title: Formal modeling and analysis of ER-αassociated Biological Regulatory Network in breast cancer.
abstract: BACKGROUND
  Breast 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.
  
  
  METHODS
  A 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.
  
  
  RESULTS
  In-silicobased discrete and continuous modeling of ER-αassociated 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.
  
  
  CONCLUSIONS
  Thein-silicoapproaches 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.
authors: Atta-ur-Rahman School of Applied Biosciences (ASAB)/Healthcare Biotechnology, National University of Science and Technology , Islamabad , Pakistan.
authors: Samra
authors: S
authors: Khalid
authors: Atta-ur-Rahman School of Applied Biosciences (ASAB)/Healthcare Biotechnology, National University of Science and Technology , Islamabad , Pakistan.
authors: Rumeza
authors: R
authors: Hanif
authors: Maastricht Centre for Systems Biology (MaCSBio), Maastricht University , Maastricht , Netherlands.
authors: Samar H K
authors: SH
authors: Tareen
authors: Research Center for Modeling & Simulation (RCMS), National University of Science and Technology , Islamabad , Pakistan.
authors: Amnah
authors: A
authors: Siddiqa
authors: Research Center for Modeling & Simulation (RCMS), National University of Science and Technology , Islamabad , Pakistan.
authors: Zurah
authors: Z
authors: Bibi
authors: Research Center for Modeling & Simulation (RCMS), National University of Science and Technology , Islamabad , Pakistan.
authors: Jamil
authors: J
authors: Ahmad
keywords: Biological Regulatory Network; Breast cancer; Continuous Hybrid Petri Net; Discrete and continuous modeling; Estrogen receptor-alpha; GENOTECH; René Thomas formalism; SMBioNet