Formal modeling and analysis of ER-αassociated Biological Regulatory Network in breast cancer

Abstract

BackgroundBreast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis.MethodsA discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities.ResultsIn-silico based discrete and continuous modeling of ER-α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC.ConclusionThe in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

Association of rs1801157 single nucleotide polymorphism of CXCL12 gene in breast cancer in Pakistan andin-silicoexpression analysis of CXCL12–CXCR4 associated biological regulatory network

“…The formal method of BRN modeling is a traditional approach which permits us to define the complexity of biological system which was more complex to identify through in-vitro experiments. A BRN consisted of two main types of biological regulations are activation (represent as positive sign) and inhibition (represent as negative sign) that have been achieved through previous experimental findings ( Khalid et al, 2016 ; Kim et al, 2014 ; Sobolik et al, 2014 ; Teicher & Fricker, 2010 ). Experimental data was used to further validate the expression levels of each entity which interlinked at diverse points, related to CXCL12–CXCR4 associated BRN.…”

“…The kinetic logic formalism was used to analyze the behaviours of genes and proteins involved in BRN. The dynamics of Rene’ Thomas formalism has been provided from Ahmad et al (2007) , Ahmad et al (2012) and Khalid et al (2016) . These dynamics are specified as respective differential equation of the system.…”

“…The overexpression of PDZK1, a newly identified estrogen regulator protein, enhances activity of growth promoting factors including estrogen growth factor receptor (EGFR) and insulin-like growth factor receptor (IGF-1R) and their related signaling pathways associated with BC ( Kim et al, 2014 ). It can lead to an increased expression of estrogen and progesterone receptor (ER and PR) that are translocated in the nucleus, causing loss of function mutations of tumor suppressor genes (TSGs) such as phosphatase and tensin homolog (PTEN), BC susceptibility genes 1 (BRCA1), p53 and mouse double minute 2 homolog (Mdm2) ( Bailey et al, 2012 ; Daniel, Hagan & Lange, 2011 ; Khalid et al, 2016 ; Liu et al, 2006 ; Werner & Maor, 2006 ).…”

An In Silico Cell Signaling-Based Approach for Exploring the Activities Involved in Pre-Metastasis and Metastasis

“…Regarding cancer research, in the last years it has found valuable support in a wide range of modeling and simulation approaches, which cover a wide spectrum ranging from mathematical models -e.g., continuous models [2][3][4] and stochastic models [5][6][7][8] to computational models -e.g., Monte Carlo method and cellular automata [9][10][11], Boolean networks [12], Petri nets [13], artificial neural networks [14][15][16] and expert systems [17]. These approaches have allowed the in silico experimentation in cancer at the cellular, system and patient level.…”

Pharmacophore modeling for identification of anti-IGF-1R drugs and in-vitro validation of fulvestrant as a potential inhibitor

“…The direct molecular interaction between IGF-1R and PDZK1 enhances expression of ER-α associated with breast cancer metastasis [ 26 ]. The IGF-1R pathway facilitates loss of function mutations of multiple tumor suppressor and oncogenes including breast cancer susceptibility genes 1/2 (BRCA1/2), p53 and mouse double minute 2 homolog (Mdm2) which drastically influence resistance to apoptosis [ 20 , 27 ]. This study focused on the identification of inhibitors against IGF-1R by using well-known in-silico approaches, i.e.…”