Browsing by Subject "Systems Biology"

Now showing 1 - 4 of 4
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    Biological Hallmarks of Cancer in Alzheimer’s Disease
    (Elsevier, 2019-04-16) Nudelman, Kelly N. H.; McDonald, Brenna C.; Lahiri, Debomoy K.; Saykin, Andrew J.; Medical and Molecular Genetics, School of Medicine
    Although Alzheimer’s disease (AD) is an international health research priority for our aging population, little therapeutic progress has been made. This lack of progress may be partially attributable to disease heterogeneity. Previous studies have identified an inverse association of cancer and AD, suggesting that cancer history may be one source of AD heterogeneity. These findings are particularly interesting in light of the number of common risk factors and two-hit models hypothesized to commonly drive both diseases. We reviewed the ten hallmark biological alterations of cancer cells to investigate overlap with the AD literature and identified overlap of all ten hallmarks in AD, including: 1) potentially common underlying risk factors, such as increased inflammation, deregulated cellular energetics, and genome instability, 2) inversely regulated mechanisms, including cell death and evading growth suppressors, and 3) functions with more complex, pleiotropic mechanisms, some of which may be stage-dependent in AD, such as cell adhesion/contact inhibition and angiogenesis. Additionally, we discuss the recent observation of a biological link between cancer and AD neuropathology. Finally, we address the therapeutic implications of this topic. The significant overlap of functional pathways and molecules between these diseases, some similarly and some oppositely regulated or functioning in each disease, supports the need for more research to elucidate cancer-related AD genetic and functional heterogeneity, with the aims of better understanding AD risk mediators, as well as further exploring the potential for some types of drug repurposing towards AD therapeutic development.
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    Developing Bottom-Up, Integrated Omics Methodologies for Big Data Biomarker Discovery
    (2020-11) Kechavarzi, Bobak David; Wu, Huanmei; Doman, Thompson; Dow, Ernst; Liu, Yunlong; Liu, Xiaowen; Yan, Jingwen
    The availability of highly-distributed computing compliments the proliferation of next generation sequencing (NGS) and genome-wide association studies (GWAS) datasets. These data sets are often complex, poorly annotated or require complex domain knowledge to sensibly manage. These novel datasets provide a rare, multi-dimensional omics (proteomics, transcriptomics, and genomics) view of a single sample or patient. Previously, biologists assumed a strict adherence to the central dogma: replication, transcription and translation. Recent studies in genomics and proteomics emphasize that this is not the case. We must employ big-data methodologies to not only understand the biogenesis of these molecules, but also their disruption in disease states. The Cancer Genome Atlas (TCGA) provides high-dimensional patient data and illustrates the trends that occur in expression profiles and their alteration in many complex disease states. I will ultimately create a bottom-up multi-omics approach to observe biological systems using big data techniques. I hypothesize that big data and systems biology approaches can be applied to public datasets to identify important subsets of genes in cancer phenotypes. By exploring these signatures, we can better understand the role of amplification and transcript alterations in cancer.
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    Development of Data-driven and AI-powered Systems Biology Methods for Understanding Human Disease
    (2024-08) Dang, Pengtao; Zhang, Chi; Salama, Paul; Cao, Sha; King, Brian; Ben-Miled, Zina
    Systems biology dynamic models, which are based on differential equations, offer a flexible and accurate framework to explain physiological properties emerging from complex biochem- ical or biological systems. These models enable explicit quantification and interpretation, allowing for simulation and perturbation analysis to study biological features and their inter- actions, as well as understanding system progression and convergence under various initial conditions. However, their application in human disease systems is limited due to unknown kinetics parameters under disease conditions and a reductionist paradigm that fails to cap- ture the complexity of diseases. Meanwhile, the advent of omics technologies provides high- resolution molecular measurements from single cells and spatially resolved samples, as well as comprehensive disease-specific molecular signatures from large patient cohorts. This wealth of data holds the promise for characterizing complex biological systems, necessitating ad- vanced systems biology models and computational tools that can harness multi-omics data to reliably depict biological processes. However, this endeavor faces the challenge of nonlinear relationships between omics data and the system’s dynamic properties, such as the global or local low-rank gene expression patterns across cell types and the nonlinear complexities within transcriptional regulatory networks revealed by single-cell RNA sequencing. The overall goal of this report is to develop new computational frameworks, AI-empowered methods, and related mathematical theories to explicitly represent and approximate the dy- namics of complex biological systems by using biological omics data. Our aim is to unravel the intricacies of context-specific dynamic systems using multi-Omics data. Specifically, we solved two different but related computational tasks and enabled the first-of-its-kind methods to (1) identify local low-rank matrices from large omics data, and (2) a robust optimization strategy to approximate metabolic flux. Subsequently, we delve into the realm of data-driven and AI-powered systems biology, harnessing the power of statistical learning and artificial intelligence to approximate differential equations or their representations. This research en- deavor not only contributes to the advancement of subspace modeling but also offers insights into a wide array of complex phenomena across diverse domains, with profound implications for computational biology and beyond.
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    The emerging genomics and systems biology research lead to systems genomics studies
    (Springer (Biomed Central Ltd.), 2014) Yang, Mary Qu; Yoshigoe, Kenji; Yang, William; Tong, Weida; Qin, Xiang; Dunker, A. Keith; Chen, Zhongxue; Arbania, Hamid R.; Liu, Jun S.; Niemierko, Andrzej; Yang, Jack Y.; Department of Biochemistry and Molecular Biology, IU School of Medicine
    Synergistically integrating multi-layer genomic data at systems level not only can lead to deeper insights into the molecular mechanisms related to disease initiation and progression, but also can guide pathway-based biomarker and drug target identification. With the advent of high-throughput next-generation sequencing technologies, sequencing both DNA and RNA has generated multi-layer genomic data that can provide DNA polymorphism, non-coding RNA, messenger RNA, gene expression, isoform and alternative splicing information. Systems biology on the other hand studies complex biological systems, particularly systematic study of complex molecular interactions within specific cells or organisms. Genomics and molecular systems biology can be merged into the study of genomic profiles and implicated biological functions at cellular or organism level. The prospectively emerging field can be referred to as systems genomics or genomic systems biology. The Mid-South Bioinformatics Centre (MBC) and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and University of Arkansas for Medical Sciences are particularly interested in promoting education and research advancement in this prospectively emerging field. Based on past investigations and research outcomes, MBC is further utilizing differential gene and isoform/exon expression from RNA-seq and co-regulation from the ChiP-seq specific for different phenotypes in combination with protein-protein interactions, and protein-DNA interactions to construct high-level gene networks for an integrative genome-phoneme investigation at systems biology level.