Browsing by Subject "Mathematical modeling"

Now showing 1 - 6 of 6
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    Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions
    (MDPI, 2022) Nunez, Roberto; Harris, Alon; Ibrahim, Omar; Keller, James; Wikle, Christopher K.; Robinson, Erin; Zukerman, Ryan; Siesky, Brent; Verticchio, Alice; Rowe, Lucas; Guidoboni, Giovanna; Ophthalmology, School of Medicine
    Recent developments in the use of artificial intelligence in the diagnosis and monitoring of glaucoma are discussed. To set the context and fix terminology, a brief historic overview of artificial intelligence is provided, along with some fundamentals of statistical modeling. Next, recent applications of artificial intelligence techniques in glaucoma diagnosis and the monitoring of glaucoma progression are reviewed, including the classification of visual field images and the detection of glaucomatous change in retinal nerve fiber layer thickness. Current challenges in the direct application of artificial intelligence to further our understating of this disease are also outlined. The article also discusses how the combined use of mathematical modeling and artificial intelligence may help to address these challenges, along with stronger communication between data scientists and clinicians.
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    Mathematical modeling of the effects of Wnt-10b on bone metabolism
    (Wiley, 2022) Cook, Carley V.; Islam, Mohammad Aminul; Smith, Brenda J.; Ford Versypt, Ashlee N.; Obstetrics and Gynecology, School of Medicine
    Bone health is determined by factors including bone metabolism or remodeling. Wnt-10b alters osteoblastogenesis through pre-osteoblast proliferation and differentiation and osteoblast apoptosis rate, which collectively lead to the increase of bone density. To model this, we adapted a previously published model of bone remodeling. The resulting model for the bone compartment includes differential equations for active osteoclasts, pre-osteoblasts, osteoblasts, osteocytes, and the amount of bone present at the remodeling site. Our alterations to the original model consist of extending it past a single remodeling cycle and implementing a direct relationship to Wnt-10b. Four new parameters were estimated and validated using normalized data from mice. The model connects Wnt-10b to bone metabolism and predicts the change in trabecular bone volume caused by a change in Wnt-10b input. We find that this model predicts the expected increase in pre-osteoblasts and osteoblasts while also pointing to a decrease in osteoclasts when Wnt-10b is increased.
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    A physical basis for quantitative ChIP-sequencing
    (Elsevier, 2020-11-20) Dickson, Bradley M.; Tiedemann, Rochelle L.; Chomiak, Alison A.; Cornett, Evan M.; Vaughan, Robert M.; Rothbart, Scott B.; Biochemistry and Molecular Biology, School of Medicine
    ChIP followed by next-generation sequencing (ChIP-Seq) is a key technique for mapping the distribution of histone posttranslational modifications (PTMs) and chromatin-associated factors across genomes. There is a perceived challenge to define a quantitative scale for ChIP-Seq data, and as such, several approaches making use of exogenous additives, or "spike-ins," have recently been developed. Herein, we report on the development of a quantitative, physical model defining ChIP-Seq. The quantitative scale on which ChIP-Seq results should be compared emerges from the model. To test the model and demonstrate the quantitative scale, we examine the impacts of an EZH2 inhibitor through the lens of ChIP-Seq. We report a significant increase in immunoprecipitation of presumed off-target histone PTMs after inhibitor treatment, a trend predicted by the model but contrary to spike-in-based indications. Our work also identifies a sensitivity issue in spike-in normalization that has not been considered in the literature, placing limitations on its utility and trustworthiness. We call our new approach the sans-spike-in method for quantitative ChIP-sequencing (siQ-ChIP). A number of changes in community practice of ChIP-Seq, data reporting, and analysis are motivated by this work.
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    Physiology-Enhanced Data Analytics to Evaluate the Effect of Altitude on Intraocular Pressure and Ocular Hemodynamics
    (MDPI, 2022) Verticchio Vercellin, Alice; Harris, Alon; Belamkar, Aditya; Zukerman, Ryan; Carichino, Lucia; Szopos, Marcela; Siesky, Brent; Quaranta, Luciano; Bruttini, Carlo; Oddone, Francesco; Riva, Ivano; Guidoboni, Giovanna; Ophthalmology, School of Medicine
    Altitude affects intraocular pressure (IOP); however, the underlying mechanisms involved and its relationship with ocular hemodynamics remain unknown. Herein, a validated mathematical modeling approach was used for a physiology-enhanced (pe-) analysis of the Mont Blanc study (MBS), estimating the effects of altitude on IOP, blood pressure (BP), and retinal hemodynamics. In the MBS, IOP and BP were measured in 33 healthy volunteers at 77 and 3466 m above sea level. Pe-retinal hemodynamics analysis predicted a statistically significant increase (p < 0.001) in the model predicted blood flow and pressure within the retinal vasculature following increases in systemic BP with altitude measured in the MBS. Decreased IOP with altitude led to a non-monotonic behavior of the model predicted retinal vascular resistances, with significant decreases in the resistance of the central retinal artery (p < 0.001) and retinal venules (p = 0.003) and a non-significant increase in the resistance in the central retinal vein (p = 0.253). Pe-aqueous humor analysis showed that a decrease in osmotic pressure difference (OPD) may underlie the difference in IOP measured at different altitudes in the MBS. Our analysis suggests that venules bear the significant portion of the IOP pressure load within the ocular vasculature, and that OPD plays an important role in regulating IOP with changes in altitude.
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    Sizing it up: The mechanical feedback hypothesis of organ growth regulation
    (Elsevier, 2014) Buchmann, Amy; Alber, Mark; Zartman, Jeremiah J.; Medicine, School of Medicine
    The question of how the physical dimensions of animal organs are specified has long fascinated both experimentalists and computational scientists working in the field of developmental biology. Research over the last few decades has identified many of the genes and signaling pathways involved in organizing the emergent multi-scale features of growth and homeostasis. However, an integrated model of organ growth regulation is still unrealized due to the numerous feedback control loops found within and between intercellular signaling pathways as well as a lack of understanding of the exact role of mechanotransduction. Here, we review several computational and experimental studies that have investigated the mechanical feedback hypothesis of organ growth control, which postulates that mechanical forces are important for regulating the termination of growth and hence the final physical dimensions of organs. In particular, we highlight selected computational studies that have focused on the regulation of growth of the Drosophila wing imaginal disc. In many ways, these computational and theoretical approaches continue to guide experimental inquiry. We demonstrate using several examples how future progress in dissecting the crosstalk between the genetic and biophysical mechanisms controlling organ growth might depend on the close coupling between computational and experimental approaches, as well as comparison of growth control mechanisms in other systems.
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    Underlying Neurobiological and Neurocognitive Mechanisms of Impulsivity in Risk-Taking Behaviors
    (MDPI, 2020-03-25) Cyders, Melissa A.; Psychology, School of Science
    Impulsivity has been widely implicated in many maladaptive risk-taking and clinical disorders associated with such behaviors [1,2], and may be the most frequently noted criteria in the Diagnostic Statistical Manual for Mental Disorders [3] across a wide variety of disorder classes [4] [...].