Letno poročilo 2005

Dolo~ene spojine, kot so estrogene kemikalije,
fitoestrogeni, naravni in sinteti~ni estrogeni, so
bili obravnavani z razli~nimi metodami: s
pristopom, ki temelji na iskanju konformacije
liganda z najmanj{o energijo in na podlagi te
konformacije izra~una deskriptorje, ter s
pristopom, osnovanim na strukturi, ki upo{teva
tudi strukturo proteina in za izra~un deskriptorjev
uporabi konformacijo, ki je rezultat
metode ugnezdenja (docking). Z namenom
dobiti najbolj{i mo`ni model smo za izbor
spremenljivk uporabili genetski algoritem (GA).
Kon~ni modeli, dobljeni za ER alfa in beta, so
bili testirani z zunanjim testnim nizom.
Velik del raziskav smo posvetili razvoju in
uporabi topolo{kih indeksov. Vzpostavili smo
{iroko sodelovanje z mnogimi strokovnjaki s
podro~ja teorije grafov. Plod tega sodelovanja
je razviden iz bibliografije; teoreti~ne invariante,
pridobljene s pomo~jo teorije grafov, smo
uspe{no uporabili v proteomiki in genomiki.
Za re{evanje zgoraj omenjene industrijske
naloge smo uporabili znanje in algoritme, ki so
izhajali iz graf-teoreti~nih raziskav.
V okviru evropskega projekta TRACE (Food Quality
and Safety) smo obravnavali podatke, ki so
dobljeni z analizo zemlje na povr{ju in analizo
sloja zemlje neposredno pod povr{jem, na treh
razli~nih mestih jemanja vzorcev, s tremi
razli~nimi sestavami zemlje (pe{~enjak, apnenec,
skrilavec). Skupno podatkovna baza zajema
180 vzorcev za vrhnjo plast ter 45 vzorcev za
sloj zemlje neposredno pod povr{jem, vzor~evanih
z devetih odvzemnih mest. Do sedaj
opravljeno delo se nana{a na pripravo normaliziranih
podatkovnih setov in vnos klju~a za
manjkajo~e podatke. Dolo~ili smo tudi oznake
glede na razli~ne parametre, ki jih `elimo slediti:
ali odvzemna mesta glede na vrsto kamnine,
ali glede na geografsko podro~je ali glede na
koncentracijo elementov v zemlji. Tako pripravljene
podatke smo s pomo~jo Kohonenovih
nevronskih mre` mapirali in klasificirali. V sklopu
TRACE projekta se je ena sodelavka aprila 2005
udele`ila prvega letnega sestanka v Yorku v
Veliki Britaniji. Sestanek je potekal na temo
Laboratorij za kemometrijo
Laboratory of Chemometrics
dants, and we expanded the applications of
graph-theoretical invariants in proteomics amd
genomics. The project “Chemometrical treatment
of endocrine disrupters” was completed
in 2005 by a joint comparative work of three
doctoral fellows. The CPNNs (Counterpropagation
Neural Networks) models for prediction
of experimental binding affinity of a range of
substances towards estrogen receptor alpha
and beta. Several compounds as estrogenic
chemicals, phytoestrogens and natural and synthetic
estrogens were treated with different
methods: ligand-based approach, that finds out
the minimum energy conformation as the most
suitable conformation on which descriptors are
calculated, and structure-based approach, that
involves the use of protein structure, and uses
conformations that are output of a docking
methodology to calculate descriptors. Models
were built up through training the counterpropagation
neural network and encoding the
information present in the two sets of molecular
descriptors. In order to reach the best possible
model, a selection of the descriptors using
Genetic Algorithm (GA) was performed for both
approaches. The final models obtained for
estrogen receptor alpha and beta were tested
with an external test set.
A large part of the research was directed into
the development and application of topological
indices. We established a broad collaboration
with many experts from the graph-theoretical
research area. The achievements can be
seen from the reference list; graph-theoretical
invariants have been successfully applied to
proteomics and genomics. It has to be stressed
here, that the knowledge and algorithms,
emerging from graph-theoretical studies, were
the basis for solving a part of the industrial
project mentioned above.
In the frame of TRACE project (Food Quality
and Safety) the first available dataset has been
processed. Three teams were involved in sampling
and analysis of soil data. 20 topsoil samples
were randomly collected on three sample
sites, all within 3 different lithologies (sand-
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