9,510 research outputs found
Local and global modes of drug action in biochemical networks
It becomes increasingly accepted that a shift is needed from the traditional target-based approach of drug development to an integrated perspective of drug action in biochemical systems. We here present an integrative analysis of the interactions between drugs and metabolism based on the concept of drug scope. The drug scope represents the set of metabolic compounds and reactions that are potentially affected by a drug. We constructed and analyzed the scopes of all US approved drugs having metabolic targets. Our analysis shows that the distribution of drug scopes is highly uneven, and that drugs can be classified into several categories based on their scopes. Some of them have small scopes corresponding to localized action, while others have large scopes corresponding to potential large-scale systemic action. These groups are well conserved throughout different topologies of the underlying metabolic network. They can furthermore be associated to specific drug therapeutic properties
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
Therapeutic target discovery using Boolean network attractors: avoiding pathological phenotypes
Target identification, one of the steps of drug discovery, aims at
identifying biomolecules whose function should be therapeutically altered in
order to cure the considered pathology. This work proposes an algorithm for in
silico target identification using Boolean network attractors. It assumes that
attractors of dynamical systems, such as Boolean networks, correspond to
phenotypes produced by the modeled biological system. Under this assumption,
and given a Boolean network modeling a pathophysiology, the algorithm
identifies target combinations able to remove attractors associated with
pathological phenotypes. It is tested on a Boolean model of the mammalian cell
cycle bearing a constitutive inactivation of the retinoblastoma protein, as
seen in cancers, and its applications are illustrated on a Boolean model of
Fanconi anemia. The results show that the algorithm returns target combinations
able to remove attractors associated with pathological phenotypes and then
succeeds in performing the proposed in silico target identification. However,
as with any in silico evidence, there is a bridge to cross between theory and
practice, thus requiring it to be used in combination with wet lab experiments.
Nevertheless, it is expected that the algorithm is of interest for target
identification, notably by exploiting the inexpensiveness and predictive power
of computational approaches to optimize the efficiency of costly wet lab
experiments.Comment: Since the publication of this article and among the possible
improvements mentioned in the Conclusion, two improvements have been done:
extending the algorithm for multivalued logic and considering the basins of
attraction of the pathological attractors for selecting the therapeutic
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Search algorithms as a framework for the optimization of drug combinations
Combination therapies are often needed for effective clinical outcomes in the
management of complex diseases, but presently they are generally based on
empirical clinical experience. Here we suggest a novel application of search
algorithms, originally developed for digital communication, modified to
optimize combinations of therapeutic interventions. In biological experiments
measuring the restoration of the decline with age in heart function and
exercise capacity in Drosophila melanogaster, we found that search algorithms
correctly identified optimal combinations of four drugs with only one third of
the tests performed in a fully factorial search. In experiments identifying
combinations of three doses of up to six drugs for selective killing of human
cancer cells, search algorithms resulted in a highly significant enrichment of
selective combinations compared with random searches. In simulations using a
network model of cell death, we found that the search algorithms identified the
optimal combinations of 6-9 interventions in 80-90% of tests, compared with
15-30% for an equivalent random search. These findings suggest that modified
search algorithms from information theory have the potential to enhance the
discovery of novel therapeutic drug combinations. This report also helps to
frame a biomedical problem that will benefit from an interdisciplinary effort
and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio
A network inference method for large-scale unsupervised identification of novel drug-drug interactions
Characterizing interactions between drugs is important to avoid potentially
harmful combinations, to reduce off-target effects of treatments and to fight
antibiotic resistant pathogens, among others. Here we present a network
inference algorithm to predict uncharacterized drug-drug interactions. Our
algorithm takes, as its only input, sets of previously reported interactions,
and does not require any pharmacological or biochemical information about the
drugs, their targets or their mechanisms of action. Because the models we use
are abstract, our approach can deal with adverse interactions,
synergistic/antagonistic/suppressing interactions, or any other type of drug
interaction. We show that our method is able to accurately predict
interactions, both in exhaustive pairwise interaction data between small sets
of drugs, and in large-scale databases. We also demonstrate that our algorithm
can be used efficiently to discover interactions of new drugs as part of the
drug discovery process
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
Two-stage flux balance analysis of metabolic networks for drug target identification
<p>Abstract</p> <p>Background</p> <p>Efficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and <it>in vitro</it> binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective alternative. Wealth of omics data generated from genomic, proteomic and metabolomic techniques changes the way researchers view drug targets and provides unprecedent opportunities for drug target identification.</p> <p>Results</p> <p>In this paper, we develop a method based on flux balance analysis (FBA) of metabolic networks to identify potential drug targets. This method consists of two linear programming (LP) models, which first finds the steady optimal fluxes of reactions and the mass flows of metabolites in the pathologic state and then determines the fluxes and mass flows in the medication state with the minimal side effect caused by the medication. Drug targets are identified by comparing the fluxes of reactions in both states and examining the change of reaction fluxes. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method, then apply it to a hyperuricemia-related purine metabolic pathway. Known drug targets for hyperuricemia are correctly identified by our two-stage FBA method, and the side effects of these targets are also taken into account. A number of other promising drug targets are found to be both effective and safe.</p> <p>Conclusions</p> <p>Our method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks. It can generate testable predictions, provide insights into drug action mechanisms and guide experimental design of drug discovery.</p
Systems biology in animal sciences
Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits
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