Role of feedback and dynamics in a gene regulatory network

Cells exhibit a diverse repertoire of dynamic behaviors. These dynamic functions are implemented by circuits of interacting biomolecules. Although these regulatory networks function deterministically by executing specific programs in response to extracellular signals, molecular interactions are inherently governed by stochastic fluctuations. This molecular noise can manifest as cell-to-cell phenotypic heterogeneity in a well-mixed environment. Single-cell variability may seem like a design flaw but the coexistence of diverse phenotypes in an isogenic population of cells can also serve a biological function by increasing the probability of survival of individual cells upon an abrupt change in environmental conditions. Decades of extensive molecular and biochemical characterization have revealed the connectivity and mechanisms that constitute regulatory networks. We are now confronted with the challenge of integrating this information to link the structure of these circuits to systems-level properties such as cellular decision making. To investigate cellular decision-making, we used the well studied galactose gene-regulatory network in \textit{Saccharomyces cerevisiae}. We analyzed the mechanism and dynamics of the coexistence of two stable on and off states for pathway activity. We demonstrate that this bimodality in the pathway activity originates from two positive feedback loops that trigger bistability in the network. By measuring the dynamics of single-cells in a mixed sugar environment, we observe that the bimodality in gene expression is a transient phenomenon. Our experiments indicate that early pathway activation in a cohort of cells prior to galactose metabolism can accelerate galactose consumption and provide a transient increase in growth rate. Together these results provide important insights into strategies implemented by cells that may have been evolutionary advantageous in competitive environments.

Chemical and mineralogical characterization of micro-inclusions in diamonds

Secondary-ion mass spectrometry (SIMS), electron probe analysis (EPMA), analytical scanning electron microscopy (SEM) and infrared (IR) spectroscopy were used to determine the chemical composition and the mineralogy of sub-micrometer inclusions in cubic diamonds and in overgrowths (coats) on octahedral diamonds from Zaire, Botswana, and some unknown localities.

The inclusions are sub-micrometer in size. The typical diameter encountered during transmission electron microscope (TEM) examination was 0.1-0.5 µm. The micro-inclusions are sub-rounded and their shape is crystallographically controlled by the diamond. Normally they are not associated with cracks or dislocations and appear to be well isolated within the diamond matrix. The number density of inclusions is highly variable on any scale and may reach 10^(11) inclusions/cm^3 in the most densely populated zones. The total concentration of metal oxides in the diamonds varies between 20 and 1270 ppm (by weight).

SIMS analysis yields the average composition of about 100 inclusions contained in the sputtered volume. Comparison of analyses of different volumes of an individual diamond show roughly uniform composition (typically ±10% relative). The variation among the average compositions of different diamonds is somewhat greater (typically ±30%). Nevertheless, all diamonds exhibit similar characteristics, being rich in water, carbonate, SiO_2, and K_2O, and depleted in MgO. The composition of micro-inclusions in most diamonds vary within the following ranges: SiO_2, 30-53%; K_2O, 12-30%; CaO, 8-19%; FeO, 6-11%; Al_2O_3, 3-6%; MgO, 2-6%; TiO_2, 2-4%; Na_2O, 1-5%; P_2O_5, 1-4%; and Cl, 1-3%. In addition, BaO, 1-4%; SrO, 0.7-1.5%; La_2O_3, 0.1-0.3%; Ce_2O_3, 0.3-0.5%; smaller amounts of other rare-earth elements (REE), as well as Mn, Th, and U were also detected by instrumental neutron activation analysis (INAA). Mg/(Fe+Mg), 0.40-0.62 is low compared with other mantle derived phases; K/ AI ratios of 2-7 are very high, and the chondrite-normalized Ce/Eu ratios of 10-21 are also high, indicating extremely fractionated REE patterns.

SEM analyses indicate that individual inclusions within a single diamond are roughly of similar composition. The average composition of individual inclusions as measured with the SEM is similar to that measured by SIMS. Compositional variations revealed by the SEM are larger than those detected by SIMS and indicate a small variability in the composition of individual inclusions. No compositions of individual inclusions were determined that might correspond to mono-mineralic inclusions.

IR spectra of inclusion- bearing zones exhibit characteristic absorption due to: (1) pure diamonds, (2) nitrogen and hydrogen in the diamond matrix; and (3) mineral phases in the micro-inclusions. Nitrogen concentrations of 500-1100 ppm, typical of the micro-inclusion-bearing zones, are higher than the average nitrogen content of diamonds. Only type IaA centers were detected by IR. A yellow coloration may indicate small concentration of type IB centers.

The absorption due to the micro-inclusions in all diamonds produces similar spectra and indicates the presence of hydrated sheet silicates (most likely, Fe-rich clay minerals), carbonates (most likely calcite), and apatite. Small quantities of molecular CO_2 are also present in most diamonds. Water is probably associated with the silicates but the possibility of its presence as a fluid phase cannot be excluded. Characteristic lines of olivine, pyroxene and garnet were not detected and these phases cannot be significant components of the inclusions. Preliminary quantification of the IR data suggests that water and carbonate account for, on average, 20-40 wt% of the micro-inclusions.

The composition and mineralogy of the micro-inclusions are completely different from those of the more common, larger inclusions of the peridotitic or eclogitic assemblages. Their bulk composition resembles that of potassic magmas, such as kimberlites and lamproites, but is enriched in H_2O, CO_3, K_2O, and incompatible elements, and depleted in MgO.

It is suggested that the composition of the micro-inclusions represents a volatile-rich fluid or a melt trapped by the diamond during its growth. The high content of K, Na, P, and incompatible elements suggests that the trapped material found in the micro-inclusions may represent an effective metasomatizing agent. It may also be possible that fluids of similar composition are responsible for the extreme enrichment of incompatible elements documented in garnet and pyroxene inclusions in diamonds.

The origin of the fluid trapped in the micro-inclusions is still uncertain. It may have been formed by incipient melting of a highly metasomatized mantle rocks. More likely, it is the result of fractional crystallization of a potassic parental magma at depth. In either case, the micro-inclusions document the presence of highly potassic fluids or melts at depths corresponding to the diamond stability field in the upper mantle. The phases presently identified in the inclusions are believed to be the result of closed system reactions at lower pressures.

New Insights from Aperiodic Variability of Young Stars

Nearly all young stars are variable, with the variability traditionally divided into two classes: periodic variables and aperiodic or "irregular" variables. Periodic variables have been studied extensively, typically using periodograms, while aperiodic variables have received much less attention due to a lack of standard statistical tools. However, aperiodic variability can serve as a powerful probe of young star accretion physics and inner circumstellar disk structure. For my dissertation, I analyzed data from a large-scale, long-term survey of the nearby North America Nebula complex, using Palomar Transient Factory photometric time series collected on a nightly or every few night cadence over several years. This survey is the most thorough exploration of variability in a sample of thousands of young stars over time baselines of days to years, revealing a rich array of lightcurve shapes, amplitudes, and timescales.

I have constrained the timescale distribution of all young variables, periodic and aperiodic, on timescales from less than a day to ~100 days. I have shown that the distribution of timescales for aperiodic variables peaks at a few days, with relatively few (~15%) sources dominated by variability on tens of days or longer. My constraints on aperiodic timescale distributions are based on two new tools, magnitude- vs. time-difference (Δm-Δt) plots and peak-finding plots, for describing aperiodic lightcurves; this thesis provides simulations of their performance and presents recommendations on how to apply them to aperiodic signals in other time series data sets. In addition, I have measured the error introduced into colors or SEDs from combining photometry of variable sources taken at different epochs. These are the first quantitative results to be presented on the distributions in amplitude and time scale for young aperiodic variables, particularly those varying on timescales of weeks to months.