1,721,019 research outputs found
Integrated optofluidic strategies for a system level understanding of pathological states during space flights
No full textMETHOD FOR THE DESIGN AND ENGINEERING OF OLIGONUCLEOTIDES
No full textThe present invention relates to a method for the design of oligonucleotides suitable for PCR, Real time PCR, microarrays and, more generally for DNA and RNA amplification and/or detection, said method allowing a quick and simultaneous computation of more parameters, and computer programs capable of carrying out the method of the invention. The present invention discloses a method and a computer program carrying out said method integrating, all in one, a high functionality, and a number of parameters that are not measured together in the state of the art, for more than one oligonucleotide spamming over a single input sequence starting from sequences in FASTA format and giving a few input data such as threshold temperatures, oligonucleotide length and sampling frequencies
Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering
Get PDFBackground: Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments. Results: We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification. Conclusion: We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the ‘data rich-data poor’ paradox in Systems Biology.MediamaticsElectrical Engineering, Mathematics and Computer Scienc
Prediction of Cellular Burden with Host–Circuit Models
Get PDFHeterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognized bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modeling paradigm can facilitate fast and robust design cycles in synthetic biology
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Intein-assisted bisection mapping systematically splits proteins for Boolean logic and inducibility engineering
Get PDFSplit inteins are powerful tools for seamless ligation of synthetic split proteins. Yet,
their use remains limited because the already intricate split site identification problem
is often complicated by the requirement of extein junction sequences. To address
this, we augmented a mini-Mu transposon-based screening approach and devised
the intein-assisted bisection mapping (IBM) method. IBM robustly revealed clusters
of split sites on five proteins, converting them into AND or NAND logic gates. We
further showed that the use of inteins expands functional sequence space for
splitting a protein. We also demonstrated the utility of our approach over rational
inference of split sites from secondary structure alignment of homologous proteins.
Furthermore, the intein inserted at an identified site could be engineered by the
transposon again to become partially chemically inducible, and to some extent
enabled post-translational tuning on host protein function. Our work offers a
generalizable and systematic route towards creating split protein-intein fusions and
conditional inteins for protein activity control
Optimisation of microfluidic experiments for model calibration of a synthetic promoter in S. cerevisiae
Get PDFThis 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
Synthetic Gene Networks Identification and Control by means of Microfluidic Devices
No full textIn this Thesis, I demonstrated how modelling and control of synthetic
gene networks can be achieved by using principles from control
and systems theory. The first step consisted in analysing the expected
challenges and in looking for suitable solutions to them. The first choice
made in this context concerned the selection of a suitable testbed to prove
real-time in-vivo controllability of gene networks in a cell population. I
needed a benchmark that had the following properties: (a) a reasonable
level of complexity (b) an available differential equation model (c) decoupled
from the rest of endogenous gene circuitry. As described in Chapter
2, IRMA satisfied these properties. IRMA is synthetic gene network
built in S. cerevisiae, thus a eukaryotic model system, made up of five
yeast genes completely rearranged in their regulatory network topology
to avoid any cross talk with wild type genome. A non-linear, hybrid,
time-delayed mathematical model for IRMA has been re-derived to test
the effectiveness of the designed control strategies in-silico. Several alternative
strategies have been designed, tested and presented in Chapter
3 to address the problem at hand. A major goal I struggled to achieve
during control system design was to minimise the level of complexity
of the proposed strategies: the risk in ignoring this lays in obtaining
solutions showing astonishing results in-silico but failing to display the
robustness required in real-world experiments. Therefore, the proposed
schemes spanned from the simple relay based control to the Smith Predictor
based configuration that includes a proportional-integral controller
and a Pulse-Width-Modulation strategy. Designs, peculiarities and performances
of these control schemes have been exposed in Chapter 3.
In order to translate these designs in in-vivo control strategies, I proposed
and implemented a new technological platform presented in Chapter
4. As previously outlined, a paradigm shift was needed in order to
demonstrate the working hypothesis. As a matter of fact, flask-based
experiments could not be used to accomplish real-time in-vivo control
experiment because of the several limitations (theoretical and practical)
listed in Chapter 4. Therefore I decided to take advantage of the latest
achievements in the field of microfluidics. Device design, optimisation
and fabrication has been presented together with the results of a collaboration
with Prof. Jeff Hasty at University of California at San Diego.
During my visit at Prof. Hasty’s laboratory in late 2009, I have been able
to acquire technical expertise that allowed me to re-engineer their platform
in Dr. di Bernardo Laboratory at TIGEM as described in Chapter
4. Once the platform was set up, I carried out experimental procedures
to obtain in-vivo real-time control whose results have been reported in
Chapter 5.
At the same time I could apply principles from control theory to
the identification of a well-known gene network motif, namely a Positive
Feedback Loop, in a mammalian cell line. The results of this study have
been presented in Chapter 6 with considerations on dynamical properties
of this simple circuit and some hints at potential advantages it can confer
to cells.
All these results, taken together confirm that control theory principles
can be successfully applied to both the identification and control of gene
regulatory networks and open new roads for research in the fields of
systems and synthetic biology
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