4,182 research outputs found
GraphCombEx: A Software Tool for Exploration of Combinatorial Optimisation Properties of Large Graphs
We present a prototype of a software tool for exploration of multiple
combinatorial optimisation problems in large real-world and synthetic complex
networks. Our tool, called GraphCombEx (an acronym of Graph Combinatorial
Explorer), provides a unified framework for scalable computation and
presentation of high-quality suboptimal solutions and bounds for a number of
widely studied combinatorial optimisation problems. Efficient representation
and applicability to large-scale graphs and complex networks are particularly
considered in its design. The problems currently supported include maximum
clique, graph colouring, maximum independent set, minimum vertex clique
covering, minimum dominating set, as well as the longest simple cycle problem.
Suboptimal solutions and intervals for optimal objective values are estimated
using scalable heuristics. The tool is designed with extensibility in mind,
with the view of further problems and both new fast and high-performance
heuristics to be added in the future. GraphCombEx has already been successfully
used as a support tool in a number of recent research studies using
combinatorial optimisation to analyse complex networks, indicating its promise
as a research software tool
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Loci specific epigenetic drug sensitivity.
Therapeutic targeting of epigenetic modulators offers a novel approach to the treatment of multiple diseases. The cellular consequences of chemical compounds that target epigenetic regulators (epi-drugs) are complex. Epi-drugs affect global cellular phenotypes and cause local changes to gene expression due to alteration of a gene chromatin environment. Despite increasing use in the clinic, the mechanisms responsible for cellular changes are unclear. Specifically, to what degree the effects are a result of cell-wide changes or disease related locus specific effects is unknown. Here we developed a platform to systematically and simultaneously investigate the sensitivity of epi-drugs at hundreds of genomic locations by combining DNA barcoding, unique split-pool encoding, and single cell expression measurements. Internal controls are used to isolate locus specific effects separately from any global consequences these drugs have. Using this platform we discovered wide-spread loci specific sensitivities to epi-drugs for three distinct epi-drugs that target histone deacetylase, DNA methylation and bromodomain proteins. By leveraging ENCODE data on chromatin modification, we identified features of chromatin environments that are most likely to be affected by epi-drugs. The measurements of loci specific epi-drugs sensitivities will pave the way to the development of targeted therapy for personalized medicine
Materials for stem cell factories of the future
The materials community is now identifying polymeric substrates that could permit translation of human pluripotent stem cells (hPSCs) from lab-based research to industrial scale biomedicine. Well defined materials are required to allow cell banking and to provide the raw material for reproducible differentiation into lineages for large scale drug screening programs and clinical use, wherein >1 billion cells for each patient are needed to replace losses during heart attack, multiple sclerosis and diabetes. Producing this number of cells for one patient is challenging and a rethink is needed to scalable technology with the potential to meet the needs of millions of patients a year. Here we consider the role of materials discovery, an emerging area of materials chemistry that is in a large part driven by the challenges posed by biologists to materials scientists1-4
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Multi-scale cellular engineering: From molecules to organ-on-a-chip.
Recent technological advances in cellular and molecular engineering have provided new insights into biology and enabled the design, manufacturing, and manipulation of complex living systems. Here, we summarize the state of advances at the molecular, cellular, and multi-cellular levels using experimental and computational tools. The areas of focus include intrinsically disordered proteins, synthetic proteins, spatiotemporally dynamic extracellular matrices, organ-on-a-chip approaches, and computational modeling, which all have tremendous potential for advancing fundamental and translational science. Perspectives on the current limitations and future directions are also described, with the goal of stimulating interest to overcome these hurdles using multi-disciplinary approaches
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Mapping genetic interactions in cancer: a road to rational combination therapies.
The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies
Innovative in silico approaches to address avian flu using grid technology
The recent years have seen the emergence of diseases which have spread very
quickly all around the world either through human travels like SARS or animal
migration like avian flu. Among the biggest challenges raised by infectious
emerging diseases, one is related to the constant mutation of the viruses which
turns them into continuously moving targets for drug and vaccine discovery.
Another challenge is related to the early detection and surveillance of the
diseases as new cases can appear just anywhere due to the globalization of
exchanges and the circulation of people and animals around the earth, as
recently demonstrated by the avian flu epidemics. For 3 years now, a
collaboration of teams in Europe and Asia has been exploring some innovative in
silico approaches to better tackle avian flu taking advantage of the very large
computing resources available on international grid infrastructures. Grids were
used to study the impact of mutations on the effectiveness of existing drugs
against H5N1 and to find potentially new leads active on mutated strains. Grids
allow also the integration of distributed data in a completely secured way. The
paper presents how we are currently exploring how to integrate the existing
data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target
Discovery of Stable and Selective Antibody Mimetics from Combinatorial Libraries of Polyvalent, Loop-Functionalized Peptoid Nanosheets.
The ability of antibodies to bind a wide variety of analytes with high specificity and high affinity makes them ideal candidates for therapeutic and diagnostic applications. However, the poor stability and high production cost of antibodies have prompted exploration of a variety of synthetic materials capable of specific molecular recognition. Unfortunately, it remains a fundamental challenge to create a chemically diverse population of protein-like, folded synthetic nanostructures with defined molecular conformations in water. Here we report the synthesis and screening of combinatorial libraries of sequence-defined peptoid polymers engineered to fold into ordered, supramolecular nanosheets displaying a high spatial density of diverse, conformationally constrained peptoid loops on their surface. These polyvalent, loop-functionalized nanosheets were screened using a homogeneous Förster resonance energy transfer (FRET) assay for binding to a variety of protein targets. Peptoid sequences were identified that bound to the heptameric protein, anthrax protective antigen, with high avidity and selectivity. These nanosheets were shown to be resistant to proteolytic degradation, and the binding was shown to be dependent on the loop display density. This work demonstrates that key aspects of antibody structure and function-the creation of multivalent, combinatorial chemical diversity within a well-defined folded structure-can be realized with completely synthetic materials. This approach enables the rapid discovery of biomimetic affinity reagents that combine the durability of synthetic materials with the specificity of biomolecular materials
An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries
Virtual, make-on-demand chemical libraries have transformed early-stage drug
discovery by unlocking vast, synthetically accessible regions of chemical
space. Recent years have witnessed rapid growth in these libraries from
millions to trillions of compounds, hiding undiscovered, potent hits for a
variety of therapeutic targets. However, they are quickly approaching a size
beyond that which permits explicit enumeration, presenting new challenges for
virtual screening. To overcome these challenges, we propose the Combinatorial
Synthesis Library Variational Auto-Encoder (CSLVAE). The proposed generative
model represents such libraries as a differentiable, hierarchically-organized
database. Given a compound from the library, the molecular encoder constructs a
query for retrieval, which is utilized by the molecular decoder to reconstruct
the compound by first decoding its chemical reaction and subsequently decoding
its reactants. Our design minimizes autoregression in the decoder, facilitating
the generation of large, valid molecular graphs. Our method performs fast and
parallel batch inference for ultra-large synthesis libraries, enabling a number
of important applications in early-stage drug discovery. Compounds proposed by
our method are guaranteed to be in the library, and thus synthetically and
cost-effectively accessible. Importantly, CSLVAE can encode out-of-library
compounds and search for in-library analogues. In experiments, we demonstrate
the capabilities of the proposed method in the navigation of massive
combinatorial synthesis libraries.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS
2022
SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces
Batch Bayesian optimisation and Bayesian quadrature have been shown to be
sample-efficient methods of performing optimisation and quadrature where
expensive-to-evaluate objective functions can be queried in parallel. However,
current methods do not scale to large batch sizes -- a frequent desideratum in
practice (e.g. drug discovery or simulation-based inference). We present a
novel algorithm, SOBER, which permits scalable and diversified batch global
optimisation and quadrature with arbitrary acquisition functions and kernels
over discrete and mixed spaces. The key to our approach is to reformulate batch
selection for global optimisation as a quadrature problem, which relaxes
acquisition function maximisation (non-convex) to kernel recombination
(convex). Bridging global optimisation and quadrature can efficiently solve
both tasks by balancing the merits of exploitative Bayesian optimisation and
explorative Bayesian quadrature. We show that SOBER outperforms 11 competitive
baselines on 12 synthetic and diverse real-world tasks.Comment: 34 pages, 12 figure
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