Digital Biomarker Models for Prediction of Infectious Disease Susceptibility

Acute respiratory viral infection (ARVI) represents one of the most prevalent infectious diseases affecting mankind. With the threat of COVID-19 still looming over us, we have witnessed the substantial threat ARVI poses to world health and economy, extinguishing millions of lives and costing trillions of dollars. This sets the context for the research of this thesis: using digital biomarkers to distinguish between individuals who are susceptible to becoming severely infected and/or infectious before an infection is clinically detectable. The development of such biomarkers can have both clinical and epidemiological impact in terms of identifying individuals who are either vulnerable to severe infection or those who may become highly infectious. The digital biomarkers and associated analysis methods are developed and validated on longitudinal data collected by our clinical collaborators from two different ARVI challenge studies. The first study provides data on healthy human volunteers who were inoculated with the common cold and the second study provides data on volunteers inoculated with the flu. Digital biomarkers include molecular, physiological and cognitive data continuously collected from blood, wearable devices and cognitive testing of the study participants. The findings of our research on digitally measurable susceptibility factors are wide-ranging. We find that circadian rhythm at the molecular scale (biochronicity) plays an important role in mediating both the susceptibility and the response to severe infection, revealing groups of gene expression markers that differentiate the responses of low infected and high infected individuals. Using a high dimensional representation of physiological signals from a wearable device, we find that an infection response and its onset time can be reliably predicted at least 24 hours before peak infection time. We find that a certain measure of variability in pre-exposure cognitive function is highly associated with the post-exposure severity of infection.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169966/1/yayazhai_1.pd

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Evolutionary Genomics

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD