24,915 research outputs found
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
Geometric Universality of Currents
We discuss a non-equilibrium statistical system on a graph or network.
Identical particles are injected, interact with each other, traverse, and leave
the graph in a stochastic manner described in terms of Poisson rates, possibly
dependent on time and instantaneous occupation numbers at the nodes of the
graph. We show that under the assumption of constancy of the relative rates,
the system demonstrates a profound statistical symmetry, resulting in geometric
universality of the statistics of the particle currents. This phenomenon
applies broadly to many man-made and natural open stochastic systems, such as
queuing of packages over the internet, transport of electrons and
quasi-particles in mesoscopic systems, and chains of reactions in bio-chemical
networks. We illustrate the utility of our general approach using two enabling
examples from the two latter disciplines.Comment: 15 pages, 5 figure
A structured approach for the engineering of biochemical network models, illustrated for signalling pathways
http://dx.doi.org/10.1093/bib/bbn026Quantitative models of biochemical networks (signal transduction cascades, metabolic pathways, gene regulatory circuits) are a central component of modern systems biology. Building and managing these complex models is a major challenge that can benefit from the application of formal methods adopted from theoretical computing science. Here we provide a general introduction to the field of formal modelling, which emphasizes the intuitive biochemical basis of the modelling process, but is also accessible for an audience with a background in computing science and/or model engineering. We show how signal transduction cascades can be modelled in a modular fashion, using both a qualitative approach { Qualitative Petri nets, and quantitative approaches { Continuous Petri Nets and Ordinary Differential Equations. We review the major elementary building blocks of a cellular signalling model, discuss which critical design decisions have to be made during model building, and present ..
Optimization of Enzymatic Logic Gates and Networks for Noise Reduction and Stability
Biochemical computing attempts to process information with biomolecules and
biological objects. In this work we review our results on analysis and
optimization of single biochemical logic gates based on enzymatic reactions,
and a network of three gates, for reduction of the "analog" noise buildup. For
a single gate, optimization is achieved by analyzing the enzymatic reactions
within a framework of kinetic equations. We demonstrate that using
co-substrates with much smaller affinities than the primary substrate, a
negligible increase in the noise output from the logic gate is obtained as
compared to the input noise. A network of enzymatic gates is analyzed by
varying selective inputs and fitting standardized few-parameters response
functions assumed for each gate. This allows probing of the individual gate
quality but primarily yields information on the relative contribution of the
gates to noise amplification. The derived information is then used to modify
experimental single gate and network systems to operate them in a regime of
reduced analog noise amplification.Comment: 7 pages in PD
Local and global modes of drug action in biochemical networks
It becomes increasingly accepted that a shift is needed from the traditional target-based approach of drug development to an integrated perspective of drug action in biochemical systems. We here present an integrative analysis of the interactions between drugs and metabolism based on the concept of drug scope. The drug scope represents the set of metabolic compounds and reactions that are potentially affected by a drug. We constructed and analyzed the scopes of all US approved drugs having metabolic targets. Our analysis shows that the distribution of drug scopes is highly uneven, and that drugs can be classified into several categories based on their scopes. Some of them have small scopes corresponding to localized action, while others have large scopes corresponding to potential large-scale systemic action. These groups are well conserved throughout different topologies of the underlying metabolic network. They can furthermore be associated to specific drug therapeutic properties
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