12,685 research outputs found
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
Gene-network inference by message passing
The inference of gene-regulatory processes from gene-expression data belongs
to the major challenges of computational systems biology. Here we address the
problem from a statistical-physics perspective and develop a message-passing
algorithm which is able to infer sparse, directed and combinatorial regulatory
mechanisms. Using the replica technique, the algorithmic performance can be
characterized analytically for artificially generated data. The algorithm is
applied to genome-wide expression data of baker's yeast under various
environmental conditions. We find clear cases of combinatorial control, and
enrichment in common functional annotations of regulated genes and their
regulators.Comment: Proc. of International Workshop on Statistical-Mechanical Informatics
2007, Kyot
Gene-network inference by message passing
The inference of gene-regulatory processes from gene-expression data belongs
to the major challenges of computational systems biology. Here we address the
problem from a statistical-physics perspective and develop a message-passing
algorithm which is able to infer sparse, directed and combinatorial regulatory
mechanisms. Using the replica technique, the algorithmic performance can be
characterized analytically for artificially generated data. The algorithm is
applied to genome-wide expression data of baker's yeast under various
environmental conditions. We find clear cases of combinatorial control, and
enrichment in common functional annotations of regulated genes and their
regulators.Comment: Proc. of International Workshop on Statistical-Mechanical Informatics
2007, Kyot
Gene-network inference by message passing
The inference of gene-regulatory processes from gene-expression data belongs
to the major challenges of computational systems biology. Here we address the
problem from a statistical-physics perspective and develop a message-passing
algorithm which is able to infer sparse, directed and combinatorial regulatory
mechanisms. Using the replica technique, the algorithmic performance can be
characterized analytically for artificially generated data. The algorithm is
applied to genome-wide expression data of baker's yeast under various
environmental conditions. We find clear cases of combinatorial control, and
enrichment in common functional annotations of regulated genes and their
regulators.Comment: Proc. of International Workshop on Statistical-Mechanical Informatics
2007, Kyot
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
Rigidity and flexibility of biological networks
The network approach became a widely used tool to understand the behaviour of
complex systems in the last decade. We start from a short description of
structural rigidity theory. A detailed account on the combinatorial rigidity
analysis of protein structures, as well as local flexibility measures of
proteins and their applications in explaining allostery and thermostability is
given. We also briefly discuss the network aspects of cytoskeletal tensegrity.
Finally, we show the importance of the balance between functional flexibility
and rigidity in protein-protein interaction, metabolic, gene regulatory and
neuronal networks. Our summary raises the possibility that the concepts of
flexibility and rigidity can be generalized to all networks.Comment: 21 pages, 4 figures, 1 tabl
Model-guided design of ligand-regulated RNAi for programmable control of gene expression
Progress in constructing biological networks will rely on the development of more advanced components that can be predictably modified to yield optimal system performance. We have engineered an RNA-based platform, which we call an shRNA switch, that provides for integrated ligand control of RNA interference (RNAi) by modular coupling of an aptamer, competing strand, and small hairpin (sh) RNA stem into a single component that links ligand concentration and target gene expression levels. A combined experimental and mathematical modelling approach identified multiple tuning strategies and moves towards a predictable framework for the forward design of shRNA switches. The utility of our platform is highlighted by the demonstration of fine-tuning, multi-input control, and model-guided design of shRNA switches with an optimized dynamic range. Thus, shRNA switches can serve as an advanced component for the construction of complex biological systems and offer a controlled means of activating RNAi in disease therapeutics
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