## 5 Conclusions  
In this study, a novel computational model is designed to demonstrate a pressure-responsive calcium carbonate precipitation system. Results are presented showing the system's performance in the context of two different soil profiles, with three laboratory genes and three hypothetical genes. The results from this study are highly preliminary and based on early lab data and assumptions which are likely to be challenged as new experimental data emerge. Nonetheless, the model can be seen as a framework for simulating biological improvements across scales with Engineered microorganisms. This study conducted with the model is also an example of a design process, in which gene expression is considered a key factor to the success of different soil improvement strategies. Consequently, one can glean valuable insights from the results and the utility of this model, as outlined below:  
1. Genes with different expression profiles have a significant influence on the precipitation patterns and relevance to different geotechnical contexts, in both soil profiles. For example, Gene A shows a quicker transcriptional response in the area under the applied stress, which might be the weakest part of the application, and finishes with a better stabilisation effect in this area. Gene C shows the best effect in reinforcing the edge area and has the greatest influenced depth. It can be used in stabilising foundations with existing surrounding structures or in the deeper underground area, e.g., the pile foundation. 2. The soil profile, therefore, also influences precipitation results. Homogeneous clay soil tends to maintain high pore water pressure levels and benefits genes with increasing trend expression (e.g., Gene A). In multi-layer soil conditions, three genes have similar stabilisation results in the top layer and tend to benefit different areas in the lower layers. For any gene, the soil in higher layers tends to have a quicker precipitation rate than in lower layers due to a greater bacteria cell number count. 3. For a given working condition, the stabilisation area can be adjusted by selecting different genes and the stabilisation effect can be enhanced through the development of biological logic gates as in the 'latched gate' circuit proposed here in which genes are switched on at a threshold pressure value and remain on even after pressure drops. Developing more detailed gene expression data will lead to the possibility of modelling different experimental and synthetic genes under different working conditions and building a gene library to give guidance for gene selection could further research purposes. The most important simulations described above demonstrate that we can build a model which maps between data on genetic responses from engineered bacteria through to large-scale geotechnical models which describe soils under load. MICP-based soil improvement has already been implemented but our system goes much further by suggesting the development of new biologically engineered systems. The paper described an innovative model to understand how these systems may work. This significantly extends the range of models available in synthetic biology to support the design-simulate-build-test cycle. Our model describes soils as large ELMs, able to be biologically programmed to adapt to their environment. The model also has utility in synthetic biology, where the design of novel organisms is driven by computational modelling. The computational model described here allows us to understand the dynamics of soil loading, elevated water pressures and factors such as microbial distribution to design the appropriate pressure-sensing capability for different geotechnical conditions. It should be recognised that we are still some distance from such an application. Many of the assumptions from which our model is derived are contestable and the actual biological implementation of a pressure-sensing system of this sort will require a deeper knowledge of microbe-soil interactions.