5,722 research outputs found
Parameter domains for Turing and stationary flow-distributed waves: I. The influence of nonlinearity
new type of instability in coupled reaction-diffusion-advection systems is analysed in a one-dimensional domain. This instability, arising due to the combined action of flow and diffusion, creates spatially periodic stationary waves termed flow and diffusion-distributed structures (FDS). Here we show, via linear stability analysis, that FDS are predicted in a considerably wider domain and are more robust (in the parameter domain) than the classical Turing instability patterns. FDS also represent a natural extension of the recently discovered flow-distributed oscillations (FDO). Nonlinear bifurcation analysis and numerical simulations in one-dimensional spatial domains show that FDS also have much richer solution behaviour than Turing structures. In the framework presented here Turing structures can be viewed as a particular instance of FDS. We conclude that FDS should be more easily obtainable in chemical systems than Turing (and FDO) structures and that they may play a potentially important role in biological pattern formation
Controlled Ecological Life Support System. Life Support Systems in Space Travel
Life support systems in space travel, in closed ecological systems were studied. Topics discussed include: (1) problems of life support and the fundamental concepts of bioregeneration; (2) technology associated with physical/chemical regenerative life support; (3) projection of the break even points for various life support techniques; (4) problems of controlling a bioregenerative life support system; (5) data on the operation of an experimental algal/mouse life support system; (6) industrial concepts of bioregenerative life support; and (7) Japanese concepts of bioregenerative life support and associated biological experiments to be conducted in the space station
Continuum and discrete approach in modeling biofilm development and structure: a review
The scientific community has recognized that almost 99% of the microbial life on earth is represented by biofilms. Considering the impacts of their sessile lifestyle on both natural and human activities, extensive experimental activity has been carried out to understand how biofilms grow and interact with the environment. Many mathematical models have also been developed to simulate and elucidate the main processes characterizing the biofilm growth. Two main mathematical approaches for biomass representation can be distinguished: continuum and discrete. This review is aimed at exploring the main characteristics of each approach. Continuum models can simulate the biofilm processes in a quantitative and deterministic way. However, they require a multidimensional formulation to take into account the biofilm spatial heterogeneity, which makes the models quite complicated, requiring significant computational effort. Discrete models are more recent and can represent the typical multidimensional structural heterogeneity of biofilm reflecting the experimental expectations, but they generate computational results including elements of randomness and introduce stochastic effects into the solutions
BlenX-based compositional modeling of complex reaction mechanisms
Molecular interactions are wired in a fascinating way resulting in complex
behavior of biological systems. Theoretical modeling provides a useful
framework for understanding the dynamics and the function of such networks. The
complexity of the biological networks calls for conceptual tools that manage
the combinatorial explosion of the set of possible interactions. A suitable
conceptual tool to attack complexity is compositionality, already successfully
used in the process algebra field to model computer systems. We rely on the
BlenX programming language, originated by the beta-binders process calculus, to
specify and simulate high-level descriptions of biological circuits. The
Gillespie's stochastic framework of BlenX requires the decomposition of
phenomenological functions into basic elementary reactions. Systematic
unpacking of complex reaction mechanisms into BlenX templates is shown in this
study. The estimation/derivation of missing parameters and the challenges
emerging from compositional model building in stochastic process algebras are
discussed. A biological example on circadian clock is presented as a case study
of BlenX compositionality
An ecological framework for the analysis of prebiotic chemical reaction networks and their dynamical behavior
It is becoming widely accepted that very early in the origin of life, even
before the emergence of genetic encoding, reaction networks of diverse small
chemicals might have manifested key properties of life, namely self-propagation
and adaptive evolution. To explore this possibility, we formalize the dynamics
of chemical reaction networks within the framework of chemical ecosystem
ecology. To capture the idea that life-like chemical systems are maintained out
of equilibrium by fluxes of energy-rich food chemicals, we model chemical
ecosystems in well-mixed containers that are subject to constant dilution by a
solution with a fixed concentration of food chemicals. Modelling all chemical
reactions as fully reversible, we show that seeding an autocatalytic cycle (AC)
with tiny amounts of one or more of its member chemicals results in logistic
growth of all member chemicals in the cycle. This finding justifies drawing an
instructive analogy between an AC and the population of a biological species.
We extend this finding to show that pairs of ACs can show competitive,
predator-prey, or mutualistic associations just like biological species.
Furthermore, when there is stochasticity in the environment, particularly in
the seeding of ACs, chemical ecosystems can show complex dynamics that can
resemble evolution. The evolutionary character is especially clear when the
network architecture results in ecological precedence (survival of the first),
which makes the path of succession historically contingent on the order in
which cycles are seeded. For all its simplicity, the framework developed here
is helpful for visualizing how autocatalysis in prebiotic chemical reaction
networks can yield life-like properties. Furthermore, chemical ecosystem
ecology could provide a useful foundation for exploring the emergence of
adaptive dynamics and the origins of polymer-based genetic systems
Diffusive coupling can discriminate between similar reaction mechanisms in an allosteric enzyme system
<p>Abstract</p> <p>Background</p> <p>A central question for the understanding of biological reaction networks is how a particular dynamic behavior, such as bistability or oscillations, is realized at the molecular level. So far this question has been mainly addressed in well-mixed reaction systems which are conveniently described by ordinary differential equations. However, much less is known about how molecular details of a reaction mechanism can affect the dynamics in diffusively coupled systems because the resulting partial differential equations are much more difficult to analyze.</p> <p>Results</p> <p>Motivated by recent experiments we compare two closely related mechanisms for the product activation of allosteric enzymes with respect to their ability to induce different types of reaction-diffusion waves and stationary Turing patterns. The analysis is facilitated by mapping each model to an associated complex Ginzburg-Landau equation. We show that a sequential activation mechanism, as implemented in the model of Monod, Wyman and Changeux (MWC), can generate inward rotating spiral waves which were recently observed as glycolytic activity waves in yeast extracts. In contrast, in the limiting case of a simple Hill activation, the formation of inward propagating waves is suppressed by a Turing instability. The occurrence of this unusual wave dynamics is not related to the magnitude of the enzyme cooperativity (as it is true for the occurrence of oscillations), but to the sensitivity with respect to changes of the activator concentration. Also, the MWC mechanism generates wave patterns that are more stable against long wave length perturbations.</p> <p>Conclusions</p> <p>This analysis demonstrates that amplitude equations, which describe the spatio-temporal dynamics near an instability, represent a valuable tool to investigate the molecular effects of reaction mechanisms on pattern formation in spatially extended systems. Using this approach we have shown that the occurrence of inward rotating spiral waves in glycolysis can be explained in terms of an MWC, but not with a Hill mechanism for the activation of the allosteric enzyme phosphofructokinase. Our results also highlight the importance of enzyme oligomerization for a possible experimental generation of Turing patterns in biological systems.</p
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