Munshi, AnjanaSharma, V.2018-07-142024-08-142018-07-142024-08-142014Munshi, A., & Sharma, V. (2014). In silico disease models of breast cancer Omics Approaches in Breast Cancer: Towards Next-Generation Diagnosis, Prognosis and Therapy (pp. 315-331).97881322084338132208420978813220842610.1007/978-81-322-0843-3_16https://kr.cup.edu.in/handle/32116/1366Breast cancer is a highly heterogeneous disease as a consequence of multiple cells and genetic aberrations. It is the second leading cause of death among women in Western countries. It has been reported that approximately 1 in 8 women is affected by breast cancer and one-third of women die from breast cancer every year. The most common type of breast cancer is infiltrating ductal carcinoma, which represents around 80 % of all malignancies. Recent advances in the area of breast cancer have increased the survival rate of women with breast cancer. The post-genomic area has provided information regarding gene mutations and their effect on pathogenesis as well as on the outcome of breast cancer. A number of interacting biomarkers belonging to different pathways have been reported to influence the progression of breast cancer. However, we need more authenticated and sophisticated technology for early diagnosis and effective treatment in the area of breast cancer. In the past few years, computational modeling or in silico modeling and simulation of disease processes has gained momentum. Computational models of breast cancer have been developed to aid both biological mechansims and oncologists. The development of in silico models is facilitated by experimental and analytical tools which generate required information and data. Statistical models of cancer at the pathway levels, genomics, and transcriptomics have been proven to be effective in developing prognostics/diagnostics. Statistically inferred network models have been proven to be useful for avoiding data overfitting. Signaling and metabolic models with the knowledge of the biochemical processes involved and metabolism, derived from research studies, can also be reconstructed. At longer length scales, agent-based and continuum models of the breast cancer microenvironment and other tissue-level interactions would enable modeling of cancer cells and predictions of tumor progression. Even though breast cancer has been studied using genomics, transcriptomics, and systems approaches, significant challenges yet remain in order to translate the enormous potential of in silico cancer biology for the betterment of patients suffering with breast cancer, thus shifting the paradigm from conventional population-based to patient-specific cancer medicine. ? 2014 Springer India. All rights reserved.en-USBook Chapterhttps://link.springer.com/chapter/10.1007%2F978-81-322-0843-3_16