Quantitative Methods
- [1] arXiv:2405.09699 [pdf, ps, other]
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Title: Mapping Differential Protein-Protein Interaction Networks using Affinity Purification Mass SpectrometryPrashant Kaushal, Manisha R. Ummadi, Gwendolyn M. Jang, Yennifer Delgado, Sara K. Makanani, Sophie F. Blanc, Decan M. Winters, Jiewei Xu, Benjamin Polacco, Yuan Zhou, Erica Stevenson, Manon Eckhardt, Lorena Zuliani-Alvarez, Robyn Kaake, Danielle L. Swaney, Nevan Krogan, Mehdi BouhaddouComments: 29 pages, 3 figuresSubjects: Quantitative Methods (q-bio.QM); Molecular Networks (q-bio.MN)
Proteins congregate into complexes to perform fundamental cellular functions. Phenotypic outcomes, in health and disease, are often mechanistically driven by the remodeling of protein complexes by protein coding mutations or cellular signaling changes in response to molecular cues. Here, we present an affinity purification mass spectrometry (APMS) proteomics protocol to quantify and visualize global changes in protein protein interaction (PPI) networks between pairwise conditions. We describe steps for expressing affinity tagged bait proteins in mammalian cells, identifying purified protein complexes, quantifying differential PPIs, and visualizing differential PPI networks. Specifically, this protocol details steps for designing affinity tagged bait gene constructs, transfection, affinity purification, mass spectrometry sample preparation, data acquisition, database search, data quality control, PPI confidence scoring, cross run normalization, statistical data analysis, and differential PPI visualization. Our protocol discusses caveats and limitations with applicability across cell types and biological areas.
- [2] arXiv:2405.10070 [pdf, ps, html, other]
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Title: HeMiTo-dynamics: a characterisation of mammalian prion toxicity using non-dimensionalisation, linear stability and perturbation analysesComments: 25 pages and 4 figuresSubjects: Quantitative Methods (q-bio.QM)
Prion-like proteins play crucial parts in biological processes in organisms ranging from yeast to humans. For instance, many neurodegenerative diseases are believed to be caused by the production of prion-like proteins in neural tissue. As such, understanding the dynamics of prion-like protein production is a vital step toward treating neurodegenerative disease. Mathematical models of prion-like protein dynamics show great promise as a tool for predicting disease trajectories and devising better treatment strategies for prion-related diseases. Herein, we investigate a generic model for prion-like dynamics consisting of a class of non-linear ordinary differential equations (ODEs), establishing constraints through a linear stability analysis that enforce the expected properties of mammalian prion-like toxicity. Furthermore, we identify that prion toxicity evolves through three distinct phases for which we provide analytical descriptions using perturbation analyses. Specifically, prion-toxicity is initially characterised by the healthy phase, where the dynamics are dominated by the healthy form of prions, thereafter the system enters the mixed phase, where both healthy and toxic prions interact, and lastly, the system enters the toxic phase, where toxic prions dominate, and we refer to these phases as HeMiTo-dynamics. These findings hold the potential to aid researchers in developing precise mathematical models for prion-like dynamics, enabling them to better understand underlying mechanisms and devise effective treatments for prion-related diseases.
- [3] arXiv:2405.10110 [pdf, ps, html, other]
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Title: Source identification using different moment hierarchiesSubjects: Quantitative Methods (q-bio.QM); Optimization and Control (math.OC)
This work considers the localization of a bioluminescent source in a tissue-mimicking phantom. To model the light propagation in the resulting inverse problem the radiative transfer equation is approximated by using hierarchical simplified moment approximations. The inverse problem is then solved by using a consensus-based optimization algorithm.
New submissions for Friday, 17 May 2024 (showing 3 of 3 entries )
- [4] arXiv:2405.09649 (cross-list from physics.bio-ph) [pdf, ps, html, other]
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Title: Challenges and opportunities for digital twins in precision medicine: a complex systems perspectiveManlio De Domenico, Luca Allegri, Guido Caldarelli, Valeria d'Andrea, Barbara Di Camillo, Luis M. Rocha, Jordan Rozum, Riccardo Sbarbati, Francesco ZambelliSubjects: Biological Physics (physics.bio-ph); Adaptation and Self-Organizing Systems (nlin.AO); Quantitative Methods (q-bio.QM)
The adoption of digital twins (DTs) in precision medicine is increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. However, the reliance on black-box predictive models, which utilize large datasets, presents limitations that could impede the broader application of DTs in clinical settings. We argue that hypothesis-driven generative models, particularly multiscale modeling, are essential for boosting the clinical accuracy and relevance of DTs, thereby making a significant impact on healthcare innovation. This paper explores the transformative potential of DTs in healthcare, emphasizing their capability to simulate complex, interdependent biological processes across multiple scales. By integrating generative models with extensive datasets, we propose a scenario-based modeling approach that enables the exploration of diverse therapeutic strategies, thus supporting dynamic clinical decision-making. This method not only leverages advancements in data science and big data for improving disease treatment and prevention but also incorporates insights from complex systems and network science, quantitative biology, and digital medicine, promising substantial advancements in patient care.
- [5] arXiv:2405.09748 (cross-list from q-bio.CB) [pdf, ps, html, other]
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Title: A Mathematical Reconstruction of Endothelial Cell NetworksComments: 14 pages, 9 figuresSubjects: Cell Behavior (q-bio.CB); Combinatorics (math.CO); Group Theory (math.GR); Quantitative Methods (q-bio.QM)
Endothelial cells form the linchpin of vascular and lymphatic systems, creating intricate networks that are pivotal for angiogenesis, controlling vessel permeability, and maintaining tissue homeostasis. Despite their critical roles, there is no rigorous mathematical framework to represent the connectivity structure of endothelial networks. Here, we develop a pioneering mathematical formalism called $\pi$-graphs to model the multi-type junction connectivity of endothelial networks. We define $\pi$-graphs as abstract objects consisting of endothelial cells and their junction sets, and introduce the key notion of $\pi$-isomorphism that captures when two $\pi$-graphs have the same connectivity structure. We prove several propositions relating the $\pi$-graph representation to traditional graph-theoretic representations, showing that $\pi$-isomorphism implies isomorphism of the corresponding unnested endothelial graphs, but not vice versa. We also introduce a temporal dimension to the $\pi$-graph formalism and explore the evolution of topological invariants in spatial embeddings of $\pi$-graphs. Finally, we outline a topological framework to represent the spatial embedding of $\pi$-graphs into geometric spaces. The $\pi$-graph formalism provides a novel tool for quantitative analysis of endothelial network connectivity and its relation to function, with the potential to yield new insights into vascular physiology and pathophysiology.
- [6] arXiv:2405.09851 (cross-list from eess.IV) [pdf, ps, html, other]
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Title: Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images -- Nevus & MelanomaComments: 5 figures, NeurIPS 2022 WorkshopSubjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology may help us to reduce costs and increase the speed and accuracy of cancer diagnosis. We started with the UNC Melanocytic Tumor Dataset cohort that contains 160 hematoxylin and eosin whole-slide images of primary melanomas (86) and nevi (74). We randomly assigned 80% (134) as a training set and built an in-house deep-learning method to allow for classification, at the slide level, of nevi and melanomas. The proposed method performed well on the other 20% (26) test dataset; the accuracy of the slide classification task was 92.3% and our model also performed well in terms of predicting the region of interest annotated by the pathologists, showing excellent performance of our model on melanocytic skin tumors. Even though we tested the experiments on the skin tumor dataset, our work could also be extended to other medical image detection problems to benefit the clinical evaluation and diagnosis of different tumors.