Systems biology is a comprehensive quantitative analysis of the manner in which all the components of a biological system interact functionally along with time. Mathematical modeling and computational methods are indispensable in such kind of studies, especially for interpreting and predicting the complex interactions among all the components so as to obtain some desirable system properties. System dynamics, system robustness and control method are three crucial properties in systems biology. In this thesis, the above properties are studied in four different biological systems. The outbreak and spread of infectious diseases have been questioned and studied for years. The spread mechanism and prediction about the disease could enable scientists to evaluate isolation plans to have significant effects on a particular epidemic. A differential equation model is proposed to study the dynamics of HIV spread in a network of prisons. In prisons, screening and quarantining are both efficient control manners. An optimization model is proposed to study optimal strategies for the control of HIV spread in a prison system. A primordium (plural: primordia) is an organ or tissue in its earliest recognizable stage of development. Primordial development in plants is critical to the proper positioning and development of plant organs. An optimization model and two control mechanisms are proposed to study the dynamics and robustness of primordial systems. Probabilistic Boolean Networks (PBNs) are mathematical models for studying the switching behavior in genetic regulatory networks. An algorithm is proposed to identify singleton and small attractors in PBNs which correspond to cell types and cell states. The captured problem is NP-hard in general. Our algorithm is theoretically and computationally demonstrated to be much more efficient than the naive algorithm that examines all the possible states. The goal of studying the long-term behavior of a genetic regulatory network is to study the control strategies such that the system can obtain desired properties. A control method is proposed to study multiple external interventions meanwhile minimizing the control cost. Robustness is a paramount property for living organisms. The impact degree is a measure of robustness of a metabolic system against the deletion of single or multiple reaction(s). An algorithm is proposed to study the impact degree in Escherichia coli metabolic system. Moreover, approximation method based on Branching process is proposed for estimating the impact degree of metabolic networks. The effectiveness of our method is assured by testing with real-world Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae and Homo Sapiens metabolic systems. / published_or_final_version / Mathematics / Doctoral / Doctor of Philosophy

Biological systems are complex in that they comprise large number of interacting entities, and their dynamics follow mechanic regulations for movement and biological function organization. Established computational modeling deals with studying and manipulating biologically relevant systems as a powerful approach. Inner structure and behavior of complex biological systems can be analyzed and understood by computable biological networks. In this thesis, models and computation methods are proposed for biological networks. The study of Genetic Regulatory Networks (GRNs) is an important research topic in genomic research. Several promising techniques have been proposed for capturing the behavior of gene regulations in biological systems. One of the promising models for GRNs, Boolean Network (BN) has gained a lot of attention. However, little light has been shed on the analysis of internal connection between the dynamics of biological molecules and network systems. Inference and completion problems of a BN from a given set of singleton attractors are considered to be important in understanding the relationship between dynamics of biological molecules and network systems. Discrete dynamic systems model has been recently proposed to model time-course microarray measurements of genes, but delay effect may be modeled as a realistic factor in studying GRNs. A delay discrete dynamic systems model is developed to model GRNs. Inference and analysis of networks is one of the grand challenges in modern statistical biology. Machine learning method, in particular, Support Vector Machine (SVM), has been successfully applied in predictions of internal connections embedded in networks. Kernels in conjunction with SVM demonstrate strong ability in performing various tasks such as biomedical diagnosis, function prediction and motif extractions. In biomedical diagnosis, data sets are always high dimensional which provide a challenging research problem in machine learning area. Novel kernels using distance-metric that are not common in machine learning framework are proposed for possible tumor differentiation discrimination problem. Protein function prediction problem is a hot topic in bioinformatics. The K-spectrum Kernel is among the top popular models in description of protein sequences. Taking into consideration of positive-semi-definiteness in kernel construction, Eigen-matrix translation technique is introduced in novel kernel formulation to give better prediction result. In a further step, power of Eigen-matrix translation technique in feature selection is demonstrated through mathematical formulation. Due to structure complexity of carbohydrates, the study of carbohydrate sugar chains has lagged behind compared to that of DNA and proteins. A weighted q-gram kernel is constructed in classifying glycan structures with limitations in feature extractions. A biochemically-weighted tree kernel is then proposed to enhance the ability in both classification as well as motif extractions. Finally the problem of metabolite biomarker discovery is researched. Human diseases, in particular metabolic diseases, can be directly caused by the lack of essential metabolites. Identification of metabolite biomarkers has significant importance in the study of biochemical reaction and signaling networks. A promising computational approach is proposed to identify metabolic biomarkers through integrating biomedical data and disease-specific gene expression data. / published_or_final_version / Mathematics / Doctoral / Doctor of Philosophy

Thesis (MSc(R)) - University of Glasgow, 2007. / MSc(R) thesis submitted to the Faculty of Information and Mathematical Sciences, Department of Computing Science, University of Glasgow, 2007. Includes bibliographical references. Print version also available.

Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 60-65). Also available in print.

Thesis (doctoral)--Aalto University School of Science and Technology, 2010. / Includes bibliographical references. Also available on the World Wide Web.

Background: Currently, the development of microbial strains for biotechnological production of chemicals and materials can be improved by using a rational metabolicengineering that may involve genetic engineering and/or systems biology techniques. Elementary ux mode analysis (EFM) and Flux balance analysis (FBA) are the twomost commonly used methods for probing the microbial network system properties for metabolic engineering purposes. EFM can be used to identify all possible pathways. However, combinatorial explosion of the number of EFMs obtained during EFM analysis, especially for large reaction networks, hinders the use of EFM data fordeveloping gene knockout strategies. The objective of this project was to identify interesting target products and design `proof of principle' Saccharomyces cerevisiaestrains capable of overproducing a target product; in this case lysine was chosen. Methods: EFMs were calculated for a reaction network from S. cerevisiae. In order to make sense of the large EFM solution space, a novel approach based on com-putational reduction and clustering of EFM datasets into subsets was developed,which aided the prediction of knockouts for lysine production. A Pattern analysismethod, based on regular expression matching, was also developed to interpret the EFM data. FBA frameworks, OptKnock and GDLS, were used to design in silcoproduction strains based on genome-scale models of yeast. Double and triple S. cerevisiae lysine producing strains were constructed using a PCR-based deletion method. Absolute and relative metabolome measurements for lysine and other metabolites in the single and double mutants were achieved using GC-TOF-MS.Results: The new computational and clustering methodology aided significantly the EFM-based in silico design of S. cerevisiae strains for enhanced yield of lysine andother value chemicals. Ethanol and lysine overproducing in silico strains were also developed by OptKnock and GDLS. Remarkably, the production strains with singledeletions, lsc2 and glt1, excreted into the medium five times the amount of lysine than the control strain. Five S. cerevisiae double mutant strains were successfullyconstructed. Two-fold increase in flux towards lysine production was demonstrated by S. cerevisiae double mutant M1, while both S. cerevisiae double mutants M4 andM5 showed about four-fold increase in lysine production. Conclusion: The general modelling and data reduction approaches developed here contributed in obviating the enormous problems associated with trying to obtainthe EFMs from large reaction network models and interpreting the resulting of large number of EFMs. EFM analysis aided the development of single and double S.cerevisiae mutant strains, capable of increased yield of lysine. The computational method was validated by construction of strains that are able to produce several foldmore lysine than the original strain.

Inflammation provides broad immunological protection that is essential for our survival. This cellular response is characterised by a biphasic cycle consisting of an initial acute pro-inflammatory phase and a subsequent resolving anti-inflammatory phase. Underlying each of these phases are changes in the expression of hundreds of immune genes, which encode for inflammatory mediators called cytokines. Importantly, the biphasic nature of inflammation requires cytokine expression to be highly regulated and coordinated to different timescales during each phase of inflammation. For the initial proinflammatory response, cytokine expression needs to be rapid and robust to efficiently initiate host defence mechanisms and provide effective immunological protection. In contrast, the expression of anti-inflammatory cytokines is temporally delayed to ensure that anti-inflammation always follows pro-inflammation. In order to choreograph the expression of these cytokines during inflammation, numerous mechanisms within the cell serve to regulate and coordinate cytokine transcription. Within the eukaryotic nucleus, multiple modes of transcriptional regulation function cooperatively to provide the regulatory capacity that is required for complex transcription patterns to emerge. These include the organisation of the genome, which confine cognate chromosomal contacts that are causal to transcription, and long-non coding RNAs (lncRNAs) that function to discretely fine tune transcriptional activity. Although many of the mechanisms that regulate transcription have been well described, their role in cytokine expression during inflammation remains largely unknown. In particular, the mechanisms that facilitate rapid and robust cytokine expression during proinflammation and the regulatory networks that coordinate the biphasic regulation of inflammation are unresolved. In this work, two novel lncRNAs were discovered to transcriptionally regulate these key features of cytokine expression during inflammation. The first, UMLILO (Upstream Master LncRNA of the Inflammatory chemokine LOcus), was found to emanate from the ELR+ CXCL chemokine TAD and regulate the transcriptional activation of the pro-inflammatory ELR+ CXCL chemokines (IL-8, CXCL1, CXCL2 and CXCL3). By exploiting the pre-formed local 3D topology, UMLILO is able to epigenetically prime the chemokines for transcriptional activation. This involves the discrete deposition of H3K4me3 onto the promoters of the chemokines, which allows for the pre-loading of transcriptional machinery prior to their signal-dependent activation. This reveals a fundamental mechanism for the epigenetic priming and rapid activation of pro-inflammatory cytokine genes. The second lncRNA, called AMANZI (A MAster Non-coding RNA antagoniZing Inflammation), was found to coordinate the transcription of two functionally opposed cytokines: the master pro-inflammatory IL-1β and the broad antiinflammatory IL-37. AMANZI is encoded in the promoter of IL-1β, which results in its concomitant expression when IL-1β is transcriptionally active. Functionally, AMANZI mediates the formation of a dynamic chromosomal contact between IL-1β and IL-37. This leads to the delayed transcriptional activation of IL-37 ensuring that the pro-inflammatory function of IL-1β precedes IL-37 mediated anti-inflammation. This revealed a novel biphasic circuit that coordinated the expression of IL-1β and IL-37, through the activity of AMANZI, to regulate the two functionally opposed states of inflammation. Clinical observations in healthy individuals revealed that a polymorphism occurring in AMANZI (rs16944) was able to augment the state of this genetic circuit and shift the relative levels of IL-1β and IL-37 to influence an individual's inflammatory capacity. This affected the establishment of innate immunological memory, which is involved in the progression of many inflammatory conditions and the efficacy of certain vaccines. The work described here uncovers novel mechanisms that transcriptionally regulate key features of the inflammatory response. Importantly, this work implicates the role of two novel lncRNAs in inflammation, essentially contributing to the functional annotation to the genome and providing novel targets for the modulation of pathogenic inflammation.

Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, February, 2020 / Cataloged from student-submitted PDF of thesis. / Includes bibliographical references (pages 183-200). / Proteins are the fundamental building blocks of life, carrying out a vast array of functions at the molecular level. Understanding these molecular machines has been a core problem in biology for decades. Recent advances in cryo-electron microscopy (cryoEM) has enabled high resolution experimental measurement of proteins in their native states. However, this technology remains expensive and low throughput. At the same time, ever growing protein databases offer new opportunities for understanding the diversity of natural proteins and for linking sequence to structure and function. This thesis introduces a variety of machine learning methods for accelerating protein structure determination by cryoEM and for learning from large protein databases. We first consider the problem of protein identification in the large images collected in cryoEM. We propose a positive-unlabeled learning framework that enables high accuracy particle detection with few labeled data points, both improving data quality and analysis speed. Next, we develop a deep denoising model for cryo-electron micrographs. By learning the denoising model from large amounts of real cryoEM data, we are able to capture the noise generation process and accurately denoise micrographs, improving the ability of experamentalists to examine and interpret their data. We then introduce a neural network model for understanding continuous variability in proteins in cryoEM data by explicitly disentangling variation of interest (structure) for nuisance variation due to rotation and translation. Finally, we move beyond cryoEM and propose a method for learning vector embeddings of proteins using information from structure and sequence. Many of the machine learning methods developed here are general purpose and can be applied to other data domains. / by Tristan Bepler. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program

Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, February, 2020 / Cataloged from student-submitted PDF of thesis. Vita. / Includes bibliographical references. / In metazoans, microRNAs (miRNAs) are short pieces of RNA that load into Argonaute (AGO) proteins and base-pair to complementary sequences in mRNAs. Upon binding an mRNA, AGO- miRNA complexes recruit machinery that translationally represses and degrades the mRNAs. Mammalian genomes encode hundreds of miRNAs, and most mRNAs in mammals have evolutionarily conserved target sites to at least one of these miRNAs. Because of the widespread and varied roles of miRNAs in regulating gene expression, there have been many efforts over the past decade to predict the extent of targeting between a miRNA and an mRNA from their sequences alone. This targeting relationship between a miRNA and an mRNA depends on the binding affinities for the AGO-miRNA complex to target sites on the mRNA, which are poorly predicted by nearest-neighbor rules used for predicting RNA-RNA duplex stabilities. / This is presumably because AGO modulates the energetics of duplexes formed between its loaded miRNA and mRNA target sites. The recent development of a high-throughput method of measuring RNA-binding affinities, RNA bind-n-seq (RBNS), has allowed us to determine the relative KD values for AGO-miRNA complexes binding to hundreds of thousands of potential target sites. In this work, we use these biochemical parameters to build a quantitative model of miRNA targeting that predicts mRNA repression by a miRNA in cells better than existing in silico models. We then expand this approach to all miRNAs, including those for which we have not measured binding affinities for, by training a convolutional neural network (CNN) to predict the binding affinity between arbitrary miRNA and target sequences. We show that CNN-predicted KD values parallel the utility of experimentally determined KD values in predicting the repression of mRNAs in cells. / By measuring the binding affinities between miRNAs and their targets, we can also estimate how much binding affinity contributes to miRNA-mediated targeting. Although the majority of the variance in targeting is attributable to binding affinity, about 40% of the variance remains unexplained, motivating future efforts to expand the deep learning framework to learn important features of mRNAs outside of target sites that influence miRNA activity. / by Kathy S. Lin. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program

Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 113-121). / Bacteriophage and their hosts are locked in an age-old arms race. Successful bacteria are subject to predation, forcing the population to diversify, and phage are also quick to adapt tactics for infecting these potential hosts. Sampling of closely related bacterial strains that differ in phage infection profiles can further elucidate the mechanisms of infection. The Polz Lab maintains the Nahant Collection - 243 Vibrio strains challenged by 241 unique phage, all with sequenced genomes. This is the largest phylogenetically resolved host-range cross test available to date. Genetically mapping out the depths of this dataset requires carefully designed analysis techniques as well as further experimental exploration. First, we narrow in on a specific phage in the Nahant Collection, 2.275.0, to characterize the pressures that may select for phage that shuttle their own translational machinery. / While translation is generally considered a hallmark of cellular life, some phage carry abundant tRNA. 2.275.0 carries 18 tRNA spanning 13 amino acids. We find that while encoding translation-related components requires shuttling a larger phage genome, it also reduces dependence on host translational machinery, allowing the phage to be more aggressive in degrading and recycling the host genome and other resources required for replication. Next we develop a systematic approach for uncovering genomic features that underlie phage-host interactions. We find that correcting for phylogenetic relationships allows us to pick out relevant signals that would otherwise be drowned out by spurious correlations resulting from statistically oversampled blooms of microbes. Using these results, we wrote an interative javascript visualization to facilitate the process of developing testable hypotheses concerning the mechanisms of phage infection and host response. / From the visualization, we are able to identify, in the hosts, mobile genetic elements containing restriction modification systems that may defend against infection, as well as membrane protein modifications that may serve as phage attachment sites. / by Joy Yang. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program