Integrated Calendar

Cell death an axonal elimination govern the peripheral nervous system development. These processes have long thought to be controlled by transcriptional programs. However, the nature of these programs remains elusive. Here I performed comprehensive transcriptome analysis of sensory neurons in response to trophic deprivation. Functional analysis of two upregulated transcriptional components, dual phosphatase Dusp16 and the pro-apoptotic protein Puma, revealed unexpectedly opposing roles in axonal elimination both in vitro and in vivo. Additional experiments uncovered both genetic and biochemical interactions between Puma and Dusp16. These results revealed a transcriptional program of regressive and progressive elements, whose balance controls developmental peripheral nervous system wiring through specific subcellular functions.

Understanding how social influence shapes biological processes is a central challenge in con-temporary science, essential for achieving progress in a variety of fields ranging from the or-ganization and evolution of coordinated collective action among cells, or animals, to the dy-namics of information exchange in human societies. Using an integrated experimental and theoretical approach I will address how, and why, animals exhibit highly-coordinated collective behavior. I will demonstrate new imaging technology that allows us to reconstruct (automatcally) the dynamic, time-varying networks that correspond to the visual cues employed by organisms when making movement decisions [1]. Sensory networks are shown to provide a much more accurate representation of how social influence propagates in groups, and their analysis allows us to identify, for any instant in time, the most socially-influential individuals within groups, and to predict the magnitude of complex behavioral cascades before they actually occur [2]. I will also investigate the coupling between spatial and information dynamics in groups and reveal that emergent problem solving is the predominant mechanism by which mobile groups sense, and respond to complex environmental gradients [3]. Evolutionary modeling demonstrates such ‘physical computation’ readily evolves within populations of selfish organisms, and allowing individuals to compute collectively the spatial distribution of rsources and to allocate themselves effectively among distinct, and distant, resource patches,
Without requiring information about the number, location or size of patches [4].
Finally I will reveal the critical role uninformed, or unbiased, individuals play in effecting fast and democratic consensus decision-making in collectives [5-7], and will test these predictions with experiments involving schooling fish [6] and wild baboons [8].

The adaptive immune system can recognize many different pathogens by maintaining a large diversity of cells with different membrane receptors. We study the complex stochastic processes that generate and shape this ensemble of immune receptors developing probabilistic models from statistical inference of high throughput sequencing data. Our technique based on transfer matrices learns the probabilistic properties of the generation process, and finds it to be amazingly universal across individuals. We then model also selection pressures on the generated cells, in terms of the composition of their receptors, again finding universality, and reduction in diversity. In general our methods allows us to characterize and study the diversity distribution of immune repertoires using available sample data. This can be invaluable as a baseline for future study of the system as well as clinical applications, but might also expand our knowledge on statistical properties of interacting ensembles.