1,288 research outputs found
<Bioinformatics Center>Mathematical Bioinformatics
This Annual Report covers from 1 January to 31 December 201
Control of complex networks requires both structure and dynamics
The study of network structure has uncovered signatures of the organization
of complex systems. However, there is also a need to understand how to control
them; for example, identifying strategies to revert a diseased cell to a
healthy state, or a mature cell to a pluripotent state. Two recent
methodologies suggest that the controllability of complex systems can be
predicted solely from the graph of interactions between variables, without
considering their dynamics: structural controllability and minimum dominating
sets. We demonstrate that such structure-only methods fail to characterize
controllability when dynamics are introduced. We study Boolean network
ensembles of network motifs as well as three models of biochemical regulation:
the segment polarity network in Drosophila melanogaster, the cell cycle of
budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in
Arabidopsis thaliana. We demonstrate that structure-only methods both
undershoot and overshoot the number and which sets of critical variables best
control the dynamics of these models, highlighting the importance of the actual
system dynamics in determining control. Our analysis further shows that the
logic of automata transition functions, namely how canalizing they are, plays
an important role in the extent to which structure predicts dynamics.Comment: 15 pages, 6 figure
Systems Perturbation Analysis of a Large Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics
Dysregulation in signal transduction pathways can lead to a variety of
complex disorders, including cancer. Computational approaches such as network
analysis are important tools to understand system dynamics as well as to
identify critical components that could be further explored as therapeutic
targets. Here, we performed perturbation analysis of a large-scale signal
transduction model in extracellular environments that stimulate cell death,
growth, motility, and quiescence. Each of the model's components was perturbed
under both loss-of-function and gain-of-function mutations. We identified the
most and least influential components based on the magnitude of their influence
on the rest of the system. Based on the premise that the most influential
components might serve as better drug targets, we characterized them for
biological functions, housekeeping genes, essential genes, and druggable
proteins. Moreover, known cancer drug targets were also classified in
influential components based on the affected components in the network.
Additionally, the systemic perturbation analysis of the model revealed a
network motif of most influential components which affect each other.
Furthermore, our analysis predicted novel combinations of cancer drug targets
with various effects on other most influential components. We found that the
combinatorial perturbation consisting of PI3K inactivation and overactivation
of IP3R1 can lead to increased activity levels of apoptosis-related components
and tumor suppressor genes, suggesting that this combinatorial perturbation may
lead to a better target for decreasing cell proliferation and inducing
apoptosis. Lastly, our results suggest that systematic perturbation analyses of
large-scale computational models may serve as an approach to prioritize and
assess signal transduction components in order to identify novel drug targets
in complex disorders.Comment: 24 pages, 9 Figure
Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli.
The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype–phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large-scale network models with sufficient accuracy
2016 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics
Schedule and abstract book for the Eighth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics
Date: October 8-9, 2016Location: UT Conference Center, KnoxvillePlenary Speaker: Jorge X. Velasco Hernández, Universidad Nacional Autónoma de MéxicoFeatured Speaker: Judy Day, University of Tennessee, Knoxvill
Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism
Motivation: Despite being often perceived as the main contributors to cell
fate and physiology, genes alone cannot predict cellular phenotype. During the
process of gene expression, 95% of human genes can code for multiple proteins
due to alternative splicing. While most splice variants of a gene carry the
same function, variants within some key genes can have remarkably different
roles. To bridge the gap between genotype and phenotype, condition- and
tissue-specific models of metabolism have been constructed. However, current
metabolic models only include information at the gene level. Consequently, as
recently acknowledged by the scientific community, common situations where
changes in splice-isoform expression levels alter the metabolic outcome cannot
be modeled. Results: We here propose GEMsplice, the first method for the
incorporation of splice-isoform expression data into genome-scale metabolic
models. Using GEMsplice, we make full use of RNA-Seq quantitative expression
profiles to predict, for the first time, the effects of splice isoform-level
changes in the metabolism of 1455 patients with 31 different breast cancer
types. We validate GEMsplice by generating cancer-versus-normal predictions on
metabolic pathways, and by comparing with gene-level approaches and available
literature on pathways affected by breast cancer. GEMsplice is freely available
for academic use at https://github.com/GEMsplice/GEMsplice_code. Compared to
state-of-the-art methods, we anticipate that GEMsplice will enable for the
first time computational analyses at transcript level with splice-isoform
resolution
TaBooN -- Boolean Network Synthesis Based on Tabu Search
Recent developments in Omics-technologies revolutionized the investigation of
biology by producing molecular data in multiple dimensions and scale. This
breakthrough in biology raises the crucial issue of their interpretation based
on modelling. In this undertaking, network provides a suitable framework for
modelling the interactions between molecules. Basically a Biological network is
composed of nodes referring to the components such as genes or proteins, and
the edges/arcs formalizing interactions between them. The evolution of the
interactions is then modelled by the definition of a dynamical system. Among
the different categories of network, the Boolean network offers a reliable
qualitative framework for the modelling. Automatically synthesizing a Boolean
network from experimental data therefore remains a necessary but challenging
issue. In this study, we present taboon, an original work-flow for synthesizing
Boolean Networks from biological data. The methodology uses the data in the
form of Boolean profiles for inferring all the potential local formula
inference. They combine to form the model space from which the most truthful
model with regards to biological knowledge and experiments must be found. In
the taboon work-flow the selection of the fittest model is achieved by a
Tabu-search algorithm. taboon is an automated method for Boolean Network
inference from experimental data that can also assist to evaluate and optimize
the dynamic behaviour of the biological networks providing a reliable platform
for further modelling and predictions
The Role Of Adult Stem Cells And Tumor Necrosis Factor In Peripheral Neuropathy
Peripheral neuropathies are a significant cause of morbidity and mortality, with a population prevalence of 2,400 per 100,000 (2.4%) that increases in the elderly to 8,000 per 100,000 (8%)(C. N. Martyn and R. A. Hughes, 1997). The variations in symptom distribution and etiologic attribution have resulted in the classification of over 100 types of peripheral neuropathy with specific patterns of development and prognoses. In the first study, we use a mouse model of hereditary peripheral neuropathy that results in hind-limb paralysis to investigate the therapeutic efficacy of adult, adipose derived stem cells (ADSC). The paralyzed mice that received ADSC transplantation demonstrated significantly improved motor function, likely due to stromal support provided by ADSCs. The ultrastructure of the nerve was not significantly improved, indicating that the threshold of functional motor improvement can be met through alternative means. In the second study, we developed a process to identify highly-connected genes in a model of peripheral nerve development using entropy maximized network analysis of gene microarrays. We found that Tumor Necrosis Factor (TNF) mediates axonal-Schwann cell communication, and that disruption of TNF signaling results in sensory and tissue dysfunction. These findings indicate that the threshold of wild-type physiological function in peripheral nerve development can be addressed by disrupting or strengthening specific signaling processes without significant changes to tissue structure
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