4,921 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
Energy-based Analysis of Biochemical Cycles using Bond Graphs
Thermodynamic aspects of chemical reactions have a long history in the
Physical Chemistry literature. In particular, biochemical cycles - the
building-blocks of biochemical systems - require a source of energy to
function. However, although fundamental, the role of chemical potential and
Gibb's free energy in the analysis of biochemical systems is often overlooked
leading to models which are physically impossible. The bond graph approach was
developed for modelling engineering systems where energy generation, storage
and transmission are fundamental. The method focuses on how power flows between
components and how energy is stored, transmitted or dissipated within
components. Based on early ideas of network thermodynamics, we have applied
this approach to biochemical systems to generate models which automatically
obey the laws of thermodynamics. We illustrate the method with examples of
biochemical cycles. We have found that thermodynamically compliant models of
simple biochemical cycles can easily be developed using this approach. In
particular, both stoichiometric information and simulation models can be
developed directly from the bond graph. Furthermore, model reduction and
approximation while retaining structural and thermodynamic properties is
facilitated. Because the bond graph approach is also modular and scaleable, we
believe that it provides a secure foundation for building thermodynamically
compliant models of large biochemical networks
A Partitioning Algorithm for Maximum Common Subgraph Problems
We introduce a new branch and bound algorithm for the maximum common subgraph and maximum common connected subgraph problems which is based around vertex labelling and partitioning. Our method in some ways resembles a traditional constraint programming approach, but uses a novel compact domain store and supporting inference algorithms which dramatically reduce the memory and computation requirements during search, and allow better dual viewpoint ordering heuristics to be calculated cheaply. Experiments show a speedup of more than an order of magnitude over the state of the art, and demonstrate that we can operate on much larger graphs without running out of memory
Graphical Conditions for Rate Independence in Chemical Reaction Networks
Chemical Reaction Networks (CRNs) provide a useful abstraction of molecular
interaction networks in which molecular structures as well as mass conservation
principles are abstracted away to focus on the main dynamical properties of the
network structure. In their interpretation by ordinary differential equations,
we say that a CRN with distinguished input and output species computes a
positive real function \rightarrow, if for any initial
concentration x of the input species, the concentration of the output molecular
species stabilizes at concentration f (x). The Turing-completeness of that
notion of chemical analog computation has been established by proving that any
computable real function can be computed by a CRN over a finite set of
molecular species. Rate-independent CRNs form a restricted class of CRNs of
high practical value since they enjoy a form of absolute robustness in the
sense that the result is completely independent of the reaction rates and
depends solely on the input concentrations. The functions computed by
rate-independent CRNs have been characterized mathematically as the set of
piecewise linear functions from input species. However, this does not provide a
mean to decide whether a given CRN is rate-independent. In this paper, we
provide graphical conditions on the Petri Net structure of a CRN which entail
the rate-independence property either for all species or for some output
species. We show that in the curated part of the Biomodels repository, among
the 590 reaction models tested, 2 reaction graphs were found to satisfy our
rate-independence conditions for all species, 94 for some output species, among
which 29 for some non-trivial output species. Our graphical conditions are
based on a non-standard use of the Petri net notions of place-invariants and
siphons which are computed by constraint programming techniques for efficiency
reasons
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
Programmable models of growth and mutation of cancer-cell populations
In this paper we propose a systematic approach to construct mathematical
models describing populations of cancer-cells at different stages of disease
development. The methodology we propose is based on stochastic Concurrent
Constraint Programming, a flexible stochastic modelling language. The
methodology is tested on (and partially motivated by) the study of prostate
cancer. In particular, we prove how our method is suitable to systematically
reconstruct different mathematical models of prostate cancer growth - together
with interactions with different kinds of hormone therapy - at different levels
of refinement.Comment: In Proceedings CompMod 2011, arXiv:1109.104
Generic Strategies for Chemical Space Exploration
Computational approaches to exploring "chemical universes", i.e., very large
sets, potentially infinite sets of compounds that can be constructed by a
prescribed collection of reaction mechanisms, in practice suffer from a
combinatorial explosion. It quickly becomes impossible to test, for all pairs
of compounds in a rapidly growing network, whether they can react with each
other. More sophisticated and efficient strategies are therefore required to
construct very large chemical reaction networks.
Undirected labeled graphs and graph rewriting are natural models of chemical
compounds and chemical reactions. Borrowing the idea of partial evaluation from
functional programming, we introduce partial applications of rewrite rules.
Binding substrate to rules increases the number of rules but drastically prunes
the substrate sets to which it might match, resulting in dramatically reduced
resource requirements. At the same time, exploration strategies can be guided,
e.g. based on restrictions on the product molecules to avoid the explicit
enumeration of very unlikely compounds. To this end we introduce here a generic
framework for the specification of exploration strategies in graph-rewriting
systems. Using key examples of complex chemical networks from sugar chemistry
and the realm of metabolic networks we demonstrate the feasibility of a
high-level strategy framework.
The ideas presented here can not only be used for a strategy-based chemical
space exploration that has close correspondence of experimental results, but
are much more general. In particular, the framework can be used to emulate
higher-level transformation models such as illustrated in a small puzzle game
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