3,868 research outputs found
Sculplexity: Sculptures of Complexity using 3D printing
We show how to convert models of complex systems such as 2D cellular automata
into a 3D printed object. Our method takes into account the limitations
inherent to 3D printing processes and materials. Our approach automates the
greater part of this task, bypassing the use of CAD software and the need for
manual design. As a proof of concept, a physical object representing a modified
forest fire model was successfully printed. Automated conversion methods
similar to the ones developed here can be used to create objects for research,
for demonstration and teaching, for outreach, or simply for aesthetic pleasure.
As our outputs can be touched, they may be particularly useful for those with
visual disabilities.Comment: Free access to article on European Physics Letter
A Monte Carlo Approach to Measure the Robustness of Boolean Networks
Emergence of robustness in biological networks is a paramount feature of
evolving organisms, but a study of this property in vivo, for any level of
representation such as Genetic, Metabolic, or Neuronal Networks, is a very hard
challenge. In the case of Genetic Networks, mathematical models have been used
in this context to provide insights on their robustness, but even in relatively
simple formulations, such as Boolean Networks (BN), it might not be feasible to
compute some measures for large system sizes. We describe in this work a Monte
Carlo approach to calculate the size of the largest basin of attraction of a
BN, which is intrinsically associated with its robustness, that can be used
regardless the network size. We show the stability of our method through
finite-size analysis and validate it with a full search on small networks.Comment: on 1st International Workshop on Robustness and Stability of
Biological Systems and Computational Solutions (WRSBS
Robust Multi-Cellular Developmental Design
This paper introduces a continuous model for Multi-cellular Developmental
Design. The cells are fixed on a 2D grid and exchange "chemicals" with their
neighbors during the growth process. The quantity of chemicals that a cell
produces, as well as the differentiation value of the cell in the phenotype,
are controlled by a Neural Network (the genotype) that takes as inputs the
chemicals produced by the neighboring cells at the previous time step. In the
proposed model, the number of iterations of the growth process is not
pre-determined, but emerges during evolution: only organisms for which the
growth process stabilizes give a phenotype (the stable state), others are
declared nonviable. The optimization of the controller is done using the NEAT
algorithm, that optimizes both the topology and the weights of the Neural
Networks. Though each cell only receives local information from its neighbors,
the experimental results of the proposed approach on the 'flags' problems (the
phenotype must match a given 2D pattern) are almost as good as those of a
direct regression approach using the same model with global information.
Moreover, the resulting multi-cellular organisms exhibit almost perfect
self-healing characteristics
Flexible RNA design under structure and sequence constraints using formal languages
The problem of RNA secondary structure design (also called inverse folding)
is the following: given a target secondary structure, one aims to create a
sequence that folds into, or is compatible with, a given structure. In several
practical applications in biology, additional constraints must be taken into
account, such as the presence/absence of regulatory motifs, either at a
specific location or anywhere in the sequence. In this study, we investigate
the design of RNA sequences from their targeted secondary structure, given
these additional sequence constraints. To this purpose, we develop a general
framework based on concepts of language theory, namely context-free grammars
and finite automata. We efficiently combine a comprehensive set of constraints
into a unifying context-free grammar of moderate size. From there, we use
generic generic algorithms to perform a (weighted) random generation, or an
exhaustive enumeration, of candidate sequences. The resulting method, whose
complexity scales linearly with the length of the RNA, was implemented as a
standalone program. The resulting software was embedded into a publicly
available dedicated web server. The applicability demonstrated of the method on
a concrete case study dedicated to Exon Splicing Enhancers, in which our
approach was successfully used in the design of \emph{in vitro} experiments.Comment: ACM BCB 2013 - ACM Conference on Bioinformatics, Computational
Biology and Biomedical Informatics (2013
An Agent-Based Spatially Explicit Epidemiological Model in MASON
This paper outlines the design and implementation of an agent-based epidemiological simulation system. The system was implemented in the MASON toolkit, a set of Java-based agent-simulation libraries. This epidemiological simulation system is robust and extensible for multiple applications, including classroom demonstrations of many types of epidemics and detailed numerical experimentation on a particular disease. The application has been made available as an applet on the MASON web site, and as source code on the author\'s web site.Epidemiology, Social Networks, Agent-Based Simulation, MASON Toolkit
An exploration and validation of computer modeling of evolution, natural selection, and evolutionary biology with cellular automata for secondary students.
The Evolutionary Tool Kit, a new software package, is the prototype of a concept simulator providing an environment for students to create microworlds of populations of artificial organisms. Its function is to model processes, concepts and arguments in natural selection and evolutionary biology, using either Mendelian asexual or sexual reproduction, or counterfactual systems such as \u27paint pot\u27 or blending inheritance. In this environment students can explore a conceptual What if? in evolutionary biology, test misconceptions and deepen understanding of inheritance and changes in populations. Populations can be defined either with typological, or with populational thinking, to inquire into the role and necessity of variation in natural selection. The approach is generative not tutorial. The interface is highly graphic with twenty traits set as icons that are moved onto the \u27phenotypes\u27. Activities include investigations of evolutionary theory of aging, reproductive advantage, sexual selection and mimicry. Design of the activities incorporates Howard Gardner\u27s Theory of Multiple Intelligences. Draft of a teacher and student manual are included
ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra
Background: Many biological systems are modeled qualitatively with discrete
models, such as probabilistic Boolean networks, logical models, Petri nets, and
agent-based models, with the goal to gain a better understanding of the system.
The computational complexity to analyze the complete dynamics of these models
grows exponentially in the number of variables, which impedes working with
complex models. Although there exist sophisticated algorithms to determine the
dynamics of discrete models, their implementations usually require
labor-intensive formatting of the model formulation, and they are oftentimes
not accessible to users without programming skills. Efficient analysis methods
are needed that are accessible to modelers and easy to use. Method: By
converting discrete models into algebraic models, tools from computational
algebra can be used to analyze their dynamics. Specifically, we propose a
method to identify attractors of a discrete model that is equivalent to solving
a system of polynomial equations, a long-studied problem in computer algebra.
Results: A method for efficiently identifying attractors, and the web-based
tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other
analysis methods for discrete models. ADAM converts several discrete model
types automatically into polynomial dynamical systems and analyzes their
dynamics using tools from computer algebra. Based on extensive experimentation
with both discrete models arising in systems biology and randomly generated
networks, we found that the algebraic algorithms presented in this manuscript
are fast for systems with the structure maintained by most biological systems,
namely sparseness, i.e., while the number of nodes in a biological network may
be quite large, each node is affected only by a small number of other nodes,
and robustness, i.e., small number of attractors
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