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The DNA replication-division cycle of eukaryotic cells is controlled by a complex network of regulatory proteins, called cyclin-dependent kinases, and their activators and inhibitors. Although comprehensive and accurate deterministic models of the control system are available for yeast cells, reliable stochastic simulations have not been carried out because the full reaction network has yet to be expressed in terms of elementary reaction steps. As a first step in this direction, we present a simplified version of the control system that is suitable for exact stochastic simulation of intrinsic noise caused by molecular fluctuations and extrinsic noise because of unequal division. The model is consistent with many characteristic features of noisy cell cycle progression in yeast populations, including the observation that mRNAs are present in very low abundance ([almost equal to]1 mRNA molecule per cell for each expressed gene). For the control system to operate reliably at such low mRNA levels, some specific mRNAs in our model must have very short half-lives (<1 min). If these mRNA molecules are longer-lived (perhaps 2 min), then the intrinsic noise in our simulations is too large, and there must be some additional noise suppression mechanisms at work in cells.

\"The official CAIA Level 1 curriculum book, updated and expanded to reflect the March 2016 exam CAIA Level I is the curriculum book for the Chartered Alternative Investment Analyst (CAIA) Level 1 professional examination. Covering the fundamentals of the alternative investment space, this book helps you build a foundation in alternative investment markets. You'll look closely at the different types of hedge fund strategies and the range of statistics used to define investment performance as you gain a deep familiarity with alternative investment terms and develop the computational ability to solve investment problems. From strategy characteristics to portfolio management strategies, this book contains the core material you will need to succeed on the CAIA Level I exam. This updated third edition tracks to the latest version of the exam, and is accompanied by the following ancillaries: a workbook, study guide, learning objectives, and an ethics handbook. Most investment analyst education programs focus primarily on the traditional asset classes, pushing alternative investments to the sidelines. The CAIA designation was developed in response to the tremendous growth of alternative investing, and is the industry's premier educational standard. This book is your official study companion, bringing you fully up to speed on everything you need to know (with the exception of the ethics material covered in a separate handbook). Understand the complexities of each alternative asset class Learn the quantitative techniques professionals use every day Dig into the unique aspects of alternative investments Master the core material covered by the CAIA Level I exam More than 300 financial institutions and hedge funds have committed key executives to the CAIA exam, and this rapidly growing trend speaks to the designation's rising status as a must-have credential for anyone in the alternative investment sphere. Increase your chances of success by getting your information straight from the source in CAIA Level I\"-- Provided by publisher.

In order for the cell's genome to be passed intact from one generation to the next, the events of the cell cycle (DNA replication, mitosis, cell division) must be executed in the correct order, despite the considerable molecular noise inherent in any protein‐based regulatory system residing in the small confines of a eukaryotic cell. To assess the effects of molecular fluctuations on cell‐cycle progression in budding yeast cells, we have constructed a new model of the regulation of Cln‐ and Clb‐dependent kinases, based on multisite phosphorylation of their target proteins and on positive and negative feedback loops involving the kinases themselves. To account for the significant role of noise in the transcription and translation steps of gene expression, the model includes mRNAs as well as proteins. The model equations are simulated deterministically and stochastically to reveal the bistable switching behavior on which proper cell‐cycle progression depends and to show that this behavior is robust to the level of molecular noise expected in yeast‐sized cells (∼50 fL volume). The model gives a quantitatively accurate account of the variability observed in the G1‐S transition in budding yeast, which is governed by an underlying sizer+timer control system. Synopsis Progression through the eukaryotic cell cycle is governed by the activation and inactivation of a family of cyclin‐dependent kinases (CDKs) and auxiliary proteins that regulate CDK activities (Morgan, 2007 ). The many components of this protein regulatory network are interconnected by positive and negative feedback loops that create bistable switches and transient pulses (Tyson and Novak, 2008 ). The network must ensure that cell‐cycle events proceed in the correct order, that cell division is balanced with respect to cell growth, and that any problems encountered (in replicating the genome or partitioning chromosomes to daughter cells) are corrected before the cell proceeds to the next phase of the cycle. The network must operate robustly in the context of unavoidable molecular fluctuations in a yeast‐sized cell. With a volume of only 5×10 −14 l, a yeast cell contains one copy of the gene for each component of the network, a handful of mRNA transcripts of each gene, and a few hundreds to thousands of protein molecules carrying out each gene's function. How large are the molecular fluctuations implied by these numbers, and what effects do they have on the functioning of the cell‐cycle control system? To answer these questions, we have built a new model (Figure 1 ) of the CDK regulatory network in budding yeast, based on the fact that the targets of CDK activity are typically phosphorylated on multiple sites. The activity of each target protein depends on how many sites are phosphorylated. The target proteins feedback on CDK activity by controlling cyclin synthesis (SBF's role) and degradation (Cdh1's role) and by releasing a CDK‐counteracting phosphatase (Cdc14). Every reaction in Figure 1 can be described by a mass‐action rate law, with an accompanying rate constant that must be estimated from experimental data. As the transcription and translation of mRNA molecules have major effects on fluctuating numbers of protein molecules (Pedraza and Paulsson, 2008 ), we have included mRNA transcripts for each protein in the model. To create a deterministic model, the rate laws are combined, according to standard principles of chemical kinetics, into a set of 60 differential equations that govern the temporal dynamics of the control system. In the stochastic version of the model, the rate law for each reaction determines the probability per unit time that a particular reaction occurs, and we use Gillespie's stochastic simulation algorithm (Gillespie, 1976 ) to compute possible temporal sequences of reaction events. Accurate stochastic simulations require knowledge of the expected numbers of mRNA and protein molecules in a single yeast cell. Fortunately, these numbers are available from several sources (Ghaemmaghami et al , 2003 ; Zenklusen et al , 2008 ). Although the experimental estimates are not always in good agreement with each other, they are sufficiently reliable to populate a stochastic model with realistic numbers of molecules. By simulating thousands of cells (as in Figure 5 ), we can build up representative samples for computing the mean and s.d. of any measurable cell‐cycle property (e.g. interdivision time, size at division, duration of G1 phase). The excellent fit of simulated statistics to observations of cell‐cycle variability is documented in the main text and Supplementary Information . Of particular interest to us are observations of Di Talia et al (2007) of the timing of a crucial G1 event (export of Whi5 protein from the nucleus) in a population of budding yeast cells growing at a specific growth rate α=ln2/(mass‐doubling time). Whi5 export is a consequence of Whi5 phosphorylation, and it occurs simultaneously with the release (activation) of SBF (see Figure 1 ). Using fluorescently labeled Whi5, Di Talia et al could easily measure (in individual yeast cells) the time, T 1 , from cell birth to the abrupt loss of Whi5 from the nucleus. Correlating T 1 to the size of the cell at birth, V birth , they found that, for a sample of daughter cells, α T 1 versus ln( V birth ) could be fit with two straight lines of slope −0.7 and −0.3. Our simulation of this experiment (Figure 7 of the main text) compares favorably with Figure 3d and e in Di Talia et al (2007) . The major sources of noise in our model (and in protein regulatory networks in yeast cells, in general) are related to gene transcription and the small number of unique mRNA transcripts. As each mRNA molecule may instruct the synthesis of dozens of protein molecules, the coefficient of variation of molecular fluctuations at the protein level (CV P ) may be dominated by fluctuations at the mRNA level, as expressed in the formula (Pedraza and Paulsson, 2008 ) where N M , N P denote the number of mRNA and protein molecules, respectively, and ρ=τ M /τ P is the ratio of half‐lives of mRNA and protein molecules. For a yeast cell, typical values of N M and N P are 8 and 800, respectively (Ghaemmaghami et al , 2003 ; Zenklusen et al , 2008 ). If ρ=1, then CV P ≈25%. Such large fluctuations in protein levels are inconsistent with the observed variability of size and age at division in yeast cells, as shown in the simplified cell‐cycle model of Kar et al (2009) and as we have confirmed with our more realistic model. The size of these fluctuations can be reduced to a more acceptable level by assuming a shorter half‐life for mRNA (say, ρ=0.1). There must be some mechanisms whereby yeast cells lessen the protein fluctuations implied by transcription–translation coupling. Following Pedraza and Paulsson (2008) , we suggest that mRNA gestation and senescence may resolve this problem. Equation (3) is based on a simple, one‐stage, birth–death model of mRNA turnover. In Supplementary Appendix 1 , we show that a model of mRNA processing, with 10 stages each of mRNA gestation and senescence, gives reasonable fluctuations at the protein level (CV P ≈5%), even if the effective half‐life of mRNA is 10 min. A one‐stage model with τ M =1 min gives comparable fluctuations (CV P ≈5%). In the main text, we use a simple birth–death model of mRNA turnover with an ‘effective’ half‐life of 1 min, in order to limit the computational complexity of the full cell‐cycle model. Multisite phosphorylation of CDK target proteins provides the requisite nonlinearity for cell cycle modeling using elementary reaction mechanisms. Stochastic simulations, based on Gillespie's algorithm and using realistic numbers of protein and mRNA molecules, compare favorably with single‐cell measurements in budding yeast. The role of transcription–translation coupling is critical in the robust operation of protein regulatory networks in yeast cells.

Cell-fate asymmetry in the predivisional cell of Caulobacter crescentus requires that the regulatory protein DivL localizes to the new pole of the cell where it up-regulates CckA kinase, resulting in a gradient of CtrA~P across the cell. In the preceding stage of the cell cycle (the \"stalked\" cell), DivL is localized uniformly along the cell membrane and maintained in an inactive form by DivK~P. It is unclear how DivL overcomes inhibition by DivK~P in the predivisional cell simply by changing its location to the new pole. It has been suggested that co-localization of DivL with PleC phosphatase at the new pole is essential to DivL's activity there. However, there are contrasting views on whether the bifunctional enzyme, PleC, acts as a kinase or phosphatase at the new pole. To explore these ambiguities, we formulated a mathematical model of the spatiotemporal distributions of DivL, PleC and associated proteins (DivJ, DivK, CckA, and CtrA) during the asymmetric division cycle of a Caulobacter cell. By varying localization profiles of DivL and PleC in our model, we show how the physiologically observed spatial distributions of these proteins are essential for the transition from a stalked cell to a predivisional cell. Our simulations suggest that PleC is a kinase in predivisional cells, and that, by sequestering DivK~P, the kinase form of PleC enables DivL to be reactivated at the new pole. Hence, co-localization of PleC kinase and DivL is essential to establishing cellular asymmetry. Our simulations reproduce the experimentally observed spatial distribution and phosphorylation status of CtrA in wild-type and mutant cells. Based on the model, we explore novel combinations of mutant alleles, making predictions that can be tested experimentally.

Throughout history and across cultures, the spy chief has been an essential advisor for heads of state and the leader of the state security apparatus. In democracies, the spy chief has become a public figure, and intelligence activities have been largely brought under the rule of law. In authoritarian regimes, the spy chief was and remains a frightening and opaque figure who designs intrigue abroad and fosters repression at home. This second volume of Spy Chiefs provides a close-up look at intelligence leadership, good and bad. The contributors to the volumes try to answer the following questions: how do intelligence leaders operate in different national, institutional and historical contexts? What role have they played in the conduct of international relations? How much power do they possess? What qualities make an effective intelligence leader? How secretive and accountable to the public have they been? This book goes beyond the commonly studied spy chiefs of the United States and Britain to examine leaders from Renaissance Venice to twentieth century Russia, Germany, India, Egypt, and Lebanon.

We study how double-stranded DNA translocates through graphene nanogaps. Nanogaps are fabricated with a novel capillary-force induced graphene nanogap formation technique. DNA translocation signatures for nanogaps are qualitatively different from those obtained with circular nanopores, owing to the distinct shape of the gaps discussed here. Translocation time and conductance values vary by ∼ 100%, which we suggest are caused by local gap width variations. We also observe exponentially relaxing current traces. We suggest that slow relaxation of the graphene membrane following DNA translocation may be responsible. We conclude that DNA-graphene interactions are important, and need to be considered for graphene-nanogap based devices. This work further opens up new avenues for direct read of single molecule activitities, and possibly sequencing.

As the alien Xenovirus reaches Britain, Prime Minister Sir Winston Churchill, now gifted with extraordinary longevity, joins with Alan Turing to set up a special organization, the Order of the Silver Helix, to outmaneuver the terrifying mutations of the virus in Britain.

The free-living aquatic bacterium, Caulobacter crescentus, exhibits two different morphologies during its life cycle. The morphological change from swarmer cell to stalked cell is a result of changes of function of two bi-functional histidine kinases, PleC and CckA. Here, we describe a detailed molecular mechanism by which the function of PleC changes between phosphatase and kinase state. By mathematical modeling of our proposed molecular interactions, we derive conditions under which PleC, CckA and its response regulators exhibit bistable behavior, thus providing a scenario for robust switching between swarmer and stalked states. Our simulations are in reasonable agreement with in vitro and in vivo experimental observations of wild type and mutant phenotypes. According to our model, the kinase form of PleC is essential for the swarmer-to-stalked transition and to prevent premature development of the swarmer pole. Based on our results, we reconcile some published experimental observations and suggest novel mutants to test our predictions.