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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

@article{Rivadeneira:2014:10.1089/biores.2014.0024,
author = {Rivadeneira, PS and Moog, CH and Stan, G-B and Brunet, C and Raffi, F and Ferré, V and Costanza, V and Mhawej, MJ and Biafore, F and Ouattara, DA and Ernst, D and Fonteneau, R and Xia, X},
doi = {10.1089/biores.2014.0024},
journal = {Biores Open Access},
pages = {233--241},
title = {Mathematical Modeling of HIV Dynamics After Antiretroviral Therapy Initiation: A Review.},
url = {http://dx.doi.org/10.1089/biores.2014.0024},
volume = {3},
year = {2014}
}

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RIS format (EndNote, RefMan)

TY - JOUR
AB - This review shows the potential ground-breaking impact that mathematical tools may have in the analysis and the understanding of the HIV dynamics. In the first part, early diagnosis of immunological failure is inferred from the estimation of certain parameters of a mathematical model of the HIV infection dynamics. This method is supported by clinical research results from an original clinical trial: data just after 1 month following therapy initiation are used to carry out the model identification. The diagnosis is shown to be consistent with results from monitoring of the patients after 6 months. In the second part of this review, prospective research results are given for the design of individual anti-HIV treatments optimizing the recovery of the immune system and minimizing side effects. In this respect, two methods are discussed. The first one combines HIV population dynamics with pharmacokinetics and pharmacodynamics models to generate drug treatments using impulsive control systems. The second one is based on optimal control theory and uses a recently published differential equation to model the side effects produced by highly active antiretroviral therapy therapies. The main advantage of these revisited methods is that the drug treatment is computed directly in amounts of drugs, which is easier to interpret by physicians and patients.
AU - Rivadeneira,PS
AU - Moog,CH
AU - Stan,G-B
AU - Brunet,C
AU - Raffi,F
AU - Ferré,V
AU - Costanza,V
AU - Mhawej,MJ
AU - Biafore,F
AU - Ouattara,DA
AU - Ernst,D
AU - Fonteneau,R
AU - Xia,X
DO - 10.1089/biores.2014.0024
EP - 241
PY - 2014///
SN - 2164-7844
SP - 233
TI - Mathematical Modeling of HIV Dynamics After Antiretroviral Therapy Initiation: A Review.
T2 - Biores Open Access
UR - http://dx.doi.org/10.1089/biores.2014.0024
UR - https://www.ncbi.nlm.nih.gov/pubmed/25371860
UR - http://hdl.handle.net/10044/1/18554
VL - 3
ER -