Dr. Nachman Iftach

Affiliation:Biochemistry, The George S.Wise faculty of life sciences
Sherman building
room 506
Tel:  (972)-3-6405900
Email: iftachn@tau.ac.il
Personal Website:

Postal Address:Biochemistry
The George S.Wise faculty of life sciences

Tel Aviv University
Tel Aviv 69978

Research Interest

Our goal is to understand how cells within a population reach developmental decisions at the phenotypic and mechanistic level. How do cells “decide” to change their state? Why do similar cells respond differently to the same signal? What properties of the cell affect its decision? Our lab will study these fundamental questions in two model systems using methods from live cell fluorescent imaging, microfluidics, statistical and computational analysis.

Propagation of information through signaling and transcriptional pathways
Cell populations in the nature face different cues from the environment that change in different frequencies. For example, a yeast colony in the vineyard senses different levels of heat, humidity, osmolarity and nutrient levels changing at different rates. Effective response to these fluctuating cues raises several challenges. Can the cells distinguish between a fleeting cue and a consistent change? Can they filter out the former to avoid mistaken decisions? How do their signaling and transcriptional networks handle these complex fluctuations? We study responses to signal fluctuations in the yeast meiosis process using live cell microscopy and custom-designed microfluidic devices capable of generating spatial and temporal signal gradients.

Differentiation dynamics in size-controlled embryoid bodies
In-vitro differentiation of embryonic stem cells into defined cell types is a field of immense importance for both regenerative medicine and for basic understanding of development. Embryoid bodies (EB's), three dimensional aggregates of differentiating embryonic stem cells, have been the method of choice for in-vitro differentiation into many cell types, including motor neurons, hepatocytes and cardiomyocytes. We are developing a microfluidic-based system for controlled EB formation, for generation and imaging of both uniform and variable size and shape EB’s. We will study correlates of specific cell fate differentiation and cell movement patterns in EB’s by imaging large numbers of such systems, in conjunction with protein reporter and lineage tracing fluorescent constructs. The project will enhance our basic understanding of cell movement and rules of differentiation during development, and can lead to improved protocols of in-vitro differentiation.

Signal perturbations with spatial or temporal gradients in microfuidic devices
a. A microfluidic flow cell capable of generating a stable concentration gradient, shown in the insets on the right. By combining this device with time varying inputs, we create a signal pulse with a spatial gradient. b. Using the setup above, we measured the meiosis decision time as a function of the spike glucose level. The results show an increase in decision time and variability which depend on the fluctuation strength over a limited range. c. By gradually changing the flow pressures in the two inputs of a Y-shaped flow channel, we obtain different spike durations along the width of the channel. Multiple Y channels allow measurement of different conditions in parallel.

Selected Publications

  • ZD. Smith*, I. Nachman*, A. Regev, A. Meissner (2010). Dynamic single-cell imaging of direct reprogramming reveals an early specifying event. Nature Biotechnol. 2010 May; 28 (5): 521-6.
  • I. Nachman, A. Regev (2009). BRNI: Integrated modular analysis of transcriptional regulatory programs. BMC Bioinformatics, 10: 155.
  • I. Nachman, A. Regev, S. Ramanathan (2007). Dissecting Timing Variability in Yeast Meiosis. Cell 131 (3), 544-556.
  • I. Nachman, A. Regev, N. Friedman (2004). Inferring Quantitative Models of Regulatory Networks from Expression Data. Bioinformatics 20: I248–I256.
  • I. Nachman †, G. Elidan †, N. Friedman (2004). “Ideal Parent” Structure Learning for Continuous Variables Networks. Proc. 20th Conference on Uncertainty in Artificial Intelligence (UAI).
  • N. Friedman, I. Nachman (2000). Gaussian Process Networks. Proc. 16th Conf. on Uncertainty in Artificial Intelligence (UAI).
  • N. Friedman, M. Linial, I. Nachman, D. Pe’er (2000). Using Bayesian Networks to Analyze Expression Data. J. Computational Biology 7 (3, 4), 601-620.
  • A. Ben-Dor, L. Bruhn, N. Friedman, I. Nachman, M. Schummer, Z. Yakhini (2000). Tissue Classification with Gene Expression Profiles. J. Computational Biology 7 (3, 4), 559-583.
  • N. Friedman, I. Nachman, D. Pe’er (1999). Learning Bayesian Network Structure from Massive Datasets: The Sparse Candidate Algorithm. Proc. 15th Conf. on Uncertainty in Artificial Intelligence (UAI).