42,594 research outputs found
Effects of cell cycle noise on excitable gene circuits
We assess the impact of cell cycle noise on gene circuit dynamics. For
bistable genetic switches and excitable circuits, we find that transitions
between metastable states most likely occur just after cell division and that
this concentration effect intensifies in the presence of transcriptional delay.
We explain this concentration effect with a 3-states stochastic model. For
genetic oscillators, we quantify the temporal correlations between daughter
cells induced by cell division. Temporal correlations must be captured properly
in order to accurately quantify noise sources within gene networks.Comment: 15 pages, 8 figure
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
GPU-powered Simulation Methodologies for Biological Systems
The study of biological systems witnessed a pervasive cross-fertilization
between experimental investigation and computational methods. This gave rise to
the development of new methodologies, able to tackle the complexity of
biological systems in a quantitative manner. Computer algorithms allow to
faithfully reproduce the dynamics of the corresponding biological system, and,
at the price of a large number of simulations, it is possible to extensively
investigate the system functioning across a wide spectrum of natural
conditions. To enable multiple analysis in parallel, using cheap, diffused and
highly efficient multi-core devices we developed GPU-powered simulation
algorithms for stochastic, deterministic and hybrid modeling approaches, so
that also users with no knowledge of GPUs hardware and programming can easily
access the computing power of graphics engines.Comment: In Proceedings Wivace 2013, arXiv:1309.712
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