348 research outputs found
Scaling theory of transport in complex networks
Transport is an important function in many network systems and understanding
its behavior on biological, social, and technological networks is crucial for a
wide range of applications. However, it is a property that is not
well-understood in these systems and this is probably due to the lack of a
general theoretical framework. Here, based on the finding that renormalization
can be applied to bio-networks, we develop a scaling theory of transport in
self-similar networks. We demonstrate the networks invariance under length
scale renormalization and we show that the problem of transport can be
characterized in terms of a set of critical exponents. The scaling theory
allows us to determine the influence of the modular structure on transport. We
also generalize our theory by presenting and verifying scaling arguments for
the dependence of transport on microscopic features, such as the degree of the
nodes and the distance between them. Using transport concepts such as diffusion
and resistance we exploit this invariance and we are able to explain, based on
the topology of the network, recent experimental results on the broad flow
distribution in metabolic networks.Comment: 8 pages, 6 figure
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
Global organization of metabolic fluxes in the bacterium, Escherichia coli
Cellular metabolism, the integrated interconversion of thousands of metabolic
substrates through enzyme-catalyzed biochemical reactions, is the most
investigated complex intercellular web of molecular interactions. While the
topological organization of individual reactions into metabolic networks is
increasingly well understood, the principles governing their global functional
utilization under different growth conditions pose many open questions. We
implement a flux balance analysis of the E. coli MG1655 metabolism, finding
that the network utilization is highly uneven: while most metabolic reactions
have small fluxes, the metabolism's activity is dominated by several reactions
with very high fluxes. E. coli responds to changes in growth conditions by
reorganizing the rates of selected fluxes predominantly within this high flux
backbone. The identified behavior likely represents a universal feature of
metabolic activity in all cells, with potential implications to metabolic
engineering.Comment: 15 pages 4 figure
Ultrafast Structural Dynamics of BlsA, a Photoreceptor from the Pathogenic Bacterium Acinetobacter baumannii
Acinetobacter baumannii is an important human pathogen that can form biofilms and persist under harsh environmental conditions. Biofilm formation and virulence are modulated by blue light, which is thought to be regulated by a BLUF protein, BlsA. To understand the molecular mechanism of light sensing, we have used steady-state and ultrafast vibrational spectroscopy to compare the photoactivation mechanism of BlsA to the BLUF photosensor AppA from Rhodobacter sphaeroides. Although similar photocycles are observed, vibrational data together with homology modeling identify significant differences in the β5 strand in BlsA caused by photoactivation, which are proposed to be directly linked to downstream signaling
A Taxonomy of Causality-Based Biological Properties
We formally characterize a set of causality-based properties of metabolic
networks. This set of properties aims at making precise several notions on the
production of metabolites, which are familiar in the biologists' terminology.
From a theoretical point of view, biochemical reactions are abstractly
represented as causal implications and the produced metabolites as causal
consequences of the implication representing the corresponding reaction. The
fact that a reactant is produced is represented by means of the chain of
reactions that have made it exist. Such representation abstracts away from
quantities, stoichiometric and thermodynamic parameters and constitutes the
basis for the characterization of our properties. Moreover, we propose an
effective method for verifying our properties based on an abstract model of
system dynamics. This consists of a new abstract semantics for the system seen
as a concurrent network and expressed using the Chemical Ground Form calculus.
We illustrate an application of this framework to a portion of a real
metabolic pathway
BLUF Domain Function Does Not Require a Metastable Radical Intermediate State
BLUF
(blue light using flavin) domain proteins are an important
family of blue light-sensing proteins which control a wide variety
of functions in cells. The primary light-activated step in the BLUF
domain is not yet established. A number of experimental and theoretical
studies points to a role for photoinduced electron transfer (PET)
between a highly conserved tyrosine and the flavin chromophore to
form a radical intermediate state. Here we investigate the role of
PET in three different BLUF proteins, using ultrafast broadband transient
infrared spectroscopy. We characterize and identify infrared active
marker modes for excited and ground state species and use them to
record photochemical dynamics in the proteins. We also generate mutants
which unambiguously show PET and, through isotope labeling of the
protein and the chromophore, are able to assign modes characteristic
of both flavin and protein radical states. We find that these radical
intermediates are not observed in two of the three BLUF domains studied,
casting doubt on the importance of the formation of a population of
radical intermediates in the BLUF photocycle. Further, unnatural amino
acid mutagenesis is used to replace the conserved tyrosine with fluorotyrosines,
thus modifying the driving force for the proposed electron transfer
reaction; the rate changes observed are also not consistent with a
PET mechanism. Thus, while intermediates of PET reactions can be observed
in BLUF proteins they are not correlated with photoactivity, suggesting
that radical intermediates are not central to their operation. Alternative
nonradical pathways including a keto–enol tautomerization induced
by electronic excitation of the flavin ring are considered
Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems
A generic mechanism - networked buffering - is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems. \ud
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Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study
The last decade has seen an explosion in models that describe phenomena in
systems medicine. Such models are especially useful for studying signaling
pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to
showcase current mathematical and statistical techniques that enable modelers
to gain insight into (models of) gene regulation, and generate testable
predictions. We introduce a range of modeling frameworks, but focus on ordinary
differential equation (ODE) models since they remain the most widely used
approach in systems biology and medicine and continue to offer great potential.
We present methods for the analysis of a single model, comprising applications
of standard dynamical systems approaches such as nondimensionalization, steady
state, asymptotic and sensitivity analysis, and more recent statistical and
algebraic approaches to compare models with data. We present parameter
estimation and model comparison techniques, focusing on Bayesian analysis and
coplanarity via algebraic geometry. Our intention is that this (non exhaustive)
review may serve as a useful starting point for the analysis of models in
systems medicine.Comment: Submitted to 'Systems Medicine' as a book chapte
Photo-Ionization in Liquids and Crystals and the Dependence of the Frequency of X-Ray Absorption Edges on Chemical Constitution
Accelerated search for biomolecular network models to interpret high-throughput experimental data
<p>Abstract</p> <p>Background</p> <p>The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables.</p> <p>Results</p> <p>Optimal parameters for the evolutionary search were identified based on artificial data, and the algorithm showed scalable and consistent performance for as many as 150 variables. The method was tested on previously published human cell cycle gene expression microarray data sets. The evolutionary search method was found to converge to the results of exhaustive search. The randomized evolutionary search was able to converge on a set of similar best-fitting network models on different training data sets after 30 generations running 30 models per generation. Consistent results were found regardless of which of the published data sets were used to train or verify the quantitative predictions of the best-fitting models for cell cycle gene dynamics.</p> <p>Conclusion</p> <p>Our results demonstrate the capability of scalable evolutionary search for fuzzy network models to address the problem of inferring models based on complex, noisy biomolecular data sets. This approach yields multiple alternative models that are consistent with the data, yielding a constrained set of hypotheses that can be used to optimally design subsequent experiments.</p
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