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Conference paperLaumann F, von Kuegelgen J, Barahona M, 2021,
Kernel two-sample and independence tests for non-stationary random processes
, ITISE 2021 (7th International conference on Time Series and Forecasting), Publisher: https://www.mdpi.com/2673-4591/5/1/31, Pages: 1-13Two-sample and independence tests with the kernel-based MMD and HSIC haveshown remarkable results on i.i.d. data and stationary random processes.However, these statistics are not directly applicable to non-stationary randomprocesses, a prevalent form of data in many scientific disciplines. In thiswork, we extend the application of MMD and HSIC to non-stationary settings byassuming access to independent realisations of the underlying random process.These realisations - in the form of non-stationary time-series measured on thesame temporal grid - can then be viewed as i.i.d. samples from a multivariateprobability distribution, to which MMD and HSIC can be applied. We further showhow to choose suitable kernels over these high-dimensional spaces by maximisingthe estimated test power with respect to the kernel hyper-parameters. Inexperiments on synthetic data, we demonstrate superior performance of ourproposed approaches in terms of test power when compared to currentstate-of-the-art functional or multivariate two-sample and independence tests.Finally, we employ our methods on a real socio-economic dataset as an exampleapplication.
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Journal articleGodoy-Lorite A, Jones N, 2021,
Inference and influence of network structure using snapshot social behavior without network data
, Science Advances, Vol: 7, ISSN: 2375-2548Population behavior, like voting and vaccination, depends on the structure of social networks. This structure can differ depending on behavior type and is typically hidden. However, we do often have behavioral data, albeit only snapshots taken at one time point. We present a method jointly inferring a model for both network structure and human behavior using only snapshot population-level behavioral data. This exploits the simplicity of a few parameter model, geometric sociodemographic network model, and a spin-based model of behavior. We illustrate, for the European Union referendum and two London mayoral elections, how the model offers both prediction and the interpretation of the homophilic inclinations of the population. Beyond extracting behavior-specific network structure from behavioral datasets, our approach yields a framework linking inequalities and social preferences to behavioral outcomes. We illustrate potential network-sensitive policies: How changes to income inequality, social temperature, and homophilic preferences might have reduced polarization in a recent election.
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Journal articleThomas P, Shahrezaei V, 2021,
Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations
, Journal of the Royal Society Interface, Vol: 18, Pages: 1-16, ISSN: 1742-5662The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation—including static extrinsic noise—exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis, a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.
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Journal articleMyall AC, Peach RL, Weiße AY, et al., 2021,
Network memory in the movement of hospital patients carrying drug-resistant bacteria
, Applied Network Science, Vol: 6, ISSN: 2364-8228Hospitals constitute highly interconnected systems that bring into contact anabundance of infectious pathogens and susceptible individuals, thus makinginfection outbreaks both common and challenging. In recent years, there hasbeen a sharp incidence of antimicrobial-resistance amongsthealthcare-associated infections, a situation now considered endemic in manycountries. Here we present network-based analyses of a data set capturing themovement of patients harbouring drug-resistant bacteria across three largeLondon hospitals. We show that there are substantial memory effects in themovement of hospital patients colonised with drug-resistant bacteria. Suchmemory effects break first-order Markovian transitive assumptions andsubstantially alter the conclusions from the analysis, specifically on noderankings and the evolution of diffusive processes. We capture variable lengthmemory effects by constructing a lumped-state memory network, which we then useto identify overlapping communities of wards. We find that these communities ofwards display a quasi-hierarchical structure at different levels of granularitywhich is consistent with different aspects of patient flows related to hospitallocations and medical specialties.
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Journal articleSaavedra-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: 0027-8424Cancer 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.
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Journal articlePeach RL, Arnaudon A, Schmidt JA, et al., 2021,
HCGA: Highly comparative graph analysis for network phenotyping
, Patterns, Vol: 2, Pages: 100227-100227, ISSN: 2666-3899<jats:title>A<jats:sc>bstract</jats:sc></jats:title><jats:p>Networks are widely used as mathematical models of complex systems across many scientific disciplines, not only in biology and medicine but also in the social sciences, physics, computing and engineering. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and some times overlapping) characteristics of a network. In the analysis of real-world graphs, it is crucial to integrate systematically a large number of diverse graph features in order to characterise and classify networks, as well as to aid network-based scientific discovery. In this paper, we introduce HCGA, a framework for highly comparative analysis of graph data sets that computes several thousands of graph features from any given network. HCGA also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterisation of graph data sets. We show that HCGA outperforms other methodologies on supervised classification tasks on benchmark data sets whilst retaining the interpretability of network features. We also illustrate how HCGA can be used for network-based discovery through two examples where data is naturally represented as graphs: the clustering of a data set of images of neuronal morphologies, and a regression problem to predict charge transfer in organic semiconductors based on their structure. HCGA is an open platform that can be expanded to include further graph properties and statistical learning tools to allow researchers to leverage the wide breadth of graph-theoretical research to quantitatively analyse and draw insights from network data.</jats:p>
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Journal articleQian Y, Expert P, Panzarasa P, et al., 2021,
Geometric graphs from data to aid classification tasks with Graph Convolutional Networks
, Patterns, Vol: 2, Pages: 100237-100237, ISSN: 2666-3899 -
Journal articleMaes A, Barahona M, Clopath C, 2021,
Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons
, PLoS Computational Biology, Vol: 17, ISSN: 1553-734XSequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.
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Journal articleKuntz Nussio J, Thomas P, Stan G, et al., 2021,
Approximations of countably-infinite linear programs over bounded measure spaces
, SIAM Journal on Optimization, Vol: 31, Pages: 604-625, ISSN: 1052-6234We study a class of countably-infinite-dimensional linear programs (CILPs)whose feasible sets are bounded subsets of appropriately defined spaces ofmeasures. The optimal value, optimal points, and minimal points of these CILPscan be approximated by solving finite-dimensional linear programs. We show howto construct finite-dimensional programs that lead to approximations witheasy-to-evaluate error bounds, and we prove that the errors converge to zero asthe size of the finite-dimensional programs approaches that of the originalproblem. We discuss the use of our methods in the computation of the stationarydistributions, occupation measures, and exit distributions of Markov~chains.
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Journal articleKuntz J, Thomas P, Stan G-B, et al., 2021,
Stationary distributions of continuous-time Markov chains: a review of theory and truncation-based approximations
, SIAM Review, ISSN: 0036-1445Computing the stationary distributions of a continuous-time Markov chaininvolves solving a set of linear equations. In most cases of interest, thenumber of equations is infinite or too large, and cannot be solved analyticallyor numerically. Several approximation schemes overcome this issue by truncatingthe state space to a manageable size. In this review, we first give acomprehensive theoretical account of the stationary distributions and theirrelation to the long-term behaviour of the Markov chain, which is readilyaccessible to non-experts and free of irreducibility assumptions made instandard texts. We then review truncation-based approximation schemes payingparticular attention to their convergence and to the errors they introduce, andwe illustrate their performance with an example of a stochastic reactionnetwork of relevance in biology and chemistry. We conclude by elaborating oncomputational trade-offs associated with error control and some open questions.
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