לוח שנה משולב

Whether we are speaking, swimming, or playing the piano, we are crucially dependent on our brain?s capacity to step through sequences of neural states. Songbirds provide a marvelous animal model in which to study this phenomenon. Their stereotyped vocalizations have hierarchical temporal structure spanning two orders or magnitude in timescale ? from individual vocal gestures lasting ten milliseconds, to song syllables (~100 msec), to song motifs (~1 sec). Several brain areas have been proposed to control timing at these different timescales. By manipulating these circuits with temperature change and observing the effect on song structure, we have been able to localize a single ?clock? circuit in the premotor vocal pathway. Intracellular neuronal recordings during singing elucidate the mechanism by which this clock circuit operates. Our findings are consistent with the predictions of a synfire-chain model? a synaptically connected chain of neurons in HVC. Our findings are inconsistent with models in which subthreshold dynamics, such as ramps or oscillations, play a role in the control of timing.

Despite being a very useful and versatile tool, Nuclear Magnetic Resonance (NMR) has a major disadvantage in the form of its inherently low signal to noise ratio (SNR) compared to other methods of spectroscopy. One approach to enhance the SNR is by increasing the initial nuclear polarization. This can be done by, among other ways, dynamic nuclear polarization (DNP), the topic of my Masters research project. In DNP the large electron polarization is transferred to nuclei by the use of MW irradiation, thereby increasing the NMR signal. In this lecture I will show experimental results from our hybrid pulsed Electron Paramagnetic Resonance and NMR DNP spectrometer, from samples of DMSO/Water and a derivative of the stable radical 2,2,6,6-Tetramethylpiperidine-1-oxyl (TEMPO). I interpret the data using a quantum mechanical theoretical model that gives good agreement with the experimental results, and through other quantum mechanical simulations of DNP systems.

The ability to anticipate and prepare in advance to changes in the environment is ascribed to neuronal systems in multi-cellular organisms. Yet by means of gene expression regulatory connectivity microbes too may have evolved to "anticipate" and prepare in advance. I will present evidence for microbial Pavlovian-like conditioning and discuss the similarities and differences to conditioning in the neuronal-cognitive context.

A paradox stems from the discrepancy between the large number of protein-protein interactions (PPIs) characterized by large-scale experiments, and the comparatively smaller number of PPIs that the scientific community can make biological sense of. This paradox fuels debates around a fundamental question: what do all these interactions mean? We argue that a large number of physical PPIs may simply be nonfunctional, or promiscuous. That is, they do exist in cells, they can be detected by typical PPI assays, but they have not evolved to achieve a particular function. We will examine this question from several different perspectives.


In a first, theoretical and computational part, (i) we will see two formalisms that anticipate the existence of promiscuous interactions (Levy, JMB, 2010; Levy, Landry, Michnick, Science Signaling, 2009); (ii) furthermore, by projecting amino-acid evolutionary conservation onto protein structures, we will see that mutations likely to induce promiscuous interactions with other proteins are selected against; (iii) finally, because non-functional interactions should not be conserved across organisms, we will assess the evolutionary conservation of a particular type of PPI: kinase-substrate interactions. This will reveal that the fraction of functional interactions could in fact be smaller than the fraction of promiscuous ones (Landry, Levy, Michnick, TiGs, 2009).


In a second, experimental part, we will see (i) how we can use promiscuous interactions to our advantage to create an in-vivo PPI microarray in yeast; and (ii) how we can filter promiscuous PPIs using information on their dynamics across different growth conditions