, (ii) reactions and (iii) metabolites stored in the database. Part B links to the query interface, Part C links to the SMDA tool and Part D links to legacy tools developed by our team. Part E shows the mainframe where contents are loaded after clicking a link. The browser page has the following flexible hierarchy that enables user to effectively browse related entities of the network.Pathways → reactions in the selected pathway → compartments the selected reaction takes place → metabolites associated with the selected reaction in the selected compartment.Reactions → compartments the selected reaction takes place and pathways this reaction is associated with → (selecting a compartment) metabolites associated with the selected reaction in the selected compartment.Metabolites → pathways the selected metabolite is associated with → compartments the selected pathway takes place in → reactions of the selected pathway that exist in selected compartment.Part B opens up the query interface, and Part C links to the implemented tools (currently only SMDA tool). Part D links the user to our legacy tools, namely, the OMA tool  and MQL Pathway: Panels for (i) loading the applet that visualizes the pathway, (ii) listing reactions taking place in the pathway, (iii) listing connected pathways in the complete metabolic network and finally (iv) listing all available pathway-related built-in queries.Reaction: Panels for (i) listing involved metabolites, (ii) listing related pathways and finally (iii) listing all available reaction-related built-in queries.Metabolite: Panels for (i) listing related pathways, (ii) listing synonyms stored in the database for this metabolite  and finally (iii) listing all available metabolite-related built-in queries.Compartment: A panel to list all reactions that take place in this compartment, and a panel to list all pathways that take place in the selected compartment.Part E shows the details page where information about the selected browser item is loaded as separate collapsible panels. In the home page, statistics about the database is shown along with a quick introduction. Users are able to click on any entity on the browser page (as described above), to open up the details page. The following information is displayed per entity:Panel B downloads the applet once clicked (only at the first usage) and loads up the XML data to visualize the pathway. A screenshot for the visualization of ‘Urea Cycle’ is shown in The details page accesses the data using AJAX calls. Therefore, once an item is clicked, the collapsible panels are loaded. Collapsible panels make calls to the server after they are displayed, so the data are accessed asynchronously. If a collapsible panel is clicked, while the data are still being fetched, user is informed that it is being loaded. This brings a huge advantage in terms of user experience, when pathways with a large number of reactions and metabolites are accessed. In the synchronous case, users would have to wait for all panels to load before they see the page. Users may also interact with the loaded panels although some panels are still loading data in the background.SMDA , 12 usesSMDA is both a constraint- and rule-based approach. It is constraint-based , as the The SMDA algorithm runs in an iteration of two phases, namely, ‘Expansion and Merge’, and lasts until all reactions and metabolite pools in the network are assigned a status. Expansion phase starts from the labeled metabolite pools (observations from a given metabolomics measurement), which are flow graphs with single metabolite pool. Then, expanded flow graph(s) are generated by adding neighboring reactions and metabolite pools to the original flow graph. SMDA generates all possible combinations of label assignments to those neighboring pools and reactions. This process continues until all reactions and metabolite pools are assigned a label.SMDA returns only two flux values for a reaction, namely, 0 (‘inactive’) and 1 (‘active’). The query output space of the SMDA tool is large. However, (i) larger numbers of observations exponentially reduce the output size, and (ii) exploratory search and browsing of the query output space allows users to mine and search for what they are looking for.Advantages of SMDA include its ease of use and simplicity; it is designed as a ‘first-step’ and ‘online’ tool for wet-lab researchers (i) to evaluate their hypotheses about observed measurements, and (ii) to be used for ‘what if’ types of questions (i.e. knowledge discovery). SMDA technique and its computational performance limits are evaluated using a mammalian metabolic network database .SMDA’s approach of evaluating the activation/inactivation scenarios of the metabolic network at steady state is related to metabolic network analysis techniques, such as MCA , FBA 2)2), metabSMDA tool implements the SMDA algorithm on server side and can be accessed via the Web interface. The tool is implemented using C# language in .NET environment. Next, we introduce the capabilities of the Web interface for the SMDA tool over an example, shown in First, there are two options to input data. (i) Users can pick one of the eight provided sample input data shown in Part A. Clicking the button loads up the subnetwork selected and corresponding observations. (ii) Users can input data manually. For the second case, the user first needs to select the metabolic subnetwork of interest. In Part B, the user picks a pathway and a compartment (e.g. organ) that the pathway is taking place in (note that a pathway can exist in more than one compartment). In this example, ‘TCA Cycle’ in liver cell is selected. Next, in Part C, users select stand-alone reactions (which are not part of a pathway). They can select transport reactions or regular reactions. Transport reactions are unique so they do not require input of compartment, whereas regular reactions require the compartment to be specified. Users are also able to simplify pathways into a single reaction as done in many works in the literature. In this example, in Part D, ‘Glycolysis’ pathway is added as a single reaction that has ‘D-Glucose’ as input and ‘Pyruvate’ as output again in liver cell. Part E lets the user to pick metabolite pools and specify the concentrations manually. In this example, the only observation is ‘D-Glucose’ in the cytosol of liver cell. Finally, Part F concludes data input part by inputting observed enzyme levels for reactions. In After inputting data, users can check whether the selected subnetwork is connected and can visualize their network, using the buttons on Part G. Finally, the user can run the algorithm till it completes, or he/she can see how the algorithm proceeds  step-by-step by selecting one of the buttons shown on Part I. Part H lets the user to pick one of the iteration algorithms as explained in . SMDA toThe results are passed from the server back to the Web client in XML format and directly visualized. SMDA Visualization uses the same infrastructure used to visualize pathways. The only differences are (i) there are possibly more than one resulting graphs, each representing a possible scenario, and (ii) thin edges represent inactive reactions and thick edges represent active reactions.MAW currently has five built-in queries to enable users to find related pathways, reactions and metabolites with respect to another entity. The currently available queries are as follows:Pathways within a given number of steps from a pathway: This query searches for pathways that are n reactions away from a given pathway.Reactions within a given number of steps from a reaction in a pathway: This query searches for reactions that are n reactions away from a given reaction in a pathway.Reactions within a given number of steps from a reaction in the metabolic network: This query searches for reactions that are n reactions away from a given reaction in the network.Reactions involving a metabolite in a pathway: This query searches for reactions that involve a given metabolite in a specified role in a particular pathway.Metabolites within a given number of steps from a metabolite in the metabolic network: This query searches for metabolites that are n reactions away from a given metabolite in the network.PathCaseThe system is equipped with AJAX calls that prunes out irrelevant selections without reloading the page. For example, for the second query, the user is asked to pick a pathway, then a reaction. Once the user picks the pathway, the next combo box to pick the reaction in that pathway is loaded with only the reactions in that pathway, and the rest is discarded. This prevents the user from selecting illegal items  and contributes to a positive user experience.MAW system, namely, the PathCaseMAW visualization interface.PathCase visualization tools visualize metabolic data, relationships in the data, as well as analysis results of the data via multiple variations of a JAVA applet. The visualization tools are components of many PathCase Systems, as shown in MAW visualization interface is responsible for displaying interactive and user-friendly depictions of metabolic (sub)networks (e.g. pathways or a collection of pathways and reactions). It is used in three subsystems of PathCaseMAW:within a collapsible panel for pathway visualization on details page of a pathway,as a tab for pathway visualization in the data editor andin the SMDA tool, as a separate window for visualizing the selected subnetwork (any connected set of pathways and reactions) and visualizing the resulting flow graphs.PathCaseMAW to scale for a large number of users. It is run within an Internet browser. The visualization interface seamlessly downloads the applet itself only at the time of the first use, and comes embedded into the corresponding pathway pages of the SMDA tool interface. This architecture has two advantages.The visualization does not require a separate installation effort (a manageability convenience for users) andthe architecture provides a platform-independent access regardless of the operating system or browser that is run on the user machine. For the editor, it comes embedded within a jar file, and displays all selected pathways and reactions.In the first two cases, the applet displays only the selected pathway. It uses the JAVA applet (based on yEd Graph Editor library) that runs on the client machine, and with no server-side intervention or communication other than an XML document exchange, through a Web service. This allows PathCaseMAW and PathCaseMAW editor has the capability of visualizing a pathway in multiple tissues (if it exists in multiple tissues) side by side.The visualization in PathCaseThe interactive graph displays metabolites (circles), reactions , compartments (rectangles that contain metabolites and reactions and subcompartments) and the relations between them (as lines) in a metabolic (sub)network. The directions of the lines set producer/ consumer relation for a reaction . Red/green line indicates inhibitor/activator relationships, respectively. Interface captures the compartment hierarchy. Metabolite pools and reactions are shown in the corresponding compartments.Users are able to interact with the visualization; they can (i) move around all visualized items, (ii) zoom in/out in different ways  and (iii) save the modified layout so that it appears the same layout on the next load for that pathway/subnetwork (password protected for data owner).2O, NAD, ATP) have high connectivity and tend to result in unclear visualizations. Such metabolites are called ‘common  metabolites’. Unlike all metabolite pools, which are displayed once, these metabolites are displayed per participated reaction to provide simpler visualization  is displayed in a little box that pops up and disappears when cursor is moved out. This lets the applet to keep truncated forms of the long names and keep the image tidy, while letting the user see the names of interest in advance. Another useful functionality is the map, which is crucial for large pathways/subnetworks. Picking this option opens up a small and zoomed-out bird’s-eye snapshot of the complete visualization. This way, the user is able to navigate through the visualization without a need to zoom-out and then zoom-in again.MAW is to provide educational tools for life scientists in biochemistry teaching and research. Users are able to have nice visual depictions of pathways and run the SMDA tool using the iPad interface. The system is written in Objective-C language using Cocoa framework. Although the system works on iPad 1, currently, the system is optimized for efficient use on iPad 2 or higher . The system has three main interfaces: browsing, visualization and SMDA tool interfaces. Next we give details of each interface.iPad has been a revolutionizing mobile device with a multi-touch interface that has sold 9.25 million only in the fiscal 2011 third quarter . Other tThe browser of the application consists of only pathway browsing that acts as a link to the visualization of a pathway. At the current stage, reaction, metabolite and compartment browsing is not supported. Pathway browser can be accessed through the home page, where pathways are listed on the right frame, or from within a visualization of any pathway by tapping the ‘Pathway’ button in the toolbar as seen on the top right of The visualization interface used for iPad looks similar to the Web interface’s visualization; however, the implementation is different. The application uses Core Animation Framework to render objects. The data are obtained in the form of an XML + GML (Graph Markup Language) document via Web services. Because of the hierarchical and interconnected nature of the data model, it is not practical to dynamically invoke the Web services at the time their data are needed. Instead, an internal representation is updated in bulk at the user’s request. When the user taps on the ‘Update Local Data’ button in the SMDA input view, a series of asynchronously executed Objective-C blocks are invoked. This design choice cuts down on the data quote usage of the device and provides fast response time for the user.SMDA tool interface uses the same server-side component that implements the SMDA algorithm. It provides a similar interface as the Web interface, which lets the user to pick the subnetwork and the measurements for metabolites. Results are visualized in the same manner (thick edges represent active reactions and thin edges represent inactive reactions). As stated before, there are possibly more than one resulting visualizations. The iPad visualization interface implements a nice feature to mark the differences between the current image and the previous one.MAW database.In this section, we describe two tools used to manage the data stored in PathCaseMAW  and populates its own metabolic network database. Consider a user entering a new reaction (with a new substrate and a new product). First, she needs to enter two new metabolites into the ‘Metabolite’ table and then two new entries into the ‘MetabolitePool’ table keeping the IDs of the metabolites in mind. Next, she needs to add the reaction into the ‘Reaction’ table. Finally, she needs to add two entries into ‘ReactionMetabolite’ table coupling the IDs of the metabolite pools and the reaction along with the role IDs of the metabolite pools. Thus, even entering a single reaction is a complicated and an error-prone task. To enable users to manage the data easily, we have developed an easy-to-use editor , provides a user-friendly tool to access and modify the metabolism database, as well as to review the modifications visually. It (i) checks data dependency internally to prevent data integrity violations, and (ii) ensures data correctness during data insertion and editing to help users input valid data, and also improve data insertion efficiency. Via a user-friendly graphical user interface, the editor allows (i) editing of pathways, reactions, compartments, enzymes, metabolites and metabolite pools, physiological states and (ii) visualization of pathways either within the main editor window or as a separate stand-alone full-screen window. Below is a more detailed summary of the editor. For more details, please see Han’s thesis  who would like to use our system to analyze the data. A user obtains components of PathCasee editor  that hous thesis .MAW editor has the following functionalities/advantages:The system hides from users the internal object IDs. All relationships are shown via object names in the GUI, and the user cannot modify object’s ID directly; this effectively eliminates much of the data inconsistency possibilities.MAW editor is designed to prevent entering illegal data by providing a drop-down box for a field with fixed set of allowed values, rather than providing a free-text box. In cases where the first measure fails, it explicitly checks the data against certain constraints.PathCaseUsers can insert new data in two ways: (i) they can insert a new entity (i.e. from scratch) and (ii) for some object types (i.e. compartment), they can copy an existing object, modify and insert it into the database as a new record.MAW editor provides an autocomplete feature for text fields. Given the complexity of the biological terminology, this feature saves unnecessary keystrokes and prevents illegal data insertion to the database.To input data efficiently, PathCaseChanges made on the data are not reflected to the database right away. Users need to approve their changes by ‘saving’ their work to avoid updating the database by mistake.The integrated visualization applet enables user to see results of the update on the fly, which acts as an efficient visual debugger.PathCaseMAW Editor’. The lowest layer, ‘database wrapper classes’, contains a set of wrapper classes for the Java Database Connection. This layer (i) initializes and maintains the database connection, and (ii) via SQL queries, provides an easy-to-use interface for select, insert, update and delete queries to the upper level. The middle layer, ‘Mediation Layer’, performs data conversion and validation. This layer contains two abstract classes, namely, ‘BaseEntity’ and ‘BaseRelation’ through which common database operations  are implemented. The actual  entity’s class is derived from BaseEntity, and the real relationship class is derived from BaseRelation. Each real entity class is mapped to one object  table in database, and each real relationship class is mapped to one relationship  table in database. The upper layer, the ‘GUI layer’, uses the ‘java reflection’ technique to dynamically retrieve real entity/relationship classes, so that the name of the real database class is transparent to the upper layer. This means that, when the database schema is changed (e.g. to add new object type), the upper layer code (e.g. the editor GUI) stays the same, providing flexibility.MAW Editor’, which includes two subpanels. The left panel contains an ‘Object Tree’ that presents the object hierarchy with the root ‘Pathway’. The object hierarchy lists reactions, compartments and metabolite pools, which are related to the given pathway. The maximum depth of the tree is 3 (levels of objects). The right panel includes two tabs: ‘Data Editor’ tab for data viewing and editing and ‘Visualization’ tab for visualization view. In the data editor Panel, the top part shows the current object’s name, ID and other information. The middle part lists tables that contain relationships to the currently edited object. The operation results (e.g. how many rows deleted from the database) are placed at the bottom part.The third layer is the GUI layer with two parts: one is the editor GUI that provides data insertion, editing and viewing operations, and the other one is the visualization viewer.Compartment tree support,Automatic metabolite pool creation,Compartment duplication for reuse,Autocomplete feature for object fields, andSelection lists for enumerated fields.Although we have presented a few examples of updating pathways, reactions or metabolites, the editor has a large number of user-friendly features :CompartThe editor also provides different convenient ways for users to interact with the database .MAW, we are dealing with metabolic networks that have been described in SBML format. PathCaseMAW SBML parser is a descendent of PathCaseSB SBML parser  , which iMAW have been implemented and released to the research and community. Currently, the system runs on a manually created generic mammalian database that consists of 26 pathways, 282 reactions and 243 metabolites. We have also parsed and released three genome-scale reconstructed metabolic networks (two for Mus musculus and one for Trypanosomacruzi) in the sister PathCaseRCMN site. Next, we provide a comparison of the existing systems with PathCaseMAW and then the challenges/shortcomings of the current system.All presented subsystems of PathCaseMAW, KEGG (i) provides limited visualization and browsing capabilities, (ii) does not capture compartment information for metabolites, (iii) provides limited functionality over the data and (iv) ignores metabolic regulation, such as covalent activation/inhibition or metabolite ratios .KEGG  has beenSB and PathCaseKEGG , browsing and visualization capabilities are limited on their Web site. BioCyc provides a SVG-based, static visualization of the complete metabolic network, and links to related sources per item, but there is no other interaction, unlike the PathCaseMAW visualization interface. ‘Pathway Tools’ (MAW editor. Computational tools provided by pathway tools focus on prediction of metabolic features, such as pathways, choke points or operons for metabolic networks. On the other hand, SMDA tool is concerned with analyzing metabolic profiles and predicting metabolic activity on metabolic networks. They also provide a FBA tool, but only in the desktop version. BioCyc does have an iPhone application for only EcoCyc (MAW’s task, which deals with already created networks such as those from already reconstructed genome-scale networks. PathCaseMAW clearly provides a much smaller subset of the information provided by BioCyc; however, for metabolic networks, it provides a user-friendly, manageable and online system with nice analysis, browsing and visualizing capabilities.BioCyc  is anothy Tools’  provide y EcoCyc , and theMAW. There is no mobile interface provided. Reactome’s goal is to have an all-inclusive one-stop-shop type of curated metabolic pathway database, whereas PathCaseMAW’s goal is to provide tools and schema for researchers to move their own data to PathCaseMAW schema to have the access, query, browsing and analysis interfaces available.Reactome  is a patMEMOSys  is a sysMAW is a unique system with its goal and capabilities. It has similarities and differences, compared with the existing systems. However, it is not a stand-alone advancement over an existing system.In summary, PathCaseMAW system is with parsing reconstructed metabolic networks. And, the reason is the misuse of the SBML format for creating networks by researchers. For instance, Edinburgh metabolic network for humans (et al., 2012 (One of the biggest challenges of the PathCaser humans  does notr humans . The secl., 2012 . For netl., 2012  with simMAW is an online multi-tool system to effectively create, store, browse, query, visualize and analyze metabolic networks. The system provides an SBML parser to create networks from a SBML document, and a user-friendly interface that lets users to update the database. Stored information can be accessed online either through a Web interface or an iPad application. PathCaseMAW is a useful system to access and user-created reconstructed networks online for researchers. An analysis made online on the SMDA tool has been published (PathCaseublished  as a proAs future work, our goal is to link the data stored in the database to outside sources such as KEGG for reaction references and HMDB for metabolite references, whenever the information is provided in the input model. Next, we plan to have a collective site that includes all of the released models for the use of research community.


The progressive aggregation of Amyloid-β (Aβ) in the brain is a major trait of Alzheimer's Disease (AD). Aβ is produced as a result of proteolytic processing of the β-amyloid precursor protein (APP). Processing of APP is mediated by multiple enzymes, resulting in the production of distinct peptide products: the non-amyloidogenic peptide sAPPα and the amyloidogenic peptides sAPPβ, Aβ40, and Aβ42. Using a pathway-based approach, we analyzed a large-scale siRNA screen that measured the production of different APP proteolytic products. Our analysis identified many of the biological processes/pathways that are known to regulate APP processing and have been implicated in AD pathogenesis, as well as revealing novel regulatory mechanisms. Furthermore, we also demonstrate that some of these processes differentially regulate APP processing, with some mechanisms favouring production of certain peptide species over others. For example, synaptic transmission having a bias towards regulating Aβ40 production over Aβ42 as well as processes involved in insulin and pancreatic biology having a bias for sAPPβ production over sAPPα. In addition, some of the pathways identified as regulators of APP processing contain genes  recently implicated with AD through genome wide association studies (GWAS) and associated meta-analysis. In addition, we provide supporting evidence and a deeper mechanistic understanding of the role of diabetes in AD. The identification of these processes/pathways, their differential impact on APP processing, and their relationships to each other, provide a comprehensive systems biology view of the “regulatory landscape” of APP. Progressive aggregation of a toxic 42 amino-acid peptide species, known as amyloid-beta (Aβ) 42 is a major hallmark of Alzheimer’s disease (AD). Accumulation of Aβ42 oligomers is thought to cause neuronal injury, synaptic dysfunction, and neuronal death leading to dementia,2. Aβ42 Several observations support the notion that alternative approaches to direct BACE1 and γ-secretase inhibition are possible. Oxidative stress, transient interactions with the γ-secretase complex, neuronal activity, cholesterol metabolism, and inflammatory cytokines can directly modulate γ-secretase activity. TherapeWhole-genome siRNA screens provide a powerful means for target identification. By knocking down a gene with siRNA nucleotide probes, one is able to measure the effect on different biological outcomes for thousands of genes simultaneously, enabling the identification of pathways and processes that regulate the biology of interest. However, there are many challenges facing the analysis of siRNA screens. The fact that siRNA probes may not be specific, due to sequence similarity with other genes, results in off-target activities and thus potentially contributes to the high rate of false-positive hits and the low level of reproducibility,11. FalsHere we apply a pathway scoring method to a large-scale siRNA screen aimed at identifying regulators of APP processing. The screen measures the production of APP proteolytic products, the non-amyloidogenic peptide (sAPPα) and the amyloidogenic peptides , as well as cell viability. By applIn order to identify the pathways/biological processes that regulate APP processing and viability, both net and absolute pathway impact (PI) scores were calculated see . BrieflyP ≤ 0.01 for either Net or ABS PI. A pathway was considered significant at a P = 0.0014; ABS PI 7.37, P = 0.0009) as defined by the KEGG database ; and “Presenilin action in Notch and Wnt signalling”. Some of the most significant pathways were those associated with gene transcription, mRNA splicing, and protein translation. These observations are also consistent with biological expectations that: (1) knock-down of APP and its corresponding proteolytic enzymes would reduce Aβ42 production; and (2) knock-down of a gene in transcription/translation related processes would result in lower levels of protein production, and hence Aβ42. Such biSince proteins can participate in more than one biological process/pathway, and because there are similarities and differences in how databases define pathways, we derived a matrix representing all vs. all overlap of these gene sets in order to identify redundancies as well as cross-talk across Aβ42-regulatory processes . The oveOther pathways and processes were also identified. Examples include "Integrin-mediated signalling", "neuron recognition", "BMP signalling pathway", "Arginine and proline metabolism", and "Lectin pathway of complement activation" ”, “adipocytokine signalling pathway (KEGG pathways)”, and processes involved in pancreas biology and development were found to be significant for the production of sAPPα but not for sAPPβ see . These oSo far, the detailed underlying mechanism by which diabetes and AD can be linked is unknown. In ordeMaturity onset of diabetes of the young, or MODY, is a monogenic form of diabetes with onset at adolescence and early adulthood. Mutations in genes in this pathway limit the ability of the pancreas to produce insulin. Knock-dIn this report we demonstrate that pathway-based analysis of genome-wide siRNA screens can be leveraged to identify key regulatory processes of different biological endpoints; in this case proteolytic processing of APP. This was achieved despite the known limitations of large-scale siRNA screens, suggesting that leveraging our knowledge of biological pathways/processes can improve our ability to interpret and leverage this technology.et al. demonstrated how proteins involved in vesicle trafficking, adhesion, and integrin signalling affect the levels of Aβ40 and Aβ42 via interactions with the γ-secretase complex[Most processes/pathways identified in this study have been implicated in AD and in APP biology as well as in ageing and neuronal vulnerability ,9,24,44 e complex. Similare complex,9  direct mechanisms (e.g. γ-secretase complex); (2) related but not direct regulatory mechanisms ; and (3) general cellular mechanisms that are likely to impact any readout . Conditioned media from these cells were then removed after 48 hrs following incubation with siRNA pools. Aliquots were used to detect different APP proteolytic products: Aβ40, Aβ42, sAPPα, and sAPPβ. In addition, cell viability was determined by incubating cells for 2 hrs with Alamar blue.The siRNA screen used in this study is described in Majercak et al.. Brieflyet al.. These cYi be the measured activity for a given well (log intensity), N, in the plate, and MADN the median absolute deviation[Raw intensities from the original screen were re-processed in order to achieve the following: (1) to normalize all peptide-related values  to viability in order to account for differences in peptide levels that may result from differences in cell number; and (2) to account for plate-edge effects that were observed in the original screen. The original screen reporteddeviation of measuwww.ingenuity.com), Reactome [www.genego.com), Panther Biological Processes[http://targetmine.nibio.go.jp/applandscape/) [We compiled several gene sets representing pathways and biological processes from the following commercial and public databases : NCI NatReactome , GeneGo Processes, and GenProcesses. These sProcesses tool frodscape/) .n is the number of genes in a pathway/process set, and Z*i is the Z* score for each individual gene in that pathway/process set. The ABS PI score is calculated to mitigate against the scenario in which small PI scores could result from equal but opposing effects of genes in a given pathway/process.PI scores are calculated as follows:P of obtaining by chance a PI score equal to or greater than the observed one for a given set of size n was calculated as follows: the Net or Absolute PI score was computed for 10,000 random selection of n genes from the screen in order to derive the null distribution for each set size. Then, the probability P was determined by the proportion of random PI scores that were equal to or more extreme than the observed PI score. Pathway/process sets were considered significant if P ≤ 0.01 for either the Net or ABS PI scores for each readout, respectively. Volcano plots for the results were created using Spotfire Decision Site 9.1 (http://spotifire.tibco.com); PI scores for Net or ABS values are reflected in the x-axis and P-values are represented on the y-axis as -log10 and hence the cut-off is -log10 0.01 = 2.The probability Oc between two sets i and j as follows:Si and Sj, and Oc was then used to create a symmetric overlap matrix of all-versus-all set comparisons. An overlap of 1 means either: (1) the sets are identical in size and composition; or (2) the smaller set is a true subset of the larger set; conversely, an overlap of 0 means that the sets share no genes. The overlap was calculated based on the composition of gene sets (pathways/processes) with respect to the screen. Hence, the degree of overlap between two gene sets in this study may differ from the overlap of these sets when considering the complete membership for each set.In order to identify the extent of overlap between different gene sets, we calculated the overlap coefficient onsidered. Oc wasOc was re-ordered to identify clusters that represented related pathways/processes. Briefly, rows and columns of the matrix were re-ordered by first calculating a distance matrix based on cosine-correlation followed by average linkage clustering [Oc matrix and ordering from the clustering run. The generation of the cosine-correlation distance matrix and average linkage clustering were performed using the pdist and linkage functions, respectively, in Statistic Tool Box of Matlab 7.4.0 (www.mathworks.com). The matrix was plotted using Spotfire Decision Site 9.1 (www.tibco.com).The overlap coefficient matrix ustering . A matriS1 Supplementary Information(DOCX)Click here for additional data file.S2 Supplementary InformationThe gene set name, number of genes, source (e.g. Reactome), and corresponding members  is provided. Note that the gene set composition used in this study may not match current pathway definitions. One reason is that pathway definitions change with time and some pathways may now contain additional members. Secondly, only genes for which siRNA data is available were considered; in a scenario where the pathway contains 10 genes, but only 5 were screened, the pathway is consider to have a set size of 5.(XLS)Click here for additional data file.S3 Supplementary Information(XLS)Click here for additional data file.S4 Supplementary Information(XLS)Click here for additional data file.S5 Supplementary Information(XLS)Click here for additional data file.S6 Supplementary InformationThe pathway contains only those genes that were present in the screen.(DOCX)Click here for additional data file.S7 Supplementary Information(DOCX)Click here for additional data file.S8 Supplementary Information(DOCX)Click here for additional data file.S9 Supplementary Information(XLS)Click here for additional data file.S10 Supplementary Information(DOCX)Click here for additional data file.S11 Supplementary Information(DOCX)Click here for additional data file.S12 Supplementary InformationThat is, genes that contain exactly the same genes are merged into one.(TXT)Click here for additional data file.


Pathway analyses are playing an increasingly important role in understanding biological mechanism, cellular function and disease states. Current pathway-identification methods generally focus on only the changes of gene expression levels; however, the biological relationships among genes are also the fundamental components of pathways, and the dysregulated relationships may also alter the pathway activities. We propose a powerful computational method, Edge Set Enrichment Analysis (ESEA), for the identification of dysregulated pathways. This provides a novel way of pathway analysis by investigating the changes of biological relationships of pathways in the context of gene expression data. Simulation studies illustrate the power and performance of ESEA under various simulated conditions. Using real datasets from p53 mutation, Type 2 diabetes and lung cancer, we validate effectiveness of ESEA in identifying dysregulated pathways. We further compare our results with five other pathway enrichment analysis methods. With these analyses, we show that ESEA is able to help uncover dysregulated biological pathways underlying complex traits and human diseases via specific use of the dysregulated biological relationships. We develop a freely available R-based tool of ESEA. Currently, ESEA can support pathway analysis of the seven public databases . The development of high-throughput experimental techniques such as microarray and next generation sequencing has led to amount of gene expression datasets. Thousands of dysregulated genes have been identified. To better understand the function of genes in the biological system, genes need to be studied in the context of the canonical biological pathways. The biological pathways analyses can help to insight into biological mechanism, cellular function and disease states124et al. is gene set enrichment analysis (GSEA)89121415The classical enrichment analysis methods are developed by using the statistical models, such as Fisher’s exact test and hypergeometric test, to detect if the differentially-expressed genes are over- or under-represented in a predefined pathwayet al. proposes an interaction-based gene set analysis method (IB-GSA), which identifies enriched gene interaction (correlation) effects on a phenotype of interest in the framework of gene set analysiset al. proposes gene interaction enrichment and network analysis (GIENA) to identify dysregulated pathways in complex diseases. GIENA defines several functions to model the biologically relevant gene interactions, and then identifies dysregulated interactions and pathways enriched in dysregulated interactions124Approaches based on the changes of relationships among genes (deemed as edge-centric methods) have been proposed to investigate dysregulated pathways. Zhang et al. proposes signaling pathway impact analysis (SPIA), which combines the positions and interactions of genes in the pathways with classical over-representation evidence in prioritizing risk signaling pathwaysSeveral recent methods effectively used pathway structure in identifying dysregulated pathways. ScorePage takes advantage of the shortest distances between genes in pathways for the analysis of changes in activity of metabolic pathwayswww.biocarta.com; NCI/Nature Pathway Interaction DatabaseIn this study, we developed a powerful edge-centric method, Edge Set Enrichment Analysis (ESEA), to identify dysregulated pathways by investigating the changes of inherent biological relationships embedded in pathways in the context of gene expression data. ESEA integrates pathway structure  and differential correlation among genes. The biological pathways were collected from the seven public databases (KEGGhttp://cran.r-project.org/web/packages/ESEA/).ESEA was developed to identify dysregulated pathways based on the changes of biological relationships of pathways in the context of gene expression data. A flow diagram of the ESEA methodology is shown in et al.et al.http://www.broadinstitute.org/gsea/index.jsp). The third case was two independent lung cancer datasets (GSE7670 and GSE10072) published by Su et al.et al.http://www.ncbi.nlm.nih.gov/geo).We used three cases to illustrate the ESEA method. The first case was p53 mutation dataset published by Olivier http://cran.r-project.org/web/packages/ESEA/).We collected human pathways from the seven popular public databases . There are more than 2300 pathways totally , which cDifferential correlation analysis was used to identify the changes of relationships among genes in the context of gene expression data. The information theoretic measure of statistical dependence, mutual information (MI), can estimate the correlation between the expression profiles of two genes30EdgeScore) was defined as:We mapped the gene expression data to the background set of edges, and retained the edges in the background when both genes in the edge were mapped. For each edge, we estimated the MI between two genes in the edge using parmigene package, which gives more precise results with less computational costsall represents the MI between the expression profiles of the two genes (i and j) in the edge across all samples; MIcontrol represents the MI between the expression profiles of the two genes (i and j) in the edge across control samples. According to the EdgeScore, each edge could be classified as either a gain of correlation (GoC), loss of correlation (LoC), or no change (NC). Specifically, we tested whether the MI increased (EdgeScore > 0) or decreased (EdgeScore < 0) when the samples with the specific phenotype were added to control samples. We defined the edge as GoC (LoC or NC) if the EdgeScore > 0 (<0 or =0), which refers to the correlation of the two genes in the edge is gained (lost or no change) in the specific phenotype. If an edge is strongly correlated with the specific phenotype, its EdgeScore will highly deviate from zero. We ranked the N edges in the background set to form a edge list L = {e1, e2, …eN} according to decreasing EdgeScore.where MIhttp://cran.r-project.org/web/packages/ESEA/). For a given database, pathways with more than 15 edges or less than 1000 edges in the expression dataset were used in the analysis. This will avoid overly narrow or broad functional pathways.For each pathway in the seven pathway database , the edge set can be extracted from the corresponding pathway graph. We therefore created the edge sets of pathways for each of the above databases, which can be obtained from our “ESEA” package , which reflects the degree to which a pathway is overrepresented toward the extremes (top or bottom) of the edge list L. This statistic has been used in GSEA previously. But, it is used as statistic test of nodes. In the paper, we used it as statistic test of edges. Specifically, at a given position i in the list L, we evaluated the fraction of edges in the pathway (FInP) weighted by their EdgeScore and the fraction of edges not in the pathway (FNotP) as follows:We mapped the edges in a predefined pathway to the ranked edge list NR = rj is the EdgeScore of edge j; NNotP represents the number of edges in the list L not in the pathway. The parameter p is used to weight the EdgeScore of the edges in the pathway, and we set p = 1 in the study. With the position i walking down the list L, the EES of the pathway (EES(P)) is calculated as the maximum deviation from zero of FInP − FNotP. The EES(P) will be high if the edges in the pathway cluster at the top or bottom of the list, but if the edges randomly distributed at the list, the EES(P) will be small. According to the sign the EES(P), the pathways could be classified as GoC pathway (EES(P) > 0), LoC pathway (EES(P) < 0) and NC pathway (EES(P) = 0), which indicate the pathways are enriched by edges with GoC, LoC and NC respectively.where p-value) of the EES(P), we performed a gene-based permutation test procedure that preserves the sample labels and gene expression data. Specifically, we permuted gene labels and recomputed the EES(P) for the permutated data. The background set of EES was generated after performing N permutations, and was designated as EESperm. When the observed EES(P) > 0, the p-value was computed as p-value = M/N, where M is the number of EESperm greater than the observed EES(P); when the observed EES(P) < 0, p-value = M/N, where M is the number of EESperm less than the observed EES(P). The permutation times N was set at 1000 for the examples in this study. Because of our method mainly studies the changes of correlation between genes, the gene-based permutation would be reasonable for identifying dysregulated pathway enriched by differential correlations relationships. To correct for multiple comparisons, we adjusted the empirical p-values by using false discovery rate (FDR) method proposed by Benjamini and HochbergTo estimate the statistical significance . Here, the core subset of edges in a significant pathway is defined as the edges appear in the ranked edge list L at and before (or after if EES(P) < 0) the point where EES(P) is obtained. The core subset of edges is expected to be more likely associated with the biological process of an interesting phenotype.Actually, only partial differential correlations relationships in a significant pathway will participate in the studied phenotype12EES, we further normalized the observed EES(P). The normalized edge enrichment score (NEES) for each pathway was computed by:To account for the size of the pathway and allow inter-pathway comparisons with orEESperm(EESperm > 0) or EESperm(EESperm < 0) represent the vector of positive or negative scores in the EESperm respectively.where To assess the performance of the ESEA approach, we performed simulated study. We simulated gene expression dataset with 2000 genes. A simulated background set of edges was constructed by selecting 10000 different genes pairs.γ, the percentage of risk edge in the causal pathway, and we considered γ ∈{0.25, 0.50, 0.75, 1.00}. Other 99 pathways were simulated from null models, namely, none of the correlations of edges in these pathways were changed between two phenotypes of interest. The edges in these null pathways were chose from the remaining 9900 edges in simulated background set, and the sizes of these null pathways were randomly drawn from a uniform distribution U.We generated 100 pathways with edges, and the edges were extracted from simulated background set of edges. Only the first pathway was defined as causal pathway including differential correlation edges (risk edges). We chose 100 edges from simulated edge background set to generate this causal pathway. Thus, the maximum number of risk edge is 100. In real biological settings, only parts of edges in a risk pathway are differential correlative. We therefore introduced a parameter For gene expression dataset, we simulated 50 controls and 50 cases with 2000 genes from multivariate normal distribution r controls the strength of correlation between genes in the risk edge. As the correlation between genes in the risk edge vary in strength, we consider r ∈{0.1, 0.2, …, 0.9}. A risk edge with larger r indicates that the edge possesses greater differential correlation degree between cases and controls. To ensure that the parameter We illustrated the ESEA method using simulated data and real biological data. The simulation study was firstly conducted to assess the power and performance of ESEA in a fully controlled setting. We then explored the effectiveness of ESEA to provide biologically meaningful insights using two real expression datasets from p53 mutation and type 2 diabetes. In each case, we searched for significantly associated pathways from one or two of the seven public pathway databases . We also provided a point of comparison by analyzing each data using GSEA. We then test the consistency of method by applying ESEA to two independent lung cancer datasets. Finally, we compared the results of ESEA with five other pathway enrichment analysis methods.γ, the percentage of risk edge in the causal pathway and r, the strength of correlation between genes in the risk edge, to test how exactly these parameters influence the power and performance of ESEA method.We conducted extensive simulation studies to illustrate the power and performance of ESEA under various conditions. We simulated a gene expression data with 2000 genes and an edge background set with 10000 edges. 100 pathways were generated by extracting edges from simulated background set. Only the one of 100 defined pathways was used as casual pathway containing risk edges. The remaining 99 pathways were used as a null model, and the size of these null pathways were randomly drawn from a uniform distribution U. Under real biological situations, not all edges in the casual pathway are differential correlative (risk), and the correlation between genes in the risk edge varies in strength. We thus introduced two parameters: r, r ∈{0.1, 0.2, …, 0.9} and γ, γ ∈{0.25, 0.50, 0.75, 1.00}. For each scenario, we performed 200 replicates and the power was calculated as the proportion of replicates for which the p-value for the causal pathway was less than 0.05. We plotted power curves by selecting each γ in {0.25, 0.50, 0.75, 1.00} and used r = {0.1, 0.2, …, 0.9} respectively  versus the false-positive rate (FPR) subject to the threshold  separating the identification results in 200 replicates. To compare different curves obtained by ROC analysis, we calculated the area under the ROC curve (AUC) for each curve  and the percentage of risk edge (γ), and increased values for each or both of these two parameters would increase the power and performance of method.We further used the receiver-operating characteristic (ROC) analysis to compare the performance of the algorithm under various scenarios . The cauch curve . When r Our first case was gene expression dataset of p53 status from the NCI-60 collection of cancer cell lineset al. demonstrated that methionine inhibited cellular growth dependent on the native p53 status of cancer cells, and this inhibited effects were loss in mutated p53 statuset al. demonstrated that the dilated cardiomyopathy caused by loss of Mdm4 (an inhibitor of the p53 tumor suppressor) was dependent on p53 doseet al. concluded that mutation of the p53 gene was one of the commonest genetic alterations in the progression of human colorectal cancerWe first applied ESEA to identify KEGG pathways associated with p53 mutation. With FDR<0.05 pathway significance threshold, ESEA yielded five statistically significant pathways enriched by edges with gain of correlation (GoC pathways) . The fulTo provide a comparison analysis, we also applied GSEA to p53 mutation dataset to identify KEGG pathways. With the default threshold of method (FDR < 0.25), GSEA identified one significant pathway: N-Glycan biosynthesis. Although this pathway may be associated with the p53 function, ESEA exclusively identified five statistically significant pathways associated with the p53 function.EdgeScore representing differential correlation degree, the edges locate close to the top or bottom of the list may tend to be dysregulated. The accumulation of multiple dysregulated edges may result in the pathway dysregulated. To reflect the degree to which the edges in the pathway cluster toward the extremes (top or bottom) of the edge list, the edge enrichment score of the pathway (EES(P)) was calculated by walking down the edge list. A running-sum statistic was calculated by increasing it when we encounter an edge in the pathway and decreasing it when we encounter edges not in the pathway (EES(P) (The detail information for each edge in the pathway was listed in the EES(P), were defined as core subset of edges. These core edges were mapped to the pathway graph. A series of dysregulated relationships were found . We then mapped the pathway P10072 to the entire ranked edge list from the GSE7670 dataset . These results indicate that the ESEA method is able to detect strong consistent signal between independently derived lung cancer datasets.To test if the ESEA method could obtain consistent results across different datasets, we used two independent derived lung cancer datasets (GSE7670 and GSE10072) for analysis. We defined two edge sets of pathways,  dataset . The pat dataset , and theet al. reported that dysregulated G2/M checkpoint function was associated with an increased risk of lung cancerWe further explored whether ESEA could provide consistent pathways in lung cancer. We performed ESEA on the two lung cancer datasets with the Reactome pathways. To provide a more general comparison, the top 20 pathways from each lung dataset were used to test how many pathways were overlapped. Interestingly, approximately half of the pathways (9 pathways) were shared between the two studies across the top 20 pathways . These oTo explore whether ESEA could provide new biological insights in identifying important pathways, we applied DAVID3437Identifying dysregulated canonical biological pathways can help us to understand biological mechanism, cellular function and disease states. According to the entities used by the pathway identification methods, these methods can be naturally classified as node-centric (gene based) and edge-centric (gene-gene relationships based) methods. The recent pathway identification methods mainly belong to node-centric methods (e.g. GSEA), which focus on investigating the changes of gene expression levels between cases and controls. Although these node-centric methods achieved good results, they did not consider the changes of relationships among genes which may also alter the activities of pathways. Some edge-centric methods were thus developed to detect the changes of relationships among genes in identifying dysregulated pathways. However, they mainly compared the difference for all gene pairs within the pathways and did not take advantage of the inherent pathway structure . Actually, pathways are models describing the pathway structure, not simple sets of genes. Thus, in these methods, some differential-correlation relationships identified in a pathway may result from other pathways. ESEA was developed as an edge-centric method by integrating pathway structure and differential correlation among genes, which may improve the specificity of results in identifying the dysregulated pathways.EdgeScore) between the expression profiles of two genes in the edge. According to the EdgeScore, the edges were classified as either a gain of correlation (GoC), loss of correlation (LoC), or no change (NC). This could provide more delicate information for the biological relationships in the development of complex diseases.Because the prior pathway structure of the recent pathway databases was generally incomplete; we thus collected more than 2300 human pathways from the seven popular pathway databases  to const30In the study, ESEA is designed to identify the dysregulated pathways by investigating the changes of inherent biological relationships  embedded in pathways in the context of gene expression data. This means that the dysregulated pathways identified by ESEA are enriched by the specific dysfunctional biological relationships between genes. The strategy of ESEA is different from the recent pathway enrichment analysis methods , which identify the dysregulated pathways based on the differentially expressed genes. To explore whether ESEA could provide new biological insight in identifying important pathways, we further applied DAVID3437By detecting the dysregulated pathways obtained from ESEA, we found that these pathways were enriched by the dysregulated biological relationships. For the colorectal cancer pathway identified in p53 mutation data, 27 core edges, such as “AKT2|PIK3R2”, “AKT2|PIK3CB” and “AKT3|PIK3R3” etc., were found. Through mapping these core edges to the original pathway, a region of PI3K/AKT and β-catenin signaling cascade (blue circle in 39404145http://cran.r-project.org/web/packages/ESEA/). The users input interesting gene expression data with case and control samples, and the dysregulated pathways can then be inferred. The edge sets of pathways have been created for each of the seven pathway databases . ESEA can be flexibly applied to the pathways in a given databases. The ESEA method was applied to gene expression microarrays in the study, and it can also be apply to the transcriptome profiling from next-generation sequencing (RNA-Seq).In order to make the EAEA to be broadly applicable, we have implemented ESEA as a flexible R-based package, which is freely available on CRAN .


Consequently, inhibition of a single component/pathway would probably not constitute a successful cancer therapy.Resistance to therapy remains a major cause of the failure of cancer treatment. A major challenge in cancer therapy is to design treatment strategies that circumvent the higher-level homeostatic functions of the robust cellular network that occurs in resistant cells. There is a lack of understanding of mechanisms responsible for the development of cancer and the basis of therapy-resistance mechanisms. Cellular signaling networks have an underlying architecture guided by universal principles. A robust system, such as cancer, has the fundamental ability to survive toxic anticancer drug treatments or a stressful environment mainly due to its mechanisms of redundancy and to predict communications among the various pathways based on network theory, using data from gene expression profiles of hepatocellular carcinoma (HCC) of patients with poor and better prognosis cancers. Our results clearly indicate that immune system pathways tightly regulate most cancer pathways, and when those pathways are targeted by drugs, the network connectivity is dramatically changed. We examined the main HCC targeted treatments that are currently being evaluated in clinical trials. One prediction of our study is that Sorafenib combined with immune system treatments will be a more effective combination strategy than Sorafenib combined with any other targeted drugs.We developed a computational method to study the mechanisms of Gene and Pathway Co-expression Networks. Our hypothesis is that redundancy is one of the major causes of drug resistance, and can be described as a function of the network structure and its properties. From this perspective, we believe that integration of the redundant variables could lead to the development of promising new methodologies to selectively identify and target the most significant resistance mechanisms of HCC. We describe three mechanisms of redundancy based on their levels of generalization and study the possible impact of those redundancy mechanisms on HCC treatments.We developed a computational framework to analyze gene expression data from HCC tumors with varying degrees of responsiveness and non-tumor samples, based on both  Redundancy means that two or more elements potentially could perform the same function and that inactivation of one of these elements has no significant effect on the biological phenotype or on the dynamic process. Consequently, inhibition of a single component or even an entire pathway would probably not constitute a successful cancer therapy. Choosing drugs for therapy is a complex task. Researchers often choose a specific element to target  using statistical analysis of gene expression, or the target’s ability to affect cell fate . However, many cancer drugs fail or underperform due to redundancies in their target’s pathways or the existence of alternative pathways. Efficiently targeting pathways is problematic, because it is unclear whether we should identify pathway targets by level of expression or by their location in the pathway . There are many examples indicating that redundancy serves as a resistance mechanism with clinical implications. For instance, several different ABC transporters can confer resistance to the same drugs, so inhibitors must target all of these transporters to be effective in reversing transporter-related multidrug resistance . Tre. Tre2]. sistance .In general, there are many computational and mathematical approaches to directly/indirectly address redundancy, such as network theory. Network theory helps to describe relationships between every pair of elements, where the elements could be genes, proteins, metabolites, etc. The relationships could be physical integration, correlations, targets, etc. The network could be as complex as we need it to be. For example, a multilayer network can include many components and types of relationships, depending on the objective . Describ1, S2, and S3), that were correlated with clinical parameters. The S1-signature reflected abnormal activation of the WNT signaling pathway, the S2-signature was described by the proliferation pathway as well as MYC and AKT activations, and the S3-signature was associated with hepatocyte differentiation. These three signatures were shown to predict the recurrence of HCC. S1 and S2 signatures had poor overall survival and those with the S3-signature had good overall survival.HCC is known to be a heterogeneous disease. Thus, numerous genomic-based classifications have been proposed to describe its various forms. These kinds of studies indicate the complexity in finding a consistent molecular classification for such a problem. Gene expression profiling has been used extensively in cancer research, providing useful information. A prediction of patient therapeutic response based on tumor gene singularities would improve overall efficacy of molecular therapies used to combat HCC. Computational algorithms that predict the recurrence of HCC based on clinical, pathological, and gene expression data are the current approach in the field . The stuHowever, gene expression profiling provides an incomplete picture, since it does not include communications among the genes. It is increasingly believed that cancer cells involve a large number of biochemical components that interact through complex networks and as a result, display nonlinear dynamics . TherefoThe problem of redundancy at the functional level has mainly been addressed by identifying differentially expressed pathways based on gene expression data by calculating activity levels for each pathway within the samples ,17]. Th. Th17]. Redundant Genes of a given pathway, Redundant Crosstalk Paths between pair of pathways, and the Redundant Circles  of a given set of pathway categories  and Pathway Co-expression Network (PCN), where the reference network is constructed based on random sample selection from the two groups. We offered three mechanisms of redundancy based on their levels of generalization: s Figure A. Using profiles , and thes ,,[www.genome.jp/kegg/). To address the question of its specialty to a specific phenotype, we compared the pathway network to 150 random pathway networks, and using a permutation test we calculated the p-value of each pathway edge. All pathway edges with p-value < 0.05 were assumed to be significant and the resulting pathway network was reported in the main text of our paper .We generalized the gene network to a pathway network, with each gene interaction translated to all possible pairs of pathways, and estimated their likelihood. The pathway network is composed of pathways as nodes and correlations as edges. Each gene correlation was translated to a pathway correlation using the final gene co-expression network and the KEGG pathways database .All data concerning genes and pathways were downloaded from the KEGG database (Kyoto Encyclopedia of Genes and Genomes) . For thehttp://www.ncbi.nlm.nih.gov/gds.The data sets supporting the results of this article are available in the Gene Expression Omnibus (GEO) repository, accession nos. GPL1528, GPL2094, GPL80, GPL257, GPL91, GPL96, GPL570 and GPL5474. These can be found at DEG: Differentially-expressed geneGCN: Gene co-expression networkHCC: Hepatocellular carcinomaThe authors declare that they have no competing interests.OL and MG conceived the project. Experiments were designed and performed by OL. Data were analyzed by OL and JS. All the authors contributed to writing the paper. All authors read and approved the final manuscript.Gene and pathway annotation.Click here for fileProperties of Gene Co-expression Network.Click here for fileGene Network properties of Random vs. Cancer Type A.Click here for fileGene Network properties of Random vs. Cancer Type B.Click here for fileProperties of the Pathway Network.Click here for file3-node circle categories.Click here for fileNode degree of gene and pathway networks.Click here for fileDrug targets.Click here for file


Escherichia coli metabolism forthree transcription factors, effectively doubling the known regulatory roles for Nac and MntR. Thisstudy suggests an underlying and fundamental principle in the evolutionary selection of pathwaystructures; namely, that pathways may be minimal, independent, and segregated.Pathways are a universal paradigm for functionally describing cellular processes. Even thoughadvances in high-throughput data generation have transformed biology, the core of our biologicalunderstanding, and hence data interpretation, is still predicated on human-defined pathways. Here,we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based onprinciples of parsimony that do not mimic canonical human-defined textbook pathways. Instead, theseminimal pathways better describe multiple independent pathway-associated biomolecular interactiondatasets suggesting a functional organization for metabolism based on parsimonious use of cellularcomponents. We use the inherent predictive capability of these pathways to experimentally discovernovel transcriptional regulatory interactions in  Such pathways are foundational to human-curated databases, such as KEGG is a mathematical description of a genome-scale metabolic network, which can bequeried by many available modeling methods  and transformations (reactions) of a particularcell's metabolism encoded on its genome and described in experimental literature  that can forthe first time define the shortest, functional pathways for metabolism at the genome scale usingmetabolic networks thereby describing the totality of steady-state phenotypes. We find that (1) theminimal pathways are biologically supported by independent biomolecular interaction networks, (2)the minimal pathways have stronger biological support than traditional human-defined metabolicpathways, and (3) the minimal pathways guided experimental discovery of novel regulatory roles forS that maintains the biological and thermodynamic constraints ofthe network. The MinSpan pathways have a couple notable properties. First, unlike convex analysisapproaches  that are linearly independent from each otherFig. The MinS has dimensions (m × n) wherem = 14 metabolites and n = 18 reactions. The linearbasis for the null space (N) has dimensions (n ×n − r) where r is the rank ofS. This S has rank (r = 14), meaning that the nullspace is four dimensional . Thus, a set of four linearly independentpathways through the network represents a linear basis for the null space of S. Thereare numerous potential sets of linearly independent pathways for a metabolic network as the linearbasis of the null space is not unique. MinSpan chooses a set representing the shortest independentpathways, and we later show that this linear basis is more biologically relevant than other linearbases.The mathematical derivation of MinSpan is provided in the Materials and Methods. Here, we beginwith an illustrative example of MinSpan for a metabolic model that contains a simplifiedrepresentation of glycolysis and the TCA cycle FigC. InthiN) to aMinSpan pathway matrix (P) that contains the four shortest, linearly independentreaction pathways  of metabolic pathways has been previously shown topreferentially share interactions compared to components outside of pathways. Thus, the genes withinpathways preferentially contain positive genetic interactions . We compared MinSpan pathways toPPI and genetic interactions in nisms FigC.S. cerevisiae MinSpan pathway protein sets, 80% ofknown yeast two-hybrid PPIs in metabolism  is pathway based with TFs acting directly on linearly independent, minimalpathways  that are equally or better suited atrepresenting pathway-associated biomolecular data types?n = 100) that had random criteria for the sparsity of the matrix. Wealso compared MinSpan to commonly used human-defined pathway databases including: KEGG Modules, and the Biological Processes ontology of GeneOntology  and generated “RandSpan” or randomly generated null spacebases . As mostPPIs occur between proteins within the same metabolic complex or adjacent metabolic reactions, mostmetabolic pathway structures should conserve PPIs. However, for positive genetic interactions, local transcriptional regulation, and intermediate transcriptionalregulation , MinSpan pathways werestatistically more representative of the interactions. None of the pathway structures were highlyconsistent with global regulation.Surprisingly, repeating the correlation analysis for these alternative network and human-definedpathways, we found that MinSpan pathways were generally more consistent with recapitulatingbiomolecular interactions FigD, E, & FThese finding have two important implications. First, MinSpan pathways have more underlyingsupport from biomolecular data types than human-defined pathways suggesting an alternative andfundamental modular organization of cellular metabolism. Defining pathways by human intuition andinterpretation is less representative of the biomolecular interactions. Second, a minimal pathwaystructure is more biologically relevant than other potential linear bases of the null spaceconfirming the principle underlying its use. The specific values in FigE. coli and S.cerevisiae metabolism  for olism Fig to answeetal, et al, E. coli, MinSpan captures most of their information, while KEGG,EcoCyc, and GO capture much less of MinSpan. For S. cerevisiae, Gene Ontology hasthe highest coverage. It has two fewer pathways than MinSpan   forboth organisms. The number of pathways that shared similarities with MinSpan was significantlydepleted , ordissimilar, from most pathway definitions in both organisms. This suggests that KEGG and BioCyc havethe most similar pathways, followed with Gene Ontology, which has more similarities with BioCyc thanKEGG. MinSpan pathways are significantly different from human-defined pathway databases and forE. coli contain many unique pathways , amino acid metabolism, and nucleotide metabolism.However, 26 of the 56 pathways missed by MinSpan for E. coli were related to fattyacid metabolism. A MinSpan pathway operates under the steady-state assumption, leading to full fluxbalance of the metabolic network . Traditional fatty acid pathway representations do not includeall necessary components, and fatty acid pathways typically require the most precursors andcofactors. Conversely, 99 of the 204 MinSpan pathways missed by traditional pathways dealt withpathways that contained the necessary cofactors and precursors, mainly for fatty acid metabolism.The second representative difference was that a few traditional pathways (representinggluconeogenesis and deoxyribonucleotide biosynthesis) were broken up into smaller MinSpan pathways.Third, MinSpan pathways for E. coli contained 54 novel pathways related to iontransport, alternate carbon metabolism, and electron transfer.Fourth, we determined what caused the significant difference in MinSpan and human-defineddatabases by looking at the individual pathways that were similar or dissimilar between the pathwaydefinitions FigD. For boS. cerevisiae, Gene Ontology has a larger coverage of metabolism thanMinSpan. This difference is due to two reasons: (1) the metabolic model of S.cerevisiae is not as comprehensive as the model for E. coli and (2) theGene Ontology for S. cerevisiae is relatively more comprehensive than the one forE. coli. There were 32 pathways missed by MinSpan due to differing representationsthan traditional pathway databases. Seven missed pathways dealt with fatty acid metabolism, andtheir MinSpan counterparts took cofactors and precursors into account. Five traditional pathways fortyrosine biosynthesis and triglyceride biosynthesis were broken up into smaller pathways by theMinSpan algorithm. The specific pathways that are missed by the MinSpan algorithm are provided inthe Supplementary Dataset S2.For E.coli that were not described in KEGG, Gene Ontology, or EcoCyc. For example, one suchpathway involves the degradation of shikimate, an aromatic compound, to l-tryptophan requires 10-formyltetrahydrofolate (10fthf). Thus, theMinSpan pathway for purine biosynthesis also includes tetrahydrofolate (THF) recycling whichcontains three reactions. The gene for the first reaction in THF recycling is transcriptionallyregulated by PurR, while the two other reactions’ genes are not transcriptionally regulated.MinSpan elucidates a coupling of THF recycling to IMP biosynthesis that is independently verified bythe co-regulation of the necessary genes.Second, MinSpan pathways are mass-balanced and are functionally independent units that take intoaccount systemic requirements. For example, the traditional human-defined pathway for purinebiosynthesis starts from phosphoribosyl pyrophosphate (prpp) and o IMP FigB, which S. cerevisiae is a small but complex network consisting of 12metabolic reactions that involve multiple amino acids , both MinSpan and human-defined pathways correctlyidentified four positive genetic interactions in the thwayFigC, suggesMinSpan is an inherent property of metabolic networks, unlike human-defined pathways, and offersthe direct ability to predict pathway-associated biomolecular properties from flux distributionscalculated by constraint-based modeling. From the above correlation analysis, we observed that geneswithin a MinSpan pathway are often co-regulated by the same TF. Thus, we tested whether TFs actdirectly on the MinSpan pathway structure during metabolic shifts to coordinate expression of themetabolic genes needed to implement a fully functional pathway.et al, et al, et al, et al,Constraint-based models can be used with Monte Carlo sampling methods to compute candidatereaction flux states through the metabolic network (Schellenberger P) is a linear basis for the null space of S, any sampled fluxdistribution (v) can be decomposed into linear weights (α) ofP  determined from Monte Carlo sampling can be converted intochanges in pathway flux loads (α) . Thus, a significant change in the flux loadof a MinSpan pathway (α) is a direct predictor of pathway-associated TFactivity.A reaction flux state can be decomposed into its constituent pathways. As the MinSpan pathwaymatrix (s (α) FigA. As Minetal, v) were decomposed into MinSpan pathway flux loads(α) to determine significantly changed pathways across nutritional conditions were then used as predictors oftranscriptional regulation.Metabolic reaction fluxes were computed by Monte Carlo sampling . Focusing on the 45 shifts, there were 247 predictedTF–environment associations. 154 of those predictions are confirmed in EcoCyc and primaryliterature. 93, or 38%, of the predicted TF activities are not known to be associated withthe corresponding shift, providing numerous novel transcriptional regulation predictions.Predicted TF activities FigB substanl-cysteine andl-methionine supplementation clustered by CysB activity. Sugar carbon sources clustered byCra. Organic acid carbon sources, including glycerol, were systemic and characterized by Fnr, Lrp,and Cra activity. Other systemic shifts included the response to the lack of oxygen and alternatenitrogen sources. Finally, predicted transcriptional regulatory changes of well-studied shifts , nucleotide supplementation (adenine), and oxygen depletion are described ingreater detail enumerating specific MinSpan pathway changes , and MntR with shift to anaerobicconditions (O2/MntR). The chosen shifts represent three distinct magnitudes of dualperturbations. In the ade/Nac case, the environment and genetic perturbations are both relativelyminor. In the l-trp/Cra case, Cra is a broad acting TF and dominates, while theenvironmental perturbation to the absence of oxygen dominates in the O2/MntR case.The three tested associations were Nac with adenine supplementation (ade/Nac), Cra withet al,et al, E. coli. For each case, wedetermined the gene set that is exclusively differentially expressed because of the combination ofthe genetic perturbation and environmental shift  in order to analyzewhether the TF plays a role in the environmental shift.We generated RNA-seq data from dual perturbation experiments , suggesting that Nac (nitrogen assimilation control) regulatessimilar genes during the shift or even regulates the corresponding TFs. In the l-trp/Cracase, there were no enriched TFs suggesting no global consensus. This discrepancy might be due to(1) Cra knockout causing a large genetic shift that might have changed how E. coliresponds to l-trp and (2) MinSpan is inaccurate for predicting global regulation. In theO2/MntR case, TFs known to be associated with the anaerobic shift (including ArcA andFnr) were enriched as a whole .Global analysis of the gene sets, based on enrichment of regulatory interactions with known TFassociations . A significant increase in gcvBsuggests a repression of Lrp and genes typically repressed by Lrp should have higher expression andgenes activated by Lrp should have lower expression. This trend was observed in all significantlychanged expression of Lrp-regulated genes  and nrdHIEF .Through differential expression and detection of high confidence binding sequence motifs, weidentified novel regulatory roles for all three tested TFs Fig. For thel-trp/Cra case, we did find that Crapotentially regulates the l-trp symporter and tryptophanase  MinSpan pathways account for all necessary components to make a pathway fully functionallyindependent . (2) MinSpan pathways represent the simplest pathwaystructure in a given network context. The improved consistency of MinSpan with biomolecularinteractions suggests that the coordinated regulation and usage of biological components in the cellhave evolved to be minimal and independent in order to adapt to perturbation with as little cost tothe cell as possible. Biologically meaningful pathways may be minimal, independent, and segregated.Future delineation of metabolic pathways, both network and human-defined, should take intoconsideration the cost of biomolecular machinery and systemic functional requirements for metabolicfunction.et al, Second, the MinSpan pathways provide an alternative, complementary, and potentially more powerfulapproach for investigators to analyze their generated data. Current pathway databases aretremendously important in conceptualizing biological function and are used by numerous investigatorsfor data analysis. As MinSpan pathways are more biologically relevant in terms of the underlyingbiomolecular interactions, the theory presented here for an unbiased pathway structure opens up thepotential for a whole new suite of pathways to be used with tools such as Gene Set EnrichmentAnalysis (GSEA)  with dimensions m × n and a rank ofr. The input is a metabolic model  andoutputs a MinSpan pathway matrix (P). P is the sparsest null space ofS that maintains biological and thermodynamic constraints (lb and ub). Coleman andPothen defined the mathematical problem for the sparsest linear basis of the null space as the“sparse null space basis problem” and then proved that a greedy algorithm must findthe globally optimal sparsest null space (least number of non-zero entries)  of S is initially defined by singularvalue decomposition. Then, the vectors of the orthonormal null space are iteratively replaced by theshortest pathways that span the removed vector's subspace. This process is continuouslyrepeated until the number of non-zero entries in P has converged to a minimum. Beforerunning the algorithm, all reactions that cannot carry a flux are removed, all exchanges are opened,and the biomass function is removed. The algorithm is summarized below:The MinSpan algorithm determines the shortest, linearly independent pathways for a stoichiometricmatrix (N = null(S)P = Nwhile (true)P0 = Pfor j = 1:n-rendP = P′P) == nnz(P0), break,endif nnz that is minimized by theoptimization problem to determine the MinSpan pathways. This is proved by contradiction. IfP′ and x are linearly dependent, then multiples of xand the vectors of P′ should linearly combine to zero:The null operator defines the orthonormal null space using singular value decomposition. Thennz operator determines the number of non-zero entries in a matrix. Vectors are in bold, andmatrices are capitalized. Tθ is a positive semi-definite matrix by definition and cannot equalzero. Thus, P and x are linearly independent. ThexT·v ≠ 0 constraint ensures that the calculatedpathway spans the proper dimension of the null space. For a MILP problem, the constraint isformulated as below, where ε is an arbitrarily small value , and f+ and f- are binary variables required toformulate a “not-equal” constraint:θE. coli andS. cerevisiae yielded little changes to the results (see SupplementaryInformation). MinSpan is available for COBRApy at https://github.com/sbrg/minspan.The termination criterion for the branch and bound method for each MILP iteration is a relativegap tolerance of 1e-3 or time limit of 30 min. These criteria were developed based on convergentproperties of solutions as the two parameters were varied. As the MinSpan algorithm is a MILPproblem, there can exist alternative optimal solutions. The total number of non-zero entries in thematrix is unique, but the pathways may not be. Rerunning the correlation analysis to determinebiological relevance with alternate MinSpan pathways for both c vector torandomly minimize and maximize the use of reactions while constructing each null space. Formaximization, the following constraints are added to link the binary and continuous variables:MaxSpan and RandSpan were generated similarly with slight modifications. For MaxSpan, theoptimization was switched to a maximization problem to make the densest null space. For RandSpan, weassigned a random value from −0.5 to 0.5 to the optimization i are dummy binary variables to allow an “OR” statement inlinear programming. If gi is either 1 or 0, one of the two above constraints is off.Where ε is an arbitrarily small value  and gP > 0.05, permutation test) were filtered from furtheranalysis. The total number of correlations remaining after filtering is the coverage criteria ) in the x-axes of FigP-values to determinestatistically different ROC convex hull curves were calculated using the approach by Hanley andMcNeil  were taken from Costanzo etal. For TF–gene interactions, all reported interactions in RegulonDB  . Pathways were considered similar if their CSI ranked in thetop 15% of CSI values.where PCC is the correlation coefficient of gene products, PCCWe also employed a k-nearest neighbors search to find the most similar pathways across thepathway databases. The Pearson's correlation was used as the distance metric. The closest hitthat also had a high CSI value (top 15%) was used as the nearest neighbor. If the pathway didnot have a high CSI value, then the pathway was deemed unique compared to the other databases.P > 0.85, hypergeometric, empiricallyderived).MinSpan pathways contain many gene products related to the mass balancing of the network, such astransporters, that are active in nearly every pathway. In order for a meaningful comparison betweenMinSpan and human-defined databases, the genes in each MinSpan pathway were filtered to only therepresentative genes of that pathway. To do so, we used a conservative filter to remove genes thatwere in nearly every pathway , as they are involved with type 3 loops . For alP< 0.01) of the TF-regulated genes as determined by RegulonDB  of the number of significantly changed TF-associated pathways.TFs regulating only one metabolic reaction or appearing in one pathway were removed due to a lack ofstatistical power.Assignment of TFs to MinSpan pathways was by hypergeometric enrichment using Bowtie  in genes of a particular transcription factor. A prediction was deemedcorrect if the enriched transcription factors had known associations with the environmentalshift.Dual perturbation experiments consisted of four RNA-seq experiments: (1) wild type (WT) onglucose minimal media, (2) WT + nutrient shift, (3) TF knockout (KO) on glucose minimalmedia, and (4) TF KO + nutrient shift. We defined differentially expressed gene sets betweenthe four conditions as: E1 (WT versus WT + nutrient), E2 (KO versus KO + nutrient), G1(WT versus KO), and G2 (WT + nutrient versus KO + nutrient). The differential gene setof the combination of environmental and genetic perturbations is defined as the union of thefollowing sets: {{E2▵E1}\G1 and {G2\G1}. The gene sets for the three experiments are providedin Supplementary Dataset S5. To globally determine prediction accuracy, we used RegulonDB todetermine whether a gene set was enriched (