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Synthetic Biology underpins advances in the bioeconomy

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

@article{Kuntz:2019:10.1063/1.5100670,
author = {Kuntz, Nussio J and Thomas, P and Stan, GB and Barahona, M},
doi = {10.1063/1.5100670},
journal = {Journal of Chemical Physics},
pages = {034109--034109},
title = {Bounding the stationary distributions of the chemical master equation via mathematical programming},
url = {http://dx.doi.org/10.1063/1.5100670},
volume = {151},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - The stochastic dynamics of biochemical networks are usually modelled with the chemical master equation (CME). The stationary distributions of CMEs are seldom solvable analytically, and numerical methods typically produce estimates with uncontrolled errors. Here, we introduce mathematical programming approaches that yield approximations of these distributions with computable error bounds which enable the verification of their accuracy. First, we use semidefinite programming to compute increasingly tighter upper and lower bounds on the moments of the stationary distributions for networks with rational propensities. Second, we use these moment bounds to formulate linear programs that yield convergent upper and lower bounds on the stationary distributions themselves, their marginals and stationary averages. The bounds obtained also provide a computational test for the uniqueness of the distribution. In the unique case, the bounds form an approximation of the stationary distribution with a computable bound on its error. In the non unique case, our approach yields converging approximations of the ergodic distributions. We illustrate our methodology through several biochemical examples taken from the literature: Schl¨ogl’s model for a chemical bifurcation, a two-dimensional toggle switch, a model for bursty gene expression, and a dimerisation model with multiple stationary distributions.
AU - Kuntz,Nussio J
AU - Thomas,P
AU - Stan,GB
AU - Barahona,M
DO - 10.1063/1.5100670
EP - 034109
PY - 2019///
SN - 0021-9606
SP - 034109
TI - Bounding the stationary distributions of the chemical master equation via mathematical programming
T2 - Journal of Chemical Physics
UR - http://dx.doi.org/10.1063/1.5100670
UR - http://hdl.handle.net/10044/1/70872
VL - 151
ER -