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  • Conference paper
    Dominguez-Huttinger E, Ono M, Barahona M, Tanaka RJet al., 2012,

    System-level investigation of risk factors for atopic dermatitis by mathematical modelling and analysis

    , 42nd Annual Meeting of the European-Society-for-Dermatological-Research (ESDR), Publisher: NATURE PUBLISHING GROUP, Pages: S25-S25, ISSN: 0022-202X
  • Conference paper
    Huettinger ED, Ono M, Barahona M, Tanaka RJet al., 2012,

    Mathematical model of the development of Atopic dermatitis

    , 22nd IUBMB Congress/37th FEBS Congress, Publisher: WILEY-BLACKWELL, Pages: 523-523, ISSN: 1742-464X
  • Journal article
    Wang B, Barahona M, Buck M, 2012,

    A modular cell-based biosensor using engineered genetic logic circuits to detect and integrate multiple environmental signals

    , Biosensors and Bioelectronics, Vol: 40, Pages: 368-376, ISSN: 0956-5663

    Cells perceive a wide variety of cellular and environmental signals, which are often processed combinatorially to generate particular phenotypic responses. Here, we employ both single and mixed cell type populations, pre-programmed with engineered modular cell signalling and sensing circuits, as processing units to detect and integrate multiple environmental signals. Based on an engineered modular genetic AND logic gate, we report the construction of a set of scalable synthetic microbe-based biosensors comprising exchangeable sensory, signal processing and actuation modules. These cellular biosensors were engineered using distinct signalling sensory modules to precisely identify various chemical signals, and combinations thereof, with a quantitative fluorescent output. The genetic logic gate used can function as a biological filter and an amplifier to enhance the sensing selectivity and sensitivity of cell-based biosensors. In particular, an Escherichia coli consortium-based biosensor has been constructed that can detect and integrate three environmental signals (arsenic, mercury and copper ion levels) via either its native two-component signal transduction pathways or synthetic signalling sensors derived from other bacteria in combination with a cell-cell communication module. We demonstrate how a modular cell-based biosensor can be engineered predictably using exchangeable synthetic gene circuit modules to sense and integrate multiple-input signals. This study illustrates some of the key practical design principles required for the future application of these biosensors in broad environmental and healthcare areas.
  • Journal article
    Schaub MT, Lambiotte R, Barahona M, 2012,

    Encoding dynamics for multiscale community detection: Markov time sweeping for the map equation

    , PHYSICAL REVIEW E, Vol: 86, ISSN: 2470-0045
  • Journal article
    Anderson J, Strelkowa N, Stan G-B, Douglas T, Savulescu J, Barahona M, Papachristodoulou Aet al., 2012,

    Engineering and ethical perspectives in synthetic biology

    , EMBO Reports, Vol: 13, Pages: 584-590, ISSN: 1469-221X

    Synthetic biology has emerged as an exciting and promising new research field, garnering significant attention from both the scientific community and the general public. This interest results from a variety of striking features: synthetic biology is a truly interdisciplinary field that engages biologists, mathematicians, physicists and engineers; its research focus is applied; and it has enormous potential to harness the power of biology to provide scientific and engineering solutions to a wide range of problems and challenges that plague humanity. However, the power of synthetic biology to engineer organisms with custom‐made functionality requires that researchers and society use this power safely and responsibly, in particular when it comes to releasing organisms into the environment. This creates new challenges for both the design of such organisms and the regulatory process governing their creation and use.
  • Journal article
    Thomas P, Matuschek H, Grima R, 2012,

    Intrinsic Noise Analyzer: A Software Package for the Exploration of Stochastic Biochemical Kinetics Using the System Size Expansion

    , PLOS One, Vol: 7, ISSN: 1932-6203

    The accepted stochastic descriptions of biochemical dynamics under well-mixed conditions are given by the Chemical Master Equation and the Stochastic Simulation Algorithm, which are equivalent. The latter is a Monte-Carlo method, which, despite enjoying broad availability in a large number of existing software packages, is computationally expensive due to the huge amounts of ensemble averaging required for obtaining accurate statistical information. The former is a set of coupled differential-difference equations for the probability of the system being in any one of the possible mesoscopic states; these equations are typically computationally intractable because of the inherently large state space. Here we introduce the software package intrinsic Noise Analyzer (iNA), which allows for systematic analysis of stochastic biochemical kinetics by means of van Kampen's system size expansion of the Chemical Master Equation. iNA is platform independent and supports the popular SBML format natively. The present implementation is the first to adopt a complementary approach that combines state-of-the-art analysis tools using the computer algebra system Ginac with traditional methods of stochastic simulation. iNA integrates two approximation methods based on the system size expansion, the Linear Noise Approximation and effective mesoscopic rate equations, which to-date have not been available to non-expert users, into an easy-to-use graphical user interface. In particular, the present methods allow for quick approximate analysis of time-dependent mean concentrations, variances, covariances and correlations coefficients, which typically outperforms stochastic simulations. These analytical tools are complemented by automated multi-core stochastic simulations with direct statistical evaluation and visualization. We showcase iNA's performance by using it to explore the stochastic properties of cooperative and non-cooperative enzyme kinetics and a gene network associated with circad
  • Journal article
    Thomas P, Straube AV, Grima R, 2012,

    The slow-scale linear noise approximation: an accurate, reduced stochastic description of biochemical networks under timescale separation conditions

    , BMC Systems Biology, Vol: 6, ISSN: 1752-0509

    BACKGROUND: It is well known that the deterministic dynamics of biochemical reaction networks can be more easily studied if timescale separation conditions are invoked (the quasi-steady-state assumption). In this case the deterministic dynamics of a large network of elementary reactions are well described by the dynamics of a smaller network of effective reactions. Each of the latter represents a group of elementary reactions in the large network and has associated with it an effective macroscopic rate law. A popular method to achieve model reduction in the presence of intrinsic noise consists of using the effective macroscopic rate laws to heuristically deduce effective probabilities for the effective reactions which then enables simulation via the stochastic simulation algorithm (SSA). The validity of this heuristic SSA method is a priori doubtful because the reaction probabilities for the SSA have only been rigorously derived from microscopic physics arguments for elementary reactions. RESULTS: We here obtain, by rigorous means and in closed-form, a reduced linear Langevin equation description of the stochastic dynamics of monostable biochemical networks in conditions characterized by small intrinsic noise and timescale separation. The slow-scale linear noise approximation (ssLNA), as the new method is called, is used to calculate the intrinsic noise statistics of enzyme and gene networks. The results agree very well with SSA simulations of the non-reduced network of elementary reactions. In contrast the conventional heuristic SSA is shown to overestimate the size of noise for Michaelis-Menten kinetics, considerably under-estimate the size of noise for Hill-type kinetics and in some cases even miss the prediction of noise-induced oscillations. CONCLUSIONS: A new general method, the ssLNA, is derived and shown to correctly describe the statistics of intrinsic noise about the macroscopic concentrations under timescale separation conditions. The ssLNA provides a sim
  • Conference paper
    Sootla A, Strelkowa N, Ernst D, Barahona M, Stan G-Bet al., 2012,

    On Periodic Reference Tracking Using Batch-Mode Reinforcement Learning with Application to Gene Regulatory Network Control

    , submitted to Conf. Decision Control

    In this paper, we consider the periodic referencetracking problem in the framework of batch-mode reinforcement learning, which studies methods for solving optimal control problems from the sole knowledge of a set of trajectories. In particular, we adapt an existing batch-mode reinforcement learning algorithm, known as Fitted Q iteration, to the periodic reference tracking problem. The presented periodic reference tracking algorithm explicitly exploits a priori knowledge of the future values of the reference trajectory and its periodicity. We discuss the properties of our approach and illustrate it onthe problem of reference tracking for a synthetic biology gene regulatory network known as the generalised repressilator. This system can produce decaying but long-lived oscillations, which makes it an interesting system for the tracking problem. In our companion paper submitted to this conference we also take a look at the regulation problem of the toggle switch system, where the main goal is to drive the systems states to a specific bounded region in the state space.
  • Journal article
    Schaub MT, Delvenne J-C, Yaliraki SN, Barahona Met al., 2012,

    Markov Dynamics as a Zooming Lens for Multiscale Community Detection: Non Clique-Like Communities and the Field-of-View Limit

    , PLOS ONE, Vol: 7, ISSN: 1932-6203
  • Journal article
    Beguerisse-Díaz M, Wang B, Desikan R, Barahona Met al., 2012,

    Squeeze-and-breathe evolutionary Monte Carlo optimization with local search acceleration and its application to parameter fitting

    , Journal of The Royal Society Interface

    Estimating parameters from data is a key stage of the modelling process, particularly in biological systems where many parameters need to be estimated from sparse and noisy datasets. Over the years, a variety of heuristics have been proposed to solve this complex optimization problem, with good results in some cases yet with limitations in the biological setting. In this work, we develop an algorithm for model parameter fitting that combines ideas from evolutionary algorithms, sequential Monte Carlo and direct search optimization. Our method performs well even when the order of magnitude and/or the range of the parameters is unknown. The method refines iteratively a sequence of parameter distributions through local optimization combined with partial resampling from a historical prior defined over the support of all previous iterations. We exemplify our method with biological models using both simulated and real experimental data and estimate the parameters efficiently even in the absence of a priori knowledge about the parameters.

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