# Fibrosis Protein-Protein Interactions from Google Matrix Analysis of MetaCore Network
## 1 Introduction  
Cardiovascular disease, a class of diseases that impact the cardiovascular system, is responsible for 31% of all deaths and remains the leading cause of mortality worldwide [1]. Myocardial fibrosis is central to the pathology of cardiovascular complications that leads to human failure and death [2]. Cardiac fibrosis results from uncontrolled fibroblast activity and excessive extracellular matrix deposition [2]. Although a number of factors have been implicated in orchestrating the fibrotic response, tissue fibrosis is dominated by a central mediator: transforming growth factor-\(\beta\) (TGF-\(\beta\)) [3]. Sustained TGF-\(\beta\) production leads to a continuous cycle of growth factor signaling and deregulated matrix turnover [3]. However, despite intensive research, the factors that undersrate fibrosis are still poorly understood and, as a result, effective strategies for reversing fibrosis are lacking [2, 4]. Considering the complex heterogeneity of fibrosis, research strategy on a system-level understanding of the disease using mathematical modeling approaches is a driving force to dissect the complex processes involved in fibrotic disorders. Recently, we have reproduced the classic hallmarks of aberrant cardiac fibroblast activation leading to fibrosis, and provided a powerful toolbox for fully characterizing cardiac fibroblast transcriptome [5]. Although the pathogenesis of fibrotic remodeling has not been well identified, accumulated evidence suggests that multiple genes/proteins and their interactions play important roles in disease scenarios [6]. Traditional research has been performed to reveal the involvement of a particular gene or protein in fibrosis physiopathology [5, 7]. Although these studies generated invaluable data, they still provide a small amount of evidence that is insufficient to clarify the complex nature of interactions between multiple genes or proteins simultaneously. Consequently, it is essential to develop new, multitiered approaches for global analysis of molecular interactions defining cell functional status in pathological conditions. In this context, protein-protein interactions (PPI) represent a highly promising, although challenging, class of potential targets for therapeutic development. The PPI control key functions and physio(pathological states of the cells. In fibrotic tissue remodeling, PPI form signalingnodes and hubs that transmit pathophysiological cues along molecular networks to achieve an integrated biological output, thereby promoting fibrosis [6]. Thus, pathway perturbation, through disruption of PPI critical for fibrosis, offers a novel and effective strategy for curtailing the transmission of profibrotic signals. Deciphering of fibrosis-specific PPI would uncover new mechanisms of fibrotic signaling for therapeutic interrogation. In this study, we propose a Google matrix-based approach for the prediction of PPI linked to myocardial fibrosis using MetaCore network database. The present work is based on the recent results presented in [5] which allowed determination of the protein profibrotic responses as a feedback on TGF protein stimulation, which is known to play an important role in tissue fibrosis [3]. These experiments identify proteins with most positive and most negative response in cardiac fibroblasts. To sum up, from the experimental results reported in [5], we select 40 proteins, including the top 20 positive and top 20 negative responses. The protein profile is given in Table 1 marked by indexes \(K_{u}=1,2,\ldots,20;K_{d}=1,2,\ldots,20\). These proteins are ordered monotonically from the strongest \(K_{u}=1\) to to weakest \(K_{u}=20\) positive responses; the same monotonic ordering is performed by modulus of negative response with strongest \(K_{d}=1\) to weakest \(K_{d}=20\) responses. An additional group of 4 TGF-\(\beta\)-associated proteins with indexes \(K_{t}=1,2,3,4\) was integrated in the primary list of factors used in experiments [5]. These 44 proteins form the internal selected fibrosis group.