5 results found
Saavedra-Garcia P, Roman-Trufero M, Al-Sadah HA, et al., 2021, Systems level profiling of chemotherapy-induced stress resolution in cancer cells reveals druggable trade-offs, Proceedings of the National Academy of Sciences of USA, Vol: 118, ISSN: 0027-8424
Cancer cells can survive chemotherapy-induced stress, but how they recover from it is not known.Using a temporal multiomics approach, we delineate the global mechanisms of proteotoxic stressresolution in multiple myeloma cells recovering from proteasome inhibition. Our observations definelayered and protracted programmes for stress resolution that encompass extensive changes acrossthe transcriptome, proteome, and metabolome. Cellular recovery from proteasome inhibitioninvolved protracted and dynamic changes of glucose and lipid metabolism and suppression ofmitochondrial function. We demonstrate that recovering cells are more vulnerable to specific insultsthan acutely stressed cells and identify the general control nonderepressable 2 (GCN2)-driven cellularresponse to amino acid scarcity as a key recovery-associated vulnerability. Using a transcriptomeanalysis pipeline, we further show that GCN2 is also a stress-independent bona fide target intranscriptional signature-defined subsets of solid cancers that share molecular characteristics. Thus,identifying cellular trade-offs tied to the resolution of chemotherapy-induced stress in tumour cellsmay reveal new therapeutic targets and routes for cancer therapy optimisation.
Liu Z, Barahona M, 2021, Similarity Measure for Sparse Time Course Data Based on Gaussian Processes
<jats:title>Abstract</jats:title><jats:p>We propose a similarity measure for sparsely sampled time course data in the form of a loglikelihood ratio of Gaussian processes (GP). The proposed GP similarity is similar to a Bayes factor and provides enhanced robustness to noise in sparse time series, such as those found in various biological settings, e.g., gene transcriptomics. We show that the GP measure is equivalent to the Euclidean distance when the noise variance in the GP is negligible compared to the noise variance of the signal. Our numerical experiments on both synthetic and real data show improved performance of the GP similarity when used in conjunction with two distance-based clustering methods.</jats:p>
Liu Z, Barahona M, 2020, Graph-based data clustering via multiscale community detection, Applied Network Science, Vol: 5, Pages: 1-20, ISSN: 2364-8228
We present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework. We show how the multiscale capabilities of the method allow the estimation of the number of clusters, as well as alleviating the sensitivity to the parameters in graph construction. We use both synthetic and benchmark real datasets to compare and evaluate several graph construction methods and clustering algorithms, and show that multiscale graph-based clustering achieves improved performance compared to popular clustering methods without the need to set externally the number of clusters.
Saavedra-Garcia P, Al-Sadah HA, Penfold L, et al., 2019, Integrated Systems Level Examination of Proteasome Inhibitor Stress Recovery in Myeloma Cells Reveals Druggable Vulnerabilities Linked to Multiple Metabolic Processes, 61st Annual Meeting and Exposition of the American-Society-of-Hematology (ASH), Publisher: AMER SOC HEMATOLOGY, ISSN: 0006-4971
Liu Z, Barahona M, 2017, Geometric multiscale community detection: Markov stability and vector partitioning, Journal of Complex Networks, Vol: 6, Pages: 157-172, ISSN: 2051-1329
Multiscale community detection can be viewed from a dynamical perspective within the Markov stability framework, which uses the diffusion of a Markov process on the graph to uncover intrinsic network substructures across all scales. Here we reformulate multiscale community detection as a max-sum length vector partitioning problem with respect to the set of time-dependent node vectors expressed in terms of eigenvectors of the transition matrix. This formulation provides a geometric interpretation of Markov stability in terms of a time-dependent spectral embedding, where the Markov time acts as an inhomogeneous geometric resolution factor that zooms the components of the node vectors at different rates. Our geometric formulation encompasses both modularity and the multi-resolution Potts model, which are shown to correspond to vector partitioning in a pseudo-Euclidean space, and is also linked to spectral partitioning methods, where the number of eigenvectors used corresponds to the dimensionality of the underlying embedding vector space. Inspired by the Louvain optimization for community detection, we then propose an algorithm based on a graph-theoretical heuristic for the vector partitioning problem. We apply the algorithm to the spectral optimization of modularity and Markov stability community detection. The spectral embedding based on the transition matrix eigenvectors leads to improved partitions with higher information content and higher modularity than the eigen-decomposition of the modularity matrix. We illustrate the results with random network benchmarks.
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