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Gene regulatory network inference from sparsely sampled noisy data Aalto, Atte ; ; et al in Nature Communications (2020), 11 The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing ... [more ▼] The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO’s superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life. [less ▲] Detailed reference viewed: 130 (16 UL)COVID-19 Crisis Management in Luxembourg: Insights from an Epidemionomic Approach ; ; Aalto, Atte et al E-print/Working paper (2020) We develop an epidemionomic model that jointly analyzes the health and economic responses to the COVID-19 crisis and to the related containment and public health policy measures implemented in Luxembourg ... [more ▼] We develop an epidemionomic model that jointly analyzes the health and economic responses to the COVID-19 crisis and to the related containment and public health policy measures implemented in Luxembourg and in the Greater Region. The model has a weekly structure and covers the whole year 2020. With a limited number of parameters, the model is calibrated to depict the pre-crisis evolution of the Luxembourg economy, and to match post-lockdown leading economic indicators and industry-specific infection curves. The nowcasting part of our analysis reveals that each week of lockdown reduces national output by about 28% (and annual GDP by 0.54%). A first peak of the infection curve was observed at the very beginning of April. If the lockdown measures had been permanent, annual GDP would have decreased by 22% in 2020, the number of COVID-19 cases would have reached zero around mid-June, and the proportion of recovered people would have reached 1.4% of the population. In an economy heavily relying on skill-intensive services, we show that the role of teleworking has been instrumental to limiting the weekly economic output loss (almost by one half) and the propagation of the virus. In the forecasting part of the analysis, we quantify the epidemiological and economic responses to gradual deconfinement measures under various public health scenarios. If the post-lockdown transmission rates could be kept constant throughout the deconfinement period, restarting all sectors would have huge effects on the economy (limiting the annual GDP loss to about 7%) and no effect on the aggregate infection curve. While it is a good time for lifting containment measures, there is also a risk that increasing the density of employees at the workplace and resuming social activities would induce a rebound in the infection curve. Preventing such a relapse is possible with PCR testing of both national and cross-border workers, and with accompanying measures such as (i) maintaining teleworking practices, (ii) reopening hotels, restaurants and cafes at half of their full capacity or with equivalent physical distancing measures and last but not least, (iii) sustaining distancing measures in social activities. Overall, in our worst-case scenario, combining bi-monthly testing with contact tracing and quarantining measures appear to be a suficient (perhaps not necessary) policy option in the aftermath of the deconfinement plan. [less ▲] Detailed reference viewed: 46 (9 UL)Assessing suppression strategies against epidemicoutbreaks like COVID-19: the SPQEIR model Proverbio, Daniele ; Kemp, Francoise ; Magni, Stefano et al E-print/Working paper (2020) The current COVID-19 outbreak represents a most serious challenge for societies worldwide. It isendangering the health of millions of people, and resulting in severe socioeconomic challenges dueto lock ... [more ▼] The current COVID-19 outbreak represents a most serious challenge for societies worldwide. It isendangering the health of millions of people, and resulting in severe socioeconomic challenges dueto lock-down measures. Governments worldwide aim to devise exit strategies to revive the economywhile keeping the pandemic under control. The problem is that the effects of distinct measures arenot well quantified. This paper compares several suppression approaches and potential exit strategiesusing a new extended epidemic SEIR model. It concludes that while rapid and strong lock-down isan effective pandemic suppression measure, a combination of other strategies such as social distanc-ing, active protection and removal can achieve similar suppression synergistically. This quantitativeunderstanding will support the establishment of mid- and long-term interventions. Finally, the paperprovides an online tool that allows researchers and decision makers to interactively simulate diversescenarios with our model. [less ▲] Detailed reference viewed: 89 (3 UL)Linear system identification from ensemble snapshot observations Aalto, Atte ; Goncalves, Jorge in Proceedings of the IEEE Conference on Decision and Control (2019, December) Developments in transcriptomics techniques have caused a large demand in tailored computational methods for modelling gene expression dynamics from experimental data. Recently, so-called single-cell ... [more ▼] Developments in transcriptomics techniques have caused a large demand in tailored computational methods for modelling gene expression dynamics from experimental data. Recently, so-called single-cell experiments have revolutionised genetic studies. These experiments yield gene expression data in single cell resolution for a large number of cells at a time. However, the cells are destroyed in the measurement process, and so the data consist of snapshots of an ensemble evolving over time, instead of time series. The problem studied in this article is how such data can be used in modelling gene regulatory dynamics. Two different paradigms are studied for linear system identification. The first is based on tracking the evolution of the distribution of cells over time. The second is based on the so-called pseudotime concept, identifying a common trajectory through the state space, along which cells propagate with different rates. Therefore, at any given time, the population contains cells in different stages of the trajectory. Resulting methods are compared in numerical experiments. [less ▲] Detailed reference viewed: 240 (26 UL)A multifactorial evaluation framework for gene regulatory network reconstruction Mombaerts, Laurent ; Aalto, Atte ; Markdahl, Johan et al in Foundations of Systems Biology in Engineering (2019) In the past years, many computational methods have been developed to infer the structure of gene regulatory networks from time series data. However, the applicability and accuracy presumptions of such ... [more ▼] In the past years, many computational methods have been developed to infer the structure of gene regulatory networks from time series data. However, the applicability and accuracy presumptions of such algorithms remain unclear due to experimental heterogeneity. This paper assesses the performance of recent and successful network inference strategies under a novel, multifactorial evaluation framework in order to highlight pragmatic tradeoffs in experimental design. The effects of data quantity and systems perturbations are addressed, thereby formulating guidelines for efficient resource management. Realistic data were generated from six widely used benchmark models of rhythmic and nonrhythmic gene regulatory systems with random perturbations mimicking the effect of gene knock-out or chemical treatments. Then, time series data of increasing lengths were provided to five state-of-the-art network inference algorithms representing distinctive mathematical paradigms. The performances of such network reconstruction methodologies are uncovered under various experimental conditions. We report that the algorithms do not benefit equally from data increments. Furthermore, at least for the studied rhythmic system, it is more profitable for network inference strategies to be run on long time series rather than short time series with multiple perturbations. By contrast, for the non-rhythmic systems, increasing the number of perturbation experiments yielded better results than increasing the sampling frequency. We expect that future benchmark and algorithm design would integrate such multifactorial considerations to promote their widespread and conscientious usage. [less ▲] Detailed reference viewed: 68 (5 UL)Convergence of discrete-time Kalman filter estimate to continuous-time estimate for systems with unbounded observation Aalto, Atte in Mathematics of Control, Signals & Systems (2018), 30(3), 9 In this article, we complement recent results on the convergence of the state estimate obtained by applying the discrete-time Kalman filter on a time-sampled continuous-time system. As the temporal ... [more ▼] In this article, we complement recent results on the convergence of the state estimate obtained by applying the discrete-time Kalman filter on a time-sampled continuous-time system. As the temporal discretization is re fined, the estimate converges to the continuous-time estimate given by the Kalman-Bucy fi lter. We shall give bounds for the convergence rates for the variance of the discrepancy between these two estimates. The contribution of this article is to generalize the convergence results to systems with unbounded observation operators under di fferent sets of assumptions, including systems with diagonalizable generators, systems with admissible observation operators, and systems with analytic semigroups. The proofs are based on applying the discrete-time Kalman fi lter on a dense, numerable subset on the time interval [0,T] and bounding the increments obtained. These bounds are obtained by studying the regularity of the underlying semigroup and the noise-free output. [less ▲] Detailed reference viewed: 110 (7 UL)Spatial discretization error in Kalman filtering for discrete-time infinite dimensional systems Aalto, Atte in IMA Journal of Mathematical Control and Information (2018), 35(suppl_1), 51-72 We derive a reduced-order state estimator for discrete-time infinite dimensional linear systems with finite dimensional Gaussian input and output noise. This state estimator is the optimal one-step ... [more ▼] We derive a reduced-order state estimator for discrete-time infinite dimensional linear systems with finite dimensional Gaussian input and output noise. This state estimator is the optimal one-step estimate that takes values in a fixed finite dimensional subspace of the system’s state space — consider, for example, a Finite Element space. The structure of the obtained state estimator is like the Kalman filter, but with an additional optimal embedding operator mapping from the reduced space to the original state space. We derive a Riccati difference equation for the error covariance and use sensitivity analysis to obtain a bound for the error of the state estimate due to the state space discretization. [less ▲] Detailed reference viewed: 131 (13 UL)Modal Locking Between Vocal Fold Oscillations and Vocal Tract Acoustics ; Aalto, Atte ; et al in Acta Acustica United with Acustica (2018), 104(2), 323-337 During voiced speech, vocal folds interact with the vocal tract acoustics. The resulting glottal source–resonator coupling has been observed using mathematical and physical models as well as in in vivo ... [more ▼] During voiced speech, vocal folds interact with the vocal tract acoustics. The resulting glottal source–resonator coupling has been observed using mathematical and physical models as well as in in vivo phonation. We propose a computational time-domain model of the full speech apparatus that contains a feedback mechanism from the vocal tract acoustics to the vocal fold oscillations. It is based on numerical solution of ordinary and partial differential equations defined on vocal tract geometries that have been obtained by magnetic resonance imaging. The model is used to simulate rising and falling pitch glides of [α, i] in the fundamental frequency (fo ) interval [145 Hz, 315 Hz]. The interval contains the first vocal tract resonance fR 1 and the first formant F 1 of [i] as well as the fractions of the first resonance fR 1 /5, fR 1 /4, and fR 1 /3 of [α]. The glide simulations reveal a locking pattern in the fo trajectory approximately at fR 1 of [i]. The resonance fractions of [α] produce perturbations in the pressure signal at the lips but no locking. [less ▲] Detailed reference viewed: 141 (7 UL)Bayesian variable selection in linear dynamical systems Aalto, Atte ; Goncalves, Jorge E-print/Working paper (2018) We develop a method for reconstructing regulatory interconnection networks between variables evolving according to a linear dynamical system. The work is motivated by the problem of gene regulatory ... [more ▼] We develop a method for reconstructing regulatory interconnection networks between variables evolving according to a linear dynamical system. The work is motivated by the problem of gene regulatory network inference, that is, finding causal effects between genes from gene expression time series data. In biological applications, the typical problem is that the sampling frequency is low, and consequentially the system identification problem is ill-posed. The low sampling frequency also makes it impossible to estimate derivatives directly from the data. We take a Bayesian approach to the problem, as it offers a natural way to incorporate prior information to deal with the ill-posedness, through the introduction of sparsity promoting prior for the underlying dynamics matrix. It also provides a framework for modelling both the process and measurement noises. We develop Markov Chain Monte Carlo samplers for the discrete-valued zero-structure of the dynamics matrix, and for the continuous-time trajectory of the system. [less ▲] Detailed reference viewed: 121 (11 UL)Iterative observer-based state and parameter estimation for linear systems Aalto, Atte in ESAIM: Control, Optimisation and Calculus of Variations (2018), 24(1), 265-288 We propose an iterative method for joint state and parameter estimation using measurements on a time interval [0,T] for systems that are backward output stabilizable. Since this time interval is fixed ... [more ▼] We propose an iterative method for joint state and parameter estimation using measurements on a time interval [0,T] for systems that are backward output stabilizable. Since this time interval is fixed, errors in initial state may have a big impact on the parameter estimate. We propose to use the back and forth nudging (BFN) method for estimating the system’s initial state and a Gauss–Newton step between BFN iterations for estimating the system parameters. Taking advantage of results on the optimality of the BFN method, we show that for systems with skew-adjoint generators, the initial state and parameter estimate minimizing an output error cost functional is an attractive fixed point for the proposed method. We treat both linear source estimation and bilinear parameter estimation problems. [less ▲] Detailed reference viewed: 119 (11 UL)Convergence of discrete time Kalman filter estimate to continuous time estimate Aalto, Atte in International Journal of Control (2016), 89(4), 668-679 Detailed reference viewed: 93 (1 UL)Output error minimizing back and forth nudging method for initial state recovery Aalto, Atte in Systems & Control Letters (2016), 94 Detailed reference viewed: 94 (8 UL)Acoustic wave guides as infinite-dimensional dynamical systems Aalto, Atte ; ; in ESAIM: Control, Optimisation and Calculus of Variations (2015), 21(2), 324-347 Detailed reference viewed: 90 (3 UL)Composition of passive boundary control systems Aalto, Atte ; in Mathematical Control and Related Fields (2013), 3(1), 1-19 Detailed reference viewed: 40 (1 UL)Interaction of vocal fold and vocal tract oscillations Aalto, Atte ; ; et al in Proceedings of the 24th Nordic Seminar on Computational Mechanics (2011) We study the mechanical feedback coupling between the human vocal folds and vocal tract (VT) by simulating fundamental frequency glides over the lowest VT resonance. In the classical source–filter theory ... [more ▼] We study the mechanical feedback coupling between the human vocal folds and vocal tract (VT) by simulating fundamental frequency glides over the lowest VT resonance. In the classical source–filter theory of speech production, the vocal folds produce a signal which is filtered by the resonator, vocal tract without any feedback. We have developed a computational model of the vocal folds and the VT that also includes a counter pressure from the VT to the vocal folds. This coupling gives rise to new computational observations (such as modal locking) that can be established experimentally. [less ▲] Detailed reference viewed: 40 (1 UL)Wave propagation in networks: a system theoretic approach Aalto, Atte ; in Proceedings of the 18th World Congress of the IFAC (2011) Detailed reference viewed: 81 (0 UL)A LF-pulse from a simple glottal flow model Aalto, Atte ; ; in Proceedings of the 6th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (2009) Detailed reference viewed: 33 (0 UL) |
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