22,549 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
Reconstruction of Directed Networks from Consensus Dynamics
This paper addresses the problem of identifying the topology of an unknown,
weighted, directed network running a consensus dynamics. We propose a
methodology to reconstruct the network topology from the dynamic response when
the system is stimulated by a wide-sense stationary noise of unknown power
spectral density. The method is based on a node-knockout, or grounding,
procedure wherein the grounded node broadcasts zero without being eliminated
from the network. In this direction, we measure the empirical cross-power
spectral densities of the outputs between every pair of nodes for both grounded
and ungrounded consensus to reconstruct the unknown topology of the network. We
also establish that in the special cases of undirected or purely unidirectional
networks, the reconstruction does not need grounding. Finally, we extend our
results to the case of a directed network assuming a general dynamics, and
prove that the developed method can detect edges and their direction.Comment: 6 page
A Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks
This paper proposes a new method to reverse engineer gene regulatory networks
from experimental data. The modeling framework used is time-discrete
deterministic dynamical systems, with a finite set of states for each of the
variables. The simplest examples of such models are Boolean networks, in which
variables have only two possible states. The use of a larger number of possible
states allows a finer discretization of experimental data and more than one
possible mode of action for the variables, depending on threshold values.
Furthermore, with a suitable choice of state set, one can employ powerful tools
from computational algebra, that underlie the reverse-engineering algorithm,
avoiding costly enumeration strategies. To perform well, the algorithm requires
wildtype together with perturbation time courses. This makes it suitable for
small to meso-scale networks rather than networks on a genome-wide scale. The
complexity of the algorithm is quadratic in the number of variables and cubic
in the number of time points. The algorithm is validated on a recently
published Boolean network model of segment polarity development in Drosophila
melanogaster.Comment: 28 pages, 5 EPS figures, uses elsart.cl
The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market
The topological properties of interbank networks have been discussed widely
in the literature mainly because of their relevance for systemic risk. Here we
propose to use the Stochastic Block Model to investigate and perform a model
selection among several possible two block organizations of the network: these
include bipartite, core-periphery, and modular structures. We apply our method
to the e-MID interbank market in the period 2010-2014 and we show that in
normal conditions the most likely network organization is a bipartite
structure. In exceptional conditions, such as after LTRO, one of the most
important unconventional measures by ECB at the beginning of 2012, the most
likely structure becomes a random one and only in 2014 the e-MID market went
back to a normal bipartite organization. By investigating the strategy of
individual banks, we explore possible explanations and we show that the
disappearance of many lending banks and the strategy switch of a very small set
of banks from borrower to lender is likely at the origin of this structural
change.Comment: 33 pages, 5 figure
A Systemic Receptor Network Triggered by Human cytomegalovirus Entry
Virus entry is a multistep process that triggers a variety of cellular
pathways interconnecting into a complex network, yet the molecular complexity
of this network remains largely unsolved. Here, by employing systems biology
approach, we reveal a systemic virus-entry network initiated by human
cytomegalovirus (HCMV), a widespread opportunistic pathogen. This network
contains all known interactions and functional modules (i.e. groups of
proteins) coordinately responding to HCMV entry. The number of both genes and
functional modules activated in this network dramatically declines shortly,
within 25 min post-infection. While modules annotated as receptor system, ion
transport, and immune response are continuously activated during the entire
process of HCMV entry, those for cell adhesion and skeletal movement are
specifically activated during viral early attachment, and those for immune
response during virus entry. HCMV entry requires a complex receptor network
involving different cellular components, comprising not only cell surface
receptors, but also pathway components in signal transduction, skeletal
development, immune response, endocytosis, ion transport, macromolecule
metabolism and chromatin remodeling. Interestingly, genes that function in
chromatin remodeling are the most abundant in this receptor system, suggesting
that global modulation of transcriptions is one of the most important events in
HCMV entry. Results of in silico knock out further reveal that this entire
receptor network is primarily controlled by multiple elements, such as EGFR
(Epidermal Growth Factor) and SLC10A1 (sodium/bile acid cotransporter family,
member 1). Thus, our results demonstrate that a complex systemic network, in
which components coordinating efficiently in time and space contributes to
virus entry.Comment: 26 page
Perspective: network-guided pattern formation of neural dynamics
The understanding of neural activity patterns is fundamentally linked to an
understanding of how the brain's network architecture shapes dynamical
processes. Established approaches rely mostly on deviations of a given network
from certain classes of random graphs. Hypotheses about the supposed role of
prominent topological features (for instance, the roles of modularity, network
motifs, or hierarchical network organization) are derived from these
deviations. An alternative strategy could be to study deviations of network
architectures from regular graphs (rings, lattices) and consider the
implications of such deviations for self-organized dynamic patterns on the
network. Following this strategy, we draw on the theory of spatiotemporal
pattern formation and propose a novel perspective for analyzing dynamics on
networks, by evaluating how the self-organized dynamics are confined by network
architecture to a small set of permissible collective states. In particular, we
discuss the role of prominent topological features of brain connectivity, such
as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the
notion of network-guided pattern formation with numerical simulations and
outline how it can facilitate the understanding of neural dynamics
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