Browsing by Author "Aalto, Atte"
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- Gene regulatory network inference from sparsely sampled noisy data
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2020-07-13) Aalto, Atte; Viitasaari, Lauri; Ilmonen, Pauliina; Mombaerts, Laurent; Gonçalves, JorgeThe 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. - Infinite Dimensional Systems: Passivity and Kalman Filter Discretization
School of Science | Doctoral dissertation (article-based)(2014) Aalto, AtteThe results of this thesis can be divided into two categories, well-posedness and passivity of boundary control systems and Kalman filter discretization. It is shown that a composition of internally well-posed, impedance passive boundary control systems through Kirchhoff type couplings is also an internally well-posed, impedance passive boundary control system. The concept of a passive majorant is defined and it is shown that boundary control systems that possess a passive majorant are internally well-posed, passive boundary control systems. The effect of both temporal and spatial discretization to Kalman filtering is studied. Firstly, convergence speed rates are derived for the convergence of the discrete time Kalman filter estimate to the continuous time estimate as the temporal discretization is refined. This result is established for various types of linear systems. Secondly, we derive the optimal one-step state estimate that takes values in a given finite dimensional subspace of the system's state space for a linear discrete-time system with Gaussian input and output noise. An upper bound is given for the error due to the spatial discretization. - A low-order glottis model with nonturbulent flow and mechanically coupled acoustic load
Helsinki University of Technology | Master's thesis(2009) Aalto, AtteA low order mass-spring model of human vocal cords is constructed. The vibration of the cords is caused by a Bernoulli-type flow. This means that increasing flow velocity in narrowing causes a drop in the pressure finally leading to closure of the glottis (the orifice between the vocal cords). When the glottis is closed, there is no flow and the forces of the tissues push the cords apart again. The flow velocity through the glottis is obtained from a separate one-dimensional incompressible flow model. The inertia of the air contained in the vocal tract is taken into account as well as viscous pressure losses in the vocal tract and the glottis. Changes in the pressure loss due to vocal cords' movement regulate the glottal flow velocity and, when the glottis closes, stop the flow altogether. The output of this model is fed to the vocal tract model, which is a transmission line model given by the Webster's equation for a curved tube. The solution of the Webster's equation gives us the air pressure distribution in the vocal tract. The air pressure at the glottis end is fed back to the glottis model, where it appears as a term in the load force for the equations of motion. One of the purposes of this work is to study the effect of this feedback on the glottal behaviour and verify results obtained by other researchers. The vocal tract data used in the Webster's equation is obtained by magnetic resonance imaging (MRI) performed by other researchers on human subjects. The vocal tract cross sectional area and the tract curvature are determined from such MRI data. For computations, the vocal tract was discretized using the Finite Element Method based on the natural energy norm of the Webster's equation.