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Characterizing the concentration and load dependence of phosphate binding to rabbit fast skeletal actomyosin

Proceedings of the National Academy of Sciences of the United States of America 2025年第20期122卷 e2504758122页

作者： Christopher P Marang Daniel J Petersen Brent D Scott Sam Walcott Edward P Debold Department of Kinesiology University of Massachusetts Amherst MA 01003. Department of Physics Worcester Polytechnic Institute Worcester MA 01609. Mathematical Sciences Department Worcester Polytechnic Institute Worcester MA 01609. Bioinformatics and Computational Biology Worcester Polytechnic Institute Worcester MA 01609.

Intensely contracting fast skeletal muscle rapidly loses the ability to generate force, due in part to the accumulation of phosphate (P) inhibiting myosin's force-generating capacity, in a process that is strain dependent. Crucial aspects of the mechanism underlying this inhibition remain unclear. Therefore, we directly determined the effects of increasing [P] on rabbit psoas muscle myosin's ability to generate force against progressively higher resistive loads in a laser trap assay, with the requisite spatial and temporal resolution to discern the mechanism of inhibition. Myosin's force-generating capacity decreased with increasing [P], an effect that became more pronounced at higher resistive loads. The decrease in force resulted from myosin's accelerated detachment from actin, which also increased at higher resistive forces. These data are well fit by a cross-bridge model in which P rebinds to actomyosin in a postpowerstroke, ADP-bound state before accelerating myosin's detachment from actin. Thus, these findings provide important molecular insight into the mechanism underlying the P-induced loss of force during muscle fatigue from intense contractile activity.

关键词： fatigue muscle myosin phosphate

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BPS2025 - Cross-scale conundrum: Slow masticatory myosin in jaw muscles that are both fast and strong

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Biophysical Journal 2025年第3期124卷 516a-516a页

作者： Varun V. Dhorajia Nicholas LaFave Brandon Reder Robert Brocklehurst Nicolai Konow Sam Walcott Jeffrey R. Moore Biomedical Engineering and Biotechnology Program University of Massachusetts Lowell Lowell MA USA Department of Biological Sciences University of Massachusetts Lowell Lowell MA USA Department of Mathematical Sciences Worcester Polytechnic Institute Worcester MA USA Department of Bioinformatics and Computational Biology Worcester Polytechnic Institute Worcester MA USA

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Characterization of Bioengineered Tissues by Digital Holographic Vibrometry and 3D Shape Deep Learning 1

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SEM Annual Conference and Exposition on Experimental and Applied Mechanics, 2022

作者： Hiscox, Colin Li, Juanyong Gao, Ziyang Korkin, Dmitry Furlong, Cosme Billiar, Kristen Mechanical Engineering Department Worcester Polytechnic Institute WorcesterMA United States Biomedical Engineering Department Worcester Polytechnic Institute WorcesterMA United States Department of Bioinformatics and Computational Biology Worcester Polytechnic Institute WorcesterMA United States

ISBN: (数字)9783031174711

ISBN: (纸本)9783031174704

One of the critical components of large-scale manufacturing of bioengineered tissues is the sensing of information for quality control and critical feedback of tissue growth. Modern sensors that measure mechanical qualities of tissues, however, are invasive and destructive. The goal of this project is to develop noninvasive methodologies to measure the mechanical properties of tissue engineering products. Our approach is to utilize acoustic waves to induce nanoscale level vibrations in the engineered tissues in which corresponding displacements are measured in full-field with quantitative optical techniques. A digital holographic system images the tissue’s vibration at significant modes and provides the displacement patterns of the tissue. These data are used to train a supervised learning classifier with a goal of using the comparisons between the experimental vibrational modes and the ones obtained by finite element simulation to estimate the physical properties of the tissue. This methodology has the promise of mechanical properties that would allow technicians to noninvasively determine when samples are ready to be packaged, if their growth deviates from expected time frames, or if there are defects in the tissue. It is expected that this approach will streamline several components of the quality control and production process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tissue

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Investigating the Efficacy of Data Mining Techniques for Crowdsourced Biological Datasets

Investigating the Efficacy of Data Mining Techniques for Cro...

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2019 IEEE MIT Undergraduate Research Technology Conference, URTC 2019

作者： Regan, Ilona Alkurdi, Dany Paisarnsrisomsuk, Sarun Ruiz, Carolina Ryder, Elizabeth F. Gegear, Rob Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA United States Princeton University Department of Near Eastern Studies PrincetonNJ United States Worcester Polytechnic Institute Department of Computer Science WorcesterMA United States Worcester Polytechnic Institute Department of Biology and Biotechnology WorcesterMA United States University of Massachusetts Dartmouth Department of Biology DartmouthMA United States

ISBN: (纸本)9781728158181

The rapid worldwide decline of wild pollinators over recent years poses a significant environmental threat due to the critical keystone role that pollinators play in terrestrial ecosystems. In order to gain insight into the major anthropogenic factors causing these declines, researchers have collected large amounts of ecological data. Yet, they often lack the computational tools needed to analyze the information contained in such datasets. We investigate various data analysis techniques for the Beecology Project, which is a citizen science based effort to rapidly collect data on foraging habits of bumblebee species native to Massachusetts. Different data mining approaches were explored, including association analysis, trend analysis, classification, regression, and clustering. It was found that different techniques were more suitable depending on the biological research question. Future work will focus on making tools utilizing these approaches available online through the Beecology website, where they can be used by the public to determine how best to protect our native pollinators and the diverse ecosystems they support. © 2019 IEEE.

关键词： Data mining

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Dynamics of Cell Fate Decision Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells 1

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丛书名： Springer Theses

1000年

作者： Iman Tavassoly

ISBN: (数字)9783319149622

ISBN: (纸本)9783319149615;9783319365121

This interdisciplinary thesis introduces a systems biology approach to study the cell fate decision mediated by autophagy. A mathematical model of interaction between Autophagy and Apoptosis in mammalian cells is proposed. In this dynamic model autophagy acts as a gradual response to stress (Rheostat) that delays the initiation of bistable switch of apoptosis to give the cells an opportunity to survive. The author shows that his dynamical model is consistent with existing quantitative measurements of time courses of autophagic responses to cisplatin treatment. To understand the function of this response in cancer cells, he has provided a systems biology experimental framework to study quantitative and dynamical aspects of autophagy in single cancer cells using live-cell imaging and quantitative fluorescence microscopy. This framework can provide new insights on function of autophagic response in cancer cells.

关键词： Biomaterials computational biology/bioinformatics Cancer Research Cell biology Biological and Medical Physics, Biophysics

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Effect-specific analysis of pathogenic SNVs in human interactome: Leveraging edge-based network robustness

Effect-specific analysis of pathogenic SNVs in human interac...

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Annual International Conference of the IEEE Engineering in Medicine and biology Society (EMBC)

作者： Hongzhu Cui Dmitry Korkin Bioinformatics and Computational Biology Program Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Worcester Polytechnic Institute Worcester MA USA

Study of genetic variants in the context of molecular networks has recently gained much attention. However, many of these studies suffer from the lack of functional information about the network rewiring effect of genetic variants. After large-scale homology modeling, plus extracting native structure from PDB database, we performed structure-based prediction about the rewiring effect using our SNP-IN tool, and it covers significantly more variants than experimental characterization. The analysis result confirms the widespread perturbations in human interactome and reveals the network rewiring behavior based on edge-based network robustness concept.

关键词： Proteins Diseases Robustness Genetics Databases bioinformatics

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Data-driven modeling and prediction of microglial cell dynamics in the ischemic penumbra

arXiv

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arXiv 2024年

作者： Amato, Sara Arnold, Andrea Bioinformatics & Computational Biology Program Worcester Polytechnic Institute WorcesterMA United States Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA United States

Neuroinflammation immediately follows the onset of ischemic stroke. During this process, microglial cells are activated in and recruited to the tissue surrounding the irreversibly injured infarct core, referred to as the penumbra. Microglial cells can be activated into two distinct phenotypes;however, the dynamics between the detrimental M1 phenotype and beneficial M2 phenotype are not fully understood. Using phenotype-specific cell count data obtained from experimental studies on middle cerebral artery occlusion-induced stroke in mice, we employ sparsity-promoting system identification techniques combined with Bayesian statistical methods for uncertainty quantification to generate continuous and discrete-time predictive models of the M1 and M2 microglial cell dynamics. The resulting data-driven models include constant and linear terms but do not include nonlinear interactions between the cells. Results emphasize an initial M2 dominance followed by a takeover of M1 cells, capture potential long-term dynamics of microglial cells, and suggest a persistent inflammatory response. Copyright © 2024, The Authors. All rights reserved.

关键词： Mammals

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A data-informed mathematical model of microglial cell dynamics during ischemic stroke in the middle cerebral artery

arXiv

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arXiv 2024年

Neuroinflammation immediately follows the onset of ischemic stroke in the middle cerebral artery. During this process, microglial cells are activated in and recruited to the penumbra. Microglial cells can be activated into two different phenotypes: M1, which can worsen brain injury;or M2, which can aid in long-term recovery. In this study, we contribute a summary of experimental data on microglial cell counts in the penumbra following ischemic stroke induced by middle cerebral artery occlusion (MCAO) in mice and compile available data sets into a single set suitable for time series analysis. Further, we formulate a mathematical model of microglial cells in the penumbra during ischemic stroke due to MCAO. Through use of global sensitivity analysis and Markov Chain Monte Carlo (MCMC)-based parameter estimation, we analyze the effects of the model parameters on the number of M1 and M2 cells in the penumbra and fit identifiable parameters to the compiled experimental data set. We utilize results from MCMC parameter estimation to ascertain uncertainty bounds and forward predictions for the number of M1 and M2 microglial cells over time. Results demonstrate the significance of parameters related to M1 and M2 activation on the number of M1 and M2 microglial cells. Simulations further suggest that potential outliers in the observed data may be omitted and forecast predictions suggest a lingering inflammatory response. Copyright © 2024, The Authors. All rights reserved.

关键词： Parameter estimation

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Incorporation of protein binding effects into likelihood ratio test for exome sequencing data

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BMC proceedings 2016年第SUPPL 7期10卷 275-281页

作者： Dongni Zhang Hongzhu Cui Dmitry Korkin Zheyang Wu Mathematics Department Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609-2280 USA. Computer Science Department Bioinformatics and Computational Biology Program Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609-2280 USA.

Statistical association studies are an important tool in detecting novel disease genes. However, for sequencing data, association studies confront the challenge of low power because of relatively small data sample size and rare variants. Incorporating biological information that reflects disease mechanism is likely to strengthen the association evidence of disease genes, and thus increase the power of association studies. In this paper, we annotate non-synonymous single-nucleotide variants according to protein binding sites (BSs) by using a more accurate BS prediction method. We then incorporate this information into association study through a statistical framework of likelihood ratio test (LRT) based on weighted burden score of single-nucleotide variants (SNVs). The strategy is applied to Genetic Analysis Workshop 19 exome-sequencing data for detecting novel genes associated to hypotension. The SNV-weighting LRT idea is empirically verified by the simulated phenotypes (336 cases and 1607 controls), and the weights based on BS annotation are applied to the real phenotypes (394 cases and 1457 controls). Such strategy of weighting the prior information on protein functional sites is shown to be superior to the unweighted LRT and serves as a good complement to the existing association tests. Several putative genes are reported; some of them are functionally related to hypertension according to the previous evidence in the literature.

关键词： Likelihood Ratio Test Real Data Analysis Burden Score Data Sample Size Genomic Inflation Factor

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Distributions and statistical power of optimal signal-detection methods in finite cases

arXiv

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arXiv 2017年

作者： Zhang, Hong Jin, Jiashun Wu, Zheyang Worcester Polytechnic Institute Carnegie Mellon University Department of Mathematical Sciences Program of Bioinformatics and Computational Biology Program of Data Science 100 Institute Road Worcester01609 Morocco

In big data analysis for detecting rare and weak signals among n features, some grouping-test methods such as Higher Criticism test (HC), Berk-Jones test (B-J), and φ-divergence test share the similar asymptotical optimality when n → ∞. However, in practical data analysis n is frequently small and moderately large at most. In order to properly apply these optimal tests and wisely choose them for practical studies, it is important to know how to get the p-values and statistical power of them. To address this problem in an even broader context, this paper provides analytical solutions for a general family of goodness-of-fit (GOF) tests, which covers these optimal tests. For any given i.i.d. and continuous distributions of the input test statistics of the n features, both p-value and statistical power of such a GOF test can be calculated. By calculation we compared the finite-sample performances of asymptotically optimal tests under the normal mixture alternative. Results show that HC is the best choice when signals are rare, while B-J is more robust over various signal patterns. In the application to a real genome-wide association study, results illustrate that the p-value calculation works well, and the optimal tests have potentials for detecting novel disease genes with weak genetic effects. The calculations have been implemented in an R package SetTest and published on the CRAN. Copyright © 2017, The Authors. All rights reserved.

关键词： Information analysis

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