1 research outputs found
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