Matching Items (157)
Description
Predicting nonlinear dynamical systems has been a long-standing challenge in science. This field is currently witnessing a revolution with the advent of machine learning methods. Concurrently, the analysis of dynamics in various nonlinear complex systems continues to be crucial. Guided by these directions, I conduct the following studies. Predicting critical transitions and transient states in nonlinear dynamics is a complex problem. I developed a solution called parameter-aware reservoir computing, which uses machine learning to track how system dynamics change with a driving parameter. I show that the transition point can be accurately predicted while trained in a sustained functioning regime before the transition. Notably, it can also predict if the system will enter a transient state, the distribution of transient lifetimes, and their average before a final collapse, which are crucial for management. I introduce a machine-learning-based digital twin for monitoring and predicting the evolution of externally driven nonlinear dynamical systems, where reservoir computing is exploited. Extensive tests on various models, encompassing optics, ecology, and climate, verify the approach’s effectiveness. The digital twins can extrapolate unknown system dynamics, continually forecast and monitor under non-stationary external driving, infer hidden variables, adapt to different driving waveforms, and extrapolate bifurcation behaviors across varying system sizes. Integrating engineered gene circuits into host cells poses a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback. I conducted systematic studies on hundreds of circuit structures exhibiting various functionalities, and identified a comprehensive categorization of growth-induced failures. I discerned three dynamical mechanisms behind these circuit failures. Moreover, my comprehensive computations reveal a scaling law between the circuit robustness and the intensity of growth feedback. A class of circuits with optimal robustness is also identified. Chimera states, a phenomenon of symmetry-breaking in oscillator networks, traditionally have transient lifetimes that grow exponentially with system size. However, my research on high-dimensional oscillators leads to the discovery of ’short-lived’ chimera states. Their lifetime increases logarithmically with system size and decreases logarithmically with random perturbations, indicating a unique fragility. To understand these states, I use a transverse stability analysis supported by simulations.
ContributorsKong, Lingwei (Author) / Lai, Ying-Cheng (Thesis advisor) / Tian, Xiaojun (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2023
Description
MicroRNAs (miRNAs) are 17-22 nucleotide non-coding RNAs that regulate gene expression by targeting non-complementary elements in the 3’ untranslated regions (3’UTRs) of mRNAs. miRNAs, which form complex networks of interaction that differ by tissue and developmental stage, display conservation in their function across metazoan species. Yet much remains unknown regarding their biogenesis, localization, strand selection, and their absolute abundance due to the difficulty of detecting and amplifying such small molecules. Here, I used an updated HT qPCR-based methodology to follow miRNA expression of 5p and 3p strands for all 190 C. elegans miRNAs described in miRBase throughout all six developmental stages in triplicates (total of 9,708 experiments), and studied their expression levels, tissue localization, and the rules underlying miRNA strand selection. My study validated previous findings and identified novel, conserved patterns of miRNA strand expression throughout C. elegans development, which at times correlate with previously observed developmental phenotypes. Additionally, my results highlighted novel structural principles underlying strand selection, which can be applied to higher metazoans.
Though optimized for use in C. elegans, this method can be easily adapted to other eukaryotic systems, allowing for more scalable quantitative investigation of miRNA biology and/or miRNA diagnostics.
ContributorsMeadows, Dalton Alexander (Author) / Mangone, Marco (Thesis advisor) / LaBaer, Joshua (Committee member) / Murugan, Vel (Committee member) / Wilson-Rawls, Jeanne (Committee member) / Arizona State University (Publisher)
Created2023
Description
Under the direction of Dr. Carolyn Compton, a group of seven Barrett honors students have embarked on a truly unique team thesis project to create a documentary on the process of creating a COVID-19 testing laboratory. This documentary tells the story of the ASU Biodesign Clinical Testing Laboratory (ABCTL), the first lab in the western United States to offer public saliva testing to identify the presence of COVID-19.
ContributorsCura, Joriel (Director, Photographer) / Foote, Hannah (Producer, Sound designer) / Raymond, Julia (Production personnel) / Bardfeld, Sierra (Narrator, Editor) / Dholaria, Nikhil (Writer of added commentary) / Liu, Tara (Writer of added commentary) / Varghese, Mahima (Writer of added commentary) / Compton, Carolyn C. (Interviewee, Project director) / Harris, Valerie (Interviewee) / LaBaer, Joshua (Interviewee) / Miceli, Joseph (Interviewee) / Nelson, Megan (Interviewee) / Ungaro, Brianna (Interviewee)
Created2021
Description
A notable challenge when assembling synthetic gene circuits is that modularity often fails to function as intended. A crucial underlying reason for this modularity failure is the existence of competition for shared and limited gene expression resources. By designing a synthetic cascading bistable switches (Syn-CBS) circuit in a single strain with two coupled self-activation modules to achieve successive cell fate transitions, nonlinear resource competition within synthetic gene circuits is unveiled. However, in vivo it can be seen that the transition path was redirected with the activation of one switch always prevailing over that of the other, contradictory to coactivation theoretically expected. This behavior is a result of resource competition between genes and follows a ‘winner-takes-all’ rule, where the winner is determined by the relative connection strength between the two modules. Despite investigation demonstrating that resource competition between gene modules can significantly alter circuit deterministic behaviors, how resource competition contributes to gene expression noise and how this noise can be controlled is still an open issue of fundamental importance in systems biology and biological physics. By utilizing a two-gene circuit, the effects of resource competition on protein expression noise levels can be closely studied. A surprising double-edged role is discovered: the competition for these resources decreases noise while the constraint on resource availability adds its own term of noise into the system, denoted “resource competitive” noise. Noise reduction effects are then studied using orthogonal resources. Results indicate that orthogonal resources are a good strategy for eliminating the contribution of resource competition to gene expression noise.
Noise propagation through a cascading circuit has been considered without resource competition. It has been noted that the noise from upstream genes can be transmitted downstream. However, resource competition’s effects on this cascading noise have yet to be studied. When studied, it is found that resource competition can induce stochastic state
switching and perturb noise propagation. Orthogonal resources can remove some of the resource competitive behavior and allow for a system with less noise.
ContributorsGoetz, Hanah Elizabeth (Author) / Tian, Xiaojun (Thesis advisor) / Wang, Xiao (Committee member) / Lai, Ying-Cheng (Committee member) / Arizona State University (Publisher)
Created2022
Description
Signal transduction networks comprising protein-protein interactions (PPIs) mediate homeostatic, diseased, and therapeutic cellular responses. Mapping these networks has primarily focused on identifying interactors, but less is known about the interaction affinity, rates of interaction or their regulation. To better understand the extent of the annotated human interactome, I first examined > 2500 protein interactions within the B cell receptor (BCR) signaling pathway using a current, cutting-edge bioluminescence-based platform called “NanoBRET” that is capable of analyzing transient and stable interactions in high throughput. Eighty-three percent (83%) of the detected interactions have not been previously reported, indicating that much of the BCR pathway is still unexplored. Unfortunately, NanoBRET, as with all other high throughput methods, cannot determine binding kinetics or affinities. To address this shortcoming, I developed a hybrid platform that characterizes > 400 PPIs quantitatively and simultaneously in < 1 hour by combining the high throughput and flexible nature of nucleic programmable protein arrays (NAPPA) with the quantitative abilities of surface plasmon resonance imaging (SPRi). NAPPA-SPRi was then used to study the kinetics and affinities of > 12,000 PPIs in the BCR signaling pathway, revealing unique kinetic mechanisms that are employed by proteins, phosphorylation and activation states to regulate PPIs. In one example, activation of the GTPase RAC1 with nonhydrolyzable GTP-γS minimally affected its binding affinities with phosphorylated proteins but increased, on average, its on- and off-rates by 4 orders of magnitude for one-third of its interactions. In contrast, this phenomenon occurred with virtually all unphosphorylated proteins. The majority of the interactions (85%) were novel, sharing 40% of the same interactions as NanoBRET as well as detecting 55% more interactions than NanoBRET. In addition, I further validated four novel interactions identified by NAPPA-SPRi using SDS-PAGE migration and Western blot analyses. In one case, we have the first evidence of a direct enzyme-substrate interaction between two well-known proto-oncogenes that are abnormally regulated in > 30% of cancers, PI3K and MYC. Herein, PI3K is demonstrated to phosphorylate MYC at serine 62, a phosphosite that increases the stability of MYC. This study provides valuable insight into how PPIs, phosphorylation, and GTPase activation regulate the BCR signal transduction pathway. In addition, these methods could be applied toward understanding other signaling pathways, pathogen-host interactions, and the effect of protein mutations on protein interactions.
ContributorsPetritis, Brianne Ogata (Author) / LaBaer, Joshua (Thesis advisor) / Lake, Douglas (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2018
Description
According to the World Health Organization, cancer is one of the leading causes of death around the world. Although early diagnostics using biomarkers and improved treatments with targeted therapy have reduced the rate of cancer related mortalities, there remain many unknowns regarding the contributions of the tumor microenvironment to cancer progression and therapeutic resistance. The tumor microenvironment plays a significant role by manipulating the progression of cancer cells through biochemical and biophysical signals from the surrounding stromal cells along with the extracellular matrix. As such, there is a critical need to understand how the tumor microenvironment influences the molecular mechanisms underlying cancer metastasis to facilitate the discovery of better therapies. This thesis described the development of microfluidic technologies to study the interplay of cancer cells with their surrounding microenvironment. The microfluidic model was used to assess how exposure to chemoattractant, epidermal growth factor (EGF), impacted 3D breast cancer cell invasion and enhanced cell motility speed was noted in the presence of EGF validating physiological cell behavior. Additionally, breast cancer and patient-derived cancer-associated fibroblast (CAF) cells were co-cultured to study cell-cell crosstalk and how it affected cancer invasion. GPNMB was identified as a novel gene of interest and it was shown that CAFs enhanced breast cancer invasion by up-regulating the expression of GPNMB on breast cancer cells resulting in increased migration speed. Lastly, this thesis described the design, biological validation, and use of this microfluidic platform as a new in vitro 3D organotypic model to study mechanisms of glioma stem cell (GSC) invasion in the context of a vascular niche. It was confirmed that CXCL12-CXCR4 signaling is involved in promoting GSC invasion in a 3D vascular microenvironment, while also demonstrating the effectiveness of the microfluidic as a drug screening assay. Taken together, the broader impacts of the microfluidic model developed in this dissertation include, a possible alternative platform to animal testing that is focused on mimicking human physiology, a potential ex vivo platform using patient-derived cells for studying the interplay of cancer cells with its surrounding microenvironment, and development of future therapeutic strategies tailored toward disrupting key molecular pathways involved in regulatory mechanisms of cancer invasion.
ContributorsTruong, Danh, Ph.D (Author) / Nikkhah, Mehdi (Thesis advisor) / LaBaer, Joshua (Committee member) / Smith, Barbara (Committee member) / Mouneimne, Ghassan (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2018
Description
Multicellular organisms use precise gene regulation, executed throughout development, to build and sustain various cell and tissue types. Post-transcriptional gene regulation is essential for metazoan development and acts on mRNA to determine its localization, stability, and translation. MicroRNAs (miRNAs) and RNA binding proteins (RBPs) are the principal effectors of post-transcriptional gene regulation and act by targeting the 3'untranslated regions (3'UTRs) of mRNA. MiRNAs are small non-coding RNAs that have the potential to regulate hundreds to thousands of genes and are dysregulated in many prevalent human diseases such as diabetes, Alzheimer's disease, Duchenne muscular dystrophy, and cancer. However, the precise contribution of miRNAs to the pathology of these diseases is not known.
MiRNA-based gene regulation occurs in a tissue-specific manner and is implemented by an interplay of poorly understood and complex mechanisms, which control both the presence of the miRNAs and their targets. As a consequence, the precise contributions of miRNAs to gene regulation are not well known. The research presented in this thesis systematically explores the targets and effects of miRNA-based gene regulation in cell lines and tissues.
I hypothesize that miRNAs have distinct tissue-specific roles that contribute to the gene expression differences seen across tissues. To address this hypothesis and expand our understanding of miRNA-based gene regulation, 1) I developed the human 3'UTRome v1, a resource for studying post-transcriptional gene regulation. Using this resource, I explored the targets of two cancer-associated miRNAs miR-221 and let-7c. I identified novel targets of both these miRNAs, which present potential mechanisms by which they contribute to cancer. 2) Identified in vivo, tissue-specific targets in the intestine and body muscle of the model organism Caenorhabditis elegans. The results from this study revealed that miRNAs regulate tissue homeostasis, and that alternative polyadenylation and miRNA expression patterns modulate miRNA targeting at the tissue-specific level. 3) Explored the functional relevance of miRNA targeting to tissue-specific gene expression, where I found that miRNAs contribute to the biogenesis of mRNAs, through alternative splicing, by regulating tissue-specific expression of splicing factors. These results expand our understanding of the mechanisms that guide miRNA targeting and its effects on tissue-specific gene expression.
MiRNA-based gene regulation occurs in a tissue-specific manner and is implemented by an interplay of poorly understood and complex mechanisms, which control both the presence of the miRNAs and their targets. As a consequence, the precise contributions of miRNAs to gene regulation are not well known. The research presented in this thesis systematically explores the targets and effects of miRNA-based gene regulation in cell lines and tissues.
I hypothesize that miRNAs have distinct tissue-specific roles that contribute to the gene expression differences seen across tissues. To address this hypothesis and expand our understanding of miRNA-based gene regulation, 1) I developed the human 3'UTRome v1, a resource for studying post-transcriptional gene regulation. Using this resource, I explored the targets of two cancer-associated miRNAs miR-221 and let-7c. I identified novel targets of both these miRNAs, which present potential mechanisms by which they contribute to cancer. 2) Identified in vivo, tissue-specific targets in the intestine and body muscle of the model organism Caenorhabditis elegans. The results from this study revealed that miRNAs regulate tissue homeostasis, and that alternative polyadenylation and miRNA expression patterns modulate miRNA targeting at the tissue-specific level. 3) Explored the functional relevance of miRNA targeting to tissue-specific gene expression, where I found that miRNAs contribute to the biogenesis of mRNAs, through alternative splicing, by regulating tissue-specific expression of splicing factors. These results expand our understanding of the mechanisms that guide miRNA targeting and its effects on tissue-specific gene expression.
ContributorsKotagama, Kasuen Indrajith Bandara (Author) / Mangone, Marco (Thesis advisor) / LaBaer, Joshua (Committee member) / Newbern, Jason (Committee member) / Rawls, Alan (Committee member) / Arizona State University (Publisher)
Created2019
Description
Biomarkers find a wide variety of applications in oncology from risk assessment to diagnosis and predicting and monitoring recurrence and response to therapy. Developing clinically useful biomarkers for cancer is faced with several challenges, including cancer heterogeneity and factors related to assay development and biomarker performance. Circulating biomarkers offer a rapid, cost-effective, and minimally-invasive window to disease and are ideal for population-based screening. Circulating immune biomarkers are stable, measurable, and can betray the underlying antigen when present below detection levels or even no longer present. This dissertation aims to investigate potential circulating immune biomarkers with applications in cancer detection and novel therapies. Over 600,000 cancers each year are attributed to the human papillomavirus (HPV), including cervical, anogenital and oropharyngeal cancers. A key challenge in understanding HPV immunobiology and developing immune biomarkers is the diversity of HPV types and the need for multiplexed display of HPV antigens. In Project 1, nucleic acid programmable protein arrays displaying the proteomes of 12 HPV types were developed and used for serum immunoprofiling of women with cervical lesions or invasive cervical cancer. These arrays provide a valuable high-throughput tool for measuring the breadth, specificity, heterogeneity, and cross-reactivity of the serologic response to HPV. Project 2 investigates potential biomarkers of immunity to the bacterial CRISPR/Cas9 system that is currently in clinical trials for cancer. Pre-existing B cell and T cell immune responses to Cas9 were detected in humans and Cas9 was modified to eliminate immunodominant epitopes while preserving its function and specificity. This dissertation broadens our understanding of the immunobiology of cervical cancer and provides insights into the immune profiles that could serve as biomarkers of various applications in cancer.
ContributorsEwaisha, Radwa Mohamed Emadeldin Mahmoud (Author) / Anderson, Karen S (Thesis advisor) / LaBaer, Joshua (Committee member) / Lake, Douglas F (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2018
Description
The research on the topology and dynamics of complex networks is one of the most focused area in complex system science. The goals are to structure our understanding of the real-world social, economical, technological, and biological systems in the aspect of networks consisting a large number of interacting units and to develop corresponding detection, prediction, and control strategies. In this highly interdisciplinary field, my research mainly concentrates on universal estimation schemes, physical controllability, as well as mechanisms behind extreme events and cascading failure for complex networked systems.
Revealing the underlying structure and dynamics of complex networked systems from observed data without of any specific prior information is of fundamental importance to science, engineering, and society. We articulate a Markov network based model, the sparse dynamical Boltzmann machine (SDBM), as a universal network structural estimator and dynamics approximator based on techniques including compressive sensing and K-means algorithm. It recovers the network structure of the original system and predicts its short-term or even long-term dynamical behavior for a large variety of representative dynamical processes on model and real-world complex networks.
One of the most challenging problems in complex dynamical systems is to control complex networks.
Upon finding that the energy required to approach a target state with reasonable precision
is often unbearably large, and the energy of controlling a set of networks with similar structural properties follows a fat-tail distribution, we identify fundamental structural ``short boards'' that play a dominant role in the enormous energy and offer a theoretical interpretation for the fat-tail distribution and simple strategies to significantly reduce the energy.
Extreme events and cascading failure, a type of collective behavior in complex networked systems, often have catastrophic consequences. Utilizing transportation and evolutionary game dynamics as prototypical
settings, we investigate the emergence of extreme events in simplex complex networks, mobile ad-hoc networks and multi-layer interdependent networks. A striking resonance-like phenomenon and the emergence of global-scale cascading breakdown are discovered. We derive analytic theories to understand the mechanism of
control at a quantitative level and articulate cost-effective control schemes to significantly suppress extreme events and the cascading process.
Revealing the underlying structure and dynamics of complex networked systems from observed data without of any specific prior information is of fundamental importance to science, engineering, and society. We articulate a Markov network based model, the sparse dynamical Boltzmann machine (SDBM), as a universal network structural estimator and dynamics approximator based on techniques including compressive sensing and K-means algorithm. It recovers the network structure of the original system and predicts its short-term or even long-term dynamical behavior for a large variety of representative dynamical processes on model and real-world complex networks.
One of the most challenging problems in complex dynamical systems is to control complex networks.
Upon finding that the energy required to approach a target state with reasonable precision
is often unbearably large, and the energy of controlling a set of networks with similar structural properties follows a fat-tail distribution, we identify fundamental structural ``short boards'' that play a dominant role in the enormous energy and offer a theoretical interpretation for the fat-tail distribution and simple strategies to significantly reduce the energy.
Extreme events and cascading failure, a type of collective behavior in complex networked systems, often have catastrophic consequences. Utilizing transportation and evolutionary game dynamics as prototypical
settings, we investigate the emergence of extreme events in simplex complex networks, mobile ad-hoc networks and multi-layer interdependent networks. A striking resonance-like phenomenon and the emergence of global-scale cascading breakdown are discovered. We derive analytic theories to understand the mechanism of
control at a quantitative level and articulate cost-effective control schemes to significantly suppress extreme events and the cascading process.
ContributorsChen, Yuzhong (Author) / Lai, Ying-Cheng (Thesis advisor) / Spanias, Andreas (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2016
Description
Biological fluids, in particular blood plasma, provide a vital source of information on the state of human health. While specific detection of biomarker species can aid in disease diagnostics, the complexity of plasma makes analysis challenging. Despite the challenge of complex sample analysis, biomarker quantification has become a primary interest in biomedical analysis. Due to the extremely specific interaction between antibody and analyte, immunoassays are attractive for the analysis of these samples and have gained popularity since their initial introduction several decades ago. Current limitations to diagnostics through blood testing include long incubation times, interference from non-specific binding, and the requirement for specialized instrumentation and personnel. Optimizing the features of immunoassay for diagnostic testing and biomarker quantification would enable early and accurate detection of disease and afford rapid intervention, potentially improving patient outcomes. Improving the limit of quantitation for immunoassay has been the primary goal of many diverse experimental platforms. While the ability to accurately quantify low abundance species in a complex biological sample is of the utmost importance in diagnostic testing, models illustrating experimental limitations have relied on mathematical fittings, which cannot be directly related to finite analytical limits or fundamental relationships. By creating models based on the law of mass action, it is demonstrated that fundamental limitations are imposed by molecular shot noise, creating a finite statistical limitation to quantitative abilities. Regardless of sample volume, 131 molecules are necessary for quantitation to take place with acceptable levels of uncertainty. Understanding the fundamental limitations of the technique can aid in the design of immunoassay platforms, and assess progress toward the development of optimal diagnostic testing. A sandwich-type immunoassay was developed and tested on three separate human protein targets: myoglobin, heart-type fatty acid binding protein, and cardiac troponin I, achieving superior limits of quantitation approaching ultimate limitations. Furthermore, this approach is compatible with upstream sample separation methods, enabling the isolation of target molecules from a complex biological sample. Isolation of target species prior to analysis allows for the multiplex detection of biomarker panels in a microscale device, making the full optimization of immunoassay techniques possible for clinical diagnostics.
ContributorsWoolley, Christine F (Author) / Hayes, Mark A. (Thesis advisor) / Ros, Alexandra (Committee member) / LaBaer, Joshua (Committee member) / Arizona State University (Publisher)
Created2015