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Modeliranje enostavnih bioloških preklopnih sistemov z uporabo mehke logike in razširjenih Petrijevih mrež
ID
BORDON, JURE
(
Author
),
ID
Mraz, Miha
(
Mentor
)
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20.500.12556/rul/ce0386d1-a855-460e-94fd-a405e9ce5bdd
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Abstract
Modeliranje in simulacija sta v zadnjih dveh desetletjih postala nepogrešljiva za načrtovanje novih in analizo obstoječih bioloških sistemov. Z uporabo njunih različnih pristopov lahko občutno znižamo čas in stroške načrtovanja ter izvajanja laboratorijskih poizkusov. Izbira metode modeliranja je odvisna od željene natančnosti simulacijskih rezultatov in podatkov o sistemu, ki jih imamo na voljo. Kvalitativni pristopi se uporabljajo za opis osnovnih dinamičnih lastnosti in topologije gensko regulatornega omrežja ter služijo kot izhodišče za bolj natančne pristope. Po drugi plati kvantitativni pristopi dinamiko sistema opišejo zelo natančno, a za izračun simulacijskih rezultatov potrebujejo natančne vrednosti kinetičnih parametrov, ki pogosto niso na voljo, njihovo pridobivanje pa je dolgotrajno in posledično drago. Kot močno grafično in matematično modelirno orodje so se tako za kvalitativne kot tudi kvantitativne pristope uveljavile Petrijeve mreže. V pričujoči doktorski disertaciji predstavimo kvantitativno metodo za modeliranje, ki temelji na mehki logiki in Petrijevih mrežah. Model procesa, ki ga vzpostavimo na podlagi opisnega izkustvenega poznavanja, za pridobitev kvantitativno relevantnih simulacijskih rezultatov ne potrebuje natančnih vrednosti kinetičnih parametrov, ki pogojujejo dinamiko sistema. Definiciji zveznih Petrijevih mrež dodamo mehke prožilne funkcije, ki jih lahko uporabimo za modeliranje procesov z manjkajočimi vrednostmi kinetičnih parametrov. Predlagan pristop se lahko uporabi tudi kot dopolnitev obstoječih metod za kvantitativno modeliranje v primeru, da so vrednosti kinetičnih parametrov sistema večinoma poznane. Metodo, ki temelji na mehki logiki, uporabimo za izgradnjo modela hipotetičnega represilatorja s tremi členi, z mehkimi zveznimi Petrijevimi mrežami pa zgradimo model cirkadianega ritma glive Neurospora. Pokažemo, da simulacijski rezultati modela, ki vsebuje mehke opise posameznih procesov, ohranijo kvantitativno biološko relevantnost simulacijskih rezultatov v primerjavi s simulacijskimi rezultati modela, ki je v celoti zgrajen na podlagi obstoječih metod, ki za vhode uporabljajo znane vrednosti kinetičnih parametrov.
Language:
Slovenian
Keywords:
modeliranje bioloških sistemov
,
neznani kinetični parametri
,
mehka logika
,
Petrijeve mreže
Work type:
Doctoral dissertation
Organization:
FRI - Faculty of Computer and Information Science
Year:
2017
PID:
20.500.12556/RUL-91340
COBISS.SI-ID:
289087744
Publication date in RUL:
28.03.2017
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2149
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678
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Secondary language
Language:
English
Title:
Modelling simple biological switching systems using fuzzy logic and extended Petri nets
Abstract:
Modelling and simulation have become indispensable in the design of novel and analysis of existing biological systems. Using various approaches, they can significantly reduce the time and cost of planning and executing laboratory experiments. The choice of modelling technique depends on the desired accuracy of simulation results and the available kinetic data of the system. Qualitative approaches can be used to describe basic dynamical properties and topology of the underlying network. On the other hand, quantitative approaches are used for detailed modeling of system's dynamics. However, exact kinetic data that are required to obtain simulation results using state-of-the-art quantitative methods are often missing and are hard or even impossible to obtain. Both qualitative and quantitative approaches can be graphically represented as Petri nets, which serve as a powerful framework for constructing a biological system model. In this dissertation we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data by using expert knowledge to model process descriptions and can thus produce quantitatively relevant simulation results even when kinetic data are incomplete or only vaguely defined. In addition, we extend the continuous Petri net definition and introduce fuzzy firing rate functions. Moreover, the approach can be used in the combination with the existing quantitative modelling techniques only in certain parts of the system, i.e. where kinetic data are missing. We use the fuzzy logic based approach to construct a model of a hypothetical three-gene repressilator and fuzzy continuous Petri nets for constructing a Neurospora circadian rhythm model. Simulation results obtained with fuzzy approach show that using fuzzy logic for describing processes with unknown kinetic does not significantly affect the quantitative aspects of the model.
Keywords:
modelling biological systems
,
unknown kinetic rates
,
fuzzy logic
,
Petri nets
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