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Data-based optimal control of synthetic biology gene circuits

基于数据的合成生物学基因电路优化控制

基本信息

批准号：
EP/J014214/1
负责人：
Guy-Bart Stan
金额：
$ 12.73万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FJ014214%2F1
关键词：
Data based optimal control synthetic

项目摘要

Synthetic Biology aims at the engineering of biological systems. Its most prominent application is the rational modification or (re-)design of living organisms, ideally in a way akin to the engineering of man-made devices, for their efficient use in sectors such as energy, biomedicine, drug production and food technology. The availability of control mechanisms that can ensure robust and optimal operation of engineered systems is one of the key factors behind the tremendous advances in engineering fields such as transportation, industrial production and energy. However, in the case of engineered biosystems, their accurate control must typically overcome two important hurdles: uncertainty and noise. Uncertainty arises from a high number of components that interact in a nonlinear (and often unknown) manner, and makes it often extremely hard to build accurate mathematical models of their behaviour. Noise, on the other hand, is ubiquitous in cellular systems since the environmental conditions in which they operate typically vary unpredictably and gene expression is inherently a stochastic process.In this research, we investigate the possibility of automatically learning to optimally control synthetic biology gene networks from input-output data collected from these gene networks, i.e. without using a mathematical model built a priori. In particular, we will develop algorithms that allow computer-based systems to autonomously learn how to vary the inputs of a given system so as to optimise its performance defined in terms of the time evolution of its measured outputs. The control strategies learned by our methods will take into account noise and uncertainties in the data and will be developed to be robust with respect to these. Such data-based strategies are analogous to, for example, the way we drive our cars: without any a priori mathematical model of the car behaviour on the road, we can effectively learn how and when to steer, accelerate and break (inputs) based on our observations of the car's position and velocity on the road (outputs) so as to, for example, minimise our lap time around an unknown track using appropriate input scheduling strategies.The algorithms we will develop will allow users to define the desired behaviour and performance objectives and will compute input-scheduling strategies that allow these objectives to be satisfied. The project will build on methods that I have developed and successfully applied to the optimal control of nonlinear systems in noisy environments, e.g., my work on data-based optimal drug-scheduling for HIV infected patients. The use of such purely data-based optimal control methods is particularly important in synthetic biology applications where the system to be controlled is typically poorly characterised and model uncertainties prevail, yet large amount of high-throughput input-output data are available or can be extracted. To showcase the potential of these computational techniques, we will develop data-based methods to optimally control two landmark synthetic biomodules: the light-inducible genetic toggle switch, and the light-inducible generalised repressilator, both of which are currently under implementation in my host Department.

合成生物学旨在生物系统工程。其最突出的应用是对生物体的合理修改或（重新）设计，理想情况下以类似于人造设备工程的方式，以使其在能源、生物医学、药物生产和食品技术等领域得到有效利用。能够确保工程系统稳健和最佳运行的控制机制的可用性是交通、工业生产和能源等工程领域取得巨大进步的关键因素之一。然而，就工程生物系统而言，其精确控制通常必须克服两个重要障碍：不确定性和噪声。不确定性是由大量以非线性（通常是未知）方式相互作用的组件产生的，并且使得建立其行为的准确数学模型通常极其困难。另一方面，噪声在细胞系统中无处不在，因为它们运行的环境条件通常难以预测，并且基因表达本质上是一个随机过程。在这项研究中，我们研究了从这些基因网络收集的输入输出数据中自动学习以最佳控制合成生物学基因网络的可能性，即不使用先验建立的数学模型。特别是，我们将开发算法，允许基于计算机的系统自主学习如何改变给定系统的输入，以便优化根据其测量输出的时间演变定义的性能。通过我们的方法学到的控制策略将考虑数据中的噪声和不确定性，并将开发得对这些具有鲁棒性。这种基于数据的策略类似于我们驾驶汽车的方式：在没有任何汽车在道路上行为的先验数学模型的情况下，我们可以根据我们对汽车在道路上的位置和速度（输出）的观察，有效地学习如何以及何时转向、加速和制动（输入），以便例如使用适当的输入调度策略最小化我们在未知赛道上的单圈时间。我们将开发的算法将允许用户定义所需的行为和性能目标，并将计算满足这些目标的输入调度策略。该项目将建立在我开发并成功应用于噪声环境中非线性系统最优控制的方法的基础上，例如，我为艾滋病毒感染者进行基于数据的最优药物调度的工作。使用这种纯粹基于数据的最优控制方法在合成生物学应用中尤其重要，其中要控制的系统通常特征描述很差并且模型不确定性普遍存在，但大量高通量输入输出数据是可用的或可以提取的。为了展示这些计算技术的潜力，我们将开发基于数据的方法来优化控制两个具有里程碑意义的合成生物模块：光诱导遗传切换开关和光诱导广义抑制器，这两种方法目前正在我的所在部门实施。