17,655 research outputs found
Synthesizing and tuning chemical reaction networks with specified behaviours
We consider how to generate chemical reaction networks (CRNs) from functional
specifications. We propose a two-stage approach that combines synthesis by
satisfiability modulo theories and Markov chain Monte Carlo based optimisation.
First, we identify candidate CRNs that have the possibility to produce correct
computations for a given finite set of inputs. We then optimise the reaction
rates of each CRN using a combination of stochastic search techniques applied
to the chemical master equation, simultaneously improving the of correct
behaviour and ruling out spurious solutions. In addition, we use techniques
from continuous time Markov chain theory to study the expected termination time
for each CRN. We illustrate our approach by identifying CRNs for majority
decision-making and division computation, which includes the identification of
both known and unknown networks.Comment: 17 pages, 6 figures, appeared the proceedings of the 21st conference
on DNA Computing and Molecular Programming, 201
The noisy edge of traveling waves
Traveling waves are ubiquitous in nature and control the speed of many
important dynamical processes, including chemical reactions, epidemic
outbreaks, and biological evolution. Despite their fundamental role in complex
systems, traveling waves remain elusive because they are often dominated by
rare fluctuations in the wave tip, which have defied any rigorous analysis so
far. Here, we show that by adjusting nonlinear model details, noisy traveling
waves can be solved exactly. The moment equations of these tuned models are
closed and have a simple analytical structure resembling the deterministic
approximation supplemented by a nonlocal cutoff term. The peculiar form of the
cutoff shapes the noisy edge of traveling waves and is critical for the correct
prediction of the wave speed and its fluctuations. Our approach is illustrated
and benchmarked using the example of fitness waves arising in simple models of
microbial evolution, which are highly sensitive to number fluctuations. We
demonstrate explicitly how these models can be tuned to account for finite
population sizes and determine how quickly populations adapt as a function of
population size and mutation rates. More generally, our method is shown to
apply to a broad class of models, in which number fluctuations are generated by
branching processes. Because of this versatility, the method of model tuning
may serve as a promising route toward unraveling universal properties of
complex discrete particle systems.Comment: For supplementary material and published open access article, see
http://www.pnas.org/content/108/5/1783.abstract?sid=693e63f3-fd1a-407a-983e-c521efc6c8c5
See also Commentary Article by D. S. Fisher,
http://www.pnas.org/content/108/7/2633.extrac
The three different phases in the dynamics of chemical reaction networks and their relationship to cancer
We investigate the catalytic reactions model used in cell modeling. The
reaction kinetic is defined through the energies of different species of
molecules following random independent distribution. The related statistical
physics model has three phases and these three phases emerged in the dynamics:
fast dynamics phase, slow dynamic phase and ultra-slow dynamic phase. The
phenomenon we found is a rather general, does not depend on the details of the
model. We assume as a hypothesis that the transition between these phases
(glassiness degrees) is related to cancer. The imbalance in the rate of
processes between key aspects of the cell (gene regulation, protein-protein
interaction, metabolical networks) creates a change in the fine tuning between
these key aspects, affects the logics of the cell and initiates cancer. It is
probable that cancer is a change of phase resulting from increased and
deregulated metabolic reactions.Comment: 5 pages, 2 figures, EPL, in pres
Field theory for a reaction-diffusion model of quasispecies dynamics
RNA viruses are known to replicate with extremely high mutation rates. These
rates are actually close to the so-called error threshold. This threshold is in
fact a critical point beyond which genetic information is lost through a
second-order phase transition, which has been dubbed the ``error catastrophe.''
Here we explore this phenomenon using a field theory approximation to the
spatially extended Swetina-Schuster quasispecies model [J. Swetina and P.
Schuster, Biophys. Chem. {\bf 16}, 329 (1982)], a single-sharp-peak landscape.
In analogy with standard absorbing-state phase transitions, we develop a
reaction-diffusion model whose discrete rules mimic the Swetina-Schuster model.
The field theory representation of the reaction-diffusion system is
constructed. The proposed field theory belongs to the same universality class
than a conserved reaction-diffusion model previously proposed [F. van Wijland
{\em et al.}, Physica A {\bf 251}, 179 (1998)]. From the field theory, we
obtain the full set of exponents that characterize the critical behavior at the
error threshold. Our results present the error catastrophe from a new point of
view and suggest that spatial degrees of freedom can modify several mean field
predictions previously considered, leading to the definition of characteristic
exponents that could be experimentally measurable.Comment: 13 page
Toward Cultural Oncology: The Evolutionary Information Dynamics of Cancer
'Racial' disparities among cancers, particularly of the breast and prostate, are something of a mystery. For the US, in the face of slavery and its sequelae, centuries of interbreeding have greatly leavened genetic differences between 'Blacks' and 'whites', but marked contrasts in disease prevalence and progression persist. 'Adjustment' for socioeconomic status and lifestyle, while statistically accounting for much of the variance in breast cancer, only begs the question of ultimate causality. Here we propose a more basic biological explanation that extends the theory of immune cognition to include elaborate tumor control mechanisms constituting the principal selection pressure acting on pathologically mutating cell clones. The interplay between them occurs in the context of an embedding, highly structured, system of culturally specific psychosocial stress which we find is able to literally write an image of itself onto disease progression. The dynamics are analogous to punctuated equilibrium in simple evolutionary proces
Model validation of simple-graph representations of metabolism
The large-scale properties of chemical reaction systems, such as the
metabolism, can be studied with graph-based methods. To do this, one needs to
reduce the information -- lists of chemical reactions -- available in
databases. Even for the simplest type of graph representation, this reduction
can be done in several ways. We investigate different simple network
representations by testing how well they encode information about one
biologically important network structure -- network modularity (the propensity
for edges to be cluster into dense groups that are sparsely connected between
each other). To reach this goal, we design a model of reaction-systems where
network modularity can be controlled and measure how well the reduction to
simple graphs capture the modular structure of the model reaction system. We
find that the network types that best capture the modular structure of the
reaction system are substrate-product networks (where substrates are linked to
products of a reaction) and substance networks (with edges between all
substances participating in a reaction). Furthermore, we argue that the
proposed model for reaction systems with tunable clustering is a general
framework for studies of how reaction-systems are affected by modularity. To
this end, we investigate statistical properties of the model and find, among
other things, that it recreate correlations between degree and mass of the
molecules.Comment: to appear in J. Roy. Soc. Intefac
Lost in translation: Toward a formal model of multilevel, multiscale medicine
For a broad spectrum of low level cognitive regulatory and other biological phenomena, isolation from signal crosstalk between them requires more metabolic free energy than permitting correlation. This allows an evolutionary exaptation leading to dynamic global broadcasts of interacting physiological processes at multiple scales. The argument is similar to the well-studied exaptation of noise to trigger stochastic resonance amplification in physiological subsystems. Not only is the living state characterized by cognition at every scale and level of organization, but by multiple, shifting, tunable, cooperative larger scale broadcasts that link selected subsets of functional modules to address problems. This multilevel dynamical viewpoint has implications for initiatives in translational medicine that have followed the implosive collapse of pharmaceutical industry 'magic bullet' research. In short, failure to respond to the inherently multilevel, multiscale nature of human pathophysiology will doom translational medicine to a similar implosion
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