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Dish-Trend: Intervention Modeling Simulator That Accounts for Trend Influences

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2021 Winter Simulation Conference, WSC 2021

作者： Andjelkovic, Stefan Miskov-Zivanov, Natasa Computational and Systems Biology Joint Carnegie Mellon University University of Pittsburgh Computational Biology PhD Program University of Pittsburgh PittsburghPA15213 United States Electrical and Computer Engineering Bioengineering Computational and Systems Biology University of Pittsburgh PittsburghPA15213 United States

ISBN: (纸本)9781665433112

Simulation on directed graphs is an important method for understanding the dynamics in the systems where connectivity graphs contain cycles. Discrete Stochastic Heterogeneous Simulator (DiSH) is one of the simulation tools with wide application, which uses regulator values to calculate state updates of regulated elements. Here we present a new simulation approach DiSH-trend which also takes into account the trends in regulating elements. We demonstrate the features of trend-based regulation, as well as hybrid regulation, which is a combination of the trend-and level-based approaches. The modeling capabilities are demonstrated on a small toy model, showcasing different functionalities. Real-world capabilities are demonstrated on a larger network model of food insecurity in the Ethiopian region Oromia. Adding trend-based regulation to models results in increased modeling flexibility, and hybrid regulation improves qualitative dynamic behavior prediction. With appropriate data, DiSH-trend becomes a powerful tool for exploring intervention strategies. © 2021 IEEE.

关键词： Directed graphs

Author Correction: Progressive plasticity during colorectal cancer metastasis

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Nature 2025年第8047期637卷 E28页

作者： Andrew Moorman Elizabeth K Benitez Francesco Cambuli Qingwen Jiang Ahmed Mahmoud Melissa Lumish Saskia Hartner Sasha Balkaran Jonathan Bermeo Simran Asawa Canan Firat Asha Saxena Fan Wu Anisha Luthra Cassandra Burdziak Yubin Xie Valeria Sgambati Kathleen Luckett Yanyun Li Zhifan Yi Ignas Masilionis Kevin Soares Emmanouil Pappou Rona Yaeger T Peter Kingham William Jarnagin Philip B Paty Martin R Weiser Linas Mazutis Michael D'Angelica Jinru Shia Julio Garcia-Aguilar Tal Nawy Travis J Hollmann Ronan Chaligné Francisco Sanchez-Vega Roshan Sharma Dana Pe'er Karuna Ganesh Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA. Molecular Pharmacology Program Memorial Sloan Kettering Cancer Center New York NY USA. Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program New York NY USA. New York Genome Center New York NY USA. Pharmacology Program Weill Cornell Graduate School New York NY USA. Department of Medicine Memorial Sloan Kettering Cancer Center New York NY USA. Case Western Reserve University Cleveland OH USA. Department of Pathology and Laboratory Medicine Memorial Sloan Kettering Cancer Center New York NY USA. Department of Surgery Memorial Sloan Kettering Cancer Center New York NY USA. Tri-Institutional PhD Program in Computational Biology and Medicine New York NY USA. Bristol Myers Squibb Princeton NJ USA. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA. peerd@***. Howard Hughes Medical Institute Chevy Chase MD USA. peerd@***. Molecular Pharmacology Program Memorial Sloan Kettering Cancer Center New York NY USA. ganeshk@***. Department of Medicine Memorial Sloan Kettering Cancer Center New York NY USA. ganeshk@***.

Spectral clustering strategies for heterogeneous disease expression data 18

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Spectral clustering strategies for heterogeneous disease exp...

18th Pacific Symposium on Biocomputing, PSB 2013

作者： Huang, Grace T. Cunningham, Kathryn I. Benos, Panayiotis V. Chennubhotla, Chakra S. Department of Computational and Systems Biology University of Pittsburgh PittsburghPA United States Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States Department of Computer Science University of Arizona TucsonAZ United States

Clustering of gene expression data simplifies subsequent data analyses and forms the basis of numerous approaches for biomarker identification, prediction of clinical outcome, and personalized therapeutic strategies. The most popular clustering methods such as K-means and hierarchical clustering are intuitive and easy to use, but they require arbitrary choices on their various parameters (number of clusters for K-means, and a threshold to cut the tree for hierarchical clustering). Human disease gene expression data are in general more difficult to cluster efficiently due to background (genotype) heterogeneity, disease stage and progression differences and disease subtyping;all of which cause gene expression datasets to be more heterogeneous. Spectral clustering has been recently introduced in many fields as a promising alternative to standard clustering methods. The idea is that pairwise comparisons can help reveal global features through the eigen techniques. In this paper, we developed a new recursive K-means spectral clustering method (ReKS) for disease gene expression data. We benchmarked ReKS on three large-scale cancer datasets and we compared it to different clustering methods with respect to execution time, background models and external biological knowledge. We found ReKS to be superior to the hierarchical methods and equally good to K-means, but much faster than them and without the requirement for a priori knowledge of K. Overall, ReKS offers an attractive alternative for efficient clustering of human disease data.

关键词： Cluster analysis

Dish-Trend: Intervention Modeling Simulator That Accounts for Trend Influences

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Dish-Trend: Intervention Modeling Simulator That Accounts fo...

Simulation Winter Conference

作者： Stefan Andjelkovic Natasa Miskov-Zivanov Computational and Systems Biology Joint Carnegie Mellon University and University of Pittsburgh Computational Biology PhD Program University of Pittsburgh Pittsburgh PA USA Electrical and Computer Engineering Bioengineering Computational and Systems Biology University of Pittsburgh Pittsburgh PA USA

ISBN: (纸本)9781665433129

Simulation on directed graphs is an important method for understanding the dynamics in the systems where connectivity graphs contain cycles. Discrete Stochastic Heterogeneous Simulator (DiSH) is one of the simulation tools with wide application, which uses regulator values to calculate state updates of regulated elements. Here we present a new simulation approach DiSH-trend which also takes into account the trends in regulating elements. We demonstrate the features of trend-based regulation, as well as hybrid regulation, which is a combination of the trend- and level-based approaches. The modeling capabilities are demonstrated on a small toy model, showcasing different functionalities. Real-world capabilities are demonstrated on a larger network model of food insecurity in the Ethiopian region Oromia. Adding trend-based regulation to models results in increased modeling flexibility, and hybrid regulation improves qualitative dynamic behavior prediction. With appropriate data, DiSH-trend becomes a powerful tool for exploring intervention strategies.

关键词： Regulators Toy manufacturing industry Stochastic processes Predictive models Market research Regulation Data models

Dish-trend: Intervention modeling simulator that accounts for trend influences

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arXiv 2021年

作者： Andjelkovic, Stefan Miskov-Zivanov, Natasa Computational and Systems Biology Joint Carnegie Mellon University University of Pittsburgh Computational Biology PhD Program University of Pittsburgh 4420 Bayard St PittsburghPA15213 United States Electrical and Computer Engineering Bioengineering Computational and Systems Biology University of Pittsburgh 4420 Bayard St PittsburghPA15213 United States

Simulation on directed graphs is an important method for understanding the dynamics in the systems where connectivity graphs contain cycles. Discrete Stochastic Heterogeneous Simulator (DiSH) is one of the simulation tools with wide application, which uses regulator values to calculate state updates of regulated elements. Here we present a new simulation approach DiSH-trend which also takes into account the trends in regulating elements. We demonstrate the features of trend-based regulation, as well as hybrid regulation, which is a combination of the trend- and level-based approaches. The modeling capabilities are demonstrated on a small toy model, showcasing different functionalities. Real-world capabilities are demonstrated on a larger network model of food insecurity in the Ethiopian region Oromia. Adding trend-based regulation to models results in increased modeling flexibility, and hybrid regulation improves qualitative dynamic behavior prediction. With appropriate data, DiSH-trend becomes a powerful tool for exploring intervention strategies. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

COEM: Cross-Modal Embedding for MetaCell Identification

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arXiv 2022年

作者： Mao, Haiyi Jia, Minxue Dou, Jason Xiaotian Zhang, Haotian Benos, Panayiotis V. Department of Computational and Systems Biology University of Pittsburgh PA United States Joint Carnegie Mellon University University of Pittsburgh PhD Program in Computational Biology United States Department of Electrical and Computer Engineering University of Pittsburgh PA United States

Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though single-cell multi-omics datasets profile multiple molecular modalities within the same cell. Here, we present Cross-MOdal Embedding for MetaCell Identification (COEM), which utilizes an embedded space leveraging the information of both scATAC-seq and scRNA-seq to perform aggregation, balancing the trade-off between fine resolution and sufficient sequencing coverage. COEM outperforms the state-of-the-art method SEACells by efficiently identifying accurate and well-separated metacells across datasets with continuous and discrete cell types. Furthermore, COEM significantly improves peak-to-gene association analyses, and facilitates complex gene regulatory inference tasks. © 2022, CC BY.

关键词： Embeddings

Mixed graphical models for causal analysis of multi-modal variables

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arXiv 2017年

作者： Sedgewick, Andrew J. Ramsey, Joseph D. Spirtes, Peter Glymour, Clark Benos, Panayiotis V. Department of Computational and Systems Biology University of Pittsburgh School of Medicine PittsburghPA United States Joint Carnegie Mellon University University of Pittsburgh PhD Program in Computational Biology PittsburghPA United States Department of Bioengineering University of Pittsburgh PittsburghPA United States Department of Philosophy Carnegie Mellon University PittsburghPA United States

Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data. Once learned, causal graphs can be used for classification, feature selection and hypothesis generation, while revealing the underlying causal network structure and thus allowing for arbitrary likelihood queries over the data. However, current algorithms for learning sparse directed graphs are generally designed to handle only one type of data (continuous-only or discrete-only), which limits their applicability to a large class of multi-modal biological datasets that include mixed type variables. To address this issue, we developed new methods that modify and combine existing methods for finding undirected graphs with methods for finding directed graphs. These hybrid methods are not only faster, but also perform better than the directed graph estimation methods alone for a variety of parameter settings and data set sizes. Here, we describe a new conditional independence test for learning directed graphs over mixed data types and we compare performances of different graph learning strategies on synthetic data. Copyright © 2017, The Authors. All rights reserved.

关键词： Directed graphs

End-to-End Differentiable Molecular Mechanics Force Field Construction

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arXiv 2020年

作者： Wang, Yuanqing Fass, Josh Kaminow, Benjamin Herr, John E. Rufa, Dominic Zhang, Ivy Pulido, Iván Henry, Mike Chodera, John D. Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medical College Cornell University New YorkNY10065 United States Physiology Biophysics and System Biology Ph.D. Program Weill Cornell Medical College Cornell University New YorkNY10065 United States Program in Creative Writing Division of Humanities and Arts The City College of New York The City University of New York New YorkNY10031 United States

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with little inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset

关键词： Atoms

Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

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Current Opinion in Structural biology 2023年 78卷 102517页

作者： Banerjee, Anupam Saha, Satyaki Tvedt, Nathan C. Yang, Lee-Wei Bahar, Ivet Computational and Systems Biology University of Pittsburgh School of Medicine Pittsburgh 15261 PA United States Computational and Applied Mathematics and Statistics The College of William and Mary Williamsburg 23185 VA United States Institute of Bioinformatics and Structural Biology and PhD Program in Biomedical Artificial Intelligence National Tsing Hua University Hsinchu 300044 Taiwan Physics Division National Center for Theoretical Sciences Taipei 106319 Taiwan

Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities. © 2022 The Authors

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