לוח שנה משולב

Throughout adult life, adult neuronal stem cells (NSCs) continuously generate neurons in discrete brain regions. I am interested in harnessing this natural regenerative process for repairing the diseased and aging brain. To effectively use this regenerative capacity in a clinical setting requires first an advanced understanding of NSCs, adult neurogenesis and neuronal regeneration during neurodegenerative diseases and aging. Study of these areas, however, is challenging, as it requires profiling rare continuous processes in the adult brain. To this end, I developed sNuc-Seq, a method for profiling RNA in complex tissues with single nuclei resolution by RNA-sequencing, and Div-Seq, for profiling RNA in individual dividing cells. I applied sNuc-Seq to study the adult hippocampus brain region, revealing new cell-type specific and spatial expression patterns. I then applied Div-Seq to track transcriptional dynamics of newborn neurons within the adult hippocampal neurogenic region and to identify and profile rare newborn GABAergic neurons in the adult spinal cord. I am currently developing follow-up technologies to sNuc-Seq and applying them to study the cross-talk between neurons, NSCs, glia and immune cells during neurodegenerative diseases and its role in inhibiting or promoting regeneration. I will continue to work towards advancing our ability to mitigate and even reverse neurodegenerative disease and age-related pathologies. Incorporating in my work techniques from molecular neuroscience, single cell genomics, genome engineering and computational biology.

Drawing upon materials developed for a course I teach at Berkeley, I will show how the atmosphere is changing, that humans are the cause, and that there are consequences. These consequences demand we consider every possible means to decarbonize the atmosphere. I am particularly keen on carbon capture and sequestration and will show how NMR studies of metal-organic frameworks help move us to the point where carbon capture in flue gas, and directly from the air, are feasible.

In the lab we integrate experimental ecology and agricultural practices with high-throughput genomics and bioinformatics to study the genetics of adaptation and domestication in crop plants and their wild relatives. Much of our work is focused on identifying the genetic changes that underlie the formation of new varieties, and more generally, the genotype-phenotype-environment interaction.

Solving the Problem of Anharmonic Densities of States*

Julius Jellinek

Chemical Sciences and Engineering Division
Argonne National Laboratory, Argonne, IL 60439, USA

Density of states (DOS) is a fundamental characteristic of systems that lies in the very foundation of statistical mechanics and all the theoretical constructs that derive from them (e.g., kinetic rate theories, phase diagrams, etc.). Knowledge of DOS is central for calculation of entropy, partition function, free energy, reaction rate constants, and other important characteristics. The accuracy of all these depends on the accuracy with which the DOS is defined. Even though virtually all real systems are anharmonic, the current practice in the computation of vibrational DOSs is largely based on the harmonic approximation. The reason is that despite major efforts over about eight decades a general and exact, yet practical in applications, solution to the problem of anharmonic DOSs stubbornly resisted resolution. The alternatives introduced are mostly limited to cases of weak anharmonicity and/or suffer from other shortcomings.

In a recent development, we formulated a general and exact solution to this long-standing problem, which is applicable to arbitrary degree of anharmonicity (i.e., any system) and that is practical and efficient in applications. The solution and its algorithmic implementations are developed within the frameworks of both classical and quantum mechanics. The quantum implementation involves generalization and significant enhancement in the efficiency of the celebrated Beyer-Swinehart counting scheme, which is the fastest to date algorithm used in the computation of the quantum harmonic DOSs. Our solution is based on simulating the actual dynamical behavior of systems on the time scale of interest, short or long, as defined by the experiment and/or the nature of the process or phenomenon at hand. As a consequence, the resulting anharmonic DOSs are fully dynamically informed and, in general, time-dependent. As such, they lay the foundation for formulation of new statistical mechanical frameworks that incorporate time and reproduce exactly the actual time-averaged dynamical behavior of systems on the temporal scale of interest irrespective of whether this behavior is statistical or not in the traditional sense.

Work has been initiated on extending this development to the general case of rotational(ro)-vibrational DOSs for systems with arbitrary degree of anharmonicity and arbitrarily strong ro-vibrational coupling.
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* This work was supported by the Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences, U.S. Department of Energy under Contract No. DE-AC02-06CH11357.

Within the last two decades it has become clear that cells having the same genetic information can behave very differently, due to inevitable stochastic fluctuations in gene expression, known as noise. How do cells in multicellular organisms achieve high precision in their developmental fate in the presence of noise, in order to reap the benefits of division of labor? We address this fundamental question from Systems Biology and Statistical Physics perspectives, with Anabaena cyanobacterial filaments as a model system, one of the earliest examples of multicellular organisms in nature. These filaments can form one-dimensional, nearly-regular patterns of cells of two types. The developmental program uses tightly regulated, non-linear processes that include activation, inhibition, and transport, in order to create spatial and temporal patterns of gene expression that we can follow in real time, at the level of individual cells. We study cellular decisions, properties of the genetic network behind pattern formation, and establish the spatial extent to which gene expression is correlated along filaments. Motivated by our experimental results, I will show that pattern formation in Anabaena can be described theoretically by a minimal, three-component model that exhibits a deterministic, diffusion-driven Turing instability. Furthermore, I will discuss how noise can enhance considerably the robustness of the developmental program, by promoting the formation of stochastic patterns in regions of parameter space for which deterministic patterns do not form, suggesting a novel mechanism for pattern formation in this and other systems.