David van Dijk

• Akiko Iwasaki
• Albert C Shaw
• Albert Ko
• Alexander B. Pine
• Allison Campbell
• Ana Luisa Perdigoto
• Anita Huttner
• Anjelica Martin
• Anne Wyllie
• Arya Mani
• Benjamin Goldman-Israelow
• Brent Vander Wyk
• Caroline Hendry
• Carrie L Lucas
• Catherine Mezzacappa
• Cesar Caraballo-Cordovez
• Chantal Vogels
• Christine Won
• Craig B. Wilen
• David A. Hafler
• Deguang Song
• Dejian Zhao
• Denyse Lutchmansingh
• Edward J Miller
• Ellen F Foxman
• Erol Fikrig
• Evangelos K. Oikonomou
• F. Perry Wilson
• George Goshua
• Guilin Wang
• Hanming Zhang
• Harlan Krumholz
• Hongyu Zhao
• Hyung Chun
• Inci Yildirim
• Janghoo Lim
• Jean-Leon Thomas
• Jeffrey Ishizuka
• Jennifer Chiarella
• Jennifer M. Kwan
• Jennifer Possick
• Jonas Christian Schupp
• Leying Guan
• Madison S. Strine
• Marie-Louise Landry
• Matt Simon
• Mia Madel Alfajaro
• Micha Sam Brickman Raredon
• Murat Gunel
• Naftali Kaminski
• Nathan Grubaugh
• Nina Brodsky
• Noah Wolcott Palm
• Peiwen Lu
• Priti Kumar
• Qin Yan
• Richard Pierce
• Rohan Khera
• Ruth R Montgomery
• Samuel Reinhardt
• Serena Spudich
• Shelli Farhadian
• Sidi Chen
• Stephanie Halene
• Steven Kleinstein
• Tomokazu S Sumida
• Tony Melillo
• Tyler Rice
• Wade Schulz
• Xiaomei Wang
• Xiting Yan
• Ya-Chi Ho
• Yuki Yasumoto

Neurosciences; Computational Biology; Single-Cell Analysis; Machine Learning; Deep Learning

• Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1Tejwani L, Ravindra N, Lee C, Cheng Y, Nguyen B, Luttik K, Ni L, Zhang S, Morrison L, Gionco J, Xiang Y, Yoon J, Ro H, Haidery F, Grijalva R, Bae E, Kim K, Martuscello R, Orr H, Zoghbi H, McLoughlin H, Ranum L, Shakkottai V, Faust P, Wang S, van Dijk D, Lim J. Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1. Neuron 2023, 112: 362-383.e15. PMID: 38016472, PMCID: PMC10922326, DOI: 10.1016/j.neuron.2023.10.039.
• Causal identification of single-cell experimental perturbation effects with CINEMA-OTDong M, Wang B, Wei J, de O. Fonseca A, Perry C, Frey A, Ouerghi F, Foxman E, Ishizuka J, Dhodapkar R, van Dijk D. Causal identification of single-cell experimental perturbation effects with CINEMA-OT. Nature Methods 2023, 20: 1769-1779. PMID: 37919419, PMCID: PMC10630139, DOI: 10.1038/s41592-023-02040-5.
• Distinguishing features of long COVID identified through immune profilingKlein J, Wood J, Jaycox J, Dhodapkar R, Lu P, Gehlhausen J, Tabachnikova A, Greene K, Tabacof L, Malik A, Silva Monteiro V, Silva J, Kamath K, Zhang M, Dhal A, Ott I, Valle G, Peña-Hernández M, Mao T, Bhattacharjee B, Takahashi T, Lucas C, Song E, McCarthy D, Breyman E, Tosto-Mancuso J, Dai Y, Perotti E, Akduman K, Tzeng T, Xu L, Geraghty A, Monje M, Yildirim I, Shon J, Medzhitov R, Lutchmansingh D, Possick J, Kaminski N, Omer S, Krumholz H, Guan L, Dela Cruz C, van Dijk D, Ring A, Putrino D, Iwasaki A. Distinguishing features of long COVID identified through immune profiling. Nature 2023, 623: 139-148. PMID: 37748514, PMCID: PMC10620090, DOI: 10.1038/s41586-023-06651-y.
• Anatomical Diversity of the Adult Corticospinal Tract Revealed by Single-Cell Transcriptional Profiling.Golan N, Ehrlich D, Bonanno J, O'Brien R, Murillo M, Kauer S, Ravindra N, Van Dijk D, Cafferty W. Anatomical Diversity of the Adult Corticospinal Tract Revealed by Single-Cell Transcriptional Profiling. Journal Of Neuroscience 2023, 43: 7929-7945. PMID: 37748862, PMCID: PMC10669816, DOI: 10.1523/jneurosci.0811-22.2023.
• Nasal host response-based screening for undiagnosed respiratory viruses: a pathogen surveillance and detection studyCheemarla N, Hanron A, Fauver J, Bishai J, Watkins T, Brito A, Zhao D, Alpert T, Vogels C, Ko A, Schulz W, Landry M, Grubaugh N, van Dijk D, Foxman E. Nasal host response-based screening for undiagnosed respiratory viruses: a pathogen surveillance and detection study. The Lancet Microbe 2023, 4: e38-e46. PMID: 36586415, PMCID: PMC9835789, DOI: 10.1016/s2666-5247(22)00296-8.
• Interspecies commensal interactions have nonlinear impacts on host immunityRice TA, Bielecka AA, Nguyen MT, Rosen CE, Song D, Sonnert ND, Yang Y, Cao Y, Khetrapal V, Catanzaro JR, Martin AL, Rashed SA, Leopold SR, Hao L, Yu X, van Dijk D, Ring AM, Flavell RA, de Zoete MR, Palm NW. Interspecies commensal interactions have nonlinear impacts on host immunity. Cell Host & Microbe 2022, 30: 988-1002.e6. PMID: 35640610, PMCID: PMC9283318, DOI: 10.1016/j.chom.2022.05.004.
• Single-cell multiomics reveals persistence of HIV-1 in expanded cytotoxic T cell clonesCollora JA, Liu R, Pinto-Santini D, Ravindra N, Ganoza C, Lama JR, Alfaro R, Chiarella J, Spudich S, Mounzer K, Tebas P, Montaner LJ, van Dijk D, Duerr A, Ho YC. Single-cell multiomics reveals persistence of HIV-1 in expanded cytotoxic T cell clones. Immunity 2022, 55: 1013-1031.e7. PMID: 35320704, PMCID: PMC9203927, DOI: 10.1016/j.immuni.2022.03.004.
• Prdm6 controls heart development by regulating neural crest cell differentiation and migrationHong L, Li N, Gasque V, Mehta S, Ye L, Wu Y, Li J, Gewies A, Ruland J, Hirschi KK, Eichmann A, Hendry C, van Dijk D, Mani A. Prdm6 controls heart development by regulating neural crest cell differentiation and migration. JCI Insight 2022, 7: e156046. PMID: 35108221, PMCID: PMC8876496, DOI: 10.1172/jci.insight.156046.
• Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19Unterman A, Sumida TS, Nouri N, Yan X, Zhao AY, Gasque V, Schupp JC, Asashima H, Liu Y, Cosme C, Deng W, Chen M, Raredon MSB, Hoehn KB, Wang G, Wang Z, DeIuliis G, Ravindra NG, Li N, Castaldi C, Wong P, Fournier J, Bermejo S, Sharma L, Casanovas-Massana A, Vogels CBF, Wyllie AL, Grubaugh ND, Melillo A, Meng H, Stein Y, Minasyan M, Mohanty S, Ruff WE, Cohen I, Raddassi K, Niklason L, Ko A, Montgomery R, Farhadian S, Iwasaki A, Shaw A, van Dijk D, Zhao H, Kleinstein S, Hafler D, Kaminski N, Dela Cruz C. Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19. Nature Communications 2022, 13: 440. PMID: 35064122, PMCID: PMC8782894, DOI: 10.1038/s41467-021-27716-4.
• Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inferenceAmodio M, Shung D, Burkhardt DB, Wong P, Simonov M, Yamamoto Y, van Dijk D, Wilson FP, Iwasaki A, Krishnaswamy S. Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference. Patterns 2021, 2: 100288. PMID: 34286302, PMCID: PMC8276014, DOI: 10.1016/j.patter.2021.100288.
• A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization.Schultz B, Zaliani A, Ebeling C, Reinshagen J, Bojkova D, Lage-Rupprecht V, Karki R, Lukassen S, Gadiya Y, Ravindra NG, Das S, Baksi S, Domingo-Fernández D, Lentzen M, Strivens M, Raschka T, Cinatl J, DeLong LN, Gribbon P, Geisslinger G, Ciesek S, van Dijk D, Gardner S, Kodamullil AT, Fröhlich H, Peitsch M, Jacobs M, Hoeng J, Eils R, Claussen C, Hofmann-Apitius M. A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization. Scientific Reports 2021, 11: 11049. PMID: 34040048, PMCID: PMC8155020, DOI: 10.1038/s41598-021-90296-2.
• Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural NetworksSehanobish A, Ravindra N, Van Dijk D. Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks. Proceedings Of The AAAI Conference On Artificial Intelligence 2021, 35: 4864-4873. DOI: 10.1609/aaai.v35i6.16619.
• A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST).Oikonomou EK, Van Dijk D, Parise H, Suchard MA, de Lemos J, Antoniades C, Velazquez EJ, Miller EJ, Khera R. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST). European Heart Journal 2021, 42: 2536-2548. PMID: 33881513, PMCID: PMC8488385, DOI: 10.1093/eurheartj/ehab223.
• Immune dysregulation and autoreactivity correlate with disease severity in SARS-CoV-2-associated multisystem inflammatory syndrome in childrenRamaswamy A, Brodsky NN, Sumida TS, Comi M, Asashima H, Hoehn KB, Li N, Liu Y, Shah A, Ravindra NG, Bishai J, Khan A, Lau W, Sellers B, Bansal N, Guerrerio P, Unterman A, Habet V, Rice AJ, Catanzaro J, Chandnani H, Lopez M, Kaminski N, Dela Cruz CS, Tsang JS, Wang Z, Yan X, Kleinstein SH, van Dijk D, Pierce RW, Hafler DA, Lucas CL. Immune dysregulation and autoreactivity correlate with disease severity in SARS-CoV-2-associated multisystem inflammatory syndrome in children. Immunity 2021, 54: 1083-1095.e7. PMID: 33891889, PMCID: PMC8043654, DOI: 10.1016/j.immuni.2021.04.003.
• Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changesRavindra NG, Alfajaro MM, Gasque V, Huston NC, Wan H, Szigeti-Buck K, Yasumoto Y, Greaney AM, Habet V, Chow RD, Chen JS, Wei J, Filler RB, Wang B, Wang G, Niklason LE, Montgomery RR, Eisenbarth SC, Chen S, Williams A, Iwasaki A, Horvath TL, Foxman EF, Pierce RW, Pyle AM, van Dijk D, Wilen CB. Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changes. PLOS Biology 2021, 19: e3001143. PMID: 33730024, PMCID: PMC8007021, DOI: 10.1371/journal.pbio.3001143.
• A neutrophil activation signature predicts critical illness and mortality in COVID-19Meizlish ML, Pine AB, Bishai JD, Goshua G, Nadelmann ER, Simonov M, Chang CH, Zhang H, Shallow M, Bahel P, Owusu K, Yamamoto Y, Arora T, Atri DS, Patel A, Gbyli R, Kwan J, Won CH, Dela Cruz C, Price C, Koff J, King BA, Rinder HM, Wilson FP, Hwa J, Halene S, Damsky W, van Dijk D, Lee AI, Chun HJ. A neutrophil activation signature predicts critical illness and mortality in COVID-19. Blood Advances 2021, 5: 1164-1177. PMID: 33635335, PMCID: PMC7908851, DOI: 10.1182/bloodadvances.2020003568.
• Quantifying the effect of experimental perturbations at single-cell resolutionBurkhardt DB, Stanley JS, Tong A, Perdigoto AL, Gigante SA, Herold KC, Wolf G, Giraldez AJ, van Dijk D, Krishnaswamy S. Quantifying the effect of experimental perturbations at single-cell resolution. Nature Biotechnology 2021, 39: 619-629. PMID: 33558698, PMCID: PMC8122059, DOI: 10.1038/s41587-020-00803-5.
• Neuroinvasion of SARS-CoV-2 in human and mouse brainSong E, Zhang C, Israelow B, Lu-Culligan A, Prado AV, Skriabine S, Lu P, Weizman OE, Liu F, Dai Y, Szigeti-Buck K, Yasumoto Y, Wang G, Castaldi C, Heltke J, Ng E, Wheeler J, Alfajaro MM, Levavasseur E, Fontes B, Ravindra NG, Van Dijk D, Mane S, Gunel M, Ring A, Kazmi SAJ, Zhang K, Wilen CB, Horvath TL, Plu I, Haik S, Thomas JL, Louvi A, Farhadian SF, Huttner A, Seilhean D, Renier N, Bilguvar K, Iwasaki A. Neuroinvasion of SARS-CoV-2 in human and mouse brain. Journal Of Experimental Medicine 2021, 218: e20202135. PMID: 33433624, PMCID: PMC7808299, DOI: 10.1084/jem.20202135.
• Genome-wide CRISPR Screens Reveal Host Factors Critical for SARS-CoV-2 InfectionWei J, Alfajaro MM, DeWeirdt PC, Hanna RE, Lu-Culligan WJ, Cai WL, Strine MS, Zhang SM, Graziano VR, Schmitz CO, Chen JS, Mankowski MC, Filler RB, Ravindra NG, Gasque V, de Miguel FJ, Patil A, Chen H, Oguntuyo KY, Abriola L, Surovtseva YV, Orchard RC, Lee B, Lindenbach BD, Politi K, van Dijk D, Kadoch C, Simon MD, Yan Q, Doench JG, Wilen CB. Genome-wide CRISPR Screens Reveal Host Factors Critical for SARS-CoV-2 Infection. Cell 2020, 184: 76-91.e13. PMID: 33147444, PMCID: PMC7574718, DOI: 10.1016/j.cell.2020.10.028.
• Transcriptomic and clonal characterization of T cells in the human central nervous systemPappalardo JL, Zhang L, Pecsok MK, Perlman K, Zografou C, Raddassi K, Abulaban A, Krishnaswamy S, Antel J, van Dijk D, Hafler DA. Transcriptomic and clonal characterization of T cells in the human central nervous system. Science Immunology 2020, 5 PMID: 32948672, PMCID: PMC8567322, DOI: 10.1126/sciimmunol.abb8786.
• Learning General Transformations of Data for Out-of-Sample ExtensionsAmodio M, van Dijk D, Wolf G, Krishnaswamy S. Learning General Transformations of Data for Out-of-Sample Extensions. 2023 IEEE 33rd International Workshop On Machine Learning For Signal Processing (MLSP) 2020, 00: 1-6. PMID: 34557339, PMCID: PMC8456777, DOI: 10.1109/mlsp49062.2020.9231660.
• Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical DecompensationHaimovich AD, Ravindra NG, Stoytchev S, Young HP, Wilson FP, van Dijk D, Schulz WL, Taylor RA. Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation. Annals Of Emergency Medicine 2020, 76: 442-453. PMID: 33012378, PMCID: PMC7373004, DOI: 10.1016/j.annemergmed.2020.07.022.
• TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics.Tong A, Huang J, Wolf G, van Dijk D, Krishnaswamy S. TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. Proceedings Of Machine Learning Research 2020, 119: 9526-9536. PMID: 34337419, PMCID: PMC8320749.
• Interpretable Neuron Structuring with Graph Spectral RegularizationTong A, van Dijk D, Stanley III J, Amodio M, Yim K, Muhle R, Noonan J, Wolf G, Krishnaswamy S. Interpretable Neuron Structuring with Graph Spectral Regularization. 2020, 12080: 509-521. PMID: 34131660, PMCID: PMC8201816, DOI: 10.1007/978-3-030-44584-3_40.
• Disease state prediction from single-cell data using graph attention networksRavindra N, Sehanobish A, Pappalardo J, Hafler D, van Dijk D. Disease state prediction from single-cell data using graph attention networks. 2020, 121-130. DOI: 10.1145/3368555.3384449.
• Single cell immune profiling of dengue virus patients reveals intact immune responses to Zika virus with enrichment of innate immune signaturesZhao Y, Amodio M, Wyk B, Gerritsen B, Kumar MM, van Dijk D, Moon K, Wang X, Malawista A, Richards MM, Cahill ME, Desai A, Sivadasan J, Venkataswamy MM, Ravi V, Fikrig E, Kumar P, Kleinstein SH, Krishnaswamy S, Montgomery RR. Single cell immune profiling of dengue virus patients reveals intact immune responses to Zika virus with enrichment of innate immune signatures. PLOS Neglected Tropical Diseases 2020, 14: e0008112. PMID: 32150565, PMCID: PMC7082063, DOI: 10.1371/journal.pntd.0008112.
• Neurohormonal Blockade and Clinical Outcomes in Patients With Heart Failure Supported by Left Ventricular Assist DevicesMcCullough M, Caraballo C, Ravindra NG, Miller PE, Mezzacappa C, Levin A, Gruen J, Rodwin B, Reinhardt S, van Dijk D, Ali A, Ahmad T, Desai NR. Neurohormonal Blockade and Clinical Outcomes in Patients With Heart Failure Supported by Left Ventricular Assist Devices. JAMA Cardiology 2020, 5: 175-182. PMID: 31738366, PMCID: PMC6865330, DOI: 10.1001/jamacardio.2019.4965.
• Uncovering axes of variation among single-cell cancer specimensChen WS, Zivanovic N, van Dijk D, Wolf G, Bodenmiller B, Krishnaswamy S. Uncovering axes of variation among single-cell cancer specimens. Nature Methods 2020, 17: 302-310. PMID: 31932777, PMCID: PMC7339867, DOI: 10.1038/s41592-019-0689-z.
• Author Correction: Visualizing structure and transitions in high-dimensional biological dataMoon KR, van Dijk D, Wang Z, Gigante S, Burkhardt DB, Chen WS, Yim K, van den Elzen A, Hirn MJ, Coifman RR, Ivanova NB, Wolf G, Krishnaswamy S. Author Correction: Visualizing structure and transitions in high-dimensional biological data. Nature Biotechnology 2020, 38: 108-108. PMID: 31896828, DOI: 10.1038/s41587-019-0395-5.
• Finding Archetypal Spaces Using Neural Networksvan Dijk D, Burkhardt D, Amodio M, Tong A, Wolf G, Krishnaswamy S. Finding Archetypal Spaces Using Neural Networks. 2019, 00: 2634-2643. DOI: 10.1109/bigdata47090.2019.9006484.
• Visualizing structure and transitions in high-dimensional biological dataMoon KR, van Dijk D, Wang Z, Gigante S, Burkhardt DB, Chen WS, Yim K, Elzen AVD, Hirn MJ, Coifman RR, Ivanova NB, Wolf G, Krishnaswamy S. Visualizing structure and transitions in high-dimensional biological data. Nature Biotechnology 2019, 37: 1482-1492. PMID: 31796933, PMCID: PMC7073148, DOI: 10.1038/s41587-019-0336-3.
• Coarse Graining of Data via Inhomogeneous Diffusion CondensationBrugnone N, Gonopolskiy A, Moyle MW, Kuchroo M, van Dijk D, Moon KR, Colon-Ramos D, Wolf G, Hirn MJ, Krishnaswamy S. Coarse Graining of Data via Inhomogeneous Diffusion Condensation. 2019, 00: 2624-2633. PMID: 32747879, PMCID: PMC7398322, DOI: 10.1109/bigdata47090.2019.9006013.
• Exploring single-cell data with deep multitasking neural networksAmodio M, van Dijk D, Srinivasan K, Chen WS, Mohsen H, Moon KR, Campbell A, Zhao Y, Wang X, Venkataswamy M, Desai A, Ravi V, Kumar P, Montgomery R, Wolf G, Krishnaswamy S. Exploring single-cell data with deep multitasking neural networks. Nature Methods 2019, 16: 1139-1145. PMID: 31591579, PMCID: PMC10164410, DOI: 10.1038/s41592-019-0576-7.
• Modeling Global Dynamics from Local Snapshots with Deep Generative Neural NetworksGigante S, van Dijk D, Moon K, Strzalkowski A, Wolf G, Krishnaswamy S. Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks. 2019, 00: 1-4. DOI: 10.1109/sampta45681.2019.9030978.
• Compressed DiffusionGigante S, Stanley J, Vu N, van Dijk D, Moon K, Wolf G, Krishnaswamy S. Compressed Diffusion. 2019, 00: 1-4. DOI: 10.1109/sampta45681.2019.9030994.
• Recovering Gene Interactions from Single-Cell Data Using Data Diffusionvan Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, Burdziak C, Moon KR, Chaffer CL, Pattabiraman D, Bierie B, Mazutis L, Wolf G, Krishnaswamy S, Pe’er D. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. Cell 2018, 174: 716-729.e27. PMID: 29961576, PMCID: PMC6771278, DOI: 10.1016/j.cell.2018.05.061.
• Manifold learning-based methods for analyzing single-cell RNA-sequencing dataMoon K, Stanley J, Burkhardt D, van Dijk D, Wolf G, Krishnaswamy S. Manifold learning-based methods for analyzing single-cell RNA-sequencing data. Current Opinion In Systems Biology 2018, 7: 36-46. DOI: 10.1016/j.coisb.2017.12.008.
• Corrigendum: Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activatorsvan Dijk D, Sharon E, Lotan-Pompan M, Weinberger A, Segal E, Carey LB. Corrigendum: Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activators. Genome Research 2017, 27: 500-500. PMID: 28250019, PMCID: PMC5340977, DOI: 10.1101/gr.220574.117.
• Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activatorsvan Dijk D, Sharon E, Lotan-Pompan M, Weinberger A, Segal E, Carey LB. Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activators. Genome Research 2016, 27: 87-94. PMID: 27965290, PMCID: PMC5204347, DOI: 10.1101/gr.212316.116.
• PD-1 marks dysfunctional regulatory T cells in malignant gliomasLowther DE, Goods BA, Lucca LE, Lerner BA, Raddassi K, van Dijk D, Hernandez AL, Duan X, Gunel M, Coric V, Krishnaswamy S, Love JC, Hafler DA. PD-1 marks dysfunctional regulatory T cells in malignant gliomas. JCI Insight 2016, 1: e85935. PMID: 27182555, PMCID: PMC4864991, DOI: 10.1172/jci.insight.85935.
• Noise in gene expression is coupled to growth rateKeren L, van Dijk D, Weingarten-Gabbay S, Davidi D, Jona G, Weinberger A, Milo R, Segal E. Noise in gene expression is coupled to growth rate. Genome Research 2015, 25: 1893-1902. PMID: 26355006, PMCID: PMC4665010, DOI: 10.1101/gr.191635.115.
• Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damagevan Dijk D, Dhar R, Missarova AM, Espinar L, Blevins WR, Lehner B, Carey LB. Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damage. Nature Communications 2015, 6: 7972. PMID: 26268986, PMCID: PMC4557116, DOI: 10.1038/ncomms8972.
• Probing the effect of promoters on noise in gene expression using thousands of designed sequencesSharon E, van Dijk D, Kalma Y, Keren L, Manor O, Yakhini Z, Segal E. Probing the effect of promoters on noise in gene expression using thousands of designed sequences. Genome Research 2014, 24: 1698-1706. PMID: 25030889, PMCID: PMC4199362, DOI: 10.1101/gr.168773.113.
• Publication metrics and success on the academic job marketvan Dijk D, Manor O, Carey LB. Publication metrics and success on the academic job market. Current Biology 2014, 24: r516-r517. PMID: 24892909, DOI: 10.1016/j.cub.2014.04.039.
• Promoter Sequence Determines the Relationship between Expression Level and NoiseCarey LB, van Dijk D, Sloot PM, Kaandorp JA, Segal E. Promoter Sequence Determines the Relationship between Expression Level and Noise. PLOS Biology 2013, 11: e1001528. PMID: 23565060, PMCID: PMC3614515, DOI: 10.1371/journal.pbio.1001528.
• Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noiseDadiani M, van Dijk D, Segal B, Field Y, Ben-Artzi G, Raveh-Sadka T, Levo M, Kaplow I, Weinberger A, Segal E. Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise. Genome Research 2013, 23: 966-976. PMID: 23403035, PMCID: PMC3668364, DOI: 10.1101/gr.149096.112.
• Inference of Surface Membrane Factors of HIV-1 Infection through Functional Interaction NetworksJaeger S, Ertaylan G, van Dijk D, Leser U, Sloot P. Inference of Surface Membrane Factors of HIV-1 Infection through Functional Interaction Networks. PLOS ONE 2010, 5: e13139. PMID: 20967291, PMCID: PMC2953485, DOI: 10.1371/journal.pone.0013139.
• Identifying potential survival strategies of HIV-1 through virus-host protein interaction networksvan Dijk D, Ertaylan G, Boucher CA, Sloot PM. Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks. BMC Systems Biology 2010, 4: 96. PMID: 20633292, PMCID: PMC2913931, DOI: 10.1186/1752-0509-4-96.
• Individual-based simulation of sexual selection: A quantitative genetic approachvan Dijk D, Sloot P, Tay J, Schut M. Individual-based simulation of sexual selection: A quantitative genetic approach. Procedia Computer Science 2010, 1: 2003-2011. DOI: 10.1016/j.procs.2010.04.224.