25,897 research outputs found
Computational Complexity of Atomic Chemical Reaction Networks
Informally, a chemical reaction network is "atomic" if each reaction may be
interpreted as the rearrangement of indivisible units of matter. There are
several reasonable definitions formalizing this idea. We investigate the
computational complexity of deciding whether a given network is atomic
according to each of these definitions.
Our first definition, primitive atomic, which requires each reaction to
preserve the total number of atoms, is to shown to be equivalent to mass
conservation. Since it is known that it can be decided in polynomial time
whether a given chemical reaction network is mass-conserving, the equivalence
gives an efficient algorithm to decide primitive atomicity.
Another definition, subset atomic, further requires that all atoms are
species. We show that deciding whether a given network is subset atomic is in
, and the problem "is a network subset atomic with respect to a
given atom set" is strongly -.
A third definition, reachably atomic, studied by Adleman, Gopalkrishnan et
al., further requires that each species has a sequence of reactions splitting
it into its constituent atoms. We show that there is a to decide whether a given network is reachably atomic, improving
upon the result of Adleman et al. that the problem is . We
show that the reachability problem for reachably atomic networks is
-.
Finally, we demonstrate equivalence relationships between our definitions and
some special cases of another existing definition of atomicity due to Gnacadja
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Sea Spray Aerosol: Where Marine Biology Meets Atmospheric Chemistry.
Atmospheric aerosols have long been known to alter climate by scattering incoming solar radiation and acting as seeds for cloud formation. These processes have vast implications for controlling the chemistry of our environment and the Earth's climate. Sea spray aerosol (SSA) is emitted over nearly three-quarters of our planet, yet precisely how SSA impacts Earth's radiation budget remains highly uncertain. Over the past several decades, studies have shown that SSA particles are far more complex than just sea salt. Ocean biological and physical processes produce individual SSA particles containing a diverse array of biological species including proteins, enzymes, bacteria, and viruses and a diverse array of organic compounds including fatty acids and sugars. Thus, a new frontier of research is emerging at the nexus of chemistry, biology, and atmospheric science. In this Outlook article, we discuss how current and future aerosol chemistry research demands a tight coupling between experimental (observational and laboratory studies) and computational (simulation-based) methods. This integration of approaches will enable the systematic interrogation of the complexity within individual SSA particles at a level that will enable prediction of the physicochemical properties of real-world SSA, ultimately illuminating the detailed mechanisms of how the constituents within individual SSA impact climate
Heuristics-Guided Exploration of Reaction Mechanisms
For the investigation of chemical reaction networks, the efficient and
accurate determination of all relevant intermediates and elementary reactions
is mandatory. The complexity of such a network may grow rapidly, in particular
if reactive species are involved that might cause a myriad of side reactions.
Without automation, a complete investigation of complex reaction mechanisms is
tedious and possibly unfeasible. Therefore, only the expected dominant reaction
paths of a chemical reaction network (e.g., a catalytic cycle or an enzymatic
cascade) are usually explored in practice. Here, we present a computational
protocol that constructs such networks in a parallelized and automated manner.
Molecular structures of reactive complexes are generated based on heuristic
rules derived from conceptual electronic-structure theory and subsequently
optimized by quantum chemical methods to produce stable intermediates of an
emerging reaction network. Pairs of intermediates in this network that might be
related by an elementary reaction according to some structural similarity
measure are then automatically detected and subjected to an automated search
for the connecting transition state. The results are visualized as an
automatically generated network graph, from which a comprehensive picture of
the mechanism of a complex chemical process can be obtained that greatly
facilitates the analysis of the whole network. We apply our protocol to the
Schrock dinitrogen-fixation catalyst to study alternative pathways of catalytic
ammonia production.Comment: 27 pages, 9 figure
Computational models for inferring biochemical networks
Biochemical networks are of great practical importance. The interaction of biological compounds in cells has been enforced to a proper understanding by the numerous bioinformatics projects, which contributed to a vast amount of biological information. The construction of biochemical systems (systems of chemical reactions), which include both topology and kinetic constants of the chemical reactions, is NP-hard and is a well-studied system biology problem. In this paper, we propose a hybrid architecture, which combines genetic programming and simulated annealing in order to generate and optimize both the topology (the network) and the reaction rates of a biochemical system. Simulations and analysis of an artificial model and three real models (two models and the noisy version of one of them) show promising results for the proposed method.The Romanian National Authority for Scientific Research, CNDI–UEFISCDI,
Project No. PN-II-PT-PCCA-2011-3.2-0917
A chemical model for the interstellar medium in galaxies
We present and test chemical models for three-dimensional hydrodynamical
simulations of galaxies. We explore the effect of changing key parameters such
as metallicity, radiation and non-equilibrium versus equilibrium metal cooling
approximations on the transition between the gas phases in the interstellar
medium. The microphysics is modelled by employing the public chemistry package
KROME and the chemical networks have been tested to work in a wide range of
densities and temperatures. We describe a simple H/He network following the
formation of H, and a more sophisticated network which includes metals.
Photochemistry, thermal processes, and different prescriptions for the H
catalysis on dust are presented and tested within a one-zone framework. The
resulting network is made publicly available on the KROME webpage. We find that
employing an accurate treatment of the dust-related processes induces a faster
HI--H transition. In addition, we show when the equilibrium assumption for
metal cooling holds, and how a non-equilibrium approach affects the thermal
evolution of the gas and the HII--HI transition. These models can be employed
in any hydrodynamical code via an interface to KROME and can be applied to
different problems including isolated galaxies, cosmological simulations of
galaxy formation and evolution, supernova explosions in molecular clouds, and
the modelling of star-forming regions. The metal network can be used for a
comparison with observational data of CII 158 m emission both for
high-redshift as well as for local galaxies.Comment: A&A accepte
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
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
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