76,290 research outputs found
Flux networks in metabolic graphs
A metabolic model can be represented as bipartite graph comprising linked
reaction and metabolite nodes. Here it is shown how a network of conserved
fluxes can be assigned to the edges of such a graph by combining the reaction
fluxes with a conserved metabolite property such as molecular weight. A similar
flux network can be constructed by combining the primal and dual solutions to
the linear programming problem that typically arises in constraint-based
modelling. Such constructions may help with the visualisation of flux
distributions in complex metabolic networks. The analysis also explains the
strong correlation observed between metabolite shadow prices (the dual linear
programming variables) and conserved metabolite properties. The methods were
applied to recent metabolic models for Escherichia coli, Saccharomyces
cerevisiae, and Methanosarcina barkeri. Detailed results are reported for E.
coli; similar results were found for the other organisms.Comment: 9 pages, 4 figures, RevTeX 4.0, supplementary data available (excel
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
Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning
Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
We present a systematic investigation using graph neural networks (GNNs) to
model organic chemical reactions. To do so, we prepared a dataset collection of
four ubiquitous reactions from the organic chemistry literature. We evaluate
seven different GNN architectures for classification tasks pertaining to the
identification of experimental reagents and conditions. We find that models are
able to identify specific graph features that affect reaction conditions and
lead to accurate predictions. The results herein show great promise in
advancing molecular machine learning.Comment: 23 pages, 10 tables, 13 figures, to appear in the ICML 2020 Workshop
on Graph Representation Learning and Beyond (GRLB
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
On RAF Sets and Autocatalytic Cycles in Random Reaction Networks
The emergence of autocatalytic sets of molecules seems to have played an
important role in the origin of life context. Although the possibility to
reproduce this emergence in laboratory has received considerable attention,
this is still far from being achieved. In order to unravel some key properties
enabling the emergence of structures potentially able to sustain their own
existence and growth, in this work we investigate the probability to observe
them in ensembles of random catalytic reaction networks characterized by
different structural properties. From the point of view of network topology, an
autocatalytic set have been defined either in term of strongly connected
components (SCCs) or as reflexively autocatalytic and food-generated sets
(RAFs). We observe that the average level of catalysis differently affects the
probability to observe a SCC or a RAF, highlighting the existence of a region
where the former can be observed, whereas the latter cannot. This parameter
also affects the composition of the RAF, which can be further characterized
into linear structures, autocatalysis or SCCs. Interestingly, we show that the
different network topology (uniform as opposed to power-law catalysis systems)
does not have a significantly divergent impact on SCCs and RAFs appearance,
whereas the proportion between cleavages and condensations seems instead to
play a role. A major factor that limits the probability of RAF appearance and
that may explain some of the difficulties encountered in laboratory seems to be
the presence of molecules which can accumulate without being substrate or
catalyst of any reaction.Comment: pp 113-12
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Message Passing Inference with Chemical Reaction Networks
Recent work on molecular programming has explored new possibilities for computational abstractions with biomolecules, including logic gates, neural networks, and linear systems. In the future such abstractions might enable nanoscale devices that can sense and control the world at a molecular scale. Just as in macroscale robotics, it is critical that such devices can learn about their environment and reason under uncertainty. At this small scale, systems are typically modeled as chemical reaction networks. In this work, we develop a procedure that can take arbitrary probabilistic graphical models, represented as factor graphs over discrete random variables, and compile them into chemical reaction networks that implement inference. In particular, we show that marginalization based on sum-product message passing can be implemented in terms of reactions between chemical species whose concentrations represent probabilities. We show algebraically that the steady state concentration of these species correspond to the marginal distributions of the random variables in the graph and validate the results in simulations. As with standard sum-product inference, this procedure yields exact results for tree-structured graphs, and approximate solutions for loopy graphs.Engineering and Applied SciencesOther Research Uni
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