53,531 research outputs found
A Multiscale Modeling Framework Based on P Systems
Cellular systems present a highly complex organization at
different scales including the molecular, cellular and colony levels. The
complexity at each one of these levels is tightly interrelated. Integrative
systems biology aims to obtain a deeper understanding of cellular systems
by focusing on the systemic and systematic integration of the different
levels of organization in cellular systems.
The different approaches in cellular modeling within systems biology
have been classified into mathematical and computational frameworks.
Specifically, the methodology to develop computational models has been
recently called executable biology since it produces executable algorithms
whose computations resemble the evolution of cellular systems.
In this work we present P systems as a multiscale modeling framework
within executable biology. P system models explicitly specify the
molecular, cellular and colony levels in cellular systems in a relevant and
understandable manner. Molecular species and their structure are represented
by objects or strings, compartmentalization is described using
membrane structures and finally cellular colonies and tissues are modeled
as a collection of interacting individual P systems.
The interactions between the components of cellular systems are described
using rewriting rules. These rules can in turn be grouped together
into modules to characterize specific cellular processes. One of our current
research lines focuses on the design of cell systems biology models
exhibiting a prefixed behavior through the automatic assembly of these
cellular modules. Our approach is equally applicable to synthetic as well
as systems biology.Kingdom's Engineering and Physical Sciences Research Council EP/ E017215/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/F01855X/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/D019613/
A Multiscale Modeling Framework Based on P Systems
Cellular systems present a highly complex organization at
different scales including the molecular, cellular and colony levels. The
complexity at each one of these levels is tightly interrelated. Integrative
systems biology aims to obtain a deeper understanding of cellular systems
by focusing on the systemic and systematic integration of the different
levels of organization in cellular systems.
The different approaches in cellular modeling within systems biology
have been classified into mathematical and computational frameworks.
Specifically, the methodology to develop computational models has been
recently called executable biology since it produces executable algorithms
whose computations resemble the evolution of cellular systems.
In this work we present P systems as a multiscale modeling framework
within executable biology. P system models explicitly specify the
molecular, cellular and colony levels in cellular systems in a relevant and
understandable manner. Molecular species and their structure are represented
by objects or strings, compartmentalization is described using
membrane structures and finally cellular colonies and tissues are modeled
as a collection of interacting individual P systems.
The interactions between the components of cellular systems are described
using rewriting rules. These rules can in turn be grouped together
into modules to characterize specific cellular processes. One of our current
research lines focuses on the design of cell systems biology models
exhibiting a prefixed behavior through the automatic assembly of these
cellular modules. Our approach is equally applicable to synthetic as well
as systems biology.Kingdom's Engineering and Physical Sciences Research Council EP/ E017215/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/F01855X/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/D019613/
Multi-level agent-based modeling - A literature survey
During last decade, multi-level agent-based modeling has received significant
and dramatically increasing interest. In this article we present a
comprehensive and structured review of literature on the subject. We present
the main theoretical contributions and application domains of this concept,
with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic
statistics updated. v7 Change of the name of the paper to reflect what it
became, many refs and text added, bibliographic statistics update
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution
framework to generate continuous (grid-free) spatio-temporal solutions from the
low-resolution inputs. While being computationally efficient, MeshfreeFlowNet
accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet
allows for: (i) the output to be sampled at all spatio-temporal resolutions,
(ii) a set of Partial Differential Equation (PDE) constraints to be imposed,
and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal
domains owing to its fully convolutional encoder. We empirically study the
performance of MeshfreeFlowNet on the task of super-resolution of turbulent
flows in the Rayleigh-Benard convection problem. Across a diverse set of
evaluation metrics, we show that MeshfreeFlowNet significantly outperforms
existing baselines. Furthermore, we provide a large scale implementation of
MeshfreeFlowNet and show that it efficiently scales across large clusters,
achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of
less than 4 minutes.Comment: Supplementary Video: https://youtu.be/mjqwPch9gDo. Accepted to SC2
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