137 research outputs found
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
TREEOME: A framework for epigenetic and transcriptomic data integration to explore regulatory interactions controlling transcription
Motivation: Predictive modelling of gene expression is a powerful framework
for the in silico exploration of transcriptional regulatory interactions
through the integration of high-throughput -omics data. A major limitation of
previous approaches is their inability to handle conditional and synergistic
interactions that emerge when collectively analysing genes subject to different
regulatory mechanisms. This limitation reduces overall predictive power and
thus the reliability of downstream biological inference.
Results: We introduce an analytical modelling framework (TREEOME: tree of
models of expression) that integrates epigenetic and transcriptomic data by
separating genes into putative regulatory classes. Current predictive modelling
approaches have found both DNA methylation and histone modification epigenetic
data to provide little or no improvement in accuracy of prediction of
transcript abundance despite, for example, distinct anti-correlation between
mRNA levels and promoter-localised DNA methylation. To improve on this, in
TREEOME we evaluate four possible methods of formulating gene-level DNA
methylation metrics, which provide a foundation for identifying gene-level
methylation events and subsequent differential analysis, whereas most previous
techniques operate at the level of individual CpG dinucleotides. We demonstrate
TREEOME by integrating gene-level DNA methylation (bisulfite-seq) and histone
modification (ChIP-seq) data to accurately predict genome-wide mRNA transcript
abundance (RNA-seq) for H1-hESC and GM12878 cell lines.
Availability: TREEOME is implemented using open-source software and made
available as a pre-configured bootable reference environment. All scripts and
data presented in this study are available online at
http://sourceforge.net/projects/budden2015treeome/.Comment: 14 pages, 6 figure
A thermodynamic framework for modelling membrane transporters
Membrane transporters contribute to the regulation of the internal
environment of cells by translocating substrates across cell membranes. Like
all physical systems, the behaviour of membrane transporters is constrained by
the laws of thermodynamics. However, many mathematical models of transporters,
especially those incorporated into whole-cell models, are not thermodynamically
consistent, leading to unrealistic behaviour. In this paper we use a
physics-based modelling framework, in which the transfer of energy is
explicitly accounted for, to develop thermodynamically consistent models of
transporters. We then apply this methodology to model two specific
transporters: the cardiac sarcoplasmic/endoplasmic Ca ATPase (SERCA) and
the cardiac Na/K ATPase
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