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Achuthan S, Chatterjee R, Kotnala S, Mohanty A, Bhattacharya S, Salgia R, Kulkarni P. Leveraging deep learning algorithms for synthetic data generation to design and analyze biological networks. J Biosci 2022. [DOI: 10.1007/s12038-022-00278-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Senthilkumar I, Howley E, McEvoy E. Thermodynamically-motivated chemo-mechanical models and multicellular simulation to provide new insight into active cell and tumour remodelling. Exp Cell Res 2022; 419:113317. [PMID: 36028058 DOI: 10.1016/j.yexcr.2022.113317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/19/2022] [Accepted: 08/08/2022] [Indexed: 11/25/2022]
Abstract
Computational models can shape our understanding of cell and tissue remodelling, from cell spreading, to active force generation, adhesion, and growth. In this mini-review, we discuss recent progress in modelling of chemo-mechanical cell behaviour and the evolution of multicellular systems. In particular, we highlight recent advances in (i) free-energy based single cell models that can provide new fundamental insight into cell spreading, cancer cell invasion, stem cell differentiation, and remodelling in disease, and (ii) mechanical agent-based models to simulate large numbers of discrete interacting cells in proliferative tumours. We describe how new biological understanding has emerged from such theoretical models, and the trade-offs and constraints associated with current approaches. Ultimately, we aim to make a case for why theory should be integrated with an experimental workflow to optimise new in-vitro studies, to predict feedback between cells and their microenvironment, and to deepen understanding of active cell behaviour.
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Affiliation(s)
- Irish Senthilkumar
- School of Computer Science, College of Science and Engineering, National University of Ireland Galway, Ireland; Biomedical Engineering, College of Science and Engineering, National University of Ireland Galway, Ireland
| | - Enda Howley
- School of Computer Science, College of Science and Engineering, National University of Ireland Galway, Ireland
| | - Eoin McEvoy
- Biomedical Engineering, College of Science and Engineering, National University of Ireland Galway, Ireland.
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LaChance J, Suh K, Clausen J, Cohen DJ. Learning the rules of collective cell migration using deep attention networks. PLoS Comput Biol 2022; 18:e1009293. [PMID: 35476698 PMCID: PMC9106212 DOI: 10.1371/journal.pcbi.1009293] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 05/13/2022] [Accepted: 03/23/2022] [Indexed: 11/18/2022] Open
Abstract
Collective, coordinated cellular motions underpin key processes in all multicellular organisms, yet it has been difficult to simultaneously express the ‘rules’ behind these motions in clear, interpretable forms that effectively capture high-dimensional cell-cell interaction dynamics in a manner that is intuitive to the researcher. Here we apply deep attention networks to analyze several canonical living tissues systems and present the underlying collective migration rules for each tissue type using only cell migration trajectory data. We use these networks to learn the behaviors of key tissue types with distinct collective behaviors—epithelial, endothelial, and metastatic breast cancer cells—and show how the results complement traditional biophysical approaches. In particular, we present attention maps indicating the relative influence of neighboring cells to the learned turning decisions of a ‘focal cell’–the primary cell of interest in a collective setting. Colloquially, we refer to this learned relative influence as ‘attention’, as it serves as a proxy for the physical parameters modifying the focal cell’s future motion as a function of each neighbor cell. These attention networks reveal distinct patterns of influence and attention unique to each model tissue. Endothelial cells exhibit tightly focused attention on their immediate forward-most neighbors, while cells in more expansile epithelial tissues are more broadly influenced by neighbors in a relatively large forward sector. Attention maps of ensembles of more mesenchymal, metastatic cells reveal completely symmetric attention patterns, indicating the lack of any particular coordination or direction of interest. Moreover, we show how attention networks are capable of detecting and learning how these rules change based on biophysical context, such as location within the tissue and cellular crowding. That these results require only cellular trajectories and no modeling assumptions highlights the potential of attention networks for providing further biological insights into complex cellular systems. Collective behaviors are crucial to the function of multicellular life, with large-scale, coordinated cell migration enabling processes spanning organ formation to coordinated skin healing. However, we lack effective tools to discover and cleanly express collective rules at the level of an individual cell. Here, we employ a carefully structured neural network to extract collective information directly from cell trajectory data. The network is trained on data from various systems, including canonical collective cell systems (HUVEC and MDCK cells) which display visually distinct forms of collective motion, and metastatic cancer cells (MDA-MB-231) which are highly uncoordinated. Using these trained networks, we can produce attention maps for each system, which indicate how a cell within a tissue takes in information from its surrounding neighbors, as a function of weights assigned to those neighbors. Thus for a cell type in which cells tend to follow the path of the cell in front, the attention maps will display high weights for cells spatially forward of the focal cell. We present results in terms of additional metrics, such as accuracy plots and number of interacting cells, and encourage future development of improved metrics.
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Affiliation(s)
- Julienne LaChance
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Kevin Suh
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Jens Clausen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Daniel J. Cohen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
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Pratapa A, Doron M, Caicedo JC. Image-based cell phenotyping with deep learning. Curr Opin Chem Biol 2021; 65:9-17. [PMID: 34023800 DOI: 10.1016/j.cbpa.2021.04.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 04/10/2021] [Indexed: 12/25/2022]
Abstract
A cell's phenotype is the culmination of several cellular processes through a complex network of molecular interactions that ultimately result in a unique morphological signature. Visual cell phenotyping is the characterization and quantification of these observable cellular traits in images. Recently, cellular phenotyping has undergone a massive overhaul in terms of scale, resolution, and throughput, which is attributable to advances across electronic, optical, and chemical technologies for imaging cells. Coupled with the rapid acceleration of deep learning-based computational tools, these advances have opened up new avenues for innovation across a wide variety of high-throughput cell biology applications. Here, we review applications wherein deep learning is powering the recognition, profiling, and prediction of visual phenotypes to answer important biological questions. As the complexity and scale of imaging assays increase, deep learning offers computational solutions to elucidate the details of previously unexplored cellular phenotypes.
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Bhattacharya S, Mohanty A, Achuthan S, Kotnala S, Jolly MK, Kulkarni P, Salgia R. Group Behavior and Emergence of Cancer Drug Resistance. Trends Cancer 2021; 7:323-334. [PMID: 33622644 PMCID: PMC8500356 DOI: 10.1016/j.trecan.2021.01.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/25/2021] [Accepted: 01/26/2021] [Indexed: 02/06/2023]
Abstract
Drug resistance is a major impediment in cancer. Although it is generally thought that acquired drug resistance is due to genetic mutations, emerging evidence indicates that nongenetic mechanisms also play an important role. Resistance emerges through a complex interplay of clonal groups within a heterogeneous tumor and the surrounding microenvironment. Traits such as phenotypic plasticity, intercellular communication, and adaptive stress response, act in concert to ensure survival of intermediate reversible phenotypes, until permanent, resistant clones can emerge. Understanding the role of group behavior, and the underlying nongenetic mechanisms, can lead to more efficacious treatment designs and minimize or delay emergence of resistance.
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Affiliation(s)
- Supriyo Bhattacharya
- Translational Bioinformatics, Center for Informatics, Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Atish Mohanty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Srisairam Achuthan
- Center for Informatics, Division of Research Informatics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Sourabh Kotnala
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Mohit Kumar Jolly
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Prakash Kulkarni
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA.
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Li H, Tian S, Li Y, Fang Q, Tan R, Pan Y, Huang C, Xu Y, Gao X. Modern deep learning in bioinformatics. J Mol Cell Biol 2020; 12:823-827. [PMID: 32573721 PMCID: PMC7883817 DOI: 10.1093/jmcb/mjaa030] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 04/01/2020] [Accepted: 04/23/2020] [Indexed: 02/01/2023] Open
Affiliation(s)
- Haoyang Li
- Cancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun 130033, China
- MOE Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Shuye Tian
- Department of Biology, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yu Li
- Computational Bioscience Research Center (CBRC), Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Qiming Fang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Renbo Tan
- Cancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun 130033, China
| | - Yijie Pan
- Ningbo Institute of Information Technology Application, Chinese Academy of Sciences, Ningbo 315040, China
| | - Chao Huang
- Ningbo Institute of Information Technology Application, Chinese Academy of Sciences, Ningbo 315040, China
| | - Ying Xu
- Cancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun 130033, China
- MOE Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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