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Publications

Building Blastocysts from Stem Cells. Preview

Pour M, Nachman I. (2019) 
Stem Cell Reports. 2019 Sep 10;13(3):437-439. doi: 10.1016/j.stemcr.2019.08.009.

Emergence and patterning dynamics of mouse definitive endoderm‏.

Pour M, Kumar AS, Farag N, Bolondi A, Kretzmer H, Walther M, Wittler L, Meissner A, Nachman I (2022)

iScience 25 (1), 103556

Differential regulation of OCT4 targets facilitates reacquisition of pluripotency.

Thakurela, S.†, Sindhu, C.†,Yurkovsky, E.†, Smith, Z.D.†, Riemenschneider, Nachman, I.,  Meissner, A. (2019)
Nature Communications, 2019 Sep 30;10(1):4444

Remodeling Membrane Binding by Mono-Ubiquitylation.

Tanner N, Kleifeld O, Nachman I, Prag G. (2019)
Biomolecules. 2019 Jul 31;9(8). pii: E325. doi: 10.3390/biom9080325.

Tungsten Disulfide-Based Nanocomposites for Photothermal Therapy

Levin, T., Sade, H., Ben-Shabat Binyamini, R., Pour, M., Nachman, I., Lellouche, JP. (2019)

Beilstein J. Nanotechnol. 2019, 10, 811-822

Evolthon: a community endeavor to evolve lab evolution. 

Kaminski Strauss, S.,  et. al.(2019)

PLoS Biology. 29;17(3):e3000182. doi: 10.1371/journal.pbio.3000182.

Prediction and control of symmetry breaking in embryoid bodies by environment and signal integration.

Sagy, N., Slovin, S., Allalouf, M., Pour, M., Savyon, G., Boxman, J., Nachman, I. (2019)

Development 2019 146: dev181917

Bifunctional Carbon-Dot-WS2 Nanorods for Photothermal Therapy and Cell Imaging.

Nandi S, Bhunia S, Zeiri L, Pour M, Nachman I, Reichmann D, Lellouche JP, Jelinek R. (2017)

Chemistry. 2017 Jan 18;23(4):963-969. doi: 10.1002/chem.201604787.

Integrated live imaging and molecular profiling of embryoid bodies reveals a synchronized progression of early differentiation.

Boxman, J.†, Sagy, N.†, Achanta, S., Vadigepalli, R., Nachman, I. (2016)

Scientific Reports 6, Article number: 31623 (2016)  doi:10.1038/srep31623

Water-Transfer Slows Aging in Saccharomyces cerevisiae.

Cohen, A., Weindling, E., Rabinovich, E., Nachman, I., Fuchs, S., Chuartzman, S., Gal, L., Schuldiner, M., Bar-Nun, S. (2016)

PLoS One 10;11(2), e0148650.

Control of relative timing and stoichiometry by a master regulator 

Y. Goldshmidt†, E. Yurkovsky†, A. Reif, R. Rosner, A. Akiva, Nachman I. (2015)

PLoS One 10(5), 2015 e0127339.  

      Epigenetic predisposition to reprogramming fates in somatic cells. 

M. Pour†, I. Pilzer†, R. Rosner, ZD. Smith, A. Meissner, I. Nachman (2015)  

EMBO reports (2015) 16, 370-378.

Event timing at the single cell level.

E. Yurkovsky, I. Nachman (2013).

Brief Funct Genomics.  2013 Mar;12(2):90-8.

Expression of Pseudomonas syringae type III effectors in yeast under stress conditions reveals that HopX1 attenuates activation of the high osmolarity glycerol MAP kinase pathway.

D. Salomon†, E. Bosis†, D. Dar, I. Nachman, G. Sessa (2012)

Microbiology 158(11), pp. 2859-69.

A microfluidic device for studying multiple distinct strains.

G. Aidelberg†, Y. Goldschmidt†, I. Nachman (2012).

​J. Vis. Exp. e4257 10.3791/4257, DOI : 10.3791/4257.​

Aggregation of PolyQ Proteins Is Increased upon Yeast Aging and Affected by Sir2 and Hsf1: Novel Quantitative Biochemical and Microscopic Assays.

A. Cohen, L. Ross, I. Nachman, S. Bar-Nun (2012).

PLoS One 7(9): e44785. doi:10.1371/journal.pone.0044785

Dynamic single cell imaging of direct reprogramming reveals an early specifying event. 

Z. D. Smith†, I. Nachman†, A. Regev, A. Meissner (2010)

 Nature Biotechnology 28(5), 521 – 526

BRNI: Modular Analysis of Transcriptional Regulatory Programs. 

I. Nachman, A, Regev (2009)

BMC Bioinformatics 2009, 10:155.

HIV-1 Positive Feedback and Lytic Fate.

I. Nachman, S. Ramanathan (2008).

Nature Genetics 2008 Apr;40(4):382-3. [News & Views].

Dissecting Timing Variability in Yeast Meiosis. 

I. Nachman, A, Regev, S. Ramanathan (2007).

Cell 131(3), 544 – 556.

 “Ideal Parent” Structure Learning for Continuous Variables Bayesian Networks. 

G. Elidan, I. Nachman, N. Friedman (2007).

 JMLR 8, 1799–1833.

 Inferring Quantitative Models of Regulatory Networks From Expression Data. Proc. 12’th International Conference on Intelligent Systems in Molecular Biology (ISMB04);

I. Nachman, A, Regev, N. Friedman (2004).

Bioinformatics 20:I248–I256.

“Ideal Parent” Structure Learning for Continuous Variables Networks. Proc.

I. Nachman†, G. Elidan†, N. Friedman (2004).

20’th Conference on Uncertainty in Artificial Intelligence (UAI04).

Gaussian Process Networks. Proc.

N. Friedman , I. Nachman (2000).

16’th Conf. on Uncertainty in Artificial Intel ligence (UAI00).

Using Bayesian Networks to Analyze Expression Data. 

N. Friedman , M. Linial, I. Nachman, D. Pe’er (2000).

 J. Computational Biology 7 (3,4), 601 – 620.

Tissue Classification with Gene Expression Profiles.

A. Ben-Dor, L. Bruhn, N. Friedman , I. Nachman, M. Schummer, Z. Yakhini (2000)

 J. Computational Biology 7 (3,4), 559 – 583.

Using Bayesian Networks to Analyze Expression Data. Proc.

N. Friedman , M. Linial, I. Nachman, D. Pe’er (2000).

 The 4’th Annual International Conference on Computational Molecular Biology (RECOMB00).

Tissue Classification with Gene Expression Profiles. Proc.

A. Ben-Dor, L. Bruhn, N. Friedman , I. Nachman, M. Schummer, Z. Yakhini (2000).

The 4’th Annual International Conference on Computational Molecular Biology (RECOMB00).

Learning Bayesian Network Structure from Massive Datasets: The Sparse Candidate Algorithm. Proc.

N. Friedman , I. Nachman, D. Pe’er (1999).

15’th Conf. on Uncertaintys in Artificial Intel ligence (UAI99).

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