One goal of synthetic biology is to understand the exciting biological phenomenon by reconstructing the systems that have the similar behavior to the native. The design of networks are always challenging to the biologists as the desired phenotype is the only hint for the design. And after the design is finished in mind, there is still a gap in realizing it in experiments.The choices of reactors, the stability of the system are still important in the wet experiments. Here we are trying to escape the biologists from the design nightmare, employing the computer instead of the human brain to do the design process.

Goal

Work Flow

The ultimate goal of our program is to assist the experimentalists to design the plasmid that works as the requirement. For example, if an oscillation behavior is the requirement as the input of the software, then the output in our imagination is a DNA sequence which works as an oscillator in E.coli or other specific organisms. It is only an imagination that we have a long way to go. Then the first goal is to make a network work stably. Generally, the desired phenotype is the input of the software, and, optionally, the restrictions extracted from the other experiments or conditions can be the input simultaneously. And the output is a list of networks that have similar phenotypes approximating to the requirement, along with the kinetic parameters and robustness evaluation.

Work Flow

The three-layer optimization is expected during the whole design process: (1) the optimization of parameters in a fixed mathematic model, (2) the selection of interaction forms in a fixed topology, (3) the comparison and screening of different topologies. And during the optimization of the parameters, there are two score functions considered. One is the RMSD(root mean square deviation) between the phenotype of the designed network and the requirement, and the other is the sensitivity of each parameter. As the cell system is noisy, the networks are hard to realize in experiments if some parameters are too sensitive to uncertainties. So the parameters' sensitivities are working as a filter to get rid of the networks that works not stably enough. After the three-layer's optimization and comparison, a list of the best selected networks will output as the final solutions.

Platform

GUI Sample

A user-friendly network-design platform is realized in our software with C# for the experimentalists. The interface is shown in Figure2.Users can input the requirement curves with uploading a data file. And the picture files for curves are also supported by our software. And the network can be designed by manually drawing the species and the interactions. The phenotypes of the designed networks will be shown with graphs that users can directly see the performances of the results and the deviation between the results and the requirement.

Algorithm

Particle Swarm Optimization Algorithm

The particle swarm optimization algorithm (PSO) is employed to optimized the parameters in a fixed mathematic model. In past several years, PSO has been successfully applied in optimizing the parameters for nonlinear system. It is demonstrated that PSO gets better results in a faster, cheaper way compared with other methods. The most important reason we choose to implement this algorithm into our software is that it is easy to realize parallelization. Since the most time-consuming part in our scheme is the optimization of parameters for a given topological structure. If we cannot find a efficient optimizer, it is impossible to deal with systems contains more than five or six nodes. Parallelization of the optimization process will be implemented in our next version.

Genetic Algorithm

Genetic Algorithm (GA) is employed to search the best topologies and the best interaction forms. It is a powerful method for complex optimization problems. It realizes an essential evolution process in a computer. Under a fitness function, the members of the population will be improved from generation to generation. And the population will fit the pressure much better by the intraspecific competition. It is suitable for our problems; because it can be converged in a moderate generations and can give a population of best topologies, not only one by other algorithms.

Future

It is just the first step. We still have a lot to do to achieve the final goal. First, the link should be established between the interaction forms and the real particles, as the promoters, the proteins, the ligands and so on. We are trying to build a database to construct the links, but the experiments data now are far than enough. And there are still some problems in the measurement of the parameters. Second, the optimization space is too large for us to search. Our program should run for a long time to finish the whole job. The parallel computation is favorable here. So we will use the parallel computation to do the optimization in the next version. Third, the on-line version is also required as it will be more convenient to the users.

Automatic Biological Circuits Design

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