Data Driven Mechanistic Models for Systems Biology
Models of biological systems, such as gene networks, are useful in extracting meaning from quantitative data obtained from specific biological systems. In this PhD proposal we are interested in how things actually work in real observable biological systems. To do this we will make use of machine learning to infer mechanistic models biological systems.
Biological systems are immensely complex, and extracting all the data necessary to characterize the system is often impossible. It is therefore important to exploit other sources of information when modelling biological systems. Once such source of information is “mechanistic models”. These are models of the underlying physical properties of the system. In this project we will ensure that such physical models can be easily combined with data driven machine learning approaches, aiming to obtain the best of both worlds: mechanistic modelling and data driven machine learning models.
The project will involve a large amount of mathematics, in particular advanced linear algebra and calculus.