Publications

Ben Yaakov, T., Wasserman, T., Savir, Y., Aged mouse ovarian immune milieu shows a shift towards adaptive immunity and attenuated cell function, bioArxiv

Daniel, N., Wasserman, T., Adler, Z., Czyzewski, T., Savir, Y. Machine Learning Reveals The Effect of Maternal Age on The Mouse Pre-Implantation Embryo Developmental Timing bioArxiv 

Larey, A, Aknin, E., Daniel, N., Osswald, G., Caldwell, J., Rochman, M., Wasserman, T., Collins, M., Arva, N., Yang, G., Rothenberg, M., Savir, Y. Harnessing Artificial Intelligence to Infer Novel Spatial Biomarkers for the Diagnosis of Eosinophilic Esophagitis (2022) Frontiers in Medicine

Sabrin Hilau, Sophia Katz, Tanya Wasserman, Ruth Hershberg, Yonatan Savir Density-dependent effects are the main determinants of variation in growth dynamics between closely related bacterial strains (2022) PLoS Computational Biology

Daniel, N., Larey, A., Aknin, E., Osswald, G., Caldwell, J., Rochman, M., Collins, M., Yang, G., Arva, N., Capocelli, K., Rothenberg, M., Savir, Y. Deep Multi-Label Segmentation Network For Eosinophilic Esophagitis Whole Slide Biopsy Diagnostics (2022) 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society

Anna Altshuler, AyaAmitai-Lange, NoamTarazi, SunandaDey, Lior Strinkovsky, Swarnabh Bhattacharya, Shira Hadad-Porat, Waseem Nasser, Jusuf Imeri, Gil Ben-David, Beatrice Tiosano, Eran Berkowitz, Nathan Karin, Yonatan Savir*, Ruby Shalom-Feuerstein* (2021) Capturing limbal epithelial stem cell population dynamics, signature, and their niche Cell stem cell

Czyzewski, T., Daniel, N., Rochman, M., Caldwell, J., Osswald, G., Collins, M., Rothenberg, M. & Savir, Y. Machine Learning Approach for Biopsy-Based Identification of Eosinophilic Esophagitis Reveals Importance of Global features. IEEE Open J. Eng. Med. Biol. 2, 218–223 (2021).

C. Ricci-Tam, J. Wang, I. Ben-Zion,J. Palme, A. Li, Y. Savir, M. Springer (2021) Decoupling transcription factor expression and activity enables dimmer-switch gene regulation Science

Abbo, A. R., Miller, A., Gazit, T., Savir, Y. & Caspi, O. Technological Developments and strategic management for overcoming the COVID-19 challenge within the hospital setting in Israel. Rambam Maimonides Med. J. 11, (2020).

Lior Strinkovsky, Evgeny Havkin, Ruby Shalom-Feuerstein, Yonatan Savir (2020) The role of replication-removal spatial correlations and cellular replicative lifespan in corneal epithelium homeostasis elife

Stroberg, H. Aktin, Y. Savir, S. Schnell (2018) How to design an optimal sensor network for the unfolded protein response Molecular Biology of the Cell 

Avesar, J, Y Blinder, H Aktin, A Szklanny, D Rosenfeld, Y Savir, M Bercovici, and S Levenberg. (2018)  Nanoliter Cell Culture Array with Tunable Chemical Gradients  Analytical chemistry

Savir, Y.* ,Martyno.A.Springer, M*. (2017), Achieving global perfect homeostasis through transporter regulation. PLoS Computational Biology 13(4): p. e1005458.
(* Corresponding authors)

Savir, Y.* and T. Tlusty. (2016), Comment on "Ribosome utilizes the minimum free energy changes to achieve the highest decoding rate and fidelity''. Physical Review E,
(* Corresponding author)

DeGennaro, CM,*, Savir, Y.*, Springer, M. (2016) Identifying Metabolic Subpopulations from Population Level Mass Spectrometry PLoS One
(* Equal contribution)

Savir, Y, Kagan, J., Tlusty, T. (2015) Binding of transcription factors adapts to resolve information-energy trade-off   Journal of Statistical Physics,

Savir, Y., Tu, BP, Springer, M. (2015) Competitive Inhibition Can Linearize Dose-Response and Generate a Linear Rectifier. Cell Systems
(see also Preview and Editorial)

Escalante R.*, Savir, Y.*, Carroll, SM., Ingraham, J.B., Wang, J., Marx, C.J., and Springer, M. (2015) Galactose metabolic genes in yeast respond to a ratio of galactose and glucose. Proc Natl Acad Sci U S A 112(5): 1636-1641.
(* Equal contribution, in alphabetical order)

Wang, J., Atolia, E., Hua, B., Savir, Y,. Escalante R.Springer, M. (2015) Cost-Benefit Tradeoff Underlies Natural Variation in Preparation for Nutrient Depletion ,PLoS Biology 13(1): e1002041.

Savir, Y., and Tlusty, T. (2013). The ribosome as an optimal decoder: a lesson in molecular recognition. Cell 153, 471-479.

Savir, Y*., Waysbrot, N*,. Antebi, YE., Tlusty, T. Friedman, N. (2012). Balancing speed and accuracy of polyclonal T cell activation: a role for extracellular feedback. BMC Syst Biol, 27;(6)
(* Equal contribution, in alphabetical order)

Bar-Even, A., Noor, E., Savir, Y., Liebermeister, W., Davidi, D., Tawfik, D. S., & Milo, R. (2011). The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry, 50(21)

Savir, Y. and Tlusty, T. (2010). RecA-Mediated Homology Search as a Nearly Optimal Signal Detection System, Mol. cell, 40(3), 388-396.
(see also cover image and preview in Mol. Cell)

Savir, Y., Noor, E., Milo, R., and Tlusty, T. (2010). Cross-species analysis traces adaptation of Rubisco toward optimality in a low-dimensional landscape. Proc Natl Acad Sci U S A 107, 3475-3480.

Savir, Y. and Tlusty, T. (2009). Molecular Recognition as an Information Channel: The Role of Conformational Changes, CISS 2009, 835-840.

Savir, Y. and Tlusty, T. (2008). Optimal Design of a Molecular Recognizer : Molecular Recognition as a Bayesian Signal Detection Problem. IEEE J Sel Topics Signal Process 2, 390-399.

Savir, Y. and Tlusty, T. (2007). Conformational proofreading: the impact of conformational changes on the specificity of molecular recognition. PLoS ONE 2, e468.
(see also review in Nature)