Stochastic Simulation to Visualize Gene Expression and Error Correction in Living Cells
Abstrak
Stochastic simulation can make the molecular processes of cellular control more vivid than the traditional differential-equation approach by generating typical system histories instead of just statistical measures such as the mean and variance of a population. Simple simulations are now easy for students to construct from scratch, that is, without recourse to black-box packages. In some cases, their results can also be compared directly to single-molecule experimental data. After introducing the stochastic simulation algorithm, this article gives two case studies, involving gene expression and error correction, respectively. Code samples and resulting animations showing results are given in the online supplements.
Topik & Kata Kunci
Penulis (3)
Kevin Y. Chen
Daniel M. Zuckerman
Philip C. Nelson
Akses Cepat
- Tahun Terbit
- 2018
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓