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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

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  • Journal article
    MacDonald JT, Barnes C, Kitney RI, Freemont PS, Stan G-BVet al., 2011,

    Computational design approaches and tools for synthetic biology

    , INTEGRATIVE BIOLOGY, Vol: 3, Pages: 97-108, ISSN: 1757-9694
  • Journal article
    Yuan Y, Stan G-B, Warnick S, Goncalves JMet al., 2011,

    Robust dynamical network structure reconstruction.

    , Automatica, Vol: 47, Pages: 1230-1235
  • Journal article
    Dalchau N, Hubbard KE, Robertson FC, Hotta CT, Briggs HM, Stan G-B, Goncalves JM, Webb AARet al., 2010,

    Correct biological timing in Arabidopsis requires multiple light-signaling pathways

    , PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 107, Pages: 13171-13176, ISSN: 0027-8424
  • Journal article
    Mhawej M-J, Brunet-Francois C, Fonteneau R, Ernst D, Ferre V, Stan G-B, Raffi F, Moog CHet al., 2009,

    Apoptosis characterizes immunological failure of HIV infected patients

    , CONTROL ENGINEERING PRACTICE, Vol: 17, Pages: 798-804, ISSN: 0967-0661
  • Journal article
    Stan G-B, Belmudes F, Fonteneau R, Zeggwagh F, Lefebvre M-A, Michelet C, Ernst Det al., 2008,

    Modelling the influence of activation-induced apoptosis of CD4(+) and CD8(+) T-cells on the immune system response of a HIV-infected patient

    , IET SYSTEMS BIOLOGY, Vol: 2, Pages: 94-102, ISSN: 1751-8849
  • Journal article
    Zhang H-T, Chen MZ, Stan G-B, Zhou T, Maciejowski JMet al., 2008,

    Collective Behavior Coordination with Predictive Mechanisms

    , IEEE CIRCUITS AND SYSTEMS MAGAZINE, Vol: 8, Pages: 67-85, ISSN: 1531-636X
  • Journal article
    Zhang H-T, Chen MZ, Zhou T, Stan G-Bet al., 2008,

    Ultrafast consensus via predictive mechanisms

    , EPL, Vol: 83, ISSN: 0295-5075
  • Journal article
    Stan G-B, Sepulchre R, 2007,

    Analysis of interconnected oscillators by dissipativity theory

    , IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 52, Pages: 256-270, ISSN: 0018-9286
  • Journal article
    Sepulchre R, Stan GB, 2005,

    Feedback mechanisms for global oscillations in Lure systems

    , SYSTEMS & CONTROL LETTERS, Vol: 54, Pages: 809-818, ISSN: 0167-6911
  • Journal article
    Stan GB, Embrechts JJ, Archambeau D, 2002,

    Comparison of different impulse response measurement techniques

    , JOURNAL OF THE AUDIO ENGINEERING SOCIETY, Vol: 50, Pages: 249-262, ISSN: 0004-7554
  • Journal article
    Embrechts JJ, Archambeau D, Stan GB, 2001,

    Determination of the scattering coefficient of random rough diffusing surfaces for room acoustics applications

    , ACUSTICA, Vol: 87, Pages: 482-494, ISSN: 0001-7884
  • Journal article
    Caro-Astorga J, Walker KT, Ellis T,

    Bacterial cellulose spheroids as building blocks for 2D and 3D engineered living materials

    <jats:title>Abstract</jats:title><jats:p>Engineered living materials (ELMs) based on bacterial cellulose (BC) offer a promising avenue for cheap-to-produce materials that can be programmed with genetically encoded functionalities. Here we explore how ELMs can be fabricated from millimetre-scale balls of cellulose occasionally produced by <jats:italic>Acetobacteriacea</jats:italic> species, which we call BC spheroids. We define a reproducible protocol to produce BC spheroids and demonstrate their potential for use as building blocks to grow ELMs in 2D and 3D shapes. These BC spheroids can be genetically functionalized and used as the method to make and grow patterned BC-based ELMs to design. We further demonstrate the use of BC spheroids for the repair and regeneration of BC materials, and measure the survival of the BC-producing bacteria in the material over time. This work forwards our understanding of BC spheroid formation and showcases their potential for creating and repairing engineered living materials.</jats:p>

  • Journal article
    Kreula SM, Kaewphan S, Ginter F, Jones PRet al.,

    Finding novel relationships with integrated gene-gene association network analysis of <i>Synechocystis sp. </i>PCC 6803 using species-independent text-mining

    <jats:p>The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for <jats:italic>Synechocystis sp.</jats:italic> PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and rule-based algorithm to (<jats:italic>i</jats:italic>) discover novel candidate associations between different genes or proteins in the network, and (<jats:italic>ii</jats:italic>) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open source resource.</jats:p>

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