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We are interested in how local changes, responses and interactions between cells lead to global coordinated effects such as multicellular self-organization, how can we use these insights to design optimal cellular interactions, and how can we infer such interactions, correlations and geometric structure in single cell data to reveal the biological processes and contexts that cellular populations are driven by. 


More broadly, we are interested in studying the emergence of complex behavior out of the interactions of simple dynamic units in biological systems. In the past we have focused on genes and their products that interact via different types of regulations to form regulatory networks underlying cellular function. We are now focusing on interacting cells that form tissues and the ways by which these interactions determine the global state of the functioning (or dysfunctioning) tissue.


Revealing latent structure in single cell data, and embeddings for biological data

Piran, Z., Cohen, N., Hoshen, Y. and Nitzan, M. 2024. Disentanglement of single-cell data with biolord. Nature Biotechnology.

Piran, Z. and Nitzan, M. 2024. Uncovering hidden biological processes by probabilistic filtering of single-cell data, Nature Communications.

Karin, J., Bornfeld, Y. and Nitzan, M., 2023. scPrisma: inference, filtering and enhancement of periodic signals in single-cell data using spectral template matching. Nature Biotechnology.

Mages, S.*, Moriel, N.*, Avraham-Davidi, I.*, Murray, E., Chen, F., Rozenblatt-Rosen, O., Klughammer, J., Regev, A. and Nitzan, M., 2023. TACCO: Unified annotation transfer and decomposition of cell identities for single-cell and spatial omics. Nature Biotechnology.


Lange, M.*, Piran, Z.*, Klein, M.*, Spanjaard, B.*, Klein, D., Junker, J.P., Theis, F.J. and Nitzan, M. 2023. Mapping lineage-traced cells across time points with moslin. bioRxiv.

Sheng, Y., Barak, B. and Nitzan, M. 2023. Robust reconstruction of single cell RNA-seq data with iterative gene weight updates. Bioinformatics 39.

Moriel, N.*, Senel, E.*, Friedman, N., Rajewsky, N., Karaiskos, N., and Nitzan, M. 2021. NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport. Nature Protocols, pp. 1-24.


Nitzan, M. and Brenner, M. 2021. Revealing lineage-related signals in single-cell gene expression using random matrix theory. Proceedings of the National Academy of Sciences (PNAS), 118(11).

Nitzan, M.*, Karaiskos, N.*, Friedman, N. and Rajewsky, N. 2019. Gene expression cartography. Nature, 576(7785), pp.132-137.

{Forrow, A., Hutter, J.C., Nitzan, M., Rigollet, P., Schiebinger, G. and Weed, J.}# 2018. Statistical optimal transport via factored couplings. arXiv 1806.07348. AISTATS19.


Modelling and design of multi-cellular expression patterns and self-organization

Adler, M. *, Moriel, N. *, Goeva, A. *, Avraham-Davidi, I., Mages, S., Adams, T.S., Kaminski, N., Macosko, E.Z., Regev, A., Medzhitov, R. and Nitzan, M. 2023. Emergence of division of labor in tissues through cell interactions and spatial cues. Cell Reports 42, 5. 

Guo, Y., Nitzan, M. and Brenner, M.P., 2021. Programming cell growth into different cluster shapes using diffusible signals. PLOS Computational Biology, 17(11), e1009576


Inference of network structure and dynamical systems embedding

Moriel, N.*, Ricci, M.* and Nitzan M., 2024. Let's do the time-warp-attend: Learning topological invariants of dynamical systems. 12th International Conference on Learning Representations.

Ricci, M., Moriel, N., Piran, Z. and Nitzan M., 2023. Phase2vec: Dynamical systems embedding with a physics-informed convolutional network. Eleventh International Conference on Learning Representations.

Nitzan, M., Casadiego, J. and Timme, M. 2017. Revealing physical interactions from statistics of collective dynamics. Science Advances, 3(2), p.e1600396. 


Casadiego, J., Nitzan, M., Hallerberg, S. and Timme, M. 2017. Model-free inference of direct network interactions from nonlinear collective dynamics. Nature Communications, 8.

Network/graph statistics and information flow

Nitzan, M., Steiman-Shimony, A., Altuvia, Y., Biham, O. and Margalit, H., 2014. Interactions between distant ceRNAs in regulatory networks. Biophysical journal, 106(10), pp.2254-2266.


Rosenfeld, N., Nitzan, M. and Globerson, A., 2016, February. Discriminative Learning of Infection Models. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (pp. 563-572). ACM.


Nitzan, M., Katzav, E., Kuehn, R. and Biham, O., 2016. Distance distribution in configuration-model networks. Physical Review E, 93(6), p.062309.


Katzav, E., Nitzan, M., ben-Avraham, D., Krapivsky, P.L., Kühn, R., Ross, N. and Biham, O., 2015. Analytical results for the distribution of shortest path lengths in random networks. Europhysics Letters, 111(2), p.26006.

Multi-layered regulatory networks and their dynamics

Nitzan, M., Rehani, R. and Margalit, H. 2017. Integration of bacterial small RNAs in regulatory networks. Annual Review of Biophysics, 46(1). 


Nitzan, M., Fechter, P., Peer, A., Altuvia, Y., Bronesky, D., Vandenesch, F., Romby, P., Biham, O. and Margalit, H., 2015. A defense-offense multi-layered regulatory switch in a pathogenic bacterium. Nucleic acids research, 43(3), pp.1357-1369.      


Nitzan, M., Shimoni, Y., Rosolio, O., Margalit, H. and Biham, O., 2015. Stochastic analysis of bistability in coherent mixed feedback loops combining transcriptional and posttranscriptional regulations. Physical Review E, 91(5), p.052706.


Sajman, J., Zenvirth, D., Nitzan, M., Margalit, H., Simpson-Lavy, K.J., Reiss, Y., Cohen, I., Ravid, T. and Brandeis, M., 2015. Degradation of Ndd1 by APC/CCdh1 generates a feed forward loop that times mitotic protein accumulation. Nature communications, 6.


Nitzan, M., Wassarman, K.M., Biham, O. and Margalit, H., 2014. Global regulation of transcription by a small RNA: a quantitative view. Biophysical journal, 106(5), pp.1205-1214.


Nitzan, M., Steiman-Shimony, A., Altuvia, Y., Biham, O. and Margalit, H., 2014. Interactions between distant ceRNAs in regulatory networks. Biophysical journal, 106(10), pp.2254-2266.

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