Computational Methods in Systems Biology
Quantitative Regular Expressions for Arrhythmia Detection Algorithms 31 Fig. 2.QREs and their combinators. (a) Basic QREφ?λmatch ...
32 H. Abbas et al. QREs are defined and what they compute. Readers familiar with QREs will notice that, when writing the QRE exp ...
Quantitative Regular Expressions for Arrhythmia Detection Algorithms 33 Letsσ,1≤σ≤n, the the scale that equals ̄s. Since the sca ...
34 H. Abbas et al. QREpeakWPMis the final QRE. It combines results obtained from scalessσ down tos 1 : peakWPM:=connδ(peakTime ...
Quantitative Regular Expressions for Arrhythmia Detection Algorithms 35 QRElatestPeakwill return a 1 at the time of the latest ...
36 H. Abbas et al. setting of ̄p(Fig. 5 (a)) for both QREs was chosen to yield the best performance. This is akin to the way car ...
Quantitative Regular Expressions for Arrhythmia Detection Algorithms 37 within a particular frequency range. The spectrogram of ...
38 H. Abbas et al. Energy calculations.We may compute the energy consumption of an algo- rithm that is expressed as a QRE, by vi ...
Quantitative Regular Expressions for Arrhythmia Detection Algorithms 39 Demri, S., Lazic, R., Nowak, D.: On the freeze quantifi ...
Detecting Attractors in Biological Models with Uncertain Parameters Jiˇr ́ı Barnat, Nikola Beneˇs(B),Luboˇs Brim, Martin Demko, ...
Detecting Attractors in Biological Models with Uncertain Parameters 41 dynamical system, i.e. the locations in the phase portrai ...
42 J. Barnat et al. computation than that achievable with a na ̈ıve execution of Tarjan’s algorithm for SCC decomposition [ 25 ] ...
Detecting Attractors in Biological Models with Uncertain Parameters 43 We may also sometimes be interested in certain simpler ve ...
44 J. Barnat et al. idea we start with a non-parametrised version of the algorithm first. The follow- ing explication is illustr ...
Detecting Attractors in Biological Models with Uncertain Parameters 45 2.2 Parametrised Algorithm We now extend the basic idea w ...
46 J. Barnat et al. 1 procedureinit(G=(V, E,P)) 2 countp←1 for allp∈P 3 V̂←[∀v∈V:v→P] 4 main(V̂) 5 proceduremain(V̂) 6 trimV̂ 7 ...
Detecting Attractors in Biological Models with Uncertain Parameters 47 first iter. second iter. (incorrect) second iter. (correc ...
48 J. Barnat et al. 3.1 Discretisation of ODE Models In this section, we briefly describe the format of the ODE models used and ...
Detecting Attractors in Biological Models with Uncertain Parameters 49 The abstraction results in a symbolic description of a pa ...
50 J. Barnat et al. d[X] dt =k^1 Kn 11 K 1 n^1 +[Y]n^1 −φX[X] d[Y] dt =k^2 Kn 22 K 2 n^2 +[X]n^2 −φY[Y] k 1 =k 2 =1,K 1 =K 2 =5, ...
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