25,231 research outputs found
Automated transition state theory calculations for high-throughput kinetics
A scarcity of known chemical kinetic parameters leads to the use of many
reaction rate estimates, which are not always sufficiently accurate, in the
construction of detailed kinetic models. To reduce the reliance on these
estimates and improve the accuracy of predictive kinetic models, we have
developed a high-throughput, fully automated, reaction rate calculation method,
AutoTST. The algorithm integrates automated saddle-point geometry search
methods and a canonical transition state theory kinetics calculator. The
automatically calculated reaction rates compare favorably to existing estimated
rates. Comparison against high level theoretical calculations show the new
automated method performs better than rate estimates when the estimate is made
by a poor analogy. The method will improve by accounting for internal rotor
contributions and by improving methods to determine molecular symmetry.Comment: 29 pages, 8 figure
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
Exploration of Reaction Pathways and Chemical Transformation Networks
For the investigation of chemical reaction networks, the identification of
all relevant intermediates and elementary reactions is mandatory. Many
algorithmic approaches exist that perform explorations efficiently and
automatedly. These approaches differ in their application range, the level of
completeness of the exploration, as well as the amount of heuristics and human
intervention required. Here, we describe and compare the different approaches
based on these criteria. Future directions leveraging the strengths of chemical
heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure
An Introduction to Rule-based Modeling of Immune Receptor Signaling
Cells process external and internal signals through chemical interactions.
Cells that constitute the immune system (e.g., antigen presenting cell, T-cell,
B-cell, mast cell) can have different functions (e.g., adaptive memory,
inflammatory response) depending on the type and number of receptor molecules
on the cell surface and the specific intracellular signaling pathways activated
by those receptors. Explicitly modeling and simulating kinetic interactions
between molecules allows us to pose questions about the dynamics of a signaling
network under various conditions. However, the application of chemical kinetics
to biochemical signaling systems has been limited by the complexity of the
systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune,
PySB) is an approach to address this complexity. In this chapter, by
application to the FcRI receptor system, we will explore the
origins of complexity in macromolecular interactions, show how rule-based
modeling can be used to address complexity, and demonstrate how to build a
model in the BioNetGen framework. Open source BioNetGen software and
documentation are available at http://bionetgen.org.Comment: 5 figure
Design and Development of Software Tools for Bio-PEPA
This paper surveys the design of software tools for the Bio-PEPA process algebra. Bio-PEPA is a high-level language for modelling biological systems such as metabolic pathways and other biochemical reaction networks. Through providing tools for this modelling language we hope to allow easier use of a range of simulators and model-checkers thereby freeing the modeller from the responsibility of developing a custom simulator for the problem of interest. Further, by providing mappings to a range of different analysis tools the Bio-PEPA language allows modellers to compare analysis results which have been computed using independent numerical analysers, which enhances the reliability and robustness of the results computed.
Quantifying the implicit process flow abstraction in SBGN-PD diagrams with Bio-PEPA
For a long time biologists have used visual representations of biochemical
networks to gain a quick overview of important structural properties. Recently
SBGN, the Systems Biology Graphical Notation, has been developed to standardise
the way in which such graphical maps are drawn in order to facilitate the
exchange of information. Its qualitative Process Diagrams (SBGN-PD) are based
on an implicit Process Flow Abstraction (PFA) that can also be used to
construct quantitative representations, which can be used for automated
analyses of the system. Here we explicitly describe the PFA that underpins
SBGN-PD and define attributes for SBGN-PD glyphs that make it possible to
capture the quantitative details of a biochemical reaction network. We
implemented SBGNtext2BioPEPA, a tool that demonstrates how such quantitative
details can be used to automatically generate working Bio-PEPA code from a
textual representation of SBGN-PD that we developed. Bio-PEPA is a process
algebra that was designed for implementing quantitative models of concurrent
biochemical reaction systems. We use this approach to compute the expected
delay between input and output using deterministic and stochastic simulations
of the MAPK signal transduction cascade. The scheme developed here is general
and can be easily adapted to other output formalisms
- …