Optimisation of microfluidic experiments for model calibration of a synthetic promoter in S. cerevisiae

This thesis explores, implements, and examines the methods to improve the efficiency of model calibration experiments for synthetic biological circuits in three aspects: experimental technique, optimal experimental design (OED), and automatic experiment abnormality screening (AEAS). Moreover, to obtain a specific benchmark that provides clear-cut evidence of the utility, an integrated synthetic orthogonal promoter in yeast (S. cerevisiae) and a corresponded model is selected as the experiment object. This work first focuses on the “wet-lab” part of the experiment. It verifies the theoretical benefit of adopting microfluidic technique by carrying out a series of in-vivo experiments on a developed automatic microfluidic experimental platform. Statistical analysis shows that compared to the models calibrated with flow-cytometry data (a representative traditional experimental technique), the models based on microfluidic data of the same experiment time give significantly more accurate behaviour predictions of never-encountered stimuli patterns. In other words, compare to flow-cytometry experiments, microfluidics can obtain models of the required prediction accuracy within less experiment time. The next aspect is to optimise the “dry-lab” part, i.e., the design of experiments and data processing. Previous works have proven that the informativeness of experiments can be improved by optimising the input design (OID). However, the amount of work and the time cost of the current OID approach rise dramatically with large and complex synthetic networks and mathematical models. To address this problem, this thesis introduces the parameter clustering analysis and visualisation (PCAV) to speed up the OID by narrowing down the parameters of interest. For the first time, this thesis proposes a parameter clustering algorithm based on the Fisher information matrix (FIMPC). Practices with in-silico experiments on the benchmarking promoter show that PCAV reduces the complexity of OID and provides a new way to explore the connections between parameters. Moreover, the analysis shows that experiments with FIMPC-based OID lead to significantly more accurate parameter estimations than the current OID approach. Automatic abnormality screening is the third aspect. For microfluidic experiments, the current identification of invalid microfluidic experiments is carried out by visual checks of the microscope images by experts after the experiments. To improve the automation level and robustness of this quality control process, this work develops an automatic experiment abnormality screening (AEAS) system supported by convolutional neural networks (CNNs). The system learns the features of six abnormal experiment conditions from images taken in actual microfluidic experiments and achieves identification within seconds in the application. The training and validation of six representative CNNs of different network depths and design strategies show that some shallow CNNs can already diagnose abnormal conditions with the desired accuracy. Moreover, to improve the training convergence of deep CNNs with small data sets, this thesis proposes a levelled-training method and improves the chance of convergence from 30% to 90%. With a benchmark of a synthetic promoter model in yeast, this thesis optimises model calibration experiments in three aspects to achieve a more efficient procedure: experimental technique, optimal experimental design (OED), and automatic experiment abnormality screening (AEAS). In this study, the efficiency of model calibration experiments for the benchmarking model can be improved by: adopting microfluidics technology, applying CAVP parameter analysis and FIMPC-based OID, and setting up an AEAS system supported by CNN. These contributions have the potential to be exploited for designing more efficient in-vivo experiments for model calibration in similar studies

Heterogeneous Activity Causes a Nonlinear Increase in the Group Energy Use of Ant Workers Isolated from Queen and Brood

Increasing evidence has shown that the energy use of ant colonies increases sublinearly with colony size so that large colonies consume less per capita energy than small colonies. It has been postulated that social environment (e.g., in the presence of queen and brood) is critical for the sublinear group energetics, and a few studies of ant workers isolated from queens and brood observed linear relationships between group energetics and size. In this paper, we hypothesize that the sublinear energetics arise from the heterogeneity of activity in ant groups, that is, large groups have relatively more inactive members than small groups. We further hypothesize that the energy use of ant worker groups that are allowed to move freely increases more slowly than the group size even if they are isolated from queen and brood. Previous studies only provided indirect evidence for these hypotheses due to technical difficulties. In this study, we applied the automated behavioral monitoring and respirometry simultaneously on isolated worker groups for long time periods, and analyzed the image with the state‐of‐the‐art algorithms. Our results show that when activity was not confined, large groups had lower per capita energy use, a lower percentage of active members, and lower average walking speed than small groups; while locomotion was confined, however, the per capita energy use was a constant regardless of the group size. The quantitative analysis shows a direct link between variation in group energy use and the activity level of ant workers when isolated from queen and brood

On-line optimal input design increases the efficiency and accuracy of the modelling of an inducible synthetic promoter

Synthetic biology seeks to design biological parts and circuits that implement new functions in cells. Major accomplishments have been reported in this field, yet predicting a priori the in vivo behaviour of synthetic gene circuits is major a challenge. Mathematical models offer a means to address this bottleneck. However, in biology, modelling is perceived as an expensive, time-consuming task. Indeed, the quality of predictions depends on the accuracy of parameters, which are traditionally inferred from poorly informative data. How much can parameter accuracy be improved by using model-based optimal experimental design (MBOED)? To tackle this question, we considered an inducible promoter in the yeast S. cerevisiae. Using in vivo data, we re-fit a dynamic model for this component and then compared the performance of standard (e.g., step inputs) and optimally designed experiments for parameter inference. We found that MBOED improves the quality of model calibration by ∼60%. Results further improve up to 84% when considering on-line optimal experimental design (OED). Our in silico results suggest that MBOED provides a significant advantage in the identification of models of biological parts and should thus be integrated into their characterisation.This research was partially supported by EC funding H2020 FET OPEN 766840-COSY-BIO and a Royal Society of Edinburgh-MoST grant (to F.M.), EPSRC funding EP/P017134/1-CONDSYC (to L.B.) and Spanish MINECO, grant ref. AGL2015-67504-C3-2-R (to E.B.-C.).Peer reviewe

Evaluation Index for IVIS Integration Test under a Closed Condition Based on the Analytic Hierarchy Process

The intelligent vehicle infrastructure system (IVIS) requires systematic testing before being put into large-scale applications. IVIS testing under closed conditions includes stress tests for typical scenarios and extreme scenario strength testing. To extract IVIS integration test indicators under closed conditions, this article constructed a hierarchical framework of IVIS’s evaluation indexes in the stress tests and the strength tests. The hierarchical framework of IVIS stress test evaluation indicators reflect the highway construction area under typical scenarios, and the hierarchical framework of IVIS strength test evaluation indicators reflect the highway merging area under extreme scenarios. Both are based on the test requirements of the stress test and strength test, with safety as the evaluation objective. Second, the analytic hierarchy process (AHP) was used to calculate the weights of the test evaluation indicators of the two scenarios. Finally, the activity-based classification (ABC) method was used after ranking the weight results in order to extract the key factors that have the maximum impact on safety in the scenarios. In this paper, we proved the practicality and feasibility of the AHP-ABC extraction method in the IVIS integration testing evaluation index and guided the development and testing of the IVIS

Numerical modelling of electrochemical deposition techniques for healing concrete damaged by alkali silica reaction

Alkali silica reaction (ASR) is a long-term factor that causes concrete cracking, and the ingress of harmful agents such as chloride can then be promoted by the ASR-induced cracks. Electrochemical deposition method (EDM) is a promising nondestructive rehabilitation technique which has two-fold advantages of crack repair and chloride removal. In this study, the entire process from ASR-induced cracking for crack repair by EDM is studied for the first time by coupling three sub-models involving different disciplines: (1) multi-ionic transport model, (2) ASR cracking model; and (3) crack repair model. The consumptions and interactions among various ionic species during ASR and electrochemical deposition are quantitively reflected in multi-ionic transport model. The ASR cracking model is developed considering the local mechanical variances of concrete composites. The crack repair model can successfully visualize the crack closure status, and the time-dependent porosity and diffusion coefficients during the treatment have also been well reflected. The proposed model is calibrated and validated against experimental data to ensure the prediction accuracy. A subsequent parameter shows that increase in alkali silica aggregates volume fraction can facilitate cracking process. Besides, for electrochemical deposition treatment on ASR-induced cracks, setting all exposed surfaces as anode can effectively improve the repair rate, and adoption of pulse current can ensure the continuous supply of magnesium ions from external anolyte. Other findings which have not been reported in existing studies are also highlighted, which is hoped to better guide the application in practical engineering