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Research

Biological systems - including the simplest cells - exhibit a broad range of functions to thrive in their environment. Research in the Imperial College Centre for Synthetic Biology is focused on the possibility of engineering the underlying biochemical processes to solve many of the challenges facing society, from healthcare to sustainable energy. In particular, we model, analyse, design and build biological and biochemical systems in living cells and/or in cell extracts, both exploring and enhancing the engineering potential of biology.

As part of our research we develop novel methods to accelerate the celebrated Design-Build-Test-Learn synthetic biology cycle. As such research in the Centre for Synthetic Biology highly multi- and interdisciplinary covering computational modelling and machine learning approaches; automated platform development and genetic circuit engineering ; multi-cellular and multi-organismal interactions, including gene drive and genome engineering; metabolic engineering; in vitro/cell-free synthetic biology; engineered phages and directed evolution; and biomimetics, biomaterials and biological engineering.

Publications

Citation

BibTex format

@inbook{Sootla:2020:10.1007/978-3-030-35713-9_11,
author = {Sootla, A and Stan, G-B and Ernst, D},
booktitle = {Lecture Notes in Control and Information Sciences},
doi = {10.1007/978-3-030-35713-9_11},
pages = {283--312},
publisher = {Springer International Publishing},
title = {Solving Optimal Control Problems for Monotone Systems Using the Koopman Operator},
url = {http://dx.doi.org/10.1007/978-3-030-35713-9_11},
year = {2020}
}

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RIS format (EndNote, RefMan)

TY  - CHAP
AB  - © Springer Nature Switzerland AG 2020. Koopman operator theory offers numerous techniques for analysis and control of complex systems. In particular, in this chapter we will argue that for the problem of convergence to an equilibrium, the Koopman operator can be used to take advantage of the geometric properties of controlled systems, thus making the optimal solutions more transparent, and easier to analyze and implement. The motivation for the study of the convergence problem comes from biological applications, where easy-to-implement and easy-to-analyze solutions are of particular value. At the moment, theoretical results have been developed for a class of nonlinear systems called monotone systems. However, the core ideas presented here can be applied heuristically to non-monotone systems. Furthermore, the convergence problem can serve as a building block for solving other control problems such as switching between stable equilibria or inducing oscillations. These applications are illustrated in biologically inspired numerical examples.
AU  - Sootla,A
AU  - Stan,G-B
AU  - Ernst,D
DO  - 10.1007/978-3-030-35713-9_11
EP  - 312
PB  - Springer International Publishing
PY  - 2020///
SN  - 9783030357122
SP  - 283
TI  - Solving Optimal Control Problems for Monotone Systems Using the Koopman Operator
T1  - Lecture Notes in Control and Information Sciences
UR  - http://dx.doi.org/10.1007/978-3-030-35713-9_11
UR  - http://hdl.handle.net/10044/1/84155
ER  -