41,556 research outputs found
Identification of control targets in Boolean molecular network models via computational algebra
Motivation: Many problems in biomedicine and other areas of the life sciences
can be characterized as control problems, with the goal of finding strategies
to change a disease or otherwise undesirable state of a biological system into
another, more desirable, state through an intervention, such as a drug or other
therapeutic treatment. The identification of such strategies is typically based
on a mathematical model of the process to be altered through targeted control
inputs. This paper focuses on processes at the molecular level that determine
the state of an individual cell, involving signaling or gene regulation. The
mathematical model type considered is that of Boolean networks. The potential
control targets can be represented by a set of nodes and edges that can be
manipulated to produce a desired effect on the system. Experimentally, node
manipulation requires technology to completely repress or fully activate a
particular gene product while edge manipulations only require a drug that
inactivates the interaction between two gene products. Results: This paper
presents a method for the identification of potential intervention targets in
Boolean molecular network models using algebraic techniques. The approach
exploits an algebraic representation of Boolean networks to encode the control
candidates in the network wiring diagram as the solutions of a system of
polynomials equations, and then uses computational algebra techniques to find
such controllers. The control methods in this paper are validated through the
identification of combinatorial interventions in the signaling pathways of
previously reported control targets in two well studied systems, a p53-mdm2
network and a blood T cell lymphocyte granular leukemia survival signaling
network.Comment: 12 pages, 4 figures, 2 table
On delayed genetic regulatory networks with polytopic uncertainties: Robust stability analysis
Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, we investigate the robust asymptotic stability problem of genetic regulatory networks with time-varying delays and polytopic parameter uncertainties. Both cases of differentiable and nondifferentiable time-delays are considered, and the convex polytopic description is utilized to characterize the genetic network model uncertainties. By using a Lyapunov functional approach and linear matrix inequality (LMI) techniques, the stability criteria for the uncertain delayed genetic networks are established in the form of LMIs, which can be readily verified by using standard numerical software. An important feature of the results reported here is that all the stability conditions are dependent on the upper and lower bounds of the delays, which is made possible by using up-to-date techniques for achieving delay dependence. Another feature of the results lies in that a novel Lyapunov functional dependent on the uncertain parameters is utilized, which renders the results to be potentially less conservative than those obtained via a fixed Lyapunov functional for the entire uncertainty domain. A genetic network example is employed to illustrate the applicability and usefulness of the developed theoretical results
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
How to understand the cell by breaking it: network analysis of gene perturbation screens
Modern high-throughput gene perturbation screens are key technologies at the
forefront of genetic research. Combined with rich phenotypic descriptors they
enable researchers to observe detailed cellular reactions to experimental
perturbations on a genome-wide scale. This review surveys the current
state-of-the-art in analyzing perturbation screens from a network point of
view. We describe approaches to make the step from the parts list to the wiring
diagram by using phenotypes for network inference and integrating them with
complementary data sources. The first part of the review describes methods to
analyze one- or low-dimensional phenotypes like viability or reporter activity;
the second part concentrates on high-dimensional phenotypes showing global
changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
figure
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
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