89,044 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
Modeling and evolving biochemical networks: insights into communication and computation from the biological domain
This paper is concerned with the modeling and evolving
of Cell Signaling Networks (CSNs) in silico. CSNs are
complex biochemical networks responsible for the coordination of cellular activities. We examine the possibility to computationally evolve and simulate Artificial Cell Signaling Networks (ACSNs) by means of Evolutionary Computation techniques. From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. For example, understanding some inherent properties of CSNs such as crosstalk may be of interest: A potential benefit of engineering crosstalking systems is that it allows the modification of a specific process according to the state of other processes in the system. This is clearly necessary in order to achieve complex control tasks. This work may also contribute to the biological understanding of the origins and evolution of real CSNs. An introduction to CSNs is first
provided, in which we describe the potential applications
of modeling and evolving these biochemical networks in
silico. We then review the different classes of techniques to model CSNs, this is followed by a presentation of two alternative approaches employed to evolve CSNs within the
ESIGNET project. Results obtained with these methods
are summarized and discussed
Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity
We present a novel formulation for biochemical reaction networks in the
context of signal transduction. The model consists of input-output transfer
functions, which are derived from differential equations, using stable
equilibria. We select a set of 'source' species, which receive input signals.
Signals are transmitted to all other species in the system (the 'target'
species) with a specific delay and transmission strength. The delay is computed
as the maximal reaction time until a stable equilibrium for the target species
is reached, in the context of all other reactions in the system. The
transmission strength is the concentration change of the target species. The
computed input-output transfer functions can be stored in a matrix, fitted with
parameters, and recalled to build discrete dynamical models. By separating
reaction time and concentration we can greatly simplify the model,
circumventing typical problems of complex dynamical systems. The transfer
function transformation can be applied to mass-action kinetic models of signal
transduction. The paper shows that this approach yields significant insight,
while remaining an executable dynamical model for signal transduction. In
particular we can deconstruct the complex system into local transfer functions
between individual species. As an example, we examine modularity and signal
integration using a published model of striatal neural plasticity. The modules
that emerge correspond to a known biological distinction between
calcium-dependent and cAMP-dependent pathways. We also found that overall
interconnectedness depends on the magnitude of input, with high connectivity at
low input and less connectivity at moderate to high input. This general result,
which directly follows from the properties of individual transfer functions,
contradicts notions of ubiquitous complexity by showing input-dependent signal
transmission inactivation.Comment: 13 pages, 5 tables, 15 figure
Stochastic Binary Modeling of Cells in Continuous Time as an Alternative to Biochemical Reaction Equations
We have developed a coarse-grained formulation for modeling the dynamic
behavior of cells quantitatively, based on stochasticity and heterogeneity,
rather than on biochemical reactions. We treat each reaction as a
continuous-time stochastic process, while reducing each biochemical quantity to
a binary value at the level of individual cells. The system can be analytically
represented by a finite set of ordinary linear differential equations, which
provides a continuous time course prediction of each molecular state. In this
letter, we introduce our formalism and demonstrate it with several examples.Comment: 10pages, 3 figure
Detailed simulations of cell biology with Smoldyn 2.1.
Most cellular processes depend on intracellular locations and random collisions of individual protein molecules. To model these processes, we developed algorithms to simulate the diffusion, membrane interactions, and reactions of individual molecules, and implemented these in the Smoldyn program. Compared to the popular MCell and ChemCell simulators, we found that Smoldyn was in many cases more accurate, more computationally efficient, and easier to use. Using Smoldyn, we modeled pheromone response system signaling among yeast cells of opposite mating type. This model showed that secreted Bar1 protease might help a cell identify the fittest mating partner by sharpening the pheromone concentration gradient. This model involved about 200,000 protein molecules, about 7000 cubic microns of volume, and about 75 minutes of simulated time; it took about 10 hours to run. Over the next several years, as faster computers become available, Smoldyn will allow researchers to model and explore systems the size of entire bacterial and smaller eukaryotic cells
An Introduction to Rule-based Modeling of Immune Receptor Signaling
Cells process external and internal signals through chemical interactions.
Cells that constitute the immune system (e.g., antigen presenting cell, T-cell,
B-cell, mast cell) can have different functions (e.g., adaptive memory,
inflammatory response) depending on the type and number of receptor molecules
on the cell surface and the specific intracellular signaling pathways activated
by those receptors. Explicitly modeling and simulating kinetic interactions
between molecules allows us to pose questions about the dynamics of a signaling
network under various conditions. However, the application of chemical kinetics
to biochemical signaling systems has been limited by the complexity of the
systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune,
PySB) is an approach to address this complexity. In this chapter, by
application to the FcRI receptor system, we will explore the
origins of complexity in macromolecular interactions, show how rule-based
modeling can be used to address complexity, and demonstrate how to build a
model in the BioNetGen framework. Open source BioNetGen software and
documentation are available at http://bionetgen.org.Comment: 5 figure
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