ArticleComputational and structural biotechnology journal2021
A control theoretic three timescale model for analyzing energy management in mammalian cancer cells.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 6 citations in OpenAlex.
- Integrating state-space modeling, parameter estimation, deep learning, and docking techniques in drug repurposing: a case study on COVID-19 cytokine storm.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Comprehensive review of drug resistance in mammalian cancer stem cells: implications for cancer therapy.Cancer cell international · 2024Review
- Modeling and optimizing callus growth and development in Cannabis sativa using random forest and support vector machine in combination with a genetic algorithm.Applied microbiology and biotechnology · 2021Article
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4 authors at 4 institutions in 1 country.
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Abstract
Interaction among different pathways, such as metabolic, signaling and gene regulatory networks, of cellular system is responsible to maintain homeostasis in a mammalian cell. Malfunctioning of this cooperation may lead to many complex diseases, such as cancer and type 2 diabetes. Timescale differences among these pathways make their integration a daunting task. Metabolic, signaling and gene regulatory networks have three different timescales, such as, ultrafast, fast and slow respectively. The article deals with this problem by developing a support vector regression (SVR) based three timescale model with the application of genetic algorithm based nonlinear controller. The proposed model can successfully capture the nonlinear transient dynamics and regulations of such integrated biochemical pathway under consideration. Besides, the model is quite capable of predicting the effects of certain drug targets for many types of complex diseases. Here, energy and cell proliferation management of mammalian cancer cells have been explored and analyzed with the help of the proposed novel approach. Previous investigations including
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