13,131 research outputs found
Setting Parameters for Biological Models With ANIMO
ANIMO (Analysis of Networks with Interactive MOdeling) is a software for
modeling biological networks, such as e.g. signaling, metabolic or gene
networks. An ANIMO model is essentially the sum of a network topology and a
number of interaction parameters. The topology describes the interactions
between biological entities in form of a graph, while the parameters determine
the speed of occurrence of such interactions. When a mismatch is observed
between the behavior of an ANIMO model and experimental data, we want to update
the model so that it explains the new data. In general, the topology of a model
can be expanded with new (known or hypothetical) nodes, and enables it to match
experimental data. However, the unrestrained addition of new parts to a model
causes two problems: models can become too complex too fast, to the point of
being intractable, and too many parts marked as "hypothetical" or "not known"
make a model unrealistic. Even if changing the topology is normally the easier
task, these problems push us to try a better parameter fit as a first step, and
resort to modifying the model topology only as a last resource. In this paper
we show the support added in ANIMO to ease the task of expanding the knowledge
on biological networks, concentrating in particular on the parameter settings
MathSBML: a package for manipulating SBML-based biological models
Summary: MathSBML is a Mathematica package designed
for manipulating Systems Biology Markup Language (SBML)
models. It converts SBML models into Mathematica data structures and provides a platform for manipulating and evaluating these models. Once a model is read by MathSBML, it is fully compatible with standard Mathematica functions such as NDSolve (a differential-algebraic equations solver). Math-SBML also provides an application programming interface for viewing, manipulating, running numerical simulations; exporting SBML models; and converting SBML models in to other formats, such as XPP, HTML and FORTRAN. By accessing the full breadth of Mathematica functionality, MathSBML is fully extensible to SBML models of any size or complexity.
Availability: Open Source (LGPL) at http://www.sbml.org and http://www.sf.net/projects/sbml.
Supplementary information: Extensive online documentation is available at http://www.sbml.org/mathsbml.html. Additional examples are provided at http://www.sbml.org/software/mathsbml/bioinformatics-application-not
Analysis of parametric biological models with non-linear dynamics
In this paper we present recent results on parametric analysis of biological
models. The underlying method is based on the algorithms for computing
trajectory sets of hybrid systems with polynomial dynamics. The method is then
applied to two case studies of biological systems: one is a cardiac cell model
for studying the conditions for cardiac abnormalities, and the second is a
model of insect nest-site choice.Comment: In Proceedings HSB 2012, arXiv:1208.315
BioDiVinE: A Framework for Parallel Analysis of Biological Models
In this paper a novel tool BioDiVinEfor parallel analysis of biological
models is presented. The tool allows analysis of biological models specified in
terms of a set of chemical reactions. Chemical reactions are transformed into a
system of multi-affine differential equations. BioDiVinE employs techniques for
finite discrete abstraction of the continuous state space. At that level,
parallel analysis algorithms based on model checking are provided. In the
paper, the key tool features are described and their application is
demonstrated by means of a case study
Quasilinear and singular elliptic systems
In this paper, we investigate a general quasilinear elliptic and singular
system. By monotonicity methods, we give some existence and uniqueness results.
Next, we give some applications to biological models
When the optimal is not the best: parameter estimation in complex biological models
Background: The vast computational resources that became available during the
past decade enabled the development and simulation of increasingly complex
mathematical models of cancer growth. These models typically involve many free
parameters whose determination is a substantial obstacle to model development.
Direct measurement of biochemical parameters in vivo is often difficult and
sometimes impracticable, while fitting them under data-poor conditions may
result in biologically implausible values.
Results: We discuss different methodological approaches to estimate
parameters in complex biological models. We make use of the high computational
power of the Blue Gene technology to perform an extensive study of the
parameter space in a model of avascular tumor growth. We explicitly show that
the landscape of the cost function used to optimize the model to the data has a
very rugged surface in parameter space. This cost function has many local
minima with unrealistic solutions, including the global minimum corresponding
to the best fit.
Conclusions: The case studied in this paper shows one example in which model
parameters that optimally fit the data are not necessarily the best ones from a
biological point of view. To avoid force-fitting a model to a dataset, we
propose that the best model parameters should be found by choosing, among
suboptimal parameters, those that match criteria other than the ones used to
fit the model. We also conclude that the model, data and optimization approach
form a new complex system, and point to the need of a theory that addresses
this problem more generally
Numerical investigation of Differential Biological-Models via GA-Kansa Method Inclusive Genetic Strategy
In this paper, we use Kansa method for solving the system of differential
equations in the area of biology. One of the challenges in Kansa method is
picking out an optimum value for Shape parameter in Radial Basis Function to
achieve the best result of the method because there are not any available
analytical approaches for obtaining optimum Shape parameter. For this reason,
we design a genetic algorithm to detect a close optimum Shape parameter. The
experimental results show that this strategy is efficient in the systems of
differential models in biology such as HIV and Influenza. Furthermore, we prove
that using Pseudo-Combination formula for crossover in genetic strategy leads
to convergence in the nearly best selection of Shape parameter.Comment: 42 figures, 23 page
- …