About the Project
5. Further Information
This project is ideal for a student with a Bachelors or Masters in Applied Mathematics, Physics, Computer Science, Engineering or a closely related field. Previous familiarity with mathematical modelling in biology is useful but not a necessity. The student will be given extensive training in stochastic modelling, control theory and machine learning in the context of biology in their first year to ensure a solid foundation. The student will be part of the group of Dr. Ramon Grima http://grimagroup.bio.ed.ac.uk/index.html, which is located in the Centre for Synthetic and Systems Biology (SynthSys) at the University of Edinburgh. The experiments will be conducted in the lab of Dr. Filippo Menolascina.
If you would like us to consider you for one of our scholarships you must apply by 12 noon on 13 December 2018 at the latest.
 Menolascina, Filippo, Mario Di Bernardo, and Diego Di Bernardo. "Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks." Automatica 47.6 (2011): 1265-1270.
 Fiore, Gianfranco, et al. "In vivo real-time control of gene expression: a comparative analysis of feedback control strategies in yeast." ACS synthetic biology 5.2 (2015): 154-162.
 Cao, Zhixing, and Ramon Grima. "Linear mapping approximation of gene regulatory networks with stochastic dynamics." Nature communications 9.1 (2018): 3305.
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