生物逆向工程设计在合成生物学中的应用

doi:10.12211/2096-8280.2020-063

摘要/Abstract

摘要：

合成生物学是一门涉及生物学、生物工程学、系统生物学、数学、物理、化学与信息科学的新生的交叉学科。它的目的是在工程化思想的指导下有目的地、可预测地设计人造生命系统。经过近二十年的蓬勃发展，合成生物学取得了重大成就，但依旧面临复杂系统理性设计的困难。在系统生物学中，运用数学物理等知识根据网络功能来研究功能背后网络结构的方法被称为逆向工程。系统生物学逆向工程的研究思路与合成生物学设计过程的一致性，启发了我们利用逆向工程指导合成生物学的理性设计。逆向工程应用到合成生物学，将大大降低复杂功能回路的设计难度。本文从合成生物学的设计思路与问题出发，根据本文作者研究团队近十年来在逆向工程研究中的经验，归纳总结了目前逆向工程设计在合成生物学中的应用方法，包括网络穷举方法、子网络拼接方法、从离散模型到连续模型的方法，论证了逆向工程指导合成生物学理性设计的可行性与有效性，分析了目前逆向工程设计在合成生物学中的发展瓶颈。

关键词: 逆向工程, 合成生物学, 理性设计, 布尔网络, 网络穷举

Abstract:

Aiming to purposefully and rationally design and construct predictable man-made life systems with pre-defined functions under the guidance of engineering principles, synthetic biology is an advanced interdisciplinary science by combining a broad range of methodologies from various disciplines, such as traditional biology, bioengineering, systems biology, mathematics, physics, chemistry, and information science. With the booming development for nearly two decades, great progress has been made in synthetic biology. However, there are a number of factors that should be taken into account for rationally designing complex systems, such as robustness and bifurcation. Because of the consistency between the research idea of biological reverse engineering and the design process of synthetic biology, we are enlightened to resolve these problems in rationally designing genetic circuits with compley pre-defined functions with the help of reverse engineering In this review, based on the accumulated experience of our research group in reverse engineering, we started with engineering principles and design difficulties in synthetic biology, and then summarized the current methods of applying reverse engineering in synthetic biology, including network enumeration, sub-network combinations, the method from Boolean network model to continuous model. In addition, we proved the efficiency of combining reverse engineering with synthetic biology to rationally design biological complex regulation networks. Finally, we concluded with the analysis of bottlenecks for the application of reverse engineering in synthetic biology.

Key words: reverse engineering, synthetic biology, rational design, Boolean network, network enumeration

中图分类号:

陈欣懋, 欧阳颀. 生物逆向工程设计在合成生物学中的应用[J]. 合成生物学, 2020, 1(1): 29-43.

CHEN Xinmao, OUYANG Qi. The application of biological reverse engineering in synthetic biology[J]. Synthetic Biology Journal, 2020, 1(1): 29-43.

图/表 16

图 1 适应性的数学描述[36]

Fig. 1 Mathematical description of the adaptation function [36]

图 2 网络穷举研究适应性功能的特定网络结构的计算过程[36]

Fig. 2 Illustration of the calculation procedure for a given topology for adaptation [36]

图 3 能较好实现完美适应的最小网络[36]

Fig. 3 Minimal adaptive topologies [36]

图 4 子网络合并法得到的最优调控网络[47]（用蛋白质相互作用和基因转录调控两个层面的子网络合并方法得到的组合网络在结构上是相似的）

Fig. 4 Results of the robust sub-network combinations with high Q-value [47]

图 5 传统网络穷举与子网络合并的计算复杂度对比[47](虚线表示传统网络穷举方法的计算复杂度随网络节点数的变化，余下折线对应子网络合并方法的计算复杂度的变化)

Fig. 5 The complexity comparison between traditional enumeration and sub-networks combination[47]

图 6 网络穷举指导细胞密度超敏开关实验构建的流程[49]

Fig. 6 The workflow of rational design guided by reverse engineering [49]

图 7 参数的理论预测与实验测量[49][通过对比(e)与(h)，(f)与(i)，(g)与(j)，发现理论设计方法具有一定的可预测性]

Fig. 7 The comparison of prediction and measurements of parameters [49]

图 8 最大路径距离、轨迹熵和随机采样三种方法在三种生物网络中的表现[54]

Fig. 8 The performances of the three methods in different biological pathways [54]

表 1 裂殖酵母细胞周期功能的动力学轨迹[55]

Tab. 1 Biological pathway of fission yeast cell cycle network [55]

Time	SK	Cdc2	Ste9	Rum1	Slp1	Cdc2*	Wee1	Cdc25	PP
1	1	0	1	1	0	0	1	0	0
2	0	0	0	0	0	0	1	0	0
3	0	1	0	0	0	0	1	0	0
4	0	1	0	0	0	0	0	1	0
5	0	1	0	0	0	1	0	1	0
6	0	1	0	0	1	1	0	1	0
7	0	0	0	0	1	0	0	1	1
8	0	0	1	1	0	0	1	0	1
9	0	0	1	1	0	0	1	0	0

图 9 随机采样法和最小网络约束在不同网络中的表现[55]

Fig. 9 The performances of the two methods in different biological pathways [55]

图 10 网络的调控熵的计算示例[56]

Fig. 10 Illustration of calculating the regulation entropy [56]

图 11 网络调控熵与动力学稳定性的关系[56](蓝色和绿色线分别表示稳定性最高的前5%和后5%的随机网络对应的动力学稳定性，红十字表示相应的生物网络的动力学稳定性和调控熵)

Fig. 11 The relationship of network regulation entropy and dynamic stability [56]

图 12 网络调控熵与结构稳定性的关系[56](红色点表示具有相同调控熵的随机网络鲁棒性的均值)

Fig. 12 The relationship of network regulation entropy and structural stability [56]

图 13 从布尔网络模型到连续模型的生物回路设计流程图[57]

Fig. 13 The workflow of the design from Boolean network model to continuous model [57]

图 14 连续模型的定量刻画与评价流程[58]

Fig 14 The workflow of continuous model [58]

图 15 在布尔网络模型和连续模型下的三个限制条件[58][第一个限制条件是ssDNA的浓度在结束计算时应下调；第二个是系统在经历响应反应后应回到初态（ssDNA除外）；第三个是节点在两种模型下的动力学行为特征应该保持一致]

Fig. 15 The three criteria in the Boolean network model and continuous model[58]

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