Signaling pathways mediate the response of external stimuli on gene expression. The signaling proteins in these pathways interact with each other and their phosphorylation levels serve as an indicator of the activity in signaling pathways. In most cases, the last protein in signaling pathway is a transcription factor which participates in regulating the transcription of specific genes. Many of these signaling pathways have been well studied. Alliance for Cellular Signaling (AfCS) has measured time-course data in RAW 264.7 macrophage cells on important phosphoproteins, such as the mitogen-activated protein kinases (MAPKs) and signal transducer and activator of transcription (STATs), in single- and double-ligand stimulation experiments for 22 ligands (total 241 experiments). In the present work, we aimed to identify the interactions between signaling pathways using a data-driven approach. We have used early time point data from these experiments to study the phosphoprotein interactions with the assumption that at early time point, degradation effects are negligible. Later time points were discarded because then the degradation effects become prominent and present complications for a linear model based approach. We have used dynamic mapping (y_t+1 = f(y_t)) and calculated the interaction coefficients using a partial least squares (PLS) approach. Significant interaction coefficients were selected based on 99% confidence in T-test by comparing with the coefficient distributions for the corresponding random models (based on random shuffling of the outputs). Using the PLS approach, we have reconstructed the phosphoproteins signaling network and have validated with the interactions already known in the literature. We have shown that this data-driven approach is able to identify most of the known signaling interactions and predicts potentially novel interactions.