Limits...
Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies.

Thiele S, Cerone L, Saez-Rodriguez J, Siegel A, Guziołowski C, Klamt S - BMC Bioinformatics (2015)

Bottom Line: Finally, we generalize the way predictions can be made by the sign consistency approach.In particular, we distinguish strong predictions (e.g. increase of a node level) and weak predictions (e.g., node level increases or remains unchanged) enlarging the overall predictive power of the approach.Overall, our work enhances the flexibility and power of the sign consistency approach for the prediction of the behavior of signaling and gene regulatory networks and, more generally, for the validation and inference of these networks.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, 39106, Germany. thieles@mpi-magdeburg.mpg.de.

ABSTRACT

Background: A rapidly growing amount of knowledge about signaling and gene regulatory networks is available in databases such as KEGG, Reactome, or RegulonDB. There is an increasing need to relate this knowledge to high-throughput data in order to (in)validate network topologies or to decide which interactions are present or inactive in a given cell type under a particular environmental condition. Interaction graphs provide a suitable representation of cellular networks with information flows and methods based on sign consistency approaches have been shown to be valuable tools to (i) predict qualitative responses, (ii) to test the consistency of network topologies and experimental data, and (iii) to apply repair operations to the network model suggesting missing or wrong interactions.

Results: We present a framework to unify different notions of sign consistency and propose a refined method for data discretization that considers uncertainties in experimental profiles. We furthermore introduce a new constraint to filter undesired model behaviors induced by positive feedback loops. Finally, we generalize the way predictions can be made by the sign consistency approach. In particular, we distinguish strong predictions (e.g. increase of a node level) and weak predictions (e.g., node level increases or remains unchanged) enlarging the overall predictive power of the approach. We then demonstrate the applicability of our framework by confronting a large-scale gene regulatory network model of Escherichia coli with high-throughput transcriptomic measurements.

Conclusion: Overall, our work enhances the flexibility and power of the sign consistency approach for the prediction of the behavior of signaling and gene regulatory networks and, more generally, for the validation and inference of these networks.

No MeSH data available.


Related in: MedlinePlus

Repair by adding signed influences example (Minimal Correction Sets - MCoS). There exist three alternative repair sets: repair set a adds a positive influence to A and repair set b includes a negative influence on B, repair set c includes a positive influence on A and a negative influence on B. Repair sets a and b are minimal containing only one repair, repair set c is not minimal having two repairs. Looking at the intersection of the labelings under minimal repairs, we can conclude that C is either responsible for an increase in A or a decrease in B. We can therefore exclude a labeling of C with 0, we can predict: pred(C)=±
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4625540&req=5

Fig7: Repair by adding signed influences example (Minimal Correction Sets - MCoS). There exist three alternative repair sets: repair set a adds a positive influence to A and repair set b includes a negative influence on B, repair set c includes a positive influence on A and a negative influence on B. Repair sets a and b are minimal containing only one repair, repair set c is not minimal having two repairs. Looking at the intersection of the labelings under minimal repairs, we can conclude that C is either responsible for an increase in A or a decrease in B. We can therefore exclude a labeling of C with 0, we can predict: pred(C)=±

Mentions: To resolve inconsistencies one may add new influences to the model if the later is considered to be potentially incomplete (which is often the case in practice). Adding an influence can be used to indicate missing (unknown) regulations or oscillations of regulators that would explain the (topology-inconsistent) measurements. We use minimal correction sets (MCoS) as defined in [17] as minimal sets of new signed (positive or negative) input influences that restore consistency of model and data. MCoS are defined as signed influences and are specific for a single experiment; they might be incompatible with other experiments. Note that every inconsistency can be repaired by adding a new influence. Therefore, adding influences is always suited to restore consistency. Also the MCoS can be interpreted as a measure of consistency of model and data. Compared to SCEN-FIT, MCoS yields always a smaller or equal number of repairs. Therefore we define the inconsistency-index of a network with respect to data as (MCoS/number of observations in the experiment). Figure 7 illustrates how repair through addition of influences works.Fig. 7


Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies.

Thiele S, Cerone L, Saez-Rodriguez J, Siegel A, Guziołowski C, Klamt S - BMC Bioinformatics (2015)

Repair by adding signed influences example (Minimal Correction Sets - MCoS). There exist three alternative repair sets: repair set a adds a positive influence to A and repair set b includes a negative influence on B, repair set c includes a positive influence on A and a negative influence on B. Repair sets a and b are minimal containing only one repair, repair set c is not minimal having two repairs. Looking at the intersection of the labelings under minimal repairs, we can conclude that C is either responsible for an increase in A or a decrease in B. We can therefore exclude a labeling of C with 0, we can predict: pred(C)=±
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4625540&req=5

Fig7: Repair by adding signed influences example (Minimal Correction Sets - MCoS). There exist three alternative repair sets: repair set a adds a positive influence to A and repair set b includes a negative influence on B, repair set c includes a positive influence on A and a negative influence on B. Repair sets a and b are minimal containing only one repair, repair set c is not minimal having two repairs. Looking at the intersection of the labelings under minimal repairs, we can conclude that C is either responsible for an increase in A or a decrease in B. We can therefore exclude a labeling of C with 0, we can predict: pred(C)=±
Mentions: To resolve inconsistencies one may add new influences to the model if the later is considered to be potentially incomplete (which is often the case in practice). Adding an influence can be used to indicate missing (unknown) regulations or oscillations of regulators that would explain the (topology-inconsistent) measurements. We use minimal correction sets (MCoS) as defined in [17] as minimal sets of new signed (positive or negative) input influences that restore consistency of model and data. MCoS are defined as signed influences and are specific for a single experiment; they might be incompatible with other experiments. Note that every inconsistency can be repaired by adding a new influence. Therefore, adding influences is always suited to restore consistency. Also the MCoS can be interpreted as a measure of consistency of model and data. Compared to SCEN-FIT, MCoS yields always a smaller or equal number of repairs. Therefore we define the inconsistency-index of a network with respect to data as (MCoS/number of observations in the experiment). Figure 7 illustrates how repair through addition of influences works.Fig. 7

Bottom Line: Finally, we generalize the way predictions can be made by the sign consistency approach.In particular, we distinguish strong predictions (e.g. increase of a node level) and weak predictions (e.g., node level increases or remains unchanged) enlarging the overall predictive power of the approach.Overall, our work enhances the flexibility and power of the sign consistency approach for the prediction of the behavior of signaling and gene regulatory networks and, more generally, for the validation and inference of these networks.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, 39106, Germany. thieles@mpi-magdeburg.mpg.de.

ABSTRACT

Background: A rapidly growing amount of knowledge about signaling and gene regulatory networks is available in databases such as KEGG, Reactome, or RegulonDB. There is an increasing need to relate this knowledge to high-throughput data in order to (in)validate network topologies or to decide which interactions are present or inactive in a given cell type under a particular environmental condition. Interaction graphs provide a suitable representation of cellular networks with information flows and methods based on sign consistency approaches have been shown to be valuable tools to (i) predict qualitative responses, (ii) to test the consistency of network topologies and experimental data, and (iii) to apply repair operations to the network model suggesting missing or wrong interactions.

Results: We present a framework to unify different notions of sign consistency and propose a refined method for data discretization that considers uncertainties in experimental profiles. We furthermore introduce a new constraint to filter undesired model behaviors induced by positive feedback loops. Finally, we generalize the way predictions can be made by the sign consistency approach. In particular, we distinguish strong predictions (e.g. increase of a node level) and weak predictions (e.g., node level increases or remains unchanged) enlarging the overall predictive power of the approach. We then demonstrate the applicability of our framework by confronting a large-scale gene regulatory network model of Escherichia coli with high-throughput transcriptomic measurements.

Conclusion: Overall, our work enhances the flexibility and power of the sign consistency approach for the prediction of the behavior of signaling and gene regulatory networks and, more generally, for the validation and inference of these networks.

No MeSH data available.


Related in: MedlinePlus