Modeling of cell signaling pathways in macrophages by semantic networks

Abstract

Background: Substantial amounts of data on cell signaling, metabolic, gene regulatory and other biological pathways have been accumulated in literature and electronic databases. Conventionally, this information is stored in the form of pathway diagrams and can be characterized as highly "compartmental" (i.e. individual pathways are not connected into more general networks). Current approaches for representing pathways are limited in their capacity to model molecular interactions in their spatial and temporal context. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways.

Results: We have applied a semantic network (SN) approach to develop and implement a model for cell signaling pathways. The semantic model has mapped biological concepts to a set of semantic agents and relationships, and characterized cell signaling events and their participants in the hierarchical and spatial context. In particular, the available information on the behaviors and interactions of the PI3K enzyme family has been integrated into the SN environment and a cell signaling network in human macrophages has been constructed. A SN-application has been developed to manipulate the locations and the states of molecules and to observe their actions under different biological scenarios. The approach allowed qualitative simulation of cell signaling events involving PI3Ks and identified pathways of molecular interactions that led to known cellular responses as well as other potential responses during bacterial invasions in macrophages.

Conclusions: We concluded from our results that the semantic network is an effective method to model cell signaling pathways. The semantic model allows proper representation and integration of information on biological structures and their interactions at different levels. The reconstruction of the cell signaling network in the macrophage allowed detailed investigation of connections among various essential molecules and reflected the cause-effect relationships among signaling events. The simulation demonstrated the dynamics of the semantic network, where a change of states on a molecule can alter its function and potentially cause a chain-reaction effect in the system.

Figure 1

An example of a semantic network. Characteristics and behaviors of a semantic agent (SA) are defined by its relationships (RE) with other agents. Semantic agents are represented as circles, and relationships are depicted as arrows. This SN-model represents that a protein A can be located at a nucleus, can interact with a protein B or catalyze a chemical reaction. For explanatory purpose, this figure illustrates an example of a semantic network. The implemented semantic network (as presented in the paper) is more complex and involves different types of relationships and agents.

Figure 2

Interactions among biological structures of different levels in the SN. The left panel shows an example of a translocation event when a protein B is moved from the cytosol to the plasma membrane. The right panel shows an example of a non-covalent interaction between a protein A and a protein B via non-covalent forces.

Figure 3

A model of a non-covalent interaction between a PI3K-p110 and a Ras. The figure was graphed from the developed SN to illustrate the relationships among different agents. The figure visualizes the agents as icons and their relationship as arrows. The left panel illustrates that a PI3K-p110 contains a "Not Bound" Ras-binding site and a "Non-Functional" catalytic domain. The right panel shows that when the PI3K-p110 has bound to a Ras, its Ras-binding site has switched to "Bound", and the catalytic domain has become "Functional" due to a positive allosteric regulation event. State changes as a result of the interaction are shown in bold. Note that the model stores the information, which specifies the non-covalent event between the prototypic Ras and the prototypic PI3K-p110, and the condition for the event to occur. This figure illustrates an instance of the Ras-binding event occurred during a simulation. The PI3K-p110 is an instance of the PI3K-p110 prototype, and it is the same agent before and after it binds to the Ras. Figure 8 shows the description of each icon.

Figure 4

A model for covalent interactions. Figure 4a shows that an Akt protein can be phosphorylated to an Akt-phosphate by an enzyme, PDK1, and an ATP is converted to an ADP in the process. Figure 4b shows a similar covalent interaction event where substrate Glucose can be converted to Glucose-6-phosphate by an enzyme Hexokinase.

Figure 5

Figure 5a- Phagocytosis of bacteria in macrophages. The picture shows macrophages ingesting green fluorescent mycobacteria (indicated by arrows). The host cell membrane was stained by red fluorochorme PKH to define the limit of the cell. (The picture was provided by Zakaria Hmama) Figure 5b- A SN-representation of the cell signaling network that regulates phagocytosis in the human macrophage. Both molecules and their interactions (non-covalent and covalent interactions) are represented as semantic agents and visualized as nodes (with distinct icons) on the diagram. Arrows represent the semantic relationships between different agents.

Figure 6

A SN- simulator: at the beginning of the simulation. The simulation showed the actions of molecules under a biological scenario. 1. The initializing buttons synthesize molecules in each subcellular compartment. 2. The localization window shows molecules present in each subcellular compartment. In this simulation, an IgG molecule was present at the extracellular space (E.S.). There were 2 ATP molecules, an Fcγ receptor (FcγR), a Gab2 and a PIP2 (PI[4,5]P2) present at the plasma membrane (P.M.). The cytosol contained a Lyn kinase, a PI3K-p85 and a PI3K-p110 subunit. There was no molecule present at the nucleus in this simulation. 3. The "Start Simulation" button creates a previously specified translocation event. In this simulation, the translocation has already occurred and moved the IgG from the extracellular space to the plasma membrane. 4. The "Next" button triggers a search that determines a proper event to occur and advances to the next step. 5. The pathway-viewer shows a series of events occurred during the simulation.

Figure 7

A SN- simulator: at the end of the simulation. The pathway-viewer shows that the initial translocation of the IgG molecule has led to the occurrence of a series of events, which include several non-covalent interactions, covalent interactions, and translocations of various molecules: Event #1: the IgG was translocated from the extracellular space to the plasma membrane. Event #2: the IgG bound to the Fcγ receptor at the plasma membrane. Event #3: the Lyn was translocated from the cytosol to the plasma membrane. Event #4: the Lyn bound to the Fcγ receptor at the plasma membrane. Event #5: the Lyn phosphorylated the Gab2 to a Gab2-phosphate (Gab2-P) at the plasma membrane. Event #6: the PI3K-p85 and p110 (already bound to each other) were translocated together from the cytosol to the plasma membrane. Event #7: the PI3K-p85 bound to the Gab2-P at the plasma membrane. Event #8: the PI3K-p110 phosphorylated the PIP2 to a PIP3 (PI[3,4,5]P3) at the plasma membrane. Event #9: The formation of the PIP3 caused phagosome formation.

Figure 8

Description of icons used in other figures.