9,192 research outputs found
Paradoxical signaling regulates structural plasticity in dendritic spines
Transient spine enlargement (3-5 min timescale) is an important event
associated with the structural plasticity of dendritic spines. Many of the
molecular mechanisms associated with transient spine enlargement have been
identified experimentally. Here, we use a systems biology approach to construct
a mathematical model of biochemical signaling and actin-mediated transient
spine expansion in response to calcium-influx due to NMDA receptor activation.
We have identified that a key feature of this signaling network is the
paradoxical signaling loop. Paradoxical components act bifunctionally in
signaling networks and their role is to control both the activation and
inhibition of a desired response function (protein activity or spine volume).
Using ordinary differential equation (ODE)-based modeling, we show that the
dynamics of different regulators of transient spine expansion including CaMKII,
RhoA, and Cdc42 and the spine volume can be described using paradoxical
signaling loops. Our model is able to capture the experimentally observed
dynamics of transient spine volume. Furthermore, we show that actin remodeling
events provide a robustness to spine volume dynamics. We also generate
experimentally testable predictions about the role of different components and
parameters of the network on spine dynamics
A Random Force is a Force, of Course, of Coarse: Decomposing Complex Enzyme Kinetics with Surrogate Models
The temporal autocorrelation (AC) function associated with monitoring order
parameters characterizing conformational fluctuations of an enzyme is analyzed
using a collection of surrogate models. The surrogates considered are
phenomenological stochastic differential equation (SDE) models. It is
demonstrated how an ensemble of such surrogate models, each surrogate being
calibrated from a single trajectory, indirectly contains information about
unresolved conformational degrees of freedom. This ensemble can be used to
construct complex temporal ACs associated with a "non-Markovian" process. The
ensemble of surrogates approach allows researchers to consider models more
flexible than a mixture of exponentials to describe relaxation times and at the
same time gain physical information about the system. The relevance of this
type of analysis to matching single-molecule experiments to computer
simulations and how more complex stochastic processes can emerge from a mixture
of simpler processes is also discussed. The ideas are illustrated on a toy SDE
model and on molecular dynamics simulations of the enzyme dihydrofolate
reductase.Comment: 11 pages / 6 figure
The thermodynamics of computational copying in biochemical systems
Living cells use readout molecules to record the state of receptor proteins,
similar to measurements or copies in typical computational devices. But is this
analogy rigorous? Can cells be optimally efficient, and if not, why? We show
that, as in computation, a canonical biochemical readout network generates
correlations; extracting no work from these correlations sets a lower bound on
dissipation. For general input, the biochemical network cannot reach this
bound, even with arbitrarily slow reactions or weak thermodynamic driving. It
faces an accuracy-dissipation trade-off that is qualitatively distinct from and
worse than implied by the bound, and more complex steady-state copy processes
cannot perform better. Nonetheless, the cost remains close to the thermodynamic
bound unless accuracy is extremely high. Additionally, we show that
biomolecular reactions could be used in thermodynamically optimal devices under
exogenous manipulation of chemical fuels, suggesting an experimental system for
testing computational thermodynamics.Comment: Accepted versio
Combined surface acoustic wave and surface plasmon resonance measurement of collagen and fibrinogen layers
We use an instrument combining optical (surface plasmon resonance) and
acoustic (Love mode acoustic wave device) real-time measurements on a same
surface for the identification of water content in collagen and fibrinogen
protein layers. After calibration of the surface acoustic wave device
sensitivity by copper electrodeposition, the bound mass and its physical
properties -- density and optical index -- are extracted from the complementary
measurement techniques and lead to thickness and water ratio values compatible
with the observed signal shifts. Such results are especially usefully for
protein layers with a high water content as shown here for collagen on an
hydrophobic surface. We obtain the following results: collagen layers include
70+/-20 % water and are 16+/-3 to 19+/-3 nm thick for bulk concentrations
ranging from 30 to 300 ug/ml. Fibrinogen layers include 50+/-10 % water for
layer thicknesses in the 6+/-1.5 to 13+/-2 nm range when the bulk concentration
is in the 46 to 460 ug/ml range.Comment: 50 pages, 8 figures, 1 tabl
Turing Patterns and Biological Explanation
Turing patterns are a class of minimal mathematical models that have been used to discover and conceptualize certain abstract features of early biological development. This paper examines a range of these minimal models in order to articulate and elaborate a philosophical analysis of their epistemic uses. It is argued that minimal mathematical models aid in structuring the epistemic practices of biology by providing precise descriptions of the quantitative relations between various features of the complex systems, generating novel predictions that can be compared with experimental data, promoting theory exploration, and acting as constitutive parts of empirically adequate explanations of naturally occurring phenomena, such as biological pattern formation. Focusing on the roles that minimal model explanations play in science motivates the adoption of a broader diachronic view of scientific explanation
A Unique Transformation from Ordinary Differential Equations to Reaction Networks
Many models in Systems Biology are described as a system of Ordinary Differential Equations, which allows for transient, steady-state or bifurcation analysis when kinetic information is available. Complementary structure-related qualitative analysis techniques have become increasingly popular in recent years, like qualitative model checking or pathway analysis (elementary modes, invariants, flux balance analysis, graph-based analyses, chemical organization theory, etc.). They do not rely on kinetic information but require a well-defined structure as stochastic analysis techniques equally do. In this article, we look into the structure inference problem for a model described by a system of Ordinary Differential Equations and provide conditions for the uniqueness of its solution. We describe a method to extract a structured reaction network model, represented as a bipartite multigraph, for example, a continuous Petri net (CPN), from a system of Ordinary Differential Equations (ODEs). A CPN uniquely defines an ODE, and each ODE can be transformed into a CPN. However, it is not obvious under which conditions the transformation of an ODE into a CPN is unique, that is, when a given ODE defines exactly one CPN. We provide biochemically relevant sufficient conditions under which the derived structure is unique and counterexamples showing the necessity of each condition. Our method is implemented and available; we illustrate it on some signal transduction models from the BioModels database. A prototype implementation of the method is made available to modellers at http://contraintes.inria.fr/~soliman/ode2pn.html, and the data mentioned in the “Results” section at http://contraintes.inria.fr/~soliman/ode2pn_data/. Our results yield a new recommendation for the import/export feature of tools supporting the SBML exchange format
- …