Team:CSU CHINA/Award Software
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We built an interactive CA Based Gene Circuit visual analytics system. The theory of cellular automata is immensely rich, with simple rules and structures being capable of producing a great variety of unexpected behaviors. Users can press the buttons in the control interface to change the metabolic rate, the glucose intake, switch on/off the blue light, and so on. Users can also find the quantity change of blood sugar vs insulin, Gal4 vs transcription factors, and VP16 vs VP*16 under different conditions defined by users themselves. It vividly describes these features of concentration correlation through the Theme-River map and the line graph, of all which can be downloaded at any moment you like. It displays the result of the experiment in the wet lab precisely as well.
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1. We simulated the working mechanism of engineered cells with the help of the ODE model constructed by the Mie equation and Hill equation. Through this equation, we can verify the existing experimental results and deepen our understanding of the principles of the work.
2. Limited to experimental equipment, we cannot perform experimental verification in many situations. With the help of models, we can understand the state of the product under these conditions. We can run ODE models through mathematical methods to predict product performance in complex environments and long-term simulation environments, and provide suggestions for subsequent product improvements. We used linear interpolation to eliminate outlier points and periodic moving averaging to process the smoothed dataset; in the process of modeling for ODEs, we transformed its constancy to obtain the second-order ODE equation. For parameters lacking reference values, we first try to fit the original data set using the fractional function and construct the mean square error loss function with the help of its approximate second-order derivative to fit the parameter approximation using the genetic algorithm. Finally, these parameters are substituted into the original ODE to obtain their analytical solutions.
3. The CABGCV developed by us provides readers with an interactive and visualized CA model of quantitative indicators, which is very helpful for them to understand the assumptions, parameters, data, and results of the model.
4. Existing models are mostly based on strict adherence to experimental data or are presented in a simple and understandable animation form from the perspective of common sense. These methods may make the model too difficult, or will not be able to transmit enough information to visualize the reader's cognition. In contrast, our CA model avoids the shortcomings of most existing models at present: the model can interact and can inspire users to explore the working mechanism of the product; although the model is animated, the main parameters are relatively quantitative, which achieves respect for the experimental data to a certain extent and ensures the model is scientific; the dashboard can visually show the approximate experimental data, which can further help users understand the content and significance of the experimental work.
5. In the process of testing the CA model, we discovered a feature of the gene circuits when it works: there is a time lag longer than expected between the insulin’s secretion and the blood sugar's reduction. In a considerable period, blood sugar has always been maintained at a high level. This observation is consistent with the corresponding experimental data, that is, after the light turns on, the reporter gene doesn't increase sharply in a short time, which indicates our visualization model is highly consistent with the real world, and during the running process we found that there was also a delay in the process of miRNA lowering the uncontrolled secretion of insulin, and blood glucose had undergone a significant decrease before insulin returned to a relatively stable level, which suggests the CA model also can predict the behavior of the product and extract the information we are interested in, guiding the optimization and improvement of the product.
We built an interactive CA Based Gene Circuit visual analytics system. Users can press the buttons in the control interface to change the metabolic rate, the glucose intake, switch on/off the blue light, and so on. Users can also find the quantity change of blood sugar vs insulin, Gal4 vs transcription factors, and VP16 vs VP*16 under different conditions defined by users themselves. It vividly describes these features of concentration correlation through the Theme-River map and the line graph, of all which can be downloaded at any moment you like. It displays the result of the experiment in the wet lab precisely as well.