<p>Embryonic development occurs with precisely timed morphogenetic cell movements directed by complex gene regulation. In this orchestrated series of events, some epithelial cells undergo extensive changes to become free moving mesenchymal cells. The transformation resulting in an epithelial cell becoming mesenchymal is called an epithelial to mesenchymal transition (EMT), a dramatic cell biological change that occurs throughout development, tissue repair, and disease. Extensive <italic>in vitro</italic> research has identified many EMT regulators. However, most <italic>in vitro</italic> studies often reduce the complicated phenotypic change to a binary choice between successful and failed EMT. Research utilizing models has generally been limited to a single aspect of EMT without considering the total transformation. Fully understanding EMT requires experiments that perturb the system via multiple channels and observe several individual components from the series of cellular changes, which together make a successful EMT.</p><p>In this study, we have taken a novel approach to understand how the sea urchin embryo coordinates an EMT. We use systems level methods to describe the dynamics of EMT by directly observing phenotypic changes created by shifting transcriptional network states over the course of primary mesenchyme cell (PMC) ingression, a classic example of developmental EMT. We systematically knocked down each transcription factor in the sea urchin's PMC gene regulatory network (GRN). In the first assay, one fluorescently labeled knockdown PMC precursor was transplanted onto an unperturbed host embryo and we observed the resulting phenotype <italic>in vivo</italic> from before ingression until two hours post ingression using time-lapse fluorescent microscopy. Movies were projected for computational analyses of several phenotypic changes relevant to EMT: apical constriction, apical basal polarity, motility, and de-adhesion. </p><p>A separate assay scored each transcription factor for its requirement in basement membrane invasion during EMT. Again, each transcription factor was knocked down one by one and embryos were immuno-stained for laminin, a major component of basement membrane, and scored on the presence or absence of a laminin hole at the presumptive entry site of ingression. </p><p>The measured results of both assays were subjected to rigorous unsupervised data analyses: principal component analysis, emergent self-organizing map data mining, and hierarchical clustering. This analytical approach objectively compared the various phenotypes that resulted from each knockdown. In most cases, perturbation of any one transcription factor resulted in a unique phenotype that shared characteristics with its upstream regulators and downstream targets. For example, Erg is a known regulator of both Hex and FoxN2/3 and all three shared a motility phenotype; additionally, Hex and Erg both regulated apical constriction but Hex additionally affected invasion and FoxN2/3 was the lone regulator of cell polarity. Measured phenotypic changes in conjunction with known GRN relationships were used to construct five unique subcircuits of the GRN that described how dynamic regulatory network states control five individual components of EMT: apical constriction, apical basal polarity, motility, de-adhesion, and invasion. The five subcircuits were built on top of the GRN and integrated existing fate specification control with the morphogenetic EMT control.</p><p>Early in the EMT study, we discovered one PMC gene, Erg, was alternatively spliced. We identified 22 splice variants of Erg that are expressed during ingression. Our Erg knockdown targeted the 5'UTR, present in all spliceoforms; therefore, the knockdown uniformly perturbed all native Erg transcripts (&#8721;Erg). Specific function was demonstrated for the two most abundant spliceoforms, Erg-0 and Erg-4, by knockdown of &#8721;Erg and mRNA rescue with a single spliceoform; the mRNA expression constructs contained no 5'UTR and were not affected by the knockdown. Different molecular phenotypes were observed, and both spliceoforms targeted Tbr, Tel, and FoxO, only Erg-0 targeted FoxN2/3 and only Erg-4 targeted Hex. Neither targeted Tgif, which was regulated by &#8721;Erg knockdown sans rescue. Our results suggest the embryo employs a minimum of three unique roles in the GRN for alternative splicing of Erg. </p><p>Overall, these experiments increase the completeness and descriptive power of the GRN with two additional levels of complexity. We uncovered five sub-circuits of EMT control, which integrated into the GRN provide a novel view of how a complex morphogenetic movement is controlled by the embryo. We also described a new functional role for alternative splicing in the GRN where the transcriptional targets for two splice variants of Erg are unique subsets of the total set of &#8721;Erg targets.</p> / Dissertation

Context. Gene regulatory network (GRN) inference is an important and challenging problem in bioinformatics. A variety of machine learning algorithms have been applied to increase the GRN inference accuracy. Ensemble learning methods are shown to yield a higher inference accuracy than individual algorithms. Objectives. We propose an ensemble GRN inference method, which is based on the principle of Mixture-of-Experts ensemble learning. The proposed method can quantitatively measure the accuracy of individual GRN inference algorithms at the network motifs level. Based on the accuracy of the individual algorithms at predicting different types of network motifs, weights are assigned to the individual algorithms so as to take advantages of their strengths and weaknesses. In this way, we can improve the accuracy of the ensemble prediction. Methods. The research methodology is controlled experiment. The independent variable is method. It has eight groups: five individual algorithms, the generic average ranking method used in the DREAM5 challenge, the proposed ensemble method including four types of network motifs and five types of network motifs. The dependent variable is GRN inference accuracy, measured by the area under the precision-recall curve (AUPR). The experiment has training and testing phases. In the training phase, we analyze the accuracy of five individual algorithms at the network motifs level to decide their weights. In the testing phase, the weights are used to combine predictions from the five individual algorithms to generate ensemble predictions. We compare the accuracy of the eight method groups on Escherichia coli microarray dataset using AUPR. Results. In the training phase, we obtain the AUPR values of the five individual algorithms at predicting each type of the network motifs. In the testing phase, we collect the AUPR values of the eight methods on predicting the GRN of the Escherichia coli microarray dataset. Each method group has a sample size of ten (ten AUPR values). Conclusions. Statistical tests on the experiment results show that the proposed method yields a significantly higher accuracy than the generic average ranking method. In addition, a new type of network motif is found in GRN, the inclusion of which can increase the accuracy of the proposed method significantly. / Genes are DNA molecules that control the biological traits and biochemical processes that comprise life. They interact with each other to realize the precise regulation of life activities. Biologists aim to understand the regulatory network among the genes, with the help of high-throughput techonologies, such as microarrays, RNA-seq, etc. These technologies produce large amount of gene expression data which contain useful information. Therefore, effective data mining is necessary to discover the information to promote biological research. Gene regulatory network (GRN) inference is to infer the gene interactions from gene expression data, such as microarray datasets. The inference results can be used to guide the direction of further experiments to discover or validate gene interactions. A variety of machine learning (data mining) methods have been proposed to solve this problem. In recent years, experiments have shown that ensemble learning methods achieve higher accuracy than the individual learning methods. Because the ensemble learning methods can take advantages of the strength of different individual methods and it is robust to different network structures. In this thesis, we propose an ensemble GRN inference method, which is based on the principle of the Mixture-of-Experts ensemble learning. By quantitatively measure the accuracy of individual methods at the network motifs level, the proposed method is able to take advantage of the complementarity among the individual methods. The proposed method yields a significantly higher accuracy than the generic average ranking method, which is the most accurate method out of 35 GRN inference methods in the DREAM5 challenge. / 0769607980

The need to analyze and closely study the gene related mechanisms motivated the research on the modeling and control of gene regulatory networks (GRN). Dierent approaches exist to model GRNs / they are mostly simulated as mathematical models that represent relationships between genes. Though it turns into a more challenging problem, we argue that partial observability would be a more natural and realistic method for handling the control of GRNs. Partial observability is a fundamental aspect of the problem / it is mostly ignored and substituted by the assumption that states of GRN are known precisely, prescribed as full observability. On the other hand, current works addressing partially observability focus on formulating algorithms for the nite horizon GRN control problem. So, in this work we explore the feasibility of realizing the problem in a partially observable setting, mainly with Partially Observable Markov Decision Processes (POMDP). We proposed a POMDP formulation for the innite horizon version of the problem. Knowing the fact that POMDP problems suer from the curse of dimensionality, we also proposed a POMDP solution method that automatically decomposes the problem by isolating dierent unrelated parts of the problem, and then solves the reduced subproblems. We also proposed a method to enrich gene expression data sets given as input to POMDP control task, because in available data sets there are thousands of genes but only tens or rarely hundreds of samples. The method is based on the idea of generating more than one model using the available data sets, and then sampling data from each of the models and nally ltering the generated samples with the help of metrics that measure compatibility, diversity and coverage of the newly generated samples.

To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. However, a poor choice of the sparsity parameter can give highly incorrect network estimates. In order to avoid such poor choices, we devised a method to optimise the sparsity parameter, which maximises the accuracy of the inferred network. We showed that it is effective on in silico data sets with a reasonable level of informativeness and demonstrated that accurate prediction of network sparsity is key to elucidate the correct network parameters. The second investigation focuses on how knowledge from association networks can be transferred to regulatory network inference procedures. It is common that the quality of expression data is inadequate for reliable gene regulatory network inference. Therefore, we constructed an algorithm to incorporate prior knowledge and demonstrated that it increases the accuracy of network inference when the quality of the data is low. The third investigation aimed to understand the influence of system and data properties on network inference accuracy. L1 regularisation methods commonly produce poor network estimates when the data used for inference is ill-conditioned, even when the signal to noise ratio is so high that all links in the network can be proven to exist for the given significance. In this study we elucidated some general principles for under what conditions we expect strongly degraded accuracy. Moreover, it allowed us to estimate expected accuracy from conditions of simulated data, which was used to predict the performance of inference algorithms on biological data. Finally, we built a software package GeneSPIDER for solving problems encountered during previous investigations. The software package supports highly controllable network and data generation as well as data analysis and exploration in the context of network inference. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p><p> </p>