8 research outputs found
The Nondeterministic Waiting Time Algorithm: A Review
We present briefly the Nondeterministic Waiting Time algorithm. Our technique
for the simulation of biochemical reaction networks has the ability to mimic
the Gillespie Algorithm for some networks and solutions to ordinary
differential equations for other networks, depending on the rules of the
system, the kinetic rates and numbers of molecules. We provide a full
description of the algorithm as well as specifics on its implementation. Some
results for two well-known models are reported. We have used the algorithm to
explore Fas-mediated apoptosis models in cancerous and HIV-1 infected T cells
Approximation and inference methods for stochastic biochemical kinetics - a tutorial review
Stochastic fluctuations of molecule numbers are ubiquitous in biological
systems. Important examples include gene expression and enzymatic processes in
living cells. Such systems are typically modelled as chemical reaction networks
whose dynamics are governed by the Chemical Master Equation. Despite its simple
structure, no analytic solutions to the Chemical Master Equation are known for
most systems. Moreover, stochastic simulations are computationally expensive,
making systematic analysis and statistical inference a challenging task.
Consequently, significant effort has been spent in recent decades on the
development of efficient approximation and inference methods. This article
gives an introduction to basic modelling concepts as well as an overview of
state of the art methods. First, we motivate and introduce deterministic and
stochastic methods for modelling chemical networks, and give an overview of
simulation and exact solution methods. Next, we discuss several approximation
methods, including the chemical Langevin equation, the system size expansion,
moment closure approximations, time-scale separation approximations and hybrid
methods. We discuss their various properties and review recent advances and
remaining challenges for these methods. We present a comparison of several of
these methods by means of a numerical case study and highlight some of their
respective advantages and disadvantages. Finally, we discuss the problem of
inference from experimental data in the Bayesian framework and review recent
methods developed the literature. In summary, this review gives a
self-contained introduction to modelling, approximations and inference methods
for stochastic chemical kinetics.Comment: 73 pages, 12 figures in J. Phys. A: Math. Theor. (2016
Synthetic transcription amplifier system for orthogonal control of gene expression in saccharomyces cerevisiae
This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications
Plant health and global change - some implications for landscape management
Global change (climate change together with other worldwide anthropogenic processes such as increasing trade, air pollution and urbanization) will affect plant health at the genetic, individual, population and landscape level. Direct effects include ecosystem stress due to natural resources shortage or imbalance. Indirect effects include (i) an increased frequency of natural detrimental phenomena, (ii) an increased pressure due to already present pests and diseases, (iii) the introduction of new invasive species either as a result of an improved suitability of the climatic conditions or as a result of increased trade, and (iv) the human response to global change. In this review, we provide an overview of recent studies on terrestrial plant health in the presence of global change factors. We summarize the links between climate change and some key issues in plant health, including tree mortality, changes in wildfire regimes, biological invasions and the role of genetic diversity for ecosystem resilience. Prediction and management of global change effects are complicated by interactions between globalization, climate and invasive plants and/or pathogens. We summarize practical guidelines for landscape management and draw general conclusions from an expanding body of literature