264 research outputs found

    Mutual information in random Boolean models of regulatory networks

    Full text link
    The amount of mutual information contained in time series of two elements gives a measure of how well their activities are coordinated. In a large, complex network of interacting elements, such as a genetic regulatory network within a cell, the average of the mutual information over all pairs is a global measure of how well the system can coordinate its internal dynamics. We study this average pairwise mutual information in random Boolean networks (RBNs) as a function of the distribution of Boolean rules implemented at each element, assuming that the links in the network are randomly placed. Efficient numerical methods for calculating show that as the number of network nodes N approaches infinity, the quantity N exhibits a discontinuity at parameter values corresponding to critical RBNs. For finite systems it peaks near the critical value, but slightly in the disordered regime for typical parameter variations. The source of high values of N is the indirect correlations between pairs of elements from different long chains with a common starting point. The contribution from pairs that are directly linked approaches zero for critical networks and peaks deep in the disordered regime.Comment: 11 pages, 6 figures; Minor revisions for clarity and figure format, one reference adde

    Double Deep-Q Learning-Based Output Tracking of Probabilistic Boolean Control Networks

    Get PDF
    In this article, a reinforcement learning (RL)-based scalable technique is presented to control the probabilistic Boolean control networks (PBCNs). In particular, a double deep- QQ network (DD QNQ\text{N} ) approach is firstly proposed to address the output tracking problem of PBCNs, and optimal state feedback controllers are obtained such that the output of PBCNs tracks a constant as well as a time-varying reference signal. The presented method is model-free and offers scalability, thereby provides an efficient way to control large-scale PBCNs that are a natural choice to model gene regulatory networks (GRNs). Finally, three PBCN models of GRNs including a 16-gene and 28-gene networks are considered to verify the presented results

    Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

    Get PDF
    Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by mainly redundant or synergistic information transfer persisting across multiple time scales or even by the alternating prevalence of redundant and synergistic source interaction depending on the time scale. Then, we apply our method to an important topic in neuroscience, i.e., the detection of causal interactions in human epilepsy networks, for which we show the relevance of partial information decomposition to the detection of multiscale information transfer spreading from the seizure onset zone

    The effect of development on spatial pattern separation in the hippocampus as quantified by the Homer1a immediate-early gene

    Get PDF
    ix, 51 leaves : ill. ; 29 cmThis study sought to determine whether the DG, CA3, and CA1 regions contain uniformly excitable populations and test the hypothesis that rapid addition of new, more excitable, granule cells in prepubescence results in a low activation probability (P1) in the DG. The immediate-early gene Homer1a was used as a neural activity marker to quantify activation in juvenile (P28) and adult (~5 mo) rats during track running. The main finding was that P1 in juveniles was substantially lower not only the DG, but also CA3 and CA1. The P1 for a DG granule cell was close to 0 in juveniles, versus 0.58 in adults. The low P1 in juveniles indicates that sparse, but non-overlapping, subpopulations participate in encoding events. Since sparse, orthogonal coding enhances a network’s ability to decorrelate input patterns (Marr, 1971; McNaughton & Morris, 1987), the findings suggest that juveniles likely possess greatly enhanced pattern separation ability

    Immediate-early gene Homer1a intranuclear transcription focus intensity as a measure of relative neural activation

    Get PDF
    Although immediate-early gene expression analyses using fluorescent in situ hybridization is an effective method to identify recently activated neurons; and non-Boolean variations in transcription foci have been documented, it remains unclear whether there is a systematic relationship between magnitude of neural activation and corresponding RNA signal. Here, we quantified the Homer1a response of hippocampal neurons in rats that ran laps on a familiar track to induce consistent cell firing. A strong linear trend (r2 > 0.9) in INF intensity (brightness) was observed between 1 and 25 laps, after which INF signal dispersed within the nucleus. When the integrated intranuclear fluorescence was considered instead, the linear relationship extended to 50 laps. But there was only an approximate doubling of Homer1a RNA detected for this 50-fold variation in total spiking. Thus, this low-gain dynamic range likely precludes INF intensity as a precise quantitative readout of neural activation, albeit a useful qualitative tool.Alberta Innovates - Health Solutions: Polaris Award Natural Sciences and Engineering Research Council of Canada: RGPIN-2017-03857 National Science Foundation: 163146

    Computer Aided Verification

    Get PDF
    This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

    Computer Aided Verification

    Get PDF
    This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications
    corecore