Cell Cycle


Cellular Differentiation

Cellular Reprogramming


  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.


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


  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.

Genome Cell Biology

Chromosome Conformation Capture

Single Cell Hi-C


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

Error Correction

  1. Karst, Søren, et al. "High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing." Nat Methods (2021).



  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

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

Immune System


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

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.



  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.


  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

Dynamical Systems



  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.


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Systems of Systems



Morse-Bott-Smale System

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

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.

Spatial Transcriptomics

Synthetic Lethality

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 (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

Additional Resources

Motivational Resources

Great Minds of Science

  1. Harold Weintraub (1945-1995)

External Resources

Lecture Notes

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

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


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