<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>

Integrated Pest Management (IPM) has long been promoted as a means of reducing reliance on pesticide inputs as compared to conventional farming systems. Reduced pesticide application could be beneficial due to the links between intensive pesticide use and negative impacts upon biodiversity and human health as well as the development of pesticide resistance. Work assessing the potential of IPM in cereal production is currently limited, however, and previous findings have generally covered the subject from the perspective of either field trial data or social science studies of farmer behaviour. This thesis attempts to help to address this knowledge gap by providing a more holistic assessment of IPM in Scottish spring barley production (selected because of its dominance in Scotland’s arable production systems), in relation to three of its most damaging fungal pathogens: Rhynchosporium commune, Blumeria graminis f.sp. hordei, and Ramularia collo-cygni. Several IPM techniques of potential relevance to the sector were identified, and the prospects of three in particular – crop rotation, varietal disease resistance, and forecasting disease pressure – were assessed in several ways. Preliminary analysis of experimental field trial data collected from 2011 – 2014 across Scotland found that the majority of spring barley trials in this period (65%) did not show a statistically significant impact of fungicide treatment on yield, with the average yield increase due to fungicide application being 0.62 t/ha. This initial analysis was expanded upon using stepwise regressions of long-term (1996 – 2014) field trial data from the same dataset. Here, the difference between treated and untreated yields could be explained by disease resistance, average seasonal rainfall (whereby wetter seasons saw an increased impact of fungicide use on yield), and high combined disease severity. Stakeholder surveying provided information about current practice and attitudes towards the selected IPM techniques amongst a group of 43 Scottish spring barley farmers and 36 agronomists. Stakeholders were broadly open to taking up IPM measures on farm; sowing of disease resistant varieties was most frequently selected as the best technique in terms of both practicality and cost, though individual preference varied. However, a disparity was seen between farmer perception of their uptake of IPM and actual, self-reported uptake for both varietal disease resistance and rotation. Farmers and agronomists also overestimated the impact of fungicide use as compared with the field trials results – the majority of stakeholders believed fungicide treatment to increase yields by 1 - 2 t/ha, while the majority of 2011 – 2014 field trials had a yield difference of under 1 t/ha. The reasons behind these differences between perception and practice are not currently known. Finally, an annual survey of commercial crops, gathered from 552 farms across Scotland (from 2009 – 2015), highlighted two gaps where IPM practice could be improved upon. Firstly, relatively few of the varieties listed in the commercial crops database were highly resistant to the three diseases – 26.1% were highly resistant to Ramularia, 14.2% to Rhynchosporium, and 58.1% to mildew. Secondly, 71% of the farms included in the database had planted barley in at least two consecutive seasons, indicating that crop rotation practices could be improved. The overarching finding of this project is that there is scope for IPM uptake to be improved upon and fungicide use to be reduced while maintaining high levels of yield in Scottish spring barley production. Incorporating experimental field data, stakeholder surveying, and commercial practice data offered a unique view into the potential for IPM in this sector, and provided insights which could not have been gained through the lens of a single discipline.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-08-01T13:42:24Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) JDiogo_Tese Final_Quimica.pdf: 8861608 bytes, checksum: b9ce8a33624820a531c0a5c298600d94 (MD5) / Made available in DSpace on 2017-08-01T13:42:24Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) JDiogo_Tese Final_Quimica.pdf: 8861608 bytes, checksum: b9ce8a33624820a531c0a5c298600d94 (MD5) Previous issue date: 2017-01-26 / CNPQ / Metodologias te´oricas s˜ao ´uteis para complementar investigac¸˜oes experimentais e guiar novos experimentos envolvendo compostos luminescentes de lantan´ıdeos. A ausˆencia de uma ferramenta computacional contendo tais m´etodos motivou o desenvolvimento do LUMPAC. Se por um lado o LUMPAC difundiu o uso dessas metodologias, por outro as suas limitac¸˜oes tamb´em foram evidenciadas. Nesse sentido, pˆode-se conhecer melhor quais m´etodos merecem uma atenc¸˜ao especial, a saber: c´alculo dos parˆametros de intensidade (Ωλ), c´alculo da energia dos estados excitados dos ligantes e c´alculo da taxa de emiss˜ao n˜ao-radiativa (Anrad). O objetivo geral do presente trabalho de doutoramento consiste em corrigir algumas dessas limitac¸˜oes. Quantoaoc´alculodosΩλ,conseguimosatenuaroproblemacomumanovaformadeajustedos fatores de carga e das polarizabilidades atrav´es de um procedimento que foi denominado de Modelo da UnicidadeQDC, o qual faz uso de um conjunto bastante reduzido de parˆametros (Q,D eC). A importˆancia do ajusteQDC ´e que todas as quantidades derivadas se tornam tamb´em ´unicas para uma dada geometria do complexo, incluindo um esquema proposto de partic¸˜ao qu´ımico da taxa de emiss˜ao radiativa (Arad) em termos dos efeitos dos ligantes. Para demonstrar uma das poss´ıveis aplicac¸˜oes dessa partic¸˜ao, foi considerado o caso de complexos tern´arios de Eu3+ de ligantes n˜ao-iˆonicos repetidos e com os ligantes betadicetonatos DBM, TTA e BTFA. A partic¸˜ao ordenou perfeitamente a combinac¸˜ao n˜ao ´obvia de pares de ligantes n˜ao-iˆonicos que levam aos compostos misturados com os maiores valores deAexp rad. Quanto ao c´alculo dos estados excitados dos ligantes, ´e proposta uma parametrizac¸˜ao do m´etodo CIS baseadonaaproximac¸˜aoNDDO,exclusivamenteparasistemaslantan´ıdicos. Al´emdisso,realizamosumestudoavaliativodemetodologiasTDDFTaplicadasaoc´alculodeestadosexcitados de ligantes em complexos de lantan´ıdeos. Dentre os funcionais e func¸˜oes de base avaliados, a combinac¸˜ao LC-ωPBE/6-31G(d) foi aquela que forneceu as energias tripleto mais concordantes com os dados obtidos na literatura, sendo o erro m´edio absoluto correspondente em torno de 1600 cm−1. Atrav´es da parametrizac¸˜ao do modelo NDDO-CIS implementado no programa ORCA foi poss´ıvel obter um modelo semiemp´ırico para o c´alculo da energia tripleto de complexosdelantan´ıdeocom qualidade bem superiora da melhormetodologiaTDDFT avaliada. / Theoretical methodologies are useful to complement experimental investigations, and to guide new experiments involving luminescent lanthanide compounds. The lack of a software containing these methods motivated us to the development of the user friendly software package LUMPAC. And indeed, LUMPAC is slowly popularising the use of these theoretical methodologies - methodologies that are being put to more frequent tests, and are, consequently, slowly revealing their limitations. In this sense, we identified which aspects of the methods would deserve a more special attention, namely: intensity parameters calculations (Ωλ), calculation of the excited state energies of the ligands, and the calculation of the non-radiative decay rate (Anrad). The overall objective of this doctoral work is to correct some of these limitations as wellastoadvancenewdevelopments. RegardingtheΩλ calculation,wemitigatedthisproblem with a new way to adjust the charge factors and polarizabilities through a procedure we called theQDC Uniqueness Model, which makes use of a fairly small set of adjustaeble parameters (Q,D, andC). The importance of theQDC adjustment is that all derived quantities become also unique for a given complex geometry, including the chemical partition of the radiative emission rate (Arad) in terms of the effects of the ligands, which is being advanced here. To demonstrate one of the possible applications of this chemical partition, we address the case of repeating non-ionic ligand ternary complexes of europium(III) with DBM, TTA, and BTFA. The chemical partition perfectly ordered the non-obvious combination of pairs of non-ionic ligands that led to the mixed ligand compounds with the highest values ofAexp rad . Regarding the calculation of the excited states of the ligands, a new parametrization of the CIS method based on the NDDO approximation is being proposed, exclusively for lanthanide complexes. In addition, we carried out a study to evaluate some TDDFT methodologies for the calculation of excited states of ligands in lanthanide complexes. Among the functionals and basis sets evaluated, the combination LC-ωPBE/6-31G(d) was the one that led to the lowest UME (unsigned mean error), of around 1600 cm−1, for the triplet energies in comparison with data from the literature. The parametrization of the NDDO-CIS model implemented into ORCA provided a semiempirical method for the triplet energy calculation of lanthanide complexes with better predictionpower thanthebestassessed TDDFT method.

<p>For many years, researchers have developed ways to diagnose degenerative disease in the retina by utilizing multiple gene analysis techniques. Retinitis pigmentosa (RP) disease can cause either partially or totally blindness in adults. For that reason, it is crucial to find a way to pinpoint the causes in order to develop a proper medication or treatment. One of the common methods is genome-wide analysis (GWA). However, it cannot fully identify the genes that are indirectly related to the changes in eye size. In this research, RNA sequencing (RNA-seq) analysis is used to link the phenotype to genotype, creating a pool of candidate genes that might associate with the RP. This will support future research in finding a therapy or treatment to cure such disease in human adults.</p> <p><br></p> <p>Using the Drosophila Genetic Reference Panel (DGRP) – a gene reference panel of fruit fly – two types of datasets are involved in this analysis: eye-size data and gene expression data with two replicates for each strain. This allows us to create a phenotype-genotype map. In other words, we are trying to trace the genes (genotype) that exhibit the RP disease guided by comparing their eye size (phenotype). The basic idea of the algorithm is to discover the best replicate combination that maximizes the correlation between gene expression and eye-size. Since there are 2N possible replicate combinations, where N is the number of selected strains, the original implementation of sequential algorithm was computationally intensive.</p> <p><br></p> <p>The original idea of finding the best replicate combination was proposed by Nguyen et al. (2022). In this research, however, we restructured the algorithms to distribute the tasks of finding the best replicate combination and run them in parallel. The implementation was done using the R programming language, utilizing doParallel and foreach packages, and able to execute on a multicore machine. The program was tested on both a laptop and a server, and the experimental results showed an outstanding improvement in terms of the execution time. For instance, while using 32 processes, the results reported up to 95% reduction in execution time when compared with the sequential version of the code. Furthermore, with the increment of computational capabilities, we were able to explore and analyze more extreme eye-size lines using three eye-size datasets representing different phenotype models. This further improved the accuracy of the results where the top candidate genes from all cases showed connection to RP.</p>

<p>Ancestry is a complex and layered concept, but it must be operationalized for its objective use in genetic studies. Critical decisions in research analyses, clinical practice, and forensic investigations are based on genetic ancestry inference. For example, in genetic association studies for clinical and applied research, investigators may need to isolate one population of interest from a worldwide dataset to avoid false positive results, or in human identification, ancestry inferences can help reveal the identity of unknown DNA evidence by narrowing down a suspect list. Many studies seek to improve ancestry inference for these reasons. The research presented here offers valuable resources for exploring and improving genetic ancestry inference and intelligence toward identity. </p> <p>First, analyses with ‘big data’ in genomics is a resource-intensive task that requires optimization. Therefore, this research introduces a suite of automated Snakemake workflows, <em>Iliad</em>, that was developed to give the research community an easy-to-learn, hands-off computational tool for genomic data processing of multiple data formats. <em>Iliad</em> can be installed and run on a Google Cloud Platform remote server instance in less than 20 minutes when using the provided installation code in the ReadTheDocs documentation. The workflows support raw data processing from various genetic data types including microarray, sequence, and compressed alignment data, as well as performing micro-workflows on variant call format (VCF) files to merge data or lift over variant positions. When compared to a similar workflow, <em>Iliad </em>completed processing one sample’s raw paired-end sequence reads to a human-legible VCF file in 7.6 hours which was three-times faster than the other workflow. This suite of workflows is paramount towards building reference population panels from human whole-genome sequence (WGS) data which is useful in many research studies including imputation, ancestry estimation, and ancestry informative marker (AIM) discovery.</p> <p>Second, there are persistent challenges in ancestry inference for individuals of the Middle East, especially with the use of AIMs. This research demonstrates a population genomics study pertaining to the Middle East, novel population data from Lebanon (n=190), and an unsupervised genetic clustering approach with WGS data from the 1000 Genomes Project and Human Genome Diversity Project. These efforts for AIM discovery identified two single nucleotide polymorphisms (SNPs) based on their high allelic frequency differences between the Middle East and populations in Eurasia, namely Europe and South/Central Asia. These candidate AIMs were evaluated with the most current and comprehensive AIM panel to date, the VISAGE Enhanced Tool (ET), using an external validation set of Middle Eastern WGS data (n=137). Instead of relying on pre-defined biogeographic ancestry labels to confirm the accuracy of validation sample ancestry inference, this research produced a deep, unsupervised ADMIXTURE analysis on 3,469 worldwide WGS samples with nearly 2 million independent SNPs (r2 < 0.1) which provided a genetic “ground truth”. This resulted in 136/137 validation samples as Middle East and provided valuable insights toward reference samples with varying co-ancestries that ultimately affects the classification of admixed individuals. Novel deep learning methods, specifically variational autoencoders, were introduced for visualizing one hundred percent of the genetic variance found using these AIMS in an alternative method to PCA and presents distinct population clusters in a robust ancestry space that remains static for the projection of unknown samples to aid in ancestry inference and human identification. </p> <p>Third, this research delves into a craniofacial study that makes improvements toward key intelligence information about physical identity by exploring the relationship between dentition and facial morphology with an advanced phenotyping approach paired with robust dental parameters used in clinical practice. Cone-beam computed tomography (CBCT) imagery was used to analyze the hard and soft tissue of the face at the same time. Low-to-moderate partial correlations were observed in several comparisons of dentition and soft tissue segments. These results included partial correlations of: i) inter-molar width and soft tissue segments nearest the nasal aperture, the lower maxillary sinuses, and a portion of the upper cheek, and ii) of lower incisor inclination and soft tissue segments overlapping the mentolabial fold. These results indicate that helpful intelligence information, potentially leading towards identity in forensic investigations, may be present where hard tissue structures are manifested in an observable way as a soft tissue phenotype. This research was a valuable preliminary study that paves the way towards the addition of facial hard tissue structures in combination with external soft tissue phenotypes to advance fundamental facial genetic research. Thus, CBCT scans greatly add to the current facial imagery landscape available for craniofacial research and provide hard and soft tissue data, each with measurable morphological variation among individuals. When paired with genetic association studies and functional biological experiments, this will ultimately lead to a greater understanding of the intricate coordination that takes place in facial morphogenesis, and in turn, guide clinical orthodontists to better treatment modalities with an emphasis on personalized medicine. Lastly, it aids intelligence methodologies when applied within the field of forensic anthropology.</p>

Genomic DNA is the template of life - the entity which is characterized by a self-sustaining anatomical development, regulated signaling processes, the ability to reproduce and to respond to stimuli. Through what is classically known as the central dogma, the genome is transcribed into mRNA, which in turn is translated into proteins. The proteins take part in most, if not all, cellular processes, and it is by unraveling these processes that we can begin to understand life and disease-causing mechanisms. In vitro and in vivo assays are two levels at which protein communication may be studied, and which permit manipulation and control over the proteins under investigation. But in order to retrieve a representation of the processes as close to reality as possible, in situ analysis may instead be applied as a complement to the other two levels of study. In situ PLA offers the ability to survey protein activity in tissue samples and primary cell lines, at a single cell level, detecting single targets in their natural unperturbed environment.   In this thesis new developments of the in situ PLA are described, along with a new technique offering in situ enzyme-free detection of proximity between biomolecules. The dynamic range of in situ PLA has now been increased by several orders of magnitude to cover analogous ranges of protein expression; the output signals have been modified to offer a greater signal-to-noise ratio and to limit false-positive-rates while also extending the dynamic range further; simultaneous detection of multiple protein complexes is now possible; proximity-HCR is presented as a robust and inexpensive enzyme-free assay for protein complex detection. The thesis also covers descriptions on how the techniques may be simultaneously applied, also together with other techniques, for the multiple data-point acquisition required by the emerging realm of systems biology. A future perspective is presented for how much more information may be simultaneously acquired from tissue samples to describe biomolecular interactions in a new manner. This will allow new types of biomarkers and drugs to be discovered, and a new holistic understanding of life.

This thesis describes the on-road analysis of criteria air pollutants (CAPs) and volatile organic compounds (VOCs) from a moving vehicle. CAPs and VOCs have numerous direct and indirect effects on the environment and public health and are generated from a variety of point and diffuse sources. The concentration of these pollutants can vary on the scale of metres and seconds due to variable emission rates of sources, meteorology, and the topography of an area. CAPs are conventionally measured on a spatial scale of tens of kilometres and one hour or longer time resolution, which limits the understanding of their impact and leaving many communities lacking information regarding their air quality. VOCs are not measured as frequently as CAPs, owing to the difficulty, challenges, and cost associated with sampling. The Mobile Mass Spectrometry Lab (MMSL) was developed to collect high geospatial (15 – 1,500 m) and temporal (1 – 10 s) resolution measurements of CAPs (O3, NOx, PM2.5), CO2, CH4, and VOCs. CAPs and greenhouse gases were monitored using standard analyzers, while VOCs were measured using a proton-transfer reaction time-of-flight mass spectrometer (PTR-MS). PTR-MS is a real-time, direct, in situ technique that can monitor VOCs in the ambient atmosphere without sample collection. The PTR-MS monitored up to mass-to-charge 330 with a sample integration time of 1 or 10 seconds and had detection limits into the low- to mid-ppt. PTR-MS is a soft ionization technique that is selective to all compounds with a proton affinity less than water, which excludes the atmospheric matrix and includes most VOCs. The measurements provided by the PTR-MS provided a rich dataset for which to develop workflow and processing methods alongside sampling strategies for the collection of high geospatial and temporal VOC data. The first on-road deployment of the MMSL was performed across the Regional District of Nanaimo and the Alberni-Clayoquot Regional District in British Columbia, Canada, from July iv 2018 – April 2019 to monitor the geospatial and temporal variation in the concentration of CAPs and VOCs. VOCs detected in the areas include hydrocarbons like toluene, C2-benzenes, and terpenes, organic acids like acetic acid, oxygenated compounds like acetone and acetaldehyde, and reduced sulfur compounds like methanethiol and dimethyl sulfide. While observed concentrations of VOCs were mostly below detection limits, concentration excursions upwards of 2,200 ppb for C2-benzenes (reported as ethylbenzene) for instance, were observed across the various communities and industries that comprise central Vancouver Island. VOCs like monoterpenes, were observed near the wood industries up to 229 ppb. Combustion related VOCs, like toluene and C2-benzenes, were often observed on major transportation corridors and was found to vary significantly between seasons, with winter measurements often exceeding those made in the summer. Reduced sulfur compounds, common components of nuisance odours, were measured around a few industries like waste management and wood industries. The second on-road deployment of the MMSL focused on the analysis of VOCs in the community around a wastewater treatment plant (WWTP) to identify the source of odours in the area. VOCs were also monitored in the odour control process of the WWTP to identify the VOCs being emitted, how much were emitted, and where potential deficiencies were in the process in a unique study. Median emission rates at the facility for methanethiol, dimethyl sulfide, and dimethyl disulfide were determined to be 100, 19, and 21 kg yr-1, respectively. VOC monitoring in the community encompassed the WWTP and the other major industries in the area, including agricultural land, a composting facility, and a marina. The highest measurements of odorous reduced sulfur compounds were observed around the WWTP, upwards of 36 ppb for methanethiol. Unsupervised multivariate analysis was performed to identify groups of VOCs present and their potential sources. Three groups were identified, one of which was related to reduced sulfur compounds. This group was observed around the WWTP, indicating that the WWTP was the likely source of malodours in the community. / Graduate

<p dir="ltr">Regulation of complex biological processes aims to achieve goals essential for an organism's survival or to exhibit specific phenotypes in response to stimuli. This regulation can occur at several levels, such as cellular metabolism, signaling pathways, gene transcription, mRNA translation into proteins, and post-translational modifications. Systems biology approaches can facilitate integrating mechanistic knowledge and high-throughput omics data to develop quantitative models that can help improve our understanding of regulations at various levels. However, computational modeling of biological processes is challenging due to the vast details of various processes with unknown mechanisms. The cybernetic modeling approach accounts for unknown control mechanisms by defining a biological goal that the system aims to optimize and subsequently mathematically formulates the cybernetic goal.</p><p dir="ltr">This thesis aims to develop a mathematical framework that integrates a cybernetic model with novel information-theoretic methods to study the inflammatory response in mammalian macrophage cells. The inflammatory response of the body is a protective mechanism that fights off infecting pathogens by inducing the production of immune signaling proteins called cytokines and chemokines, as well as specific lipids known as eicosanoids. However, excessive levels of cytokines and eicosanoids may result in chronic inflammatory diseases such as hyper-inflammation syndrome, COVID-19, and asthma. Only a few studies have focused on quantitative modeling of the role of lipid metabolism in inflammation. One key lipid is Arachidonic acid (AA), which during inflammation, gets converted into inflammatory lipids called eicosanoids. Previous models utilize Michaelis-Menten kinetics or assume the linear form and can, at best, include control at the gene expression level only. The distinguishing feature of a cybernetic model is that by defining a cybernetic objective, it can account for control at multiple levels, including transcriptional, translational, and post-translational modifications.</p><p dir="ltr">The following paragraphs address a specific research problem, outline the approaches to investigate it, and summarize the key findings.</p><p dir="ltr">First, we studied the cellular response to inflammatory stimuli that produce eicosanoids—prostanoids (PRs) and leukotrienes (LTs)—and signaling molecules—cytokines and chemokines—by macrophages. A few studies suggest that targeting eicosanoid metabolism could be a promising new approach to regulating cytokine storm in COVID-19 infection. We developed a cybernetic model combined with novel information-theoretic approaches to study the integrated system of eicosanoids and cytokines. Our cybernetic model formulates a cybernetic goal, which requires the causal relationship between the eicosanoid and cytokine secretion processes; however, this causal relationship is unknown due to insufficient mechanistic information. We developed novel information-theoretic approaches (discussed later in detail) to understand the causality between eicosanoids and cytokines. The causality result from information theory suggests that Arachidonic acid (AA) may be the cause for initiating the secretion of cytokine TNF. The model captured the data for all experimental conditions, including control, treatment with Adenosine triphosphate (ATP), (3-deoxy-d-manno-octulosonic acid) 2-lipid A (Kdo2-Lipid A, abbreviated as KLA), and a combined treatment of ATP and KLA in mouse bone marrow-derived macrophages (BMDM). The model explains the dynamics of metabolites for all experimental conditions, validating the hypothesis. It also enhanced our understanding of enzyme dynamics by predicting their profiles. The results indicated that the dominant metabolites are PGD2 (a PR) and LTB4 (an LT), aligning with their corresponding known prominent biological roles during inflammation. Based on the causality and cybernetic model result and using heuristic arguments, we also infer that AA overproduction can lead to increased secretion of cytokines/chemokines. Consequently, a potential clinical implication of this study is that modulating eicosanoid levels could lower TNFα expression, suggesting eicosanoids could be a viable strategy for managing hyperinflammation.</p><p dir="ltr">Second, we studied the dynamics of the anti-inflammatory lipid mediators from eicosapentaenoic acid (EPA) metabolism, which can be beneficial in reducing the severity of diseases such as cancer and cardiovascular effects and promoting visual and neurological development. This study employed a cybernetic model to study the enzyme competition between AA and EPA metabolism in murine macrophages. The cybernetic model adequately captured the experimental data for control non-supplemented and EPA-supplemented conditions in RAW 264.7 macrophages. The cybernetic variables provide insights into the competition between AA and EPA for the COX enzyme. Predictions from our model suggest that the system undergoes a switch from a predominantly pro-inflammatory state in control to an anti-inflammatory state with EPA supplementation. A potential application of this study is utilizing the model estimation of the ratio of concentrations required for the switch to occur as 2.2, which aligns with the experimental observations and falls within the recommended range of 1-5 needed to promote anti-inflammatory response.</p><p dir="ltr">Third, we focused on predicting novel causal connections between AA and cytokines using time series analysis as mechanistic information connecting AA and cytokines is unknown. In this work, we developed Time delay Renyi Symbolic Transfer Entropy (TDRSTE), a novel model-free information-theoretic metric. We computed it from high-throughput omics datasets for bivariate non-stationary time series to quantify causal time delays. The TDRSTE method adequately estimated time delay for the synthetic dataset, captured causality for the real-world biological dataset of the AA metabolic network with a prediction accuracy of 80.6%, where it correctly identified 25 out of 31 connections, and detected novel connections between non-stationary lipidomics and transcriptomics profiles for eicosanoids and cytokines, respectively. The results indicate that AA may initiate the secretion of cytokines like TNFα, IL1α, IL18, and IL10. Conversely, cytokines such as IL6 and IL1β may have an early causal impact on AA. These findings suggest a potential causal link between AA and cytokines, paving the way for further exploration with more extensive experimental data in future investigations.</p><p dir="ltr">This thesis develops a theoretical framework that integrates the cybernetic modeling technique with novel information-theoretic approaches to study the inflammatory response in mouse macrophages. As described in previous paragraphs, the success of the cybernetic framework in capturing the dynamic behavior of multiple processes serves to validate the idea that regulation is driven toward achieving cellular goals. The cybernetic framework can be applied to better understand the mechanisms underlying the normal and diseased states and to predict the behavior of the system given a perturbation.</p>