Deterministic and stochastic descriptions of gene expression dynamics

A key goal of systems biology is the predictive mathematical description of gene regulatory circuits. Different approaches are used such as deterministic and stochastic models, models that describe cell growth and division explicitly or implicitly etc. Here we consider simple systems of unregulated (constitutive) gene expression and compare different mathematical descriptions systematically to obtain insight into the errors that are introduced by various common approximations such as describing cell growth and division by an effective protein degradation term. In particular, we show that the population average of protein content of a cell exhibits a subtle dependence on the dynamics of growth and division, the specific model for volume growth and the age structure of the population. Nevertheless, the error made by models with implicit cell growth and division is quite small. Furthermore, we compare various models that are partially stochastic to investigate the impact of different sources of (intrinsic) noise. This comparison indicates that different sources of noise (protein synthesis, partitioning in cell division) contribute comparable amounts of noise if protein synthesis is not or only weakly bursty. If protein synthesis is very bursty, the burstiness is the dominant noise source, independent of other details of the model. Finally, we discuss two sources of extrinsic noise: cell-to-cell variations in protein content due to cells being at different stages in the division cycles, which we show to be small (for the protein concentration and, surprisingly, also for the protein copy number per cell) and fluctuations in the growth rate, which can have a significant impact.Comment: 23 pages, 5 figures; Journal of Statistical physics (2012

A narrative analysis of career transition themes and outcomes using chaos theory as a guiding metaphor

In a rapidly changing world of work little research exists on mid-career transitions. We investigated these using the open-systems approach of chaos theory as a guiding metaphor and conducted interviews with seven mid-career individuals chosen for their experience of a significant mid-career transition. Four common themes were identified through narrative analysis, where ‘false starts’ to a career were a common experience prior to finding a career ‘fit’. Career transitions, precipitated by a trigger state and/or event such as a period of disillusionment, were an important part of this ‘finding a fit’ process. Overall, career success outcomes were shaped by a combination of chaos elements: chance, unplanned events, and non-linearity of resultant outcomes. We discuss implications for future research and for practice

Global Policy Barriers and Enablers to Exercise and Physical Activity in Kidney Care

Objective: Impairment in physical function and physical performance leads to decreased independence and health-related quality of life in people living with chronic kidney disease and end-stage kidney disease. Physical activity and exercise in kidney care are not priorities in policy development. We aimed to identify global policy-related enablers, barriers, and strategies to increase exercise participation and physical activity behavior for people living with kidney disease. Design and Methods: Guided by the Behavior Change Wheel theoretical framework, 50 global renal exercise experts developed policy barriers and enablers to exercise program implementation and physical activity promotion in kidney care. The consensus process consisted of developing themes from renal experts from North America, South America, Continental Europe, United Kingdom, Asia, and Oceania. Strategies to address enablers and barriers were identified by the group, and consensus was achieved. Results: We found that policies addressing funding, service provision, legislation, regulations, guidelines, the environment, communication, and marketing are required to support people with kidney disease to be physically active, participate in exercise, and improve health-related quality of life. We provide a global perspective and highlight Japanese, Canadian, and other regional examples where policies have been developed to increase renal physical activity and rehabilitation. We present recommendations targeting multiple stakeholders including nephrologists, nurses, allied health clinicians, organizations providing renal care and education, and renal program funders. Conclusions: We strongly recommend the nephrology community and people living with kidney disease take action to change policy now, rather than idly waiting for indisputable clinical trial evidence that increasing physical activity, strength, fitness, and function improves the lives of people living with kidney disease

On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)

Steady-state expression of self-regulated genes.

MOTIVATION: Regulatory gene networks contain generic modules such as feedback loops that are essential for the regulation of many biological functions. The study of the stochastic mechanisms of gene regulation is instrumental for the understanding of how cells maintain their expression at levels commensurate with their biological role, as well as to engineer gene expression switches of appropriate behavior. The lack of precise knowledge on the steady-state distribution of gene expression requires the use of Gillespie algorithms and Monte-Carlo approximations. METHODOLOGY: In this study, we provide new exact formulas and efficient numerical algorithms for computing/modeling the steady-state of a class of self-regulated genes, and we use it to model/compute the stochastic expression of a gene of interest in an engineered network introduced in mammalian cells. The behavior of the genetic network is then analyzed experimentally in living cells. RESULTS: Stochastic models often reveal counter-intuitive experimental behaviors, and we find that this genetic architecture displays a unimodal behavior in mammalian cells, which was unexpected given its known bimodal response in unicellular organisms. We provide a molecular rationale for this behavior, and we implement it in the mathematical picture to explain the experimental results obtained from this network