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

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

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

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

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

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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EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment

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arXiv 2023年

作者： Wang, Yuanqing Pulido, Iván Takaba, Kenichiro Kaminow, Benjamin Scheen, Jenke Wang, Lily Chodera, John D. Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Physiology Biophysics and System Biology Ph.D. Program Weill Cornell Medical College Cornell University New YorkNY10065 United States Pharmaceutical Research Center Advanced Drug Discovery Asahi Kasei Pharma Corporation Shizuoka410-2321 Japan Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medical College Cornell University New YorkNY10065 United States Open Molecular Sciences Foundation DavisCA95618 United States

Atomic partial charges are crucial parameters in molecular dynamics (MD) simulation, dictating the electrostatic contributions to intermolecular energies, and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC, and is expensive for large systems or large numbers of molecules. We propose a hybrid physical / graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserves total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling, for the first time, the use of fully consistent charge models for small molecules and biopolymers for the construction of nextgeneration self-consistent biomolecular force fields. Implemented in the free and open source package espaloma_charge, this approach provides drop-in replacements for both AmberTools antechamber and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at https://***/choderalab/espaloma_charge. © 2023, CC BY.

关键词： Molecules

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

Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials

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arXiv 2024年

作者： Zariquiey, Francesc Sabanés Galvelis, Raimondas Gallicchio, Emilio Chodera, John D. Markland, Thomas E. De Fabritiis, Gianni C Dr. Aiguader 88 Barcelona08003 Spain Acellera Labs C Dr Trueta 183 Barcelona08005 Spain Department of Chemistry Brooklyn College of the City University of New York PhD Program in Chemistry Graduate Center of the City University of New York PhD Program in Biochemistry Graduate Center of the City University of New York NY11210 United States Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Department of Chemistry Stanford University 337 Campus Drive StanfordCA94305 United States Passeig Lluis Companys 23 Barcelona08010 Spain

This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies (RBFE) with the Alchemical Transfer Method (ATM) and validate its performance against established benchmarks and find significant enhancements compared to conventional MM force fields like GAFF2. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecular dynamics

Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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@***.