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Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

Postal Subscription Code 80-971

Quant. Biol.    2013, Vol. 1 Issue (3) : 209-220    https://doi.org/10.1007/s40484-013-0020-4
RESEARCH ARTICLE
Rational design of a biosensor circuit with semi-log dose-response function in Escherichia coli
Haoqian Zhang1,2,3, Ying Sheng1, Qianzhu Wu1, Ao Liu1, Yuheng Lu1, Zhenzhen Yin1, Yuansheng Cao3,4, Weiqian Zeng3,4, Qi Ouyang3,4()
1. Peking University Team for the International Genetic Engineering Machine Competition (iGEM), Peking University, Beijng 100871, China; 2. Peking-Tsinghua Joint Center for Life Sciences, School of Life Sciences, Peking University, Beijing 100871, China; 3. Center for Quantitative Biology, Peking University, Beijng 100871, China; 4. State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing 100871, China
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Abstract

A central goal of synthetic biology is to apply successful principles that have been developed in electronic and chemical engineering to construct basic biological functional modules, and through rational design, to build synthetic biological systems with predetermined functions. Here, we apply the reverse engineering design principle of biological networks to synthesize a gene circuit that executes semi-log dose-response, a logarithmically linear sensing function, in Escherichia coli cells. We first mathematically define the object function semi-log dose-response, and then search for tri-node network topologies that can most robustly execute the object function. The simplest topology, transcriptional coherent feed-forward loop (TCFL), among the searching results is mathematically analyzed; we find that, in TCFL topology, the semi-log dose-response function arises from the additive effect of logarithmical linearity intervals of Hill functions. TCFL is then genetically implemented in E. coli as a logarithmically linear sensing biosensor for heavy metal ions [mercury (II)]. Functional characterization shows that this rationally designed biosensor circuit works as expected. Through this study we demonstrated the potential application of biological network reverse engineering to broaden the computational power of synthetic biology.

Keywords synthetic biology      gene circuit design      reverse engineering      logarithmically linear sensing     
Corresponding Author(s): Ouyang Qi,Email:qi@pku.edu.cn   
Issue Date: 05 September 2013
 Cite this article:   
Haoqian Zhang,Ying Sheng,Qianzhu Wu, et al. Rational design of a biosensor circuit with semi-log dose-response function in Escherichia coli[J]. Quant. Biol., 2013, 1(3): 209-220.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-013-0020-4
https://academic.hep.com.cn/qb/EN/Y2013/V1/I3/209
Fig.1  (A) The mathematical definition of semi-log dose-response function (SLDRC function). is Pearson correlation coefficient and output range was defined as HIGHLEVEL minus LOWLEVEL. (B)The flow chart briefly describing the network reverse engineering approach.
Fig.2  (A) All possible tri-node network topologies. Node A receives input signal, Node C transmits output, and Node B plays regulatory roles. The links between nodes stand for TF interaction through transcription and translation process with three possible types: positive, negative, or no regulation. There are 16038 possible network motifs in total. (B) For each topology, 10000 sets of network parameters were sampled using LHS method. When sampling a function containing N variables, the range of each variable was divided into M equally probable intervals, and M sample points were then sampled to meet the requirements of Latin Hypercube Sampling method: in each axis-aligned hyper-plane, only one cube was filled with sample point. (C) An example network topology that has only one link from node A to node C. Whether this link is positive, negative or no regulation was determined by, a constant described in context.
Fig.3  (A) The schematic of calculating process. For each network topology, we sampled 10000 sets of parameters, calculated the character () of response curve under each set of parameters to assess whether it fits the criteria (). Q value was defined as the number of functional parameter sets for each network topology. (B) Ranking of all tri-node networks according to their Q values. The top 74 topologies with Q values higher than 100 were extracted for further analysis in Figure 4. (C) The simplest network topology is a transcriptional coherent feed-forward loop (TCFL). 6 other simplest topologies with Q values higher than 100 were also listed. Their ranks were indicated in brackets. All of them contain TCFL motif implying that it is a common feature.
Fig.4  (A) Venn diagram shows that all of the network topologies that can achieve SLDRC function contain the TCFL motif. (B) Clustergram of top 74 SLDRC network topologies. Nine vertical rectangle bars stand for nine links in Figure 2A, which correspond to Node A self-regulation, Node A to Node B, Node A to Node C, Node B to Node A, Node B self-regulation, Node B to Node C, Node C to Node A, Node C to Node B, and Node C self-regulation, respectively. Red color stands for activation, green for repression and black for no regulation. The network topologies on the right are the minimal motifs revealed by clustering.
Fig.5  (A) The detailed schematics of conventional bi-node biosensor circuit. The mercury binding transcription activator MerR was chosen as Node A. The input is mercury (II), sinceit can bind to the MerR dimer and activate transcription at promoter. The outputis GFP expression level. Activation of MerR dimer by mercury (II) binding will initiate transcription of gene at promoter. (B) On the basis of biosensor circuit in (A), TCLF, the simplest network topology motif for SLDRC function, was genetically implemented. Node A is MerR. For Node B, the gene is transcription activator that can activate transcription at promoter. Node C is GFP reporter gene whose expression was driven by both (activated by ogr activator) and (activated by MerR and mercury (II) complex).
Fig.6  The response of conventional bi-node biosensor circuit to mercury (II) doses presents a typical Hill function. The TCFL biosensor circuit, however, exhibits semi-log dose-response function as in predicted. The logarithmically linear sensing range for mercury concentration was significantly extended from 7 fold to nearly 250 fold. The solid lines stand for the logarithmically linear fitting on the dose response curves of bi-node and TCFL biosensor circuit, respectively. The red dashed curve denotes the fitting on the dose response curves of bi-node biosensor circuit using Hill function . The value of was then taken as the apparent constant of Hill function term denoting the contribution of Node A-to-Node C direct pathway in TCFL, thus to fit the dose response curve of TCFL biosensor circuit, as black dashed curve shows. The formulas used for fitting is two additively combined Hill function terms, , with and . describes the attenuation of Node A-to-Node C direct pathway due to the competition between the direct and indirect pathways, given the consideration of saturation effect in biochemical reactions. The putative “dose response curve” of the Node B-mediated Node A-to-Node C pathway is also presented in the dashed blue curve, showing the contribution of this indirect pathway to the SLDRC function of TCFL biosensor circuit; it was numerically calculated using the dose response curves of bi-node and tri-node biosensor circuits.
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