Application of in silico simulations to genetic studies
Application of in silico simulations to genetic studies
zhila shokrian,1,*Nahid shokrian,2Danyal Farajnia Ric,3Mobina Movahed Majd,4Amir Samiei,5
1. Student of research Committee,Medical University of Sarab 2. Member Midwifery Scientific Association of Iran 3. Student of research Committee Medical University of Sarab 4. Student of research Committee,Medical University of Sarab 5. Student of research Committee,Medical University of Sarab
Introduction: About 13% of all deaths worldwide are due to cancer. Oncology is fundamentally based on prognostic aspects. These days, biomedical science relies heavily on computer support to analyze extensive data, quantify dynamic and multiscale events, or similarly simulate complex models. Computational models have been used for intracellular and intercellular aspects, tissue and organ-specific. In this regard, we focus on assessing and predicting tumor growth. The mathematical basis of tumor growth was explained in the middle of the last century. We take in silico modeling of tumor growth as an initial tool and further develop it into a new web-based simulation that is uniformly accessible to biomedical scientists and clinicians. Focusing on visualizing features is key to learning and understanding ding. Therefore, features are essential for knowledge discovery. The possibilities and accessibility of our simulation and visualization approach may ultimately encourage researchers and clinicians to advance the tumor research field toward personalized medicine. Future integrations will include biomolecular networks such as drug-protein interactions or patterns of genetic variation.
Methods: We studied and reviewed related articles by searching for keywords such as nanotechnology and in silico methods on reliable scientific sites such as PubMed and Science Direct and by entering the time filter from 2019 to 2022. We succeeded in presenting this review article.
Results: Our goal is to provide a comprehensive and extensible simulation tool to visualize tumor growth. According to tumor growth activity, computational models for different types of tumors, from animal and human models, that deal with individual stages of tumor development. Understanding tumor heterogeneity concerning personalized cancer therapy represents the ultimate goal of computational tumor growth modeling. Using tumor growth data and related gene data and providing an open source database for tumor growth data are significant steps forward to support scientific collaborations and clinical programs and ultimately help fight cancer.
Conclusion: We believe our approach provides an impetus to advance in silico modeling towards 3R and a better understanding of tumor dynamics. We emphasize the computational modeling approach of biological systems and the development of computational modeling tools for simulation and reproducibility experiments in biological research. In silico methods overcome the lack of wet testing facilities and succeed as dry methods in terms of reduction, modification and replacement. Animal testing is also known as the 3R principles. Our visualization approach to simulation allows for more flexible use and accessible extension to facilitate understanding and gain new insights. In silico modeling and other computational techniques help answer critical questions in cancer research.