Cell Cycle

• 1. Ahmad, Kami, and Steven Henikoff. "No strand left behind." Science 361.6409 (2018): 1311-1312. 

  2. Boward, Ben, Tianming Wu, and Stephen Dalton. "Concise review: control of cell fate through cell cycle and pluripotency networks." Stem cells 34.6 (2016): 1427-1436. 

  3. Coller, Hilary A. "Regulation of Cell Cycle Entry and Exit: A Single Cell Perspective." Comprehensive Physiology 10.1 (2011): 317-344. 

  4. Gibcus, Johan H., et al. "A pathway for mitotic chromosome formation." Science 359.6376 (2018): eaao6135. 

  5. Gorgoulis V, Adams PD, Alimonti A, Bennett DC, Bischof O, Bishop C, Campisi J, Collado M, Evangelou K, Ferbeyre G, Gil J. "Cellular senescence: defining a path forward." Cell. 2019 Oct 31;179(4):813-27. 

  6. Graf, Thomas, and Tariq Enver. "Forcing cells to change lineages." Nature 462.7273 (2009): 587-594. 

  7. Grant, Gavin D., et al. "Accurate delineation of cell cycle phase transitions in living cells with PIP-FUCCI." Cell Cycle 17.21-22 (2018): 2496-2516.

  8. Halevy, Orna, et al. "Correlation of terminal cell cycle arrest of skeletal muscle with induction of p21 by MyoD." Science 267.5200 (1995): 1018-1021. 

  9. Halley-Stott, Richard P., and John B. Gurdon. "Epigenetic memory in the context of nuclear reprogramming and cancer." Briefings in functional genomics 12.3 (2013): 164-173. 

  10. Halley-Stott, Richard P., et al. "Mitosis gives a brief window of opportunity for a change in gene transcription." PLoS biology 12.7 (2014). 

  11. Hartman, John L., Barbara Garvik, and Lee Hartwell. "Principles for the buffering of genetic variation." Science 291.5506 (2001): 1001-1004. 

  12. Hartwell, Leland H., and Ted A. Weinert. "Checkpoints: controls that ensure the order of cell cycle events." Science 246.4930 (1989): 629-634. 

  13. Hartwell, Leland H. "Yeast and cancer." Biosci. Rep 22 (2002): 373-394. 

  14. Massagué, Joan. "G1 cell-cycle control and cancer." Nature 432.7015 (2004): 298-306. 

  15. Naumova, Natalia, et al. "Organization of the mitotic chromosome." Science 342.6161 (2013): 948-953. 

  16. Zhang, Haoyue, et al. "Chromatin structure dynamics during the mitosis-to-G1 phase transition." Nature 576.7785 (2019): 158-162. 

  17. Oomen, Marlies E., et al. "CTCF sites display cell cycle–dependent dynamics in factor binding and nucleosome positioning." Genome research 29.2 (2019): 236-249. 

  18. Soufi, Abdenour, and Stephen Dalton. "Cycling through developmental decisions: how cell cycle dynamics control pluripotency, differentiation and reprogramming." Development 143.23 (2016): 4301-4311. 

  19. Rubin SM, Sage J, Skotheim JM. "Integrating old and new paradigms of G1/S control." Molecular Cell. 2020 Sep 17.

  20. D. Mahdessian et al., “Spatiotemporal dissection of the cell cycle with single-cell proteogenomics,” Nature, vol. 590, no. 7847, pp. 649–654, Feb. 2021

Imaging

• 21. Spencer, Sabrina L., et al. "The proliferation-quiescence decision is controlled by a bifurcation in CDK2 activity at mitotic exit." Cell 155.2 (2013): 369-383. 

  22. Yang, Hee Won, et al. "Competing memories of mitogen and p53 signalling control cell-cycle entry." Nature 549.7672 (2017): 404-408. 

  23. Min M, Rong Y, Tian C, Spencer SL. "Temporal integration of mitogen history in mother cells controls proliferation of daughter cells." Science. 2020 Jun 12;368(6496):1261-5. 

  24. Nathans J, et al. "Cell cycle inertia underlies a bifurcation in cell fates after DNA damage." Science advances (2021)

  25. Yang HW, Cappell SD, Jaimovich A, Liu C, Chung M, Daigh LH, Pack LR, Fan Y, Regot S, Covert M, Meyer T. "Stress-mediated exit to quiescence restricted by increasing persistence in CDK4/6 activation." Elife. 2020 Apr 7;9:e44571. 

  26. Tian C, Yang C, Spencer SL. "EllipTrack: A Global-Local Cell-Tracking Pipeline for 2D Fluorescence Time-Lapse Microscopy." Cell Reports. 2020 Aug 4;32(5):107984. 

  27. Mathey-Prevot B, Parker BT, Im C, Hong C, Dong P, Yao G, You L. "Quantifying E2F1 protein dynamics in single cells." Quantitative Biology. 2020 Mar;8(1):20-30. 

  28. Zhao, Michael L., et al. "Molecular Competition in G1 Controls When Cells Simultaneously Commit to Terminally Differentiate and Exit the Cell Cycle." Cell reports 31.11 (2020): 107769.

  29. Gookin, Sara, et al. "A map of protein dynamics during cell-cycle progression and cell-cycle exit." PLoS biology 15.9 (2017): e2003268. 

Cellular Differentiation

• 1. Felsenfeld, Gary, and Mark Groudine. "Controlling the double helix." Nature 421.6921 (2003): 448-453. 

  2. Groudine, Mark, and Harold Weintraub. "Propagation of globin DNAase I-hypersensitive sites in absence of factors required for induction: a possible mechanism for determination." Cell 30.1 (1982): 131-139. 

  3. MacArthur, Ben D., and Ihor R. Lemischka. "Statistical mechanics of pluripotency." Cell 154.3 (2013): 484-489. 

  4. Ng, Ray K., and John B. Gurdon. "Epigenetic inheritance of cell differentiation status." Cell cycle 7.9 (2008): 1173-1177. 

  5. Weintraub, Harold, and Mark Groudine. "Chromosomal subunits in active genes have an altered conformation." Science 193.4256 (1976): 848-856. 

  6. Weintraub, Harold. "Tissue-specific gene expression and chromatin structure." Harvey lectures 79 (1983): 217. 

  7. Weintraub, Harold "Summary: genetic tinkering—local problems, local solutions." Cold Spring Harbor symposia on quantitative biology. Vol. 58. Cold Spring Harbor Laboratory Press, 1993. 

  8. C. F. Bentzinger, Y. X. Wang, and M. A. Rudnicki, “Building Muscle: Molecular Regulation of Myogenesis,” Cold Spring Harb Perspect Biol, vol. 4, no. 2, p. a008342, Feb. 2012

Cellular Reprogramming

• 1. Cahan, Patrick. "Enabling direct fate conversion with network biology." Nature genetics 48.3 (2016): 226. 

  2. Gurdon, J. B., and D. A. Melton. "Nuclear reprogramming in cells." science 322.5909 (2008): 1811-1815. 

  3. Horisawa, Kenichi, and Atsushi Suzuki. "Direct cell-fate conversion of somatic cells: Toward regenerative medicine and industries." Proceedings of the Japan Academy, Series B 96.4 (2020): 131-158. 

  4. Kamaraj, Uma S., et al. "Computational methods for direct cell conversion." Cell Cycle 15.24 (2016): 3343-3354. 

  5. Ladewig, Julia, Philipp Koch, and Oliver Brüstle. "Leveling Waddington: the emergence of direct programming and the loss of cell fate hierarchies." Nature reviews Molecular cell biology 14.4 (2013): 225-236. 

  6. Morris, Samantha A. "Cell identity reprogrammed." Nature (2019): 44-45. 

  7. Ng, Alex HM, et al. "A comprehensive library of human transcription factors for cell fate engineering." Nature Biotechnology (2020): 1-10.

  8. Ouyang, John F., et al. "Molecular Interaction Networks to Select Factors for Cell Conversion." Computational Stem Cell Biology. Humana, New York, NY, 2019. 333-361. 

  9. Rackham, Owen JL, et al. "A predictive computational framework for direct reprogramming between human cell types." Nature genetics 48.3 (2016): 331. 

  10. Schiebinger, Geoffrey, et al. "Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming." Cell 176.4 (2019): 928-943. 

  11. Srivastava, Deepak, and Natalie DeWitt. "In vivo cellular reprogramming: the next generation." Cell 166.6 (2016): 1386-1396. 

  12. Takahashi, Kazutoshi, and Shinya Yamanaka. "A decade of transcription factor-mediated reprogramming to pluripotency." Nature reviews Molecular cell biology 17.3 (2016): 183. 

  13. Takahashi, Kazutoshi, et al. "Induction of pluripotent stem cells from adult human fibroblasts by defined factors." cell 131.5 (2007): 861-872. 

  14. Weintraub, Harold, et al. "Activation of muscle-specific genes in pigment, nerve, fat, liver, and fibroblast cell lines by forced expression of MyoD." Proceedings of the National Academy of Sciences 86.14 (1989): 5434-5438. 

  15. Weintraub, Harold. "The MyoD family and myogenesis: redundancy, networks, and thresholds." Cell 75.7 (1993): 1241-1244. 

  16. Xu, Jun, Yuanyuan Du, and Hongkui Deng. "Direct lineage reprogramming: strategies, mechanisms, and applications." Cell stem cell 16.2 (2015): 119-134. 

  17. Yamanaka, Shinya. "Elite and stochastic models for induced pluripotent stem cell generation." Nature 460.7251 (2009): 49-52. 

  18. Wang, Haofei, et al. "Direct cell reprogramming: approaches, mechanisms and progress." Nature Reviews Molecular Cell Biology (2021): 1-15. 

  19. Grath, Alexander, and Guohao Dai. "Direct cell reprogramming for tissue engineering and regenerative medicine." Journal of Biological Engineering 13.1 (2019): 1-15. 

  20. Alle, Quentin, et al. "Reprogramming: Emerging Strategies to Rejuvenate Aging Cells and Tissues." International Journal of Molecular Sciences 22.8 (2021): 3990. 

  21. K. Ahmad and S. Henikoff, “The H3.3K27M oncohistone antagonizes reprogramming in Drosophila,” PLOS Genetics, vol. 17, no. 7, p. e1009225, Jul. 2021

  22. T. Pederson, “A layperson encounter, on the ‘modified’ RNA world,” Proceedings of the National Academy of Sciences, vol. 118, no. 46, p. e2110706118, Nov. 2021, doi: 10.1073/pnas.2110706118.

  23. R. Eguchi, M. Hamano, M. Iwata, T. Nakamura, S. Oki, and Y. Yamanishi, “TRANSDIRE: data-driven direct reprogramming by a pioneer factor-guided trans-omics approach,” Bioinformatics, vol. 38, no. 10, pp. 2839–2846, May 2022

  24. A. Balsalobre and J. Drouin, “Pioneer factors as master regulators of the epigenome and cell fate,” Nat Rev Mol Cell Biol, vol. 23, no. 7, Art. no. 7, Jul. 2022

  25. S. Sellahewa, J. Li, and Q. Xiao, "Updated perspectives on direct vascular cellular reprogramming and their potential applications in tissue engineered vascular grafts," J. Funct. Biomater., 14, 21. (2023) 

  26. M. Missinato, S. Murphy, M. Lynott et al. "Conserved transcription factors promote cell fate stability and restrict reprogramming potential in differentiated cells," Nat Commun vol. 14, Art. no. 1709 (2023). 

Software

1. Balwierz, Piotr J., et al. "ISMARA: automated modeling of genomic signals as a democracy of regulatory motifs." Genome research 24.5 (2014): 869-884.

2. Cahan, Patrick, et al. "Computational Stem Cell Biology: Open Questions and Guiding Principles." Cell Stem Cell 28.1: 20-32.

3. Rackham, Owen, et al. "Challenges for Computational Stem Cell Biology: A Discussion for the Field." Stem Cell Reports 16.1: 3-9.

4. Jung, Sascha, et al. "A computer-guided design tool to increase the efficiency of cellular conversions." Nature Communications 12.1 (2021): 1-12. 

5. X. Qiu et al., “Mapping transcriptomic vector fields of single cells,” Cell, p. S0092867421015774, Feb. 2022.

Fibroblast

1. Tomaru, Yasuhiro, et al. "A transient disruption of fibroblastic transcriptional regulatory network facilitates trans-differentiation." Nucleic acids research 42.14 (2014): 8905-8913. 

HSC

1. Bernitz JM, Kim HS, MacArthur B, Sieburg H, Moore K. "Hematopoietic stem cells count and remember self-renewal divisions." Cell. 2016 Nov 17;167(5):1296-309. 

2. Gomes AM, Kurochkin I, Chang B, Daniel M, Law K, Satija N, Lachmann A, Wang Z, Ferreira L, Ma’ayan A, Chen BK. "Cooperative transcription factor induction mediates hemogenic reprogramming." Cell reports. 2018 Dec 4;25(10):2821-35. 

3. Daniel, Michael G., Ihor R. Lemischka, and Kateri Moore. "Converting cell fates: generating hematopoietic stem cells de novo via transcription factor reprogramming." Annals of the New York Academy of Sciences 1370.1 (2016): 24. 

 Transcription Factors

1. Cao Y, Yao Z, Sarkar D, Lawrence M, Sanchez GJ, Parker MH, MacQuarrie KL, Davison J, Morgan MT, Ruzzo WL, Gentleman RC. "Genome-wide MyoD binding in skeletal muscle cells: a potential for broad cellular reprogramming." Developmental cell. 2010 Apr 20;18(4):662-74. 

2. Gurdon, John B. "Cell fate determination by transcription factors." Current topics in developmental biology. Vol. 116. Academic Press, 2016. 445-454.

3. Gurdon, J. B., et al. "Long-term association of a transcription factor with its chromatin binding site can stabilize gene expression and cell fate commitment." Proceedings of the National Academy of Sciences (2020). 

4. Lambert, Samuel A., et al. "The human transcription factors." Cell 172.4 (2018): 650-665. 

5. Liu, Yang, et al. "High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue." Cell (2020). 

6. Ng AH, Khoshakhlagh P, Arias JE, Pasquini G, Wang K, Swiersy A, Shipman SL, Appleton E, Kiaee K, Kohman RE, Vernet A. "A comprehensive library of human transcription factors for cell fate engineering." Nature Biotechnology. 2020 Nov 30:1-0. 

7. Paull EO, Aytes A, Jones SJ, Subramaniam PS, Giorgi FM, Douglass EF, Tagore S, Chu B, Vasciaveo A, Zheng S, Verhaak R. "A modular master regulator landscape controls cancer transcriptional identity." Cell. 

8. Mellis, Ian A., et al. "Responsiveness to perturbations is a hallmark of transcription factors that maintain cell identity." bioRxiv (2020). 

9. Tapscott SJ. The circuitry of a master switch: "Myod and the regulation of skeletal muscle gene transcription. Development." 2005 Jun 15;132(12):2685–95. 

10. Iwafuchi-Doi, Makiko, and Kenneth S. Zaret. "Pioneer transcription factors in cell reprogramming." Genes & development 28.24 (2014): 2679-2692. 

Transcription Factories

1. Cook, Peter R. "Predicting three-dimensional genome structure from transcriptional activity." Nature genetics 32.3 (2002): 347-352. 

2. Cook, Peter R. "The organization of replication and transcription." Science 284.5421 (1999): 1790-1795. 

3. Cook, Peter R., and Davide Marenduzzo. "Transcription-driven genome organization: a model for chromosome structure and the regulation of gene expression tested through simulations." Nucleic acids research 46.19 (2018): 9895-9906. 

4. Ghirlando, Rodolfo, and Gary Felsenfeld. "CTCF: making the right connections." Genes & development 30.8 (2016): 881-891. 

5. Osborne, Cameron S., et al. "Active genes dynamically colocalize to shared sites of ongoing transcription." Nature genetics 36.10 (2004): 1065-1071. 

6. Papantonis, Argyris, and Peter R. Cook. "Transcription factories: genome organization and gene regulation." Chemical reviews 113.11 (2013): 8683-8705. 

7. Plys, Aaron J., and Robert E. Kingston. "Dynamic condensates activate transcription." Science 361.6400 (2018): 329-330. 

8. Sutherland, Heidi, and Wendy A. Bickmore. "Transcription factories: gene expression in unions?." Nature Reviews Genetics 10.7 (2009): 457-466.

9. Xu, Meng, and Peter R. Cook. "Similar active genes cluster in specialized transcription factories." Journal of Cell Biology 181.4 (2008): 615-623. 

Cancer Cell Reprogramming

1. Brown M, Dotson G, Ronquist S, Emons G, Rajapakse I, Ried T. "TCF7L2 Silencing Reprograms the 4D Nucleome of Colorectal Cancer Cells." Neoplasia, February 2021, Pages 257-269 

2. Califano A, Alvarez MJ. "The recurrent architecture of tumour initiation, progression and drug sensitivity." Nature reviews Cancer. 2017 Feb;17(2):116.

3. Casella G, Munk R, Kim KM, Piao Y, De S, Abdelmohsen K, Gorospe M. "Transcriptome signature of cellular senescence." Nucleic acids research. 2019 Aug 22;47(14):7294-305.  

4. de Thé H. "Differentiation therapy revisited." Nature Reviews Cancer. 2018 Feb;18(2):117. 

5. Paull EO, Aytes A, Jones SJ, Subramaniam PS, Giorgi FM, Douglass EF, Tagore S, Chu B, Vasciaveo A, Zheng S, Verhaak R. "A modular master regulator landscape controls cancer transcriptional identity." Cell. 

6. Ronquist S, Patterson G, Muir LA, Lindsly S, Chen H, Brown M, Wicha M, Bloch A, Brockett R and Rajapakse I. "Algorithm for Cellular Reprogramming." Proceedings of the National Academy of Sciences 114.45 (2017): 11832-11837. 

Isoforms

1. C. J. Wright, C. W. J. Smith, and C. D. Jiggins, “Alternative splicing as a source of phenotypic diversity,” Nat Rev Genet, vol. 23, no. 11, Art. no. 11, Nov. 2022

2. M. Gabut et al., “An Alternative Splicing Switch Regulates Embryonic Stem Cell Pluripotency and Reprogramming,” Cell, vol. 147, no. 1, pp. 132–146, Sep. 2011

Genome Cell Biology

• 1. Alon, Uri. "Biological networks: the tinkerer as an engineer." Science 301.5641 (2003): 1866-1867. 

  2. Bergen, Volker, et al. "Generalizing RNA velocity to transient cell states through dynamical modeling." Nature Biotechnology (2020): 1-7.

  3. Burrill, Devin R., and Pamela A. Silver. "Making cellular memories." Cell 140.1 (2010): 13-18. 

  4. Ptashne, Mark. "Epigenetics: core misconcept." Proceedings of the National Academy of Sciences 110, no. 18 (2013): 7101-7103. 

  5. Dixon, Jesse R., et al. "Chromatin architecture reorganization during stem cell differentiation." Nature 518.7539 (2015): 331-336.

  6. Dixon, Jesse R., David U. Gorkin, and Bing Ren. "Chromatin domains: the unit of chromosome organization." Molecular cell 62.5 (2016): 668-680.

  7. Dixon, Jesse R., et al. "Topological domains in mammalian genomes identified by analysis of chromatin interactions." Nature 485.7398 (2012): 376-380.

  8. Finn, Elizabeth H., and Tom Misteli. "Molecular basis and biological function of variability in spatial genome organization." Science 365.6457 (2019). 

  9. Halley-Stott, Richard P., and John B. Gurdon. "Epigenetic memory in the context of nuclear reprogramming and cancer." Briefings in functional genomics 12.3 (2013): 164-173. 

  10. Hartwell, Leland H., et al. "From molecular to modular cell biology." Nature 402.6761 (1999): C47-C52.

  11. Henikoff, Steven, and John M. Greally. "Epigenetics, cellular memory and gene regulation." Current biology 26.14 (2016): R644-R648. 

  12. Huang, Harvey, et al. "A subset of topologically associating domains fold into mesoscale core-periphery networks." Scientific reports 9.1 (2019): 1-13.

  13. La Manno, Gioele, et al. "RNA velocity of single cells." Nature 560.7719 (2018): 494-498.

  14. Maass, Philipp G., A. Rasim Barutcu, and John L. Rinn. "Interchromosomal interactions: a genomic love story of kissing chromosomes." Journal of Cell Biology 218.1 (2019): 27-38.

  15. Misteli, Tom. "The self-organizing genome: principles of genome architecture and function." Cell (2020). 

  16. Németh, Attila, et al. "Initial genomics of the human nucleolus." PLoS Genet 6.3 (2010): e1000889.  

  17. Nurse, Paul. "Life, logic and information." Nature 454.7203 (2008): 424-426. 

  18. Nurse, Paul, and Jacqueline Hayles. "The cell in an era of systems biology." Cell 144.6 (2011): 850-854. 

  19. Onuchic, Vitor, et al. "Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci." Science 361.6409 (2018): eaar3146. 

  20. Qiu, Xiaojie, et al. "Mapping transcriptomic vector fields of single cells." Cell 185.4 (2022): 690-711. 

  21. Rao, Suhas SP, et al. "A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping." Cell 159.7 (2014): 1665-1680.

  22. Trask, Barbara J. "Human cytogenetics: 46 chromosomes, 46 years and counting." Nature Reviews Genetics 3.10 (2002): 769-778.  

  23. Weintraub, H. "Summary: genetic tinkering—local problems, local solutions." Cold Spring Harbor symposia on quantitative biology. Vol. 58. Cold Spring Harbor Laboratory Press, 1993. 

  24. Weintraub H, Groudine M. "Chromosomal subunits in active genes have an altered conformation." Science. 1976 Sep 3;193(4256):848-56. 

  25. Xue, Jia, et al. "Transcriptome-based network analysis reveals a spectrum model of human macrophage activation." Immunity 40.2 (2014): 274-288.

  26.  D. L. Barabási and A.-L. Barabási, “A Genetic Model of the Connectome,” Neuron, vol. 105, no. 3, pp. 435-445.e5, Feb. 2020, doi: 10.1016/j.neuron.2019.10.031.

  27. M. B. Buechler et al., “Cross-tissue organization of the fibroblast lineage,” Nature, vol. 593, no. 7860, Art. no. 7860, May 2021, doi: 10.1038/s41586-021-03549-5.

Chromosome Conformation Capture 

• 1. Dekker, Job. "Two ways to fold the genome during the cell cycle: insights obtained with chromosome conformation capture." Epigenetics & chromatin 7.1 (2014): 1-12. 

  2. Hildebrand, Erica M., and Job Dekker. "Mechanisms and functions of chromosome compartmentalization." Trends in Biochemical Sciences (2020). 

  3. Ji, Xiong, et al. "3D chromosome regulatory landscape of human pluripotent cells." Cell stem cell 18.2 (2016): 262-275. 

  4. Lieberman-Aiden, Erez, et al. "Comprehensive mapping of long-range interactions reveals folding principles of the human genome." science 326.5950 (2009): 289-293. 

  5. Yardımcı, Galip Gürkan, et al. "Measuring the reproducibility and quality of Hi-C data." Genome biology 20.1 (2019): 1-19. 

  6. Naumova, Natalia, et al. "Organization of the mitotic chromosome." Science 342.6161 (2013): 948-953.

Single Cell Hi-C

• 1. Nagano, Takashi, et al. "Single-cell Hi-C reveals cell-to-cell variability in chromosome structure." Nature 502.7469 (2013): 59-64. 

  2. Stevens, Tim J., et al. "3D structures of individual mammalian genomes studied by single-cell Hi-C." Nature 544.7648 (2017): 59-64. 

  3. Tan, Longzhi, et al. "Three-dimensional genome structures of single diploid human cells." Science 361.6405 (2018): 924-928. 

  4. Zhou, Jingtian, et al. "Robust single-cell Hi-C clustering by convolution-and random-walk–based imputation." Proceedings of the National Academy of Sciences 116.28 (2019): 14011-14018. 

Modeling

• 1. Bianco, S., Lupiáñez, D.G., Chiariello, A.M. et al. Polymer physics predicts the effects of structural variants on chromatin architecture. Nat Genet 50, 662–667 (2018).

  2. Fiorillo, L., Musella, F., Conte, M. et al. Comparison of the Hi-C, GAM and SPRITE methods using polymer models of chromatin. Nat Methods 18, 482–490 (2021).

  3. Liang, Jie, and Alan Perez-Rathke. "Minimalistic 3D chromatin models: Sparse interactions in single cells drive the chromatin fold and form many-body units." Current opinion in structural biology 71 (2021): 200-214.

Data Formats

1. Abdennur, Nezar, and Leonid A. Mirny. "Cooler: scalable storage for Hi-C data and other genomically labeled arrays." Bioinformatics 36.1 (2020): 311-316.

Nanopore Methodologies

Long-Read Sequencing

1. Logsdon, Glennis A., Mitchell R. Vollger, and Evan E. Eichler. "Long-read human genome sequencing and its applications." Nature Reviews Genetics 21.10 (2020): 597-614.

Error Correction

2. Karst, Søren, et al. "High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing." Nat Methods (2021). https://doi.org/10.1038/s41592-020-01041-y 

Pore-C

• 1. Allahyar, Amin, et al. "Enhancer hubs and loop collisions identified from single-allele topologies." Nature genetics 50.8 (2018): 1151-1160.

  2. Ulahannan, Netha, et al. "Nanopore sequencing of DNA concatemers reveals higher-order features of chromatin structure." bioRxiv (2019): 833590. 

Hypergraph

1. Zhang, Ruochi, and Jian Ma. "MATCHA: Probing Multi-way Chromatin Interaction with Hypergraph Representation Learning." Cell Systems 10.5 (2020): 397-407. 

Nanopore Single Cell RNA Sequencing

• 1. Singh, Mandeep, et al. "High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes." Nature communications 10.1 (2019): 1-13. 

  2. Volden, Roger, and Christopher Vollmers. "Highly Multiplexed Single-Cell Full-Length cDNA Sequencing of human immune cells with 10X Genomics and R2C2." bioRxiv (2020). 

Complete Sequence of Human Genome'

1. Nurk S, Koren S, Rhie A, Rautiainen M, Bzikadze AV, Mikheenko A, Vollger MR, Altemose N, Uralsky L, Gershman A, Aganezov S. The complete sequence of a human genome. Science. (2022) Apr 1;376(6588):44-53. 

2. Gershman A, Sauria ME, Hook PW, Hoyt SJ, Razaghi R, Koren S, Altemose N, Caldas GV, Vollger MR, Logsdon GA, Rhie A. Epigenetic patterns in a complete human genome. BioRxiv. 2021 Jan 1. 

Topological Data Analysis

• 1. Babaei, Sepideh, et al. "Hi-C chromatin interaction networks predict co-expression in the mouse cortex." PLoS computational biology 11.5 (2015): e1004221. 

  2. Edelsbrunner, Herbert, David Letscher, and Afra Zomorodian. "Topological persistence and simplification." Proceedings 41st annual symposium on foundations of computer science. IEEE, 2000.

  3. Krishnagopal, Sanjukta, and Ginestra Bianconi. "Spectral Detection of Simplicial Communities via Hodge Laplacians." arXiv preprint arXiv:2108.06547 (2021).

  4. Lee, Geon, and Kijung Shin. "THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting." arXiv preprint arXiv:2109.08341 (2021).

  5. R. Ghrist, “Barcodes: The persistent topology of data,” Bull. Amer. Math. Soc., vol. 45, no. 1, pp. 61–75, 2008, doi: 10.1090/S0273-0979-07-01191-3.

  6. Gong, Yixiao, et al. "Stratification of TAD boundaries reveals preferential insulation of super-enhancers by strong boundaries." Nature communications 9.1 (2018): 1-12.

Immune System

Biology

• 1. Amitai, Assaf, et al. "A population dynamics model for clonal diversity in a germinal center." Frontiers in microbiology 8 (2017): 1693.

  2. Azagra, Alba, et al. "From Loops to Looks: Transcription Factors and Chromatin Organization Shaping Terminal B Cell Differentiation." Trends in Immunology (2019). 

  3. Buchauer, Lisa, and Hedda Wardemann. "Calculating germinal centre reactions." Current Opinion in Systems Biology 18 (2019): 1-8. 

  4. De Silva, Nilushi S., and Ulf Klein. "Dynamics of B cells in germinal centres." Nature reviews immunology 15.3 (2015): 137-148. 

  5. Lau, Angelica WY, and Robert Brink. "Selection in the germinal center." Current Opinion in Immunology 63 (2020): 29-34. 

  6. McHeyzer-Williams, Louise J., and Michael G. McHeyzer-Williams. "Antigen-specific memory B cell development." Annu. Rev. Immunol. 23 (2005): 487-513. 

  7. Mempel, Thorsten R., Sarah E. Henrickson, and Ulrich H. Von Andrian. "T-cell priming by dendritic cells in lymph nodes occurs in three distinct phases." Nature 427.6970 (2004): 154-159. 

  8. Mesin, Luka, Jonatan Ersching, and Gabriel D. Victora. "Germinal center B cell dynamics." Immunity 45.3 (2016): 471-482. 

  9. Mesin, Luka, et al. "Restricted Clonality and Limited Germinal Center Reentry Characterize Memory B Cell Reactivation by Boosting." Cell 180.1 (2020): 92-106. 

  10. Meyer-Hermann, Michael, et al. "A theory of germinal center B cell selection, division, and exit." Cell reports 2.1 (2012): 162-174. 

  11. Nutt, Stephen L., et al. "The generation of antibody-secreting plasma cells." Nature Reviews Immunology 15.3 (2015): 160-171. 

  12. Shinnakasu, Ryo, et al. "Regulated selection of germinal-center cells into the memory B cell compartment." Nature immunology 17.7 (2016): 861.

  13. Suan, Dan, Christopher Sundling, and Robert Brink. "Plasma cell and memory B cell differentiation from the germinal center." Current opinion in immunology 45 (2017): 97-102.  

  14. Tas, Jeroen MJ, et al. "Visualizing antibody affinity maturation in germinal centers." Science 351.6277 (2016): 1048-1054. 

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  16. Voss, James E., et al. "Reprogramming the antigen specificity of B cells using genome-editing technologies." Elife 8 (2019): e42995.  

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B Cell Dynamics

1. Francesconi, Mirko, et al. "Single cell RNA-seq identifies the origins of heterogeneity in efficient cell transdifferentiation and reprogramming." Elife 8 (2019): e41627. 

2. Stadhouders, Ralph, et al. "Transcription factors orchestrate dynamic interplay between genome topology and gene regulation during cell reprogramming." Nature genetics 50.2 (2018): 238-249. 

3. Stadhouders, Ralph, Guillaume J. Filion, and Thomas Graf. "Transcription factors and 3D genome conformation in cell-fate decisions." Nature 569.7756 (2019): 345-354. 

4. van Schoonhoven, Anne, et al. "3D genome organization during lymphocyte development and activation." Briefings in functional genomics 19.2 (2020): 71-82. 

Cellular Imaging

1. de Roo, Jolanda JD, et al. "Development of an in vivo model to study clonal lineage relationships in hematopoietic cells using Brainbow2. 1/Confetti mice." Future science OA 5.10 (2019): FSO427.

2. Martinez, Ryan J., Dennis K. Neeld, and Brian D. Evavold. "Identification of T cell clones without the need for sequencing." Journal of immunological methods 424 (2015): 28-31.

3. Weissman, Tamily A., and Y. Albert Pan. "Brainbow: new resources and emerging biological applications for multicolor genetic labeling and analysis." Genetics 199.2 (2015): 293-306.

4. Wu, Juwell W., et al. "Defining clonal color in fluorescent multi-clonal tracking." Scientific reports 6.1 (2016): 1-10.

Machine Learning and Mathematics

• 1. Cisse, Moustapha, et al. "Parseval networks: Improving robustness to adversarial examples." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017. 

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Sequence Analysis and Data

1. Allman, Elizabeth S., Colby Long, and John A. Rhodes. "Species tree inference from genomic sequences using the log-det distance." SIAM Journal on Applied Algebra and Geometry 3.1 (2019): 107-127. 

2. DeWitt, William S., et al. "A public database of memory and naive B-cell receptor sequences." PLoS One 11.8 (2016): e0160853. 

3. Larimore, Kevin, et al. "Shaping of human germline IgH repertoires revealed by deep sequencing." The Journal of Immunology 189.6 (2012): 3221-3230. 

4. Nouri, Nima, and Steven H. Kleinstein. "Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data." PLoS computational biology 16.6 (2020): e1007977. 

5. Ralph, Duncan K., and Frederick A. Matsen IV. "Consistency of VDJ rearrangement and substitution parameters enables accurate B cell receptor sequence annotation." PLoS computational biology 12.1 (2016): e1004409. 

6. Ralph, Duncan K., and Frederick A. Matsen IV. "Likelihood-based inference of B cell clonal families." PLoS computational biology 12.10 (2016): e1005086. 

7. Ralph, Duncan K., and Frederick A. Matsen IV. "Per-sample immunoglobulin germline inference from B cell receptor deep sequencing data." PLoS computational biology 15.7 (2019): e1007133. 

8. Ralph, Duncan K., and Frederick A. Matsen IV. "Using B cell receptor lineage structures to predict affinity." arXiv preprint arXiv:2004.11868 (2020). 

9. Robins, Harlan S., et al. "Overlap and effective size of the human CD8+ T cell receptor repertoire." Science translational medicine 2.47 (2010): 47ra64-47ra64. 

10. Robins, Harlan S., et al. "Comprehensive assessment of T-cell receptor β-chain diversity in αβ T cells." Blood, The Journal of the American Society of Hematology 114.19 (2009): 4099-4107. 

Mathematics

Entropy

1. J. C. Baez and B. S. Pollard, “Relative Entropy in Biological Systems,” Entropy, vol. 18, no. 2, Art. no. 2, Feb. 2016, doi: 10.3390/e18020046.

Clustering

1. Gao, Katelyn, et al. "An Approach to Identify the Number of Clusters." (2012). 

2. Meyer, Carl D., and Charles D. Wessell. "Stochastic data clustering." SIAM Journal on Matrix Analysis and Applications 33.4 (2012): 1214-1236. 

Compressive sensing

1. Candes, Emmanuel J., and Terence Tao. "Near-optimal signal recovery from random projections: Universal encoding strategies?." IEEE transactions on information theory 52.12 (2006): 5406-5425. 

2. Candes, Emmanuel, and Terence Tao. "The Dantzig selector: Statistical estimation when p is much larger than n." The annals of Statistics 35.6 (2007): 2313-2351. 

3. Moler, Cleve. "“Magic” Reconstruction: Compressed Sensing." Mathworks News & Notes (2010). 

Dimension reduction

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  2. Coifman, Ronald R., and Stéphane Lafon. "Diffusion maps." Applied and computational harmonic analysis 21.1 (2006): 5-30. 

  3. Maaten, Laurens van der, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of machine learning research 9.Nov (2008): 2579-2605.

  4. McInnes, Leland, John Healy, and James Melville. "Umap: Uniform manifold approximation and projection for dimension reduction." arXiv preprint arXiv:1802.03426 (2018).  

Dynamical Systems

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  4. Cucker, Felipe, and Steve Smale. "Emergent behavior in flocks." IEEE Transactions on automatic control 52.5 (2007): 852-862.

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Networks

• 1. Abbas, Waseem, and Magnus Egerstedt. "Robust graph topologies for networked systems." IFAC Proceedings Volumes 45.26 (2012): 85-90.

  2. Battiston, Federico, et al. "Networks beyond pairwise interactions: structure and dynamics." Physics Reports (2020). 

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  8. Cowen, Lenore, et al. "Network propagation: a universal amplifier of genetic associations." Nature Reviews Genetics 18.9 (2017): 551. 

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  18. Kitano, Hiroaki. "Biological robustness." Nature Reviews Genetics 5.11 (2004): 826-837.

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  21. Mesbahi, Mehran. "On state-dependent dynamic graphs and their controllability properties." IEEE Transactions on Automatic Control 50.3 (2005): 387-392.

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  35. Spielman, Daniel A., and Nikhil Srivastava. "Graph sparsification by effective resistances." SIAM Journal on Computing 40.6 (2011): 1913-1926.

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  46. D. J. Higham, “Centrality-friendship paradoxes: when our friends are more important than us,” Journal of Complex Networks, vol. 7, no. 4, pp. 515–528, Aug. 2019

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  54. Abbas, Waseem, and Magnus Egerstedt. "Robust graph topologies for networked systems." IFAC Proceedings Volumes 45.26 (2012): 85-90.

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Hypergraphs

1. Gleich, David F., Nate Veldt, and Anthony Wirth. "Correlation clustering generalized." arXiv preprint arXiv:1809.09493(2018).

2. Benson, Austin R., David F. Gleich, and Desmond J. Higham. "Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data." arXiv preprint arXiv:2103.05031 (2021).

3. Liu, Meng, et al. "Strongly Local Hypergraph Diffusions for Clustering and Semi-supervised Learning." arXiv preprint arXiv:2011.07752 (2020).

4. Wu, Tao, Austin R. Benson, and David F. Gleich. "General tensor spectral co-clustering for higher-order data." arXiv preprint arXiv:1603.00395 (2016).

5. Benson, Austin R., David F. Gleich, and Jure Leskovec. "Higher-order organization of complex networks." Science 353.6295 (2016): 163-166. 

6. Sahasrabuddhe, Rohit, Leonie Neuhäuser, and Renaud Lambiotte. "Modelling Non-Linear Consensus Dynamics on Hypergraphs." arXiv preprint arXiv:2007.09391 (2020). 

7. de Arruda, Guilherme Ferraz, Michele Tizzani, and Yamir Moreno. "Phase transitions and stability of dynamical processes on hypergraphs." Communications Physics 4.1 (2021): 1-9.

8. Chen, Yannan, Liqun Qi, and Xiaoyan Zhang. "The Fiedler vector of a Laplacian tensor for hypergraph partitioning." SIAM Journal on Scientific Computing 39.6 (2017): A2508-A2537.

9. Chodrow, Philip S., Nate Veldt, and Austin R. Benson. "Hypergraph clustering: from blockmodels to modularity." arXiv preprint arXiv:2101.09611 (2021).

10. Valdivia, Paola, et al. "Analyzing Dynamic Hypergraphs with Parallel Aggregated Ordered Hypergraph Visualization." IEEE Transactions on Visualization and Computer Graphics (2019).

11. Higham, Desmond John, and Henry-Louis de Kergorlay. "Epidemics on Hypergraphs: Spectral Thresholds for Extinction." arXiv preprint arXiv:2103.07319 (2021).

12. Tudisco, Francesco, and Desmond J. Higham. "Node and Edge Eigenvector Centrality for Hypergraphs." arXiv preprint arXiv:2101.06215 (2021).

13. Benson, Austin R. "Three hypergraph eigenvector centralities." SIAM Journal on Mathematics of Data Science 1.2 (2019): 293-312.

14. Bick, Christian, et al. "What are higher-order networks?." arXiv preprint arXiv:2104.11329 (2021).

15. M. M. Wolf, A. M. Klinvex, and D. M. Dunlavy. “Advantages to modeling relational data using hypergraphs versus graphs,” IEEE High Performance Extreme Computing Conference (HPEC) (2016) 

16. J.-G. Young, G. Petri, and T. P. Peixoto, “Hypergraph reconstruction from network data,” Commun Phys, vol. 4, no. 1, p. 135, (2021)

17. A. Sarker, J.-B. Seby, A. R. Benson, and A. Jadbabaie, “Higher Order Information Identifies Tie Strength,” arXiv:2108.02091 August (2021)

18. Y. Gao, Z. Zhang, H. Lin, X. Zhao, S. Du, and C. Zou, “Hypergraph Learning: Methods and Practices,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2020, doi: 10.1109/TPAMI.2020.3039374.

19. Chung, Fan, "The Laplacian of a hypergraph," Expanding graphs (DIMACS series) (1993): 21-36.

20. Lü, L., & Zhou, T. (2011). "Link prediction in complex networks: A survey." Physica A: statistical mechanics and its applications, 390(6), 1150-1170. 

21. Zhu, Y. X., Lü, L., Zhang, Q. M., & Zhou, T. (2012). "Uncovering missing links with cold ends." Physica A: Statistical Mechanics and its Applications, 391(22), 5769-5778. 

22. Zhou Y, Rathore A, Purvine E, Wang B. Topological Simplifications of Hypergraphs. arXiv preprint arXiv:2104.11214. 2021 Apr 22. 

23. Aksoy SG, Joslyn C, Marrero CO, Praggastis B, Purvine E. Hypernetwork science via high-order hypergraph walks. EPJ Data Science. 2020 Dec 1;9(1):16. 

24. Sarker A, Seby JB, Benson AR, Jadbabaie A. Higher order information identifies tie strength. arXiv preprint arXiv:2108.02091. 2021 Aug 4. 

25. Agarwal S, Branson K, Belongie S. Higher order learning with graphs. In Proceedings of the 23rd international conference on Machine learning 2006 Jun 25 (pp. 17-24). 

26. Aksoy, Sinan G., et al. "Models and Methods for Sparse (Hyper) Network Science in Business, Industry, and Government." NOTICES OF THE AMERICAN MATHEMATICAL SOCIETY 69.2. 

27. T. Carletti, D. Fanelli, and S. Nicoletti, “Dynamical systems on hypergraphs,” J. Phys. Complex., vol. 1, no. 3, p. 035006, Aug. 2020, doi: 10.1088/2632-072X/aba8e1.

28. M. Faccin, “Measuring dynamical systems on directed hypergraphs,” Phys. Rev. E, vol. 106, no. 3, p. 034306, Sep. 2022, doi: 10.1103/PhysRevE.106.034306.

29. Carletti, Timoteo, Duccio Fanelli, and Sara Nicoletti. "Dynamical systems on hypergraphs." Journal of Physics: Complexity 1.3 (2020): 035006. 

30. Sahasrabuddhe, Rohit, Leonie Neuhäuser, and Renaud Lambiotte. "Modelling non-linear consensus dynamics on hypergraphs." Journal of Physics: Complexity 2.2 (2021): 025006. 

31. Bairey, Eyal, Eric D. Kelsic, and Roy Kishony. "High-order species interactions shape ecosystem diversity." Nature communications 7, no. 1 (2016): 1-7. 

32. Levine, Jonathan M., Jordi Bascompte, Peter B. Adler, and Stefano Allesina. "Beyond pairwise mechanisms of species coexistence in complex communities." Nature 546, no. 7656 (2017): 56-64. 

33. Dal Cengio, Sara, Vivien Lecomte, and Matteo Polettini. "Geometry of nonequilibrium reaction networks." (2022). 

Hypergraph Observability

1. Kawano, Yu, and Toshiyuki Ohtsuka. "Global observability of polynomial systems." Proceedings of SICE Annual Conference 2010. IEEE, 2010. 

Multi-Correlation

1. P. J. Rousseeuw and G. Molenberghs, “The Shape of Correlation Matrices,” The American Statistician, vol. 48, no. 4, pp. 276–279, Nov. 1994, doi: 10.1080/00031305.1994.10476079.

2. Z. Drezner, “Multirelation — a correlation among more than two variables,” Computational Statistics & Data Analysis, vol. 19, no. 3, pp. 283–292, Mar. 1995, doi: 10.1016/0167-9473(93)E0046-7.

3. J. Wang and N. Zheng, “Measures of Correlation for Multiple Variables,” arXiv:1401.4827 [math, stat], Jan. 2020, Accessed: Nov. 29, 2021. [Online]. Available: http://arxiv.org/abs/1401.4827

4. B. M. Taylor, “A Multi-Way Correlation Coefficient,” arXiv:2003.02561 [stat], Mar. 2020, Accessed: Nov. 29, 2021. [Online]. Available: http://arxiv.org/abs/2003.02561

5. Y. Chen, L. Qi, and X. Zhang, “The Fiedler Vector of a Laplacian Tensor for Hypergraph Partitioning,” SIAM J. Sci. Comput., vol. 39, no. 6, pp. A2508–A2537, Jan. 2017, doi: 10.1137/16M1094828.

Systems of Systems

• 1. A. P. Millán, J. J. Torres, and G. Bianconi, “Explosive Higher-Order Kuramoto Dynamics on Simplicial Complexes,” Phys. Rev. Lett., vol. 124, no. 21, p. 218301, May 2020, doi: 10.1103/PhysRevLett.124.218301.

  2. P. Cisneros-Velarde and F. Bullo, “Multi-group SIS Epidemics with Simplicial and Higher-Order Interactions,” arXiv:2005.11404 [physics], Oct. 2021, Accessed: Nov. 12, 2021. [Online]. Available: http://arxiv.org/abs/2005.11404

  3. I. Iacopini, G. Petri, A. Barrat, and V. Latora, “Simplicial models of social contagion,” Nat Commun, vol. 10, no. 1, p. 2485, Jun. 2019, doi: 10.1038/s41467-019-10431-6.

  4. A. Ritz, A. N. Tegge, H. Kim, C. L. Poirel, and T. M. Murali, “Signaling hypergraphs,” Trends in Biotechnology, vol. 32, no. 7, pp. 356–362, Jul. 2014, doi: 10.1016/j.tibtech.2014.04.007.

  5. J. Grilli, G. Barabás, M. J. Michalska-Smith, and S. Allesina, “Higher-order interactions stabilize dynamics in competitive network models,” Nature, vol. 548, no. 7666, pp. 210–213, Aug. 2017, doi: 10.1038/nature23273.

  6. F. Battiston et al., “The physics of higher-order interactions in complex systems,” Nat. Phys., vol. 17, no. 10, pp. 1093–1098, Oct. 2021, doi: 10.1038/s41567-021-01371-4.

  7. A. Muhammad and M. Egerstedt, “Control Using Higher Order Laplacians in Network Topologies,” in Proc. of 17th International Symposium on Mathematical Theory of Networks and Systems, Kyoto, 2006, pp. 1024–1038.

Synchronization

• 8. Dörfler, Florian, and Francesco Bullo. "On the critical coupling for Kuramoto oscillators." SIAM Journal on Applied Dynamical Systems 10.3 (2011): 1070-1099. 

  9. Dörfler, Florian, and Francesco Bullo. "Synchronization in complex networks of phase oscillators: A survey." Automatica 50.6 (2014): 1539-1564. 

  10. Dörfler, Florian, Michael Chertkov, and Francesco Bullo. "Synchronization in complex oscillator networks and smart grids." Proceedings of the National Academy of Sciences 110.6 (2013): 2005-2010. 

  11. Izhikevich, Eugene M., and Yoshiki Kuramoto. "Weakly coupled oscillators." Encyclopedia of mathematical physics 5 (2006): 448. 

  12. Olfati-Saber, Reza. "Swarms on sphere: A programmable swarm with synchronous behaviors like oscillator networks." Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006.

  13. Olfati-Saber, Reza, J. Alex Fax, and Richard M. Murray. "Consensus and cooperation in networked multi-agent systems." Proceedings of the IEEE 95.1 (2007): 215-233.  

  14. Strogatz, Steven H., and Ian Stewart. "Coupled oscillators and biological synchronization." Scientific American 269.6 (1993): 102-109. 

  15. Strogatz, Steven H. "From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators." Physica D: Nonlinear Phenomena 143.1-4 (2000): 1-20. 

  16. Winfree, Arthur T. "On emerging coherence." Science 298.5602 (2002): 2336-2337. 

  17. Sepulchre R, Paley DA, Leonard NE. Stabilization of planar collective motion: All-to-all communication. IEEE Transactions on automatic control. 2007 May 15;52(5):811-24. 

  18. Scardovi L, Sarlette A, Sepulchre R. Synchronization and balancing on the N-torus. Systems & Control Letters. 2007 May 1;56(5):335-41. 

Tensors

• 1. Bader, Brett William, and Tamara Gibson Kolda. MATLAB tensor classes for fast algorithm prototyping. No. SAND2004-5187. Sandia National Laboratories, 2004.

  2. Banerjee, Anirban. "On the spectrum of hypergraphs." Linear Algebra and its Applications (2020).

  3. Benson, Austin R. "Three hypergraph eigenvector centralities." SIAM Journal on Mathematics of Data Science 1.2 (2019): 293-312.

  4. Brazell, Michael, et al. "Solving multilinear systems via tensor inversion." SIAM Journal on Matrix Analysis and Applications34.2 (2013): 542-570.

  5. De Lathauwer, Lieven, Bart De Moor, and Joos Vandewalle. "A multilinear singular value decomposition." SIAM journal on Matrix Analysis and Applications 21.4 (2000): 1253-1278.

  6. Kolda, Tamara G., and Jackson R. Mayo. "An adaptive shifted power method for computing generalized tensor eigenpairs." SIAM Journal on Matrix Analysis and Applications 35.4 (2014): 1563-1581.

  7. Kolda, Tamara Gibson. Multilinear operators for higher-order decompositions. No. SAND2006-2081. Sandia National Laboratories, 2006.

  8. Kolda, Tamara G., and Brett W. Bader. "Tensor decompositions and applications." SIAM review 51.3 (2009): 455-500.

  9. Lim, Lek-Heng. "Singular values and eigenvalues of tensors: a variational approach." 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.. IEEE, 2005.

  10. Oseledets, Ivan V., and Sergey V. Dolgov. "Solution of linear systems and matrix inversion in the TT-format." SIAM Journal on Scientific Computing 34.5 (2012): A2718-A2739.

  11. Oseledets, Ivan V. "Tensor-train decomposition." SIAM Journal on Scientific Computing 33.5 (2011): 2295-2317.

  12. Qi, Liqun. "Eigenvalues of a real supersymmetric tensor." Journal of Symbolic Computation 40.6 (2005): 1302-1324.

  13. Ragnarsson, Stefan, and Charles F. Van Loan. "Block tensor unfoldings." SIAM Journal on Matrix Analysis and Applications33.1 (2012): 149-169.

  14. Sidiropoulos, Nicholas D., et al. "Tensor decomposition for signal processing and machine learning." IEEE Transactions on Signal Processing 65.13 (2017): 3551-3582. 

  15. Williams, Alex H., et al. "Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis." Neuron 98.6 (2018): 1099-1115. 

  16. Sturmfels, Bernd. "Tensors and their eigenvectors." Notices of the AMS 63.6 (2016). 

  17. L.-H. Lim, “Tensors in computations,” Acta Numerica, vol. 30, pp. 555–764, (May 2021)

Morse-Bott-Smale System

• 1. Bonatti, Ch, V. Grines, and O. Pochinka. "Topological classification of Morse–Smale diffeomorphisms on 3-manifolds." Duke Mathematical Journal 168.13 (2019): 2507-2558. 

  2. Bott, Raoul. "Lectures on Morse theory, old and new." Bulletin of the american mathematical society 7.2 (1982): 331-358. 

  3. Bott, Raoul. "Morse theory indomitable." Publications Mathématiques de l'IHÉS 68 (1988): 99-114. 

  4. Guest, Martin. "Morse theory in the 1990's." arXiv preprint math/0104155 (2001). 

  5. Guest, Martin. "Morse theory." Notes (2010). 

  6. Grines, Vyacheslav Zigmuntovich, et al. "Classification of Morse–Smale systems and topological structure of the underlying manifolds." Russian Mathematical Surveys 74.1 (2019): 37. 

  7. Zhuzhoma, Evgenii Viktorovich, and Vladislav Sergeevich Medvedev. "Global dynamics of Morse-Smale systems." Proceedings of the Steklov Institute of Mathematics 261.1 (2008): 112. 

  8. Lewiner, Thomas, Hélio Lopes, and Geovan Tavares. "Toward optimality in discrete Morse theory." Experimental Mathematics 12.3 (2003): 271-285.

Singular Value Decomposition

1. Allen, Genevera I., Logan Grosenick, and Jonathan Taylor. "A generalized least-square matrix decomposition." Journal of the American Statistical Association 109.505 (2014): 145-159.

2. Gavish, Matan, and David L. Donoho. "The optimal hard threshold for singular values is 4/sqrt(3)." IEEE Transactions on Information Theory 60.8 (2014): 5040-5053. 

3. Moler, Cleve. "How SVD Saved the Universe."

4. Moler, Cleve . "Numerical computing with MATLAB." Society for Industrial and Applied Mathematics, (2004). 

5. Strang, Gilbert. "The fundamental theorem of linear algebra." The American Mathematical Monthly 100.9 (1993): 848-855.

6. Troje, Nikolaus F. "Decomposing biological motion: A framework for analysis and synthesis of human gait patterns." Journal of vision 2.5 (2002): 2-2.

7. Von Luxburg, Ulrike. "A tutorial on spectral clustering." Statistics and computing 17.4 (2007): 395-416. 

8. A. Ng, M. Jordan, and Y. Weiss, “On Spectral Clustering: Analysis and an algorithm,” in Advances in Neural Information Processing Systems, 2002, vol. 14. Accessed: Jan. 31, 2022. 

Highly Optimized Tolerance

• 1. Carlson, Jean M., and John Doyle. "Complexity and robustness." Proceedings of the national academy of sciences 99.suppl 1 (2002): 2538-2545. 

  2. Csete, Marie E., and John C. Doyle. "Reverse engineering of biological complexity." science 295.5560 (2002): 1664-1669. 

Missing Links and Matrix Completion

1. Kunegis, Jérôme, and Andreas Lommatzsch. "Learning spectral graph transformations for link prediction." Proceedings of the 26th Annual International Conference on Machine Learning. 2009.

2. Cai, Jian-Feng, Emmanuel J. Candès, and Zuowei Shen. "A singular value thresholding algorithm for matrix completion." SIAM Journal on optimization 20.4 (2010): 1956-1982.

3. Zhang, Muhan, and Yixin Chen. "Link prediction based on graph neural networks." Advances in neural information processing systems 31 (2018).

4. Menon, Aditya Krishna, and Charles Elkan. "Link prediction via matrix factorization." Joint european conference on machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg, 2011.

5. Kim, Myunghwan, and Jure Leskovec. "The network completion problem: Inferring missing nodes and edges in networks." Proceedings of the 2011 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, 2011.

6. Sun, Qingyun, Mengyuan Yan, and David Donoho. "Convolutional imputation of matrix networks." International Conference on Machine Learning. PMLR, 2018.

7. Van Dijk, David, et al. "Recovering gene interactions from single-cell data using data diffusion." Cell 174.3 (2018): 716-729.

8. Liu, Weiping, and Linyuan Lü. "Link prediction based on local random walk." EPL (Europhysics Letters) 89.5 (2010): 58007.

9. Yan, Bowen, and Steve Gregory. "Finding missing edges and communities in incomplete networks." Journal of Physics A: Mathematical and Theoretical 44.49 (2011): 495102.

10. Candès, Emmanuel J., et al. "Robust principal component analysis?." Journal of the ACM (JACM) 58.3 (2011): 1-37.

11. Zhang, Muhan, et al. "Beyond link prediction: Predicting hyperlinks in adjacency space." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 32. No. 1. 2018.

Distance Matrices

1.  I. Dokmanic, R. Parhizkar, J. Ranieri, and M. Vetterli, “Euclidean Distance Matrices: Essential Theory, Algorithms and Applications,” IEEE Signal Process. Mag., vol. 32, no. 6, pp. 12–30, Nov. 2015

Spatial Transcriptomics

• 1. Äijö, Tarmo, et al. "Splotch: Robust estimation of aligned spatial temporal gene expression data." bioRxiv (2019): 757096. 

  2. Almet, Axel A., et al. "The landscape of cell-cell communication through single-cell transcriptomics." Current opinion in systems biology (2021). 

  3. Armingol, Erick, et al. "Deciphering cell–cell interactions and communication from gene expression." Nature Reviews Genetics 22.2 (2021): 71-88. 

  4. Asp, Michaela, et al. "A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart." Cell 179.7 (2019): 1647-1660. 

  5. Baccin, Chiara, et al. "Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization." Nature Cell Biology (2019): 1-11. 

  6. Bäckdahl, Jesper, et al. "Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin." Cell Metabolism 33.9 (2021): 1869-1882. 

  7. Gross, Thilo, and Hiroki Sayama. "Adaptive networks." Adaptive networks. Springer, Berlin, Heidelberg, 2009. 1-8. 

  8. Ji, Andrew L., et al. "Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma." Cell 182.2 (2020): 497-514. 

  9. Jin, Suoqin, et al. "Inference and analysis of cell-cell communication using CellChat." Nature communications 12.1 (2021): 1-20. 

  10. Levy-Jurgenson, Alona, et al. "Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer." Scientific reports 10.1 (2020): 1-11. 

  11. Longo, Sophia K., et al. "Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics." Nature Reviews Genetics (2021): 1-18. 

  12. Maniatis, Silas, et al. "Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis." Science 364.6435 (2019): 89-93. 

  13. Moncada, Reuben, et al. "Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas." Nature Biotechnology 38.3 (2020): 333-342. 

  14. Rao, Anjali, et al. "Exploring tissue architecture using spatial transcriptomics." Nature 596.7871 (2021): 211-220. 

  15. Salamon, J., Qian, X., Nilsson, M., & Lynn, D. J. (2018). Network visualization and analysis of spatially aware gene expression data with InsituNet. Cell systems, 6(5), 626-630. 

  16. Sayama, Hiroki, et al. "Modeling complex systems with adaptive networks." Computers & Mathematics with Applications 65.10 (2013): 1645-1664. 

  17. Ståhl, Patrik L., et al. "Visualization and analysis of gene expression in tissue sections by spatial transcriptomics." Science 353.6294 (2016): 78-82. 

Synthetic Lethality

• 1. Blomen, Vincent A., et al. "Gene essentiality and synthetic lethality in haploid human cells." Science 350.6264 (2015): 1092-1096. 

  2. Costanzo, Michael, et al. "A global genetic interaction network maps a wiring diagram of cellular function." Science 353.6306 (2016): aaf1420. 

  3. Hartwell, Leland H., et al. "Integrating genetic approaches into the discovery of anticancer drugs." Science 278.5340 (1997): 1064-1068. 

  4. Luck, Katja, et al. "A reference map of the human binary protein interactome." Nature (2020): 1-7. 

  5. Szklarczyk, Damian, et al. "STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets." Nucleic acids research 47.D1 (2019): D607-D613.

  6. Nijman, Sebastian MB. "Synthetic lethality: general principles, utility and detection using genetic screens in human cells." FEBS letters 585.1 (2011): 1-6. 

  7. Wang, Tim, et al. "Identification and characterization of essential genes in the human genome." Science 350.6264 (2015): 1096-1101. 

  8. Partridge EC, Chhetri SB, Prokop JW, Ramaker RC, Jansen CS, Goh ST, Mackiewicz M, Newberry KM, Brandsmeier LA, Meadows SK, Messer CL. "Occupancy maps of 208 chromatin-associated proteins in one human cell type." Nature. 2020 Jul;583(7818):720-8. 

  9. X. Lin et al., “Nested epistasis enhancer networks for robust genome regulation,” Science, vol. 0, no. 0, p. eabk3512, Aug. 2022

Turing System

1. Ball, Philip. "Forging patterns and making waves from biology to geology: a commentary on Turing (1952)‘The chemical basis of morphogenesis’." Philosophical Transactions of the Royal Society B: Biological Sciences 370.1666 (2015): 20140218. 

2. Chua, Leon O. "Local activity is the origin of complexity." International journal of bifurcation and chaos 15.11 (2005): 3435-3456. 

3. Gierer, Alfred, and Hans Meinhardt. "A theory of biological pattern formation." Kybernetik 12.1 (1972): 30-39. 

4. Guillot, Charlène, and Thomas Lecuit. "Mechanics of epithelial tissue homeostasis and morphogenesis." Science 340.6137 (2013): 1185-1189. 

5. Koch, A. J., and Hans Meinhardt. "Biological pattern formation: from basic mechanisms to complex structures." Reviews of modern physics 66.4 (1994): 1481. 

6. Kondo, Shigeru, and Takashi Miura. "Reaction-diffusion model as a framework for understanding biological pattern formation." science 329.5999 (2010): 1616-1620. 

7. Nelson, Celeste M. "Geometric control of tissue morphogenesis." Biochimica et Biophysica Acta (BBA)-Molecular Cell Research 1793.5 (2009): 903-910. 

8. Rauch, Erik M., and Mark M. Millonas. "The role of trans-membrane signal transduction in Turing-type cellular pattern formation." Journal of theoretical biology 226.4 (2004): 401-407. 

9. Smale, Steve. "A mathematical model of two cells via Turing’s equation." The Hopf bifurcation and its applications. Springer, New York, NY, 1976. 354-367.

10. Turing, Alan Mathison. "The chemical basis of morphogenesis." Bulletin of mathematical biology 52.1-2 (1990): 153-197. 

Two Cell Systems

1. Adler, Miri, et al. "Endocytosis as a stabilizing mechanism for tissue homeostasis." Proceedings of the National Academy of Sciences 115.8 (2018): E1926-E1935.

2. Litviňuková, M. et al. "Cells of the adult human heart." Nature https://doi.org/10.1038/s41586-020-2797-4 (2020).

3. Zhou, Xu, et al. "Circuit design features of a stable two-cell system." Cell 172.4 (2018): 744-757. 

4. Watanabe, Kazuhide, et al. "OVOL2 induces mesenchymal-to-epithelial transition in fibroblasts and enhances cell-state reprogramming towards epithelial lineages." Scientific reports 9.1 (2019): 1-12.

5. Chen, Yifang, Devendra S. Mistry, and George L. Sen. "Highly rapid and efficient conversion of human fibroblasts to keratinocyte-like cells." Journal of Investigative Dermatology 134.2 (2014): 335-344.

Wound Healing

• 1. Chang, Howard Y., et al. "Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds." PLoS Biol 2.2 (2004): e7. 

  2. Cucker, Felipe, and Steve Smale. "On the mathematics of emergence." Japanese Journal of Mathematics 2.1 (2007): 197-227.

  3. Grada, Ayman, et al. "Research techniques made simple: analysis of collective cell migration using the wound healing assay." Journal of Investigative Dermatology 137.2 (2017): e11-e16.

  4. Grant, Gavin D., et al. "Accurate delineation of cell cycle phase transitions in living cells with PIP-FUCCI." Cell Cycle 17.21-22 (2018): 2496-2516.

  5. Iyer, Vishwanath R., et al. "The transcriptional program in the response of human fibroblasts to serum." science 283.5398 (1999): 83-87. 

  6. McDougall, Steven, et al. "Fibroblast migration and collagen deposition during dermal wound healing: mathematical modelling and clinical implications." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 364.1843 (2006): 1385-1405.

  7. Murray, J. D., et al. "Spatial pattern formation in biology: I. Dermal wound healing. II. Bacterial patterns." Journal of the Franklin Institute 335.2 (1998): 303-332.

  8. Schäfer, Matthias, and Sabine Werner. "Transcriptional control of wound repair." Annu. Rev. Cell Dev. Biol. 23 (2007): 69-92.

  9. Sherratt, Jonathan A., and James Dickson Murray. "Models of epidermal wound healing." Proceedings of the Royal Society of London. Series B: Biological Sciences 241.1300 (1990): 29-36.

  10. Sherratt, Jonathan A., and John C. Dallon. "Theoretical models of wound healing: past successes and future challenges." Comptes rendus biologies 325.5 (2002): 557-564.

  11. Rodrigues, Melanie, et al. "Wound healing: a cellular perspective." Physiological reviews 99.1 (2019): 665-706.

  12. Chen, Zhuo J., et al. "A novel three-dimensional wound healing model." Journal of Developmental Biology 2.4 (2014): 198-209.

  13. Rhee, Sangmyung. "Fibroblasts in three dimensional matrices: cell migration and matrix remodeling." Experimental & molecular medicine 41.12 (2009): 858-865.

  14. Ngo, Peter, et al. "Collagen gel contraction assay." Cell-Cell Interactions (2006): 103-109.

  15. Di Costanzo, Ezio, et al. "A discrete in continuous mathematical model of cardiac progenitor cells formation and growth as spheroid clusters (Cardiospheres)." Mathematical Medicine and Biology: a Journal of the IMA 35.1 (2018): 121-144.

  16. Di Costanzo, Ezio, et al. "A hybrid mathematical model of collective motion under alignment and chemotaxis." arXiv preprint arXiv:1507.02980 (2015).

  17. Kurita, Masakazu, et al. "In vivo reprogramming of wound-resident cells generates skin epithelial tissue." Nature 561.7722 (2018): 243-247.

  18. Mercado, Nicolas, et al. "IRF2 is a master regulator of human keratinocyte stem cell fate." Nature communications 10.1 (2019): 1-19.

  19. Zhang, Yuehou, et al. "Reprogramming of keratinocytes as donor or target cells holds great promise for cell therapy and regenerative medicine." Stem Cell Reviews and Reports 15.5 (2019): 680-689.

  20. Boudra, Rafik, and Matthew R. Ramsey. "Focus: Skin: Understanding Transcriptional Networks Regulating Initiation of Cutaneous Wound Healing." The Yale journal of biology and medicine 93.1 (2020): 161.

  21. S. McDougall, J. Dallon, J. Sherratt, and P. Maini, “Fibroblast migration and collagen deposition during dermal wound healing: mathematical modelling and clinical implications,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 364, no. 1843, pp. 1385–1405, Jun. 2006, doi: 10.1098/rsta.2006.1773.

  22. L. MacCarthy-Morrogh and P. Martin, “The hallmarks of cancer are also the hallmarks of wound healing,” Sci. Signal., vol. 13, no. 648, Sep. 2020

  23. Sundaram, G. M., Quah, S., & Sampath, P. (2018). Cancer: the dark side of wound healing. The FEBS journal, 285(24), 4516-4534.

  24. Deyell, M., Garris, C. S., & Laughney, A. M. (2021). Cancer metastasis as a non-healing wound. British Journal of Cancer, 124(9), 1491-1502.

Additional Resources

Motivational Resources

• 1. The Heilmeier Catechism

  2. Simplicity, Rigor, and Magic

  3. Advice from Elmer Gilbert

  4. Quotes from the NBA legend Kobe Bryant

Great Minds of Science

1. Harold Weintraub (1945-1995)

External Resources

• 1. Peter R Cook's Nuclear Structure and Function Research Group Resources

  2. Ordinary Differential Equations (Wiggins)

  3. Wolfram|Alpha: Computational Intelligence 

  4. Analyzing Dynamic Hypergraphs

  5. MATCONT: User Manual for MatCont  and  User Manual for MatContM 

  6. Eigenwalker - MathWorks Summary Slides    

  7. DMDSP – Sparsity-Promoting Dynamic Mode Decomposition

  8. Concinnitas Studio 

  9. James Simons: My Guiding  Principles

Lecture Notes

1. Bifurcation Theory: Leonid Shilnikov, Andrey Shilnikov, Dmitry Turaev, Leon Chua 

2. Stability of Synchronized Motion in Complex Networks: Tiago Pereira

Books

1. Bullo F. Lectures on Network Systems, CreateSpace, 2018, with contributions by J. Cortes, F. Dorfler, and S. Martinez. 

2. Boyd, Stephen, and Lieven Vandenberghe. "Introduction to applied linear algebra: vectors, matrices, and least squares." Cambridge university press, 2018. 

3. Cover TM. Elements of information theory. John Wiley & Sons; 1999. 

4. Ghrist RW. Elementary applied topology. Seattle: Createspace; 2014 Sep. 

5. Hansen, Lars, and Sargent, Thomas J. "Robustness." Princeton university press, 2008. 

6. Hirsch, Morris W., and Smale, Steve. "Differential equations, dynamical systems, and linear algebra." Vol. 60. Academic press, 1974. 

7. Lovász, László. "Large networks and graph limits." American Mathematical Society, (2012). 

8. Marsden JE, McCracken M. The Hopf bifurcation and its applications. Springer Science & Business Media; 2012 Dec 6. 

9. Morgan, David Owen. "The cell cycle: principles of control." New Science Press, 2007. 

10. Petersen, Kaare Brandt, and Michael Syskind Pedersen. "The Matrix Cookbook." (2012).

11. Shannon CE. A mathematical theory of communication. The Bell system technical journal. 1948 Jul;27(3):379-423. 

12. Smale, Steve. "The Mathematics of Time." New York: Springer-Verlag, 1980. 

13. Spielman, Daniel A. "Spectral and Algebraic Graph Theory." (2019)

14. Strogatz, Steven H. Nonlinear dynamics and chaos: with applications to physics. Biology, Chemistry and Engineering (1994): 1.

15. Winfree, Arthur T. "The geometry of biological time." Vol. 12. Springer Science & Business Media, 2001. 

16. Bapat, Ravindra B. Graphs and matrices. Vol. 27. London: Springer, 2010. 

17. Moler, Cleve . "Numerical computing with MATLAB." Society for Industrial and Applied Mathematics, (2004). 

18. Robbin, Joel W., and Dietmar A. Salamon. "Introduction to differential geometry." ETH, Lecture Notes, preliminary version (2011): 18. 

19. Kuznetsov, Yuri A. Elements of applied bifurcation theory. Vol. 112. Springer Science & Business Media, 2013. 

20. Perko, Lawrence. Differential Equations and Dynamical Systems. New York: Springer, 2000

21. Palis, J. Jr, and Welington De Melo. Geometric theory of dynamical systems: an introduction. Springer Science & Business Media, 2012. 

22. Porter MA, Gleeson JP. Dynamical systems on networks. Frontiers in Applied Dynamical Systems: Reviews and Tutorials. 2016;4. 

23. Branton D, Deamer DW. Nanopore sequencing: an introduction. World Scientific; 2019 Apr 5. 

24. Pugh CC. Real mathematical analysis. New York/Heidelberg/Berlin: Springer; 2002 Mar. 

25. Banyaga A, Hurtubise D. Lectures on Morse homology. Springer Science & Business Media; 2004 Oct 29. 

26. Ye YQ, Ye Y, Yeh YC, Cai SL, Lan-sun C. Theory of limit cycles. American Mathematical Soc.; 1986. 

27. Strogatz S. Sync: The emerging science of spontaneous order. 

28. Chern, Shiing-Shen, and Stephen Smale, eds. Global analysis. Vol. 1. American Mathematical Soc., 1970. 

Notes

• 1. BLOSUM Matrix: Henikoff, Steven, and Henikoff, Jorja. "Amino acid substitution matrices from protein blocks." Proceedings of the National Academy of Sciences 89.22 (1992): 10915-10919. 

  2. Testing the equality of covariance matrices 

  3. Rajapakse, Indika, Lindsey Muir, and Paul Martin. "Hardy’s “Small” Discovery Remembered." Notices of the AMS 55.3 (2008).