Abstract
In a cell, any information about extra- or intra-cellular changes is transferred and processed through a signaling network and dysregulation of signal flow often leads to disease such as cancer. So, understanding of signal flow in the signaling network is critical to identify drug targets. Owing to the development of high-throughput measurement technologies, the structure of a signaling network is becoming more available, but detailed kinetic parameter information about molecular interactions is still very limited. A question then arises as to whether we can estimate the signal flow based only on the structure information of a signaling network. To answer this question, we develop a novel algorithm that can estimate the signal flow using only the topological information and apply it to predict the direction of activity change in various signaling networks. Interestingly, we find that the average accuracy of the estimation algorithm is about 60–80% even though we only use the topological information. We also find that this predictive power gets collapsed if we randomly alter the network topology, showing the importance of network topology. Our study provides a basis for utilizing the topological information of signaling networks in precision medicine or drug target discovery.
Highlights
A cell processes any information about extra- or intra-cellular changes through a signaling network (Fig. 1a)
We further found that the topological information of signaling networks is highly informative for predicting the activity changes by comparing the predictions to the cases of randomized network topologies
Our study is expected to provide a basis for utilizing the topological information of signaling networks in precision medicine or drug target discovery
Summary
A cell processes any information about extra- or intra-cellular changes through a signaling network (Fig. 1a). Detailed kinetic parameter information or logical relationships about molecular interactions for constructing mathematical models of complex signaling networks are still very limited. The construction of a rigorous mathematical model for a signaling network requires kinetic parameter values and the interaction logics as well as the information on network topology[8,9]. A question arises as to whether we can estimate the activity change of signaling molecules based only on the information of network topology. To address this question, we have developed a novel algorithm that can estimate the signal flow using only the topological information of signaling networks. Our study is expected to provide a basis for utilizing the topological information of signaling networks in precision medicine or drug target discovery
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