2,187 research outputs found
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
Metabolite essentiality elucidates robustness of Escherichia coli metabolism
Complex biological systems are very robust to genetic and environmental
changes at all levels of organization. Many biological functions of Escherichia
coli metabolism can be sustained against single-gene or even multiple-gene
mutations by using redundant or alternative pathways. Thus, only a limited
number of genes have been identified to be lethal to the cell. In this regard,
the reaction-centric gene deletion study has a limitation in understanding the
metabolic robustness. Here, we report the use of flux-sum, which is the
summation of all incoming or outgoing fluxes around a particular metabolite
under pseudo-steady state conditions, as a good conserved property for
elucidating such robustness of E. coli from the metabolite point of view. The
functional behavior, as well as the structural and evolutionary properties of
metabolites essential to the cell survival, was investigated by means of a
constraints-based flux analysis under perturbed conditions. The essential
metabolites are capable of maintaining a steady flux-sum even against severe
perturbation by actively redistributing the relevant fluxes. Disrupting the
flux-sum maintenance was found to suppress cell growth. This approach of
analyzing metabolite essentiality provides insight into cellular robustness and
concomitant fragility, which can be used for several applications, including
the development of new drugs for treating pathogens.Comment: Supplements available at
http://stat.kaist.ac.kr/publication/2007/PJKim_pnas_supplement.pd
Improved Network Performance via Antagonism: From Synthetic Rescues to Multi-drug Combinations
Recent research shows that a faulty or sub-optimally operating metabolic
network can often be rescued by the targeted removal of enzyme-coding
genes--the exact opposite of what traditional gene therapy would suggest.
Predictions go as far as to assert that certain gene knockouts can restore the
growth of otherwise nonviable gene-deficient cells. Many questions follow from
this discovery: What are the underlying mechanisms? How generalizable is this
effect? What are the potential applications? Here, I will approach these
questions from the perspective of compensatory perturbations on networks.
Relations will be drawn between such synthetic rescues and naturally occurring
cascades of reaction inactivation, as well as their analogues in physical and
other biological networks. I will specially discuss how rescue interactions can
lead to the rational design of antagonistic drug combinations that select
against resistance and how they can illuminate medical research on cancer,
antibiotics, and metabolic diseases.Comment: Online Open "Problems and Paradigms" articl
Complex networks theory for analyzing metabolic networks
One of the main tasks of post-genomic informatics is to systematically
investigate all molecules and their interactions within a living cell so as to
understand how these molecules and the interactions between them relate to the
function of the organism, while networks are appropriate abstract description
of all kinds of interactions. In the past few years, great achievement has been
made in developing theory of complex networks for revealing the organizing
principles that govern the formation and evolution of various complex
biological, technological and social networks. This paper reviews the
accomplishments in constructing genome-based metabolic networks and describes
how the theory of complex networks is applied to analyze metabolic networks.Comment: 13 pages, 2 figure
Backup machinery of yeast transcriptional regulatory network
Several studies have suggested the existence of backup machinery of transcriptional regulatory networks (TRNs). Here, we have quantified the backup machinery of yeast gene's TRNs under five different conditions in terms of alternate paths and have revealed that a statistically significant (p<0.0001) stronger backup is maintained for endogenous processes (ENPs) than exogenous processes (EXPs). A number of biologically important genes (SUC2, MF(ALPHA)2, CLN2 etc) are observed that maintain a higher backup. Hub and random transcription factor (TF) knockouts in TRNs have showed ENPs are more robust to deletion than EXPs. While higher average connectivity of TFs in EXPs than ENPs can't explain the higher robustness in ENPs, we have found that the later have a densely interconnectedness explaining their specialized architecture that may have evolved due to evolutionary pressure. Some non-hub TFs identified here are more likely to be essential, and if not essential, have a larger impact on fitness
In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks
Background Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. Results Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. Conclusions The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis
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