4,212 research outputs found
Augmented Sparse Reconstruction of Protein Signaling Networks
The problem of reconstructing and identifying intracellular protein signaling
and biochemical networks is of critical importance in biology today. We sought
to develop a mathematical approach to this problem using, as a test case, one
of the most well-studied and clinically important signaling networks in biology
today, the epidermal growth factor receptor (EGFR) driven signaling cascade.
More specifically, we suggest a method, augmented sparse reconstruction, for
the identification of links among nodes of ordinary differential equation (ODE)
networks from a small set of trajectories with different initial conditions.
Our method builds a system of representation by using a collection of integrals
of all given trajectories and by attenuating block of terms in the
representation itself. The system of representation is then augmented with
random vectors, and minimization of the 1-norm is used to find sparse
representations for the dynamical interactions of each node. Augmentation by
random vectors is crucial, since sparsity alone is not able to handle the large
error-in-variables in the representation. Augmented sparse reconstruction
allows to consider potentially very large spaces of models and it is able to
detect with high accuracy the few relevant links among nodes, even when
moderate noise is added to the measured trajectories. After showing the
performance of our method on a model of the EGFR protein network, we sketch
briefly the potential future therapeutic applications of this approach.Comment: 24 pages, 6 figure
Systems approaches and algorithms for discovery of combinatorial therapies
Effective therapy of complex diseases requires control of highly non-linear
complex networks that remain incompletely characterized. In particular, drug
intervention can be seen as control of signaling in cellular networks.
Identification of control parameters presents an extreme challenge due to the
combinatorial explosion of control possibilities in combination therapy and to
the incomplete knowledge of the systems biology of cells. In this review paper
we describe the main current and proposed approaches to the design of
combinatorial therapies, including the empirical methods used now by clinicians
and alternative approaches suggested recently by several authors. New
approaches for designing combinations arising from systems biology are
described. We discuss in special detail the design of algorithms that identify
optimal control parameters in cellular networks based on a quantitative
characterization of control landscapes, maximizing utilization of incomplete
knowledge of the state and structure of intracellular networks. The use of new
technology for high-throughput measurements is key to these new approaches to
combination therapy and essential for the characterization of control
landscapes and implementation of the algorithms. Combinatorial optimization in
medical therapy is also compared with the combinatorial optimization of
engineering and materials science and similarities and differences are
delineated.Comment: 25 page
Network estimation in State Space Model with L1-regularization constraint
Biological networks have arisen as an attractive paradigm of genomic science
ever since the introduction of large scale genomic technologies which carried
the promise of elucidating the relationship in functional genomics. Microarray
technologies coupled with appropriate mathematical or statistical models have
made it possible to identify dynamic regulatory networks or to measure time
course of the expression level of many genes simultaneously. However one of the
few limitations fall on the high-dimensional nature of such data coupled with
the fact that these gene expression data are known to include some hidden
process. In that regards, we are concerned with deriving a method for inferring
a sparse dynamic network in a high dimensional data setting. We assume that the
observations are noisy measurements of gene expression in the form of mRNAs,
whose dynamics can be described by some unknown or hidden process. We build an
input-dependent linear state space model from these hidden states and
demonstrate how an incorporated regularization constraint in an
Expectation-Maximization (EM) algorithm can be used to reverse engineer
transcriptional networks from gene expression profiling data. This corresponds
to estimating the model interaction parameters. The proposed method is
illustrated on time-course microarray data obtained from a well established
T-cell data. At the optimum tuning parameters we found genes TRAF5, JUND, CDK4,
CASP4, CD69, and C3X1 to have higher number of inwards directed connections and
FYB, CCNA2, AKT1 and CASP8 to be genes with higher number of outwards directed
connections. We recommend these genes to be object for further investigation.
Caspase 4 is also found to activate the expression of JunD which in turn
represses the cell cycle regulator CDC2.Comment: arXiv admin note: substantial text overlap with arXiv:1308.359
Introduction to Focus Issue : Dynamics in Systems Biology
Peer reviewedPublisher PD
Information processing and signal integration in bacterial quorum sensing
Bacteria communicate using secreted chemical signaling molecules called
autoinducers in a process known as quorum sensing. The quorum-sensing network
of the marine bacterium {\it Vibrio harveyi} employs three autoinducers, each
known to encode distinct ecological information. Yet how cells integrate and
interpret the information contained within the three autoinducer signals
remains a mystery. Here, we develop a new framework for analyzing signal
integration based on Information Theory and use it to analyze quorum sensing in
{\it V. harveyi}. We quantify how much the cells can learn about individual
autoinducers and explain the experimentally observed input-output relation of
the {\it V. harveyi} quorum-sensing circuit. Our results suggest that the need
to limit interference between input signals places strong constraints on the
architecture of bacterial signal-integration networks, and that bacteria likely
have evolved active strategies for minimizing this interference. Here we
analyze two such strategies: manipulation of autoinducer production and
feedback on receptor number ratios.Comment: Supporting information is in appendi
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