Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies, a large amount of quantitative gene expression data have been continuously acquired. The springing up omics data sets such as proteomics has facilitated the investigation on disease relevant pathways. Although much work has previously been done to explore the single omics data, little work has been reported using multi-omics data integration, mainly due to methodological and technological limitations. While a single omic data can provide useful information about the underlying biological processes, multi-omics data integration would be much more comprehensive about the cause-effect processes responsible for diseases and their subtypes. This project investigates the combination of miRNAseq, proteomics, and RNAseq data on seven types of muscular dystrophies and control group. These unique multi-omics data sets provide us with the opportunity to identify disease-specific and most relevant biological pathways. We first perform t-test and OVEPUG test separately to define the differential expressed genes in protein and mRNA data sets. In multi-omics data sets, miRNA also plays a significant role in muscle development by regulating their target genes in mRNA dataset. To exploit the relationship between miRNA and gene expression, we consult with the commonly used gene library - Targetscan to collect all paired miRNA-mRNA and miRNA-protein co-expression pairs. Next, by conducting statistical analysis such as Pearson's correlation coefficient or t-test, we measured the biologically expected correlation of each gene with its upstream miRNAs and identify those showing negative correlation between the aforementioned miRNA-mRNA and miRNA-protein pairs. Furthermore, we identify and assess the most relevant disease-specific pathways by inputting the differential expressed genes and negative correlated genes into the gene-set libraries respectively, and further characterize these prioritized marker subsets using IPA (Ingenuity Pathway Analysis) or KEGG. We will then use Fisher method to combine all these p-values derived from separate gene sets into a joint significance test assessing common pathway relevance. In conclusion, we will find all negative correlated paired miRNA-mRNA and miRNA-protein, and identifying several pathophysiological pathways related to muscular dystrophies by gene set enrichment analysis. This novel multi-omics data integration study and subsequent pathway identification will shed new light on pathophysiological processes in muscular dystrophies and improve our understanding on the molecular pathophysiology of muscle disorders, preventing and treating disease, and make people become healthier in the long term. / Master of Science / Identification of biological pathways play a central role in understanding both human health and diseases. A biological pathway is a series of information processing steps via interactions among molecules in a cell that partially determines the phenotype of a cell. Specifically, identifying disease-specific pathway will guide focused studies on complex diseases, thus potentially improve the prevention and treatment of diseases. To identify disease-specific pathways, it is crucial to develop computational methods and statistical tests that can integrate multi-omics (multiple omes such as genome, proteome, etc) data. Compared to single omics data, multi-omics data will help gaining a more comprehensive understanding on the molecular architecture of disease processes. In this thesis, we propose a novel data analytics pipeline for multi-omics data integration. We test and apply our method on/to the real proteomics data sets on muscular dystrophy subtypes, and identify several biologically plausible pathways related to muscular dystrophies.

2024 / This dissertation focuses on the development of advanced multivariate analysis methods for the analysis of genetics and genomics data with multiple sources of correlations. The dissertation describes three novel topics: (1) a method to learn partial correlation networks, also known as Gaussian Graphical Models, to analyze multi-omics data (2) a sparse network method to reduce network complexity, and (3) a Genome-Wide Association Study pipeline to analyze genome-wide genotype data in longitudinal and familial settings. In the first part of my dissertation I propose a cluster-based Bootstrap algorithm for learning Gaussian Graphical Models from correlated data. The Bootstrap algorithm is validated to effectively control Type I errors without compromising statistical power compared to alternative solutions through extensive simulations in family-based studies. Additionally the algorithm is applied to learn the partial correlation networks of 47 Polygenic Risk Scores generated from genome-wide genotype data in the Long Life Family Study to unveil the complex relationships of these Polygenic Risk Scores. The second part of the dissertation extends the Bootstrap algorithm to learn sparse Gaussian Graphical Models in correlated data. Simulation studies shows that this extended Bootstrap algorithm maintains control over the Type I errors. By varying the values of the tuning parameter, the dynamic changes of networks reveal their contraction and dissection as edges with small partial correlations are systematically removed. The application of this method in real data analysis identifies meaningful clusters in the dynamic changes of the Polygenic Risk Scores and lipids networks. In the third part, I developed a Nextflow Genome-Wide Association Study pipeline, providing a fully automated analysis tool for managing, analyzing, and visualizing genome-wide genotype data for continuous and binary traits with correlated genetics data. Applying this pipeline to investigate processing speed in the Long Life Family Study leads to the identification of 17 rare protective Single Nucleotide Polymorphisms located in/near Retinoic Acid Receptor Beta and Thyroid Hormone Receptor Beta genes on chromosome 3. These findings shed light on potential mechanisms supporting the preservation of processing speed in aging individuals. / 2026-02-11T00:00:00Z

Les terres rares (TRs) sont des métaux stratégiques du XXIe siècle dont la demande croissante résulte de leurs propriétés essentielles, notamment dans les domaines des énergies renouvelables, de la médecine et des hautes technologies. Ils sont classés en TR lourdes (HTRs), terres légères légères (LTRs) et non-lanthanides. Leur dissémination dans l'environnement, associée à une faible recyclabilité, fait des TRs des contaminants émergents pour lesquels les études de toxicité sont jusqu'à présent très disparates. Afin d’établir une base générale de la réponse cellulaire et moléculaire à un stress TRs, nous avons premièrement utilisé des stratégies complémentaires et à haut débit pour étudier la réponse, ainsi que l’absorption des TRs chez le modèle eucaryote Saccharomyces cerevisiae. Les deletome, transcriptome, protéome et ionome de la levure ont été analysés et approfondis par des analyses physiologiques ciblées. Bien que des réponses communes aux TRs et à d'autres métaux aient été mises en évidence, les réponses spécifiques aux TRs étaient prédominantes. La composition de la paroi cellulaire, la biosynthèse des sphingolipides, la voie ESCRT et l'endocytose sont des éléments clés de la réponse aux TRs. Deuxièmement, nous avons exploré les effets des TRs sur le transcriptome et le ionome du modèle végétal Arabidopsis thaliana. L'exposition des TRs a négativement impacté l'architecture racinaire, comme l'a révélé la modulation de gènes liés à l'auxine. De plus, le ionome a été modifié et les gènes liés à une carence en Fe largement représentés parmi les gènes les plus différentiellement exprimés. Afin d'identifier de nouvelles plantes modèles accumulant des TRs, des espèces de Phytolacca et de fougères ont été criblées. Malgré un trait d’accumulation des TRs conservé chez quelques genres de fougères et Phytolacca, un enrichissement en HTRs chez Phytolacca et en LTRs chez les fougères a été observé. Cependant, plusieurs espèces de Dryopteris présentent des teneurs contrastées en TRs dans les frondes et représentent de nouveaux modèles pertinents pour décrypter les mécanismes d’accumulation et de tolérance aux TRs. Globalement, des divergences ont été mises en évidence dans la réponse aux différentes TRs, en fonction de leur rayon ionique. La composition de la paroi cellulaire, la détoxification vacuolaire, mais aussi l’accumulation et le fractionnement des TRs ont souligné ces différences. Nous avons confirmé que les LTRs empruntaient les canaux calciques, tandis que de nouvelles preuves ont été données sur le rôle des transporteurs de Fe dans l'accumulation de HTRs. En conclusion, nous apportons ici de nouveaux éléments sur la toxicité et les spécificités des TRs, ainsi que des explications moléculaires pour certains effets déjà connus. Ce travail constitue un premier travail de base complet et multi-approches pour de futures études afin d’approfondir la compréhension de la toxicité des TRs chez les organismes vivants. / Rare earth elements (REEs) are strategic metals whose demand in the 21st century is increasing as a result of their essential properties useful to the fields of renewable energies, medicine, and high-technologies. They are classified as heavy REEs (HREEs), light REEs (LREEs) and non-lanthanides. Their dissemination in the environment, together with poor recyclability, leads REEs to be considered emerging contaminants, for which toxicity studies are currently very fragmented. To build a strong general foundation on the cellular and molecular response to REEs, we first adopted high-throughput and complementary strategies to study the REE stress response and their uptake in the unicellular eukaryotic model Saccharomyces cerevisiae. The deletome, transcriptome, proteome and ionome of yeast were analysed together with in-depth physiological experiments. Although common responses between REEs and other metals were highlighted, REE-specific responses were predominant. Cell wall composition, sphingolipid biosynthesis, the ESCRT pathway and endocytosis were emphasized as key elements in the cellular response to REEs. Second, we explored how REEs affect the transcriptome and ionome of the plant model Arabidopsis thaliana. REE exposure negatively affected the root architecture, as revealed by the modulation of auxin-related genes. REEs impaired the ionome, and Fe deficiency-related genes were largely represented among the most differentially expressed genes in both roots and leaves. Additionally, to identify new REE-accumulating plant models, collections of ferns and Phytolacca species were screened. Despite a conserved REE accumulation trait for Phytolacca and a few fern genera, HREE enrichment was observed in Phytolacca, while LREEs were preferentially transferred into the fronds of all fern species. However, several Dryopteris species harboured contrasting REE contents in the fronds. The latter species will be of great importance in deciphering the mechanisms of REE accumulation and tolerance. Overall, the response towards REEs differed according to their ionic radius. The cell wall composition, vacuolar detoxification, and the accumulation and fractionation of REEs notably accounted for these differences. Our findings support LREE-mediated entry through calcium channels, while new evidence was provided for the role of Fe transporters in the accumulation of HREEs. In conclusion, we have provided new insights into REE toxicity and specificities, together with the molecular elucidation of REE effects that have not previously been mechanistically explained. This work is a first multi-approach comprehensive groundwork that will be used for future studies to deepen the understanding and assessment of REE toxicity in organisms.

One of the greatest risk factors for disease is advanced age. As the human lifespan has increased, so too have the burdens of caring for an increasingly older population suffering from rising rates of cardiovascular disease, kidney disease, diabetes, and dementia. The need for improving medical technology and developing new therapies for age-related diseases is manifest. Yet our understanding of the processes of aging and how to attenuate the effects of aging remains incomplete. Various studies have established calorie restriction as a robust method for extending lifespan in laboratory organisms; however the mechanism is a topic of much debate. Advancing our understanding of calorie restriction holds promise for illuminating biochemical processes involved in the aging process. One of the best explanations for the lifespan extension benefits of calorie restriction is that it improves cellular protein homeostasis (proteostasis), but because proteostasis is dynamic, it can be difficult to measure. We developed a novel combined omics methodology integrating kinetic proteomics, and applied it to a mouse model placed on calorie restriction. Our unbiased approach integrating just three measurements (kinetic proteomics, quantitative proteomics, and transcriptomics) enabled us to characterize the synthesis and degradation of thousands of proteins, and determine that calorie restriction largely alters proteostasis by slowing global protein synthesis post-transcriptionally. Validating our omics approach, we were able to replicate many previous results found in the literature, demonstrating the differential regulation of various protein ontologies in response to the nutrient stress of calorie restriction. Moreover, we were able to detect differential degradation of the large and small ribosomal subunits under calorie restriction, and proposed a model in which the rate of protein synthesis could be attenuated by the depletion of the large ribosomal subunit relative to the small subunit. The flexibility of our dynamic combined omics approach was demonstrated by the expansion of measurements to include nucleic acids and lipids. Flux measurements of DNA, ribosomal RNA, and lipids yielded cellular division rates, ribosome turnover, and lipid metabolism insights, respectively. We also adapted this approach to two-dimensional tissue imaging by DESI-MS in a proof-of-concept study to demonstrate its utility for studying regional differences in metabolism. The future integration of metabolomics and lipidomics into our combined omics approach would be facile, and add unprecedented depth to systems-wide studies involving cellular metabolism. Applied to the regulation of cellular homeostasis in humans, this has the potential to open new avenues for elucidating the etiology of aging, understanding the pathology of age-related diseases, and identifying novel targets for therapeutics.

No description available.

This thesis develops an Artificial Intelligence (AI) approach intended for accurate patient stratification and precise diagnostics/prognostics in clinical and preclinical applications. The rapid advance in high throughput technologies and bioinformatics tools is still far from linking precisely the genome-phenotype interactions with the biological mechanisms that underlie pathophysiological conditions. In practice, the incomplete knowledge on individual heterogeneity in complex diseases keeps forcing clinicians to settle for surrogate endpoints and therapies based on a generic one-size-fits-all approach. The working hypothesis is that AI can add new tools to elaborate and integrate together in new features or structures the rich information now available from high-throughput omics and bioimaging data, and that such re- structured information can be applied through predictive models for the precision medicine paradigm, thus favoring the creation of safer tailored treatments for specific patient subgroups. The computational techniques in this thesis are based on the combination of dimensionality reduction methods with Deep Learning (DL) architectures to learn meaningful transformations between the input and the predictive endpoint space. The rationale is that such transformations can introduce intermediate spaces offering more succinct representations, where data from different sources are summarized. The research goal was attacked at increasing levels of complexity, starting from single input modalities (omics and bioimaging of different types and scales), to their multimodal integration. The approach also deals with the key challenges for machine learning (ML) on biomedical data, i.e. reproducibility, stability, and interpretability of the models. Along this path, the thesis contribution is thus the development of a set of specialized AI models and a core framework of three tools of general applicability: i. A Data Analysis Plan (DAP) for model selection and evaluation of classifiers on omics and imaging data to avoid selection bias. ii. The histolab Python package that standardizes the reproducible pre-processing of Whole Slide Images (WSIs), supported by automated testing and easily integrable in DL pipelines for Digital Pathology. iii. Unsupervised and dimensionality reduction techniques based on the UMAP and TDA frameworks for patient subtyping. The framework has been successfully applied on public as well as original data in precision oncology and predictive toxicology. In the clinical setting, this thesis has developed1: 1. (DAPPER) A deep learning framework for evaluation of predictive models in Digital Pathology that controls for selection bias through properly designed data partitioning schemes. 2. (RADLER) A unified deep learning framework that combines radiomics fea- tures and imaging on PET-CT images for prognostic biomarker development in head and neck squamous cell carcinoma. The mixed deep learning/radiomics approach is more accurate than using only one feature type. 3. An ML framework for automated quantification tumor infiltrating lymphocytes (TILs) in onco-immunology, validated on original pathology Neuroblastoma data of the Bambino Gesu’ Children’s Hospital, with high agreement with trained pathologists. The network-based INF pipeline, which applies machine learning models over the combination of multiple omics layers, also providing compact biomarker signatures. INF was validated on three TCGA oncogenomic datasets. In the preclinical setting the framework has been applied for: 1. Deep and machine learning algorithms to predict DILI status from gene expression (GE) data derived from cancer cell lines on the CMap Drug Safety dataset. 2. (ML4TOX) Deep Learning and Support Vector Machine models to predict potential endocrine disruption of environmental chemicals on the CERAPP dataset. 3. (PathologAI) A deep learning pipeline combining generative and convolutional models for preclinical digital pathology. Developed as an internal project within the FDA/NCTR AIRForce initiative and applied to predict necrosis on images from the TG-GATEs project, PathologAI aims to improve accuracy and reduce labor in the identification of lesions in predictive toxicology. Furthermore, GE microarray data were integrated with histology features in a unified multi-modal scheme combining imaging and omics data. The solutions were developed in collaboration with domain experts and considered promising for application.

<p dir="ltr">Crohn’s disease and ulcerative colitis are chronic inflammatory bowel diseases (IBD) with a rising global prevalence, influenced by clinical and demographics factors. The pathogenesis of IBD involves complex interactions between gut microbiome dysbiosis, epithelial cell barrier disruption, and immune hyperactivity, which are poorly understood. This necessitates the development of novel approaches to integrate and model multiple clinical and molecular data modalities from patients, animal models, and <i>in-vitro</i> systems to discover effective biomarkers for disease progression and drug response. As sequencing technologies advance, the amount of molecular and compositional data from paired measurements of host and microbiome systems is exploding. While it is become routine to generate such rich, deep datasets, tools for their interpretation lag behind. Here, I present a computational framework for integrative modeling of microbiome multi-omics data titled: Latent Interacting Variable Effects (LIVE) modeling. LIVE combines various types of microbiome multi-omics data using single-omic latent variables (LV) into a structured meta-model to determine the most predictive combinations of multi-omics features predicting an outcome, patient group, or phenotype. I implemented and tested LIVE using publicly available metagenomic and metabolomics data set from Crohn’s Disease (CD) and ulcerative colitis (UC) status patients in the PRISM and LLDeep cohorts. The findings show that LIVE reduced the number of features interactions from the original datasets for CD to tractable numbers and facilitated prioritization of biological associations between microbes, metabolites, enzymes, clinical variables, and a disease status outcome. LIVE modeling makes a distinct and complementary contribution to the current methods to integrate microbiome data to predict IBD status because of its flexibility to adapt to different types of microbiome multi-omics data, scalability for large and small cohort studies via reliance on latent variables and dimensionality reduction, and the intuitive interpretability of the meta-model integrating -omic data types.</p><p dir="ltr">A novel application of LIVE modeling framework was associated with sex-based differences in UC. Men are 20% more likely to develop this condition and 60% more likely to progress to colitis-associated cancer compared to women. A possible explanation for this observation is differences in estrogen signaling among men and women in which estrogen signaling may be protective against UC. Extracting causal insights into how gut microbes and metabolites regulate host estrogen receptor β (ERβ) signaling can facilitate the study of the gut microbiome’s effects on ERβ’s protective role against UC. Supervised LIVE models<b> </b>ERβ signaling using high-dimensional gut microbiome data by controlling clinical covariates such as: sex and disease status. LIVE models predicted an inhibitory effect on ER-UP and ER-DOWN signaling activities by pairs of gut microbiome features, generating a novel of catalog of metabolites, microbial species and their interactions, capable of modulating ER. Two strongly positively correlated gut microbiome features: <i>Ruminoccocus gnavus</i><i> </i>with acesulfame and <i>Eubacterium rectale</i><i> </i>with 4-Methylcatechol were prioritized as suppressors ER-UP and ER-DOWN signaling activities. An <i>in-vitro</i> experimental validation roadmap is proposed to study the synergistic relationships between metabolites and microbiota suppressors of ERβ signaling in the context of UC. Two i<i>n-vitro</i> systems, HT-29 female colon cancer cell and female epithelial gut organoids are described to evaluate the effect of gut microbiome on ERβ signaling. A detailed experimentation is described per each system including the selection of doses, treatments, metrics, potential interpretations and limitations. This experimental roadmap attempts to compare experimental conditions to study the inhibitory effects of gut microbiome on ERβ signaling and how it could elevate or reduce the risk of developing UC. The intuitive interpretability of the meta-model integrating -omic data types in conjunction with the presented experimental validation roadmap aim to transform an artificial intelligence-generated big data hypothesis into testable experimental predictions.</p>

No description available.

Weed resistance to control measures, particularly herbicides, is a growing problem in agriculture. In the case of herbicides, resistance is sometimes connected to genetic changes that directly affect the target site of the herbicide. Other cases are less straightforward where resistance arises without such a clear-cut mechanism. Understanding the genetic and gene regulatory mechanisms that may lead to the rapid evolution of resistance in weedy species is critical to securing our food supply. To study this phenomenon, we exposed young Arabidopsis plants to sublethal levels of one of four weed management stressors, glyphosate herbicide, trifloxysulfuron herbicide, mechanical clipping, and shading. To evaluate responses to these stressors we collected data on gene expression and regulation via epigenetic modification (methylation) and small RNA (sRNA). For all of the treatments except shade, the stress was limited in duration, and the plants were allowed to recover until flowering, to identify changes that persist to reproduction. At flowering, DNA for methylation bisulfite sequencing, RNA, and sRNA were extracted from newly formed rosette leaf tissue. Analyzing the individual datasets revealed many differential responses when compared to the untreated control for gene expression, methylation, and sRNA expression. All three measures showed increases in differential abundance that were unique to each stressor, with very little overlap between stressors. Herbicide treatments tended to exhibit the largest number of significant differential responses, with glyphosate treatment most often associated with the greatest differences and contributing to overlap. To evaluate how large datasets from methylation, gene expression, and sRNA analyses could be connected and mined to link regulatory information with changes in gene expression, the information from each dataset and for each gene was united in a single large matrix and mined with classification algorithms. Although our models were able to differentiate patterns in a set of simulated data, the raw datasets were too noisy for the models to consistently identify differentially expressed genes. However, by focusing on responses at a local level, we identified several genes with differential expression, differential sRNA, and differential methylation. While further studies will be needed to determine whether these epigenetic changes truly influence gene expression at these sites, the changes detected at the treatment level could prime the plants for future incidents of stress, including herbicides. / Doctor of Philosophy / Growing resistance to herbicides, particularly glyphosate, is one of the many problems facing agriculture. The rapid rise of resistance across herbicide classes has caused some to wonder if there is a mechanism of adaptation that does not involve mutations. Epigenetics is the study of changes in the phenotype that cannot be attributed to changes in the genotype. Typically, studies revolve around two features of the chromosomes: cytosine methylation and histone modifications. The former can influence how proteins interact with DNA, and the latter can influence protein access to DNA. Both can affect each other in self-reinforcing loops. They can affect gene expression, and DNA methylation can be directed by small RNA (sRNA), which can also influence gene expression through other pathways. To study these processes and their role in abiotic stress response, we aimed to analyze sRNA, RNA, and DNA from Arabidopsis thaliana plants under stress. The stresses applied were sublethal doses of the herbicides, glyphosate and trifloxysulfuron, as well as mechanical clipping and shade to represent other weed management stressors. The focus of the project was to analyze these responses individually and together to find epigenetic responses to stresses routinely encountered by weeds. We tested RNA for gene expression changes under our stress conditions and identified many, including some pertaining to DNA methylation regulation. The herbicide treatments were associated with upregulated defense genes and downregulated growth genes. Shade treated plants had many downregulated defense and other stress response genes. We also detected differential methylation and sRNA responses when compared to the control plants. Changes to methylation and sRNA only accounted for about 20% of the variation in gene expression. While attempting to link the epigenetic process of methylation to gene expression, we connected all the data sets and developed computer programs to try to make correlations. While these methods worked on a simulated dataset, we did not detect broad patterns of changes to epigenetic pathways that correlated strongly with gene expression in our experiment's data. There are many factors that can influence gene expression that could create noise that would hinder the algorithms' abilities to detect differentially expressed genes. This does not, however, rule out the possibility of epigenetic influence on gene expression in local contexts. Through scoring the traits of individual genes, we found several that interest us for future studies.

Most of microorganisms are ubiquitously able to live in both planktonic and biofilm states, which can be applied to dissolve the energy and environmental issues (e.g., producing biofuels and purifying waste water), but can also lead to serious public health problems. To better harness microorganisms, plenty of studies have been implemented to investigate the metabolism of planktonic and/or biofilm cells via multi-omics approaches (e.g., transcriptomics and proteomics analysis). However, these approaches are limited to provide the direct description of intracellular metabolism (e.g., metabolic fluxes) of microorganisms. Therefore, in this study, I have applied computational modeling approaches (i.e., 13C assisted pathway and flux analysis, flux balance analysis, and machine learning) to both planktonic and biofilm cells for better understanding intracellular metabolisms and providing valuable biological insights. First, I have summarized recent advances in synergizing 13C assisted pathway and flux analysis and metabolic engineering. Second, I have applied 13C assisted pathway and flux analysis to investigate the intracellular metabolisms of planktonic and biofilm cells. Various biological insights have been elucidated, including the metabolic responses under mixed stresses in the planktonic states, the metabolic rewiring in homogenous and heterologous chemical biosynthesis, key pathways of biofilm cells for electricity generation, and mechanisms behind the electricity generation. Third, I have developed a novel platform (i.e., omFBA) to integrate multi-omics data with flux balance analysis for accurate prediction of biological insights (e.g., key flux ratios) of both planktonic and biofilm cells. Fourth, I have designed a computational tool (i.e., CRISTINES) for the advanced genome editing tool (i.e., CRISPR-dCas9 system) to facilitate the sequence designs of guide RNA for programmable control of metabolic fluxes. Lastly, I have also accomplished several outreaches in metabolic engineering. In summary, during my Ph.D. training, I have systematically applied computational modeling approaches to investigate the microbial metabolisms in both planktonic and biofilm states. The biological findings and computational tools can be utilized to guide the scientists and engineers to derive more productive microorganisms via metabolic engineering and synthetic biology. In the future, I will apply 13C assisted pathway analysis to investigate the metabolism of pathogenic biofilm cells for reducing their antibiotic resistance. / Ph. D. / Most of microorganisms are ubiquitously able to live in both planktonic and biofilm states (i.e., floating in a flow and anchoring on a surface, respectively), which can be applied to dissolve the energy and environmental issues (e.g., producing biofuels and purifying waste water), but can also lead to serious public health problems (e.g., chronic infections). Therefore, deciphering the metabolism of both planktonic and biofilm cells are of great importance to better harness microorganism. Plenty of studies have been implemented to investigate the metabolism of planktonic and/or biofilm cells by measuring the abundances of single type of biological components (e.g., gene expression and proteins). However, these approaches are limited to provide the direct description of intracellular metabolism (e.g., enzyme activities) of microorganisms. Therefore, in this study, I have applied computational modeling approaches to both planktonic and biofilm cells for providing valuable biological insights (e.g., enzyme activities). The biological insights include 1) how planktonic cells response to mixed stresses (e.g., acids and organics) 2) how planktonic cells produce various chemicals, and 3) how biofilm cells generate electricity by rewiring the intracellular metabolic pathways. I also developed a novel platform to utilize multiple types of biological data for improving the prediction accuracy of biological insights of both planktonic and biofilm cells. In addition, I designed a computational tool to facilitate the sequence designs of an advanced genome editing tool for precisely controlling the corresponding enzyme activities. Lastly, I have also accomplished several outreaches in metabolic engineering. In summary, during my Ph.D. training, I have systematically applied computational modeling approaches to investigate the microbial metabolisms in both planktonic and biofilm states. The biological findings and computational tools can be utilized to guide the metabolic engineered to derive more productive microorganisms via metabolic engineering and synthetic biology. In the future, I plan to investigate how the pathogenic biofilm cells improve their antibiotic resistance and attempt to reduce such strong resistance.