The document describes research analyzing the growth of Golden Root (Rhodiola Rosea) in vitro cultures using quantitative structure-activity relationship (QSAR) modeling. Artificial neural networks were created to analyze experimental data on plant growth in various nutrient media combinations, with the goals of improving growth and rooting while reducing costs. The models identified several low-cost media combinations that effectively promoted root or shoot growth depending on explant type. This analysis aims to optimize Golden Root biotechnology methods for commercial horticulture and agriculture applications.

1. IMPROVEMENT OF
GOLDEN ROOT IN
VITRO CULTURES
GROWTH BY QSAR
Autors:
Valeriya Simeonova
Krasimira Tasheva
Georgina Kosturkova
BIOMATH 2012
Sofia, Bulgaria
YOUNG SCHOOL
SCIENTISTS

2. About Golden
Root
Rhodiola Rosea (Golden root)
is an endangered medicinal
plant with phytoconstituents
and antioxidant potential
known to affect positively
various physiological
functions, including
cognition, working
ability, cardio protection, etc.
The initial material was the
explants isolated from wild
growing plants.

3. Objectives:
The different protocols for development of in vitro
cultures were established previously. The objectives
of the study were:
 to analyze the components of the system for its
improvement by bioinformatics methods. For this
purpose results (number and size of
buds, shoots, plants, calli, roots and nutrient media
parameters – concentration of phytohormones
and their price) from the biological experiment
were used for initial data.
 Based on analysis - to propose an effective way to
improve the growth and rooting as from
biological side as from economical point of view.

4. Biotechnology and in vitro
cultures
The recent years, in vitro cultures is used as one
of the advanced biotechnological systems for
obtaining a large number of identical plants
free of pathogens for short period of time for
horticulture industry, agriculture and forestry.
This is especially useful for species with high
demand or with slow and difficult cultivation in
natural conditions.

5. Bionformatics Methods:
 QSAR method was adapted for the purpose:
 Artificial Neural Networks were created to
analyze the trends hidden in data. The process
is divided on two stages because of the
specifics of the data.
 Graphical interpretation includes non-standard
type as ―wind of rose‖, also because of the
multidimensional aspects of the data it wasn’t
possible to make 3D scatter plot analysis.

6. Architecture and Parameters
of ANN about Stage 1 & 2
Characteristics of ANN Stage 1 Stage 2
Base statistics for
ANN
Learning cycles 6351 887
Training error 0,00002 0,000065
Input columns 15 36
Output columns 6 10
Excluded columns 4 7
Training example rows 23 7
Querying example rows 2415 2431
Duplicated example rows 0 0
Architecture of the
ANN
Input nodes connected 15 36
Hidden layer 1 nodes 16 20
Hidden layer 2 nodes 10 8
Output nodes 6 10
Parametres of the
ANN
Learning rate 0,6 0,6
Momentum 0,8 0,8
Target error 0,0001 0,0001

7. Common Input columns on Stage 1 & 2
Used at Stage № Column name Data Type
Filtered by Easy
NN Plus,
Field Type
1 & 2 Explant Text Included Emperical data
1 & 2 IPM.AC ratio Real Included Calculated field
1 & 2 IPM Type Integer Included Calculated field
1 & 2 Zeatin Real Included Emperical data
1 & 2 BAP Real Included Emperical data
1 & 2 Kinetin Real Included Emperical data
1 & 2 2-iP Real Included Emperical data
1 & 2 Thidiazuron Real Excluded Emperical data
1 & 2 GA3 Real Excluded Emperical data
1 & 2 IAA Real Included Emperical data
1 & 2 NAA Real Included Emperical data
1 & 2 2,4 D Real Included Emperical data
1 & 2 IBA Real Excluded Emperical data
1 & 2 Glutamine Integer Included Emperical data
1 & 2 Casein Integer Included Emperical data
1 & 2 Sucrose Integer Excluded Emperical data
1 & 2 Agar Integer Excluded Emperical data
1 & 2 IPM Value Real Included Calculated field

8. Additional Input columns on Stage 2
Used at Stage
№
Column name Data Type
Filtered by
Easy NN Plus,
Field Type
2 CM.AC ratio Real Included Calculated field
2 CM Type Integer Included Calculated field
2 Zeatin.2 Real Included Emperical data
2 BAP.2 Real Included Emperical data
2 Kinetin.2 Real Excluded Emperical data
2 2-iP.2 Real Included Emperical data
2 Thidiazuron.2 Real Included Emperical data
2 GA3.2 Real Included Emperical data
2 IAA.2 Real Included Emperical data
2 NAA.2 Real Included Emperical data
2 2,4 D.2 Real Included Emperical data
2 IBA.2 Real Included Emperical data
2 Glutamine.2 Integer Included Emperical data
2 Casein.2 Integer Excluded Emperical data
2 Sucrose.2 Integer Included Emperical data
2 Agar.2 Integer Included Emperical data
2 CM Value Real Included Calculated field
2 MC Value Real Included Calculated field

9. Output columns for Stage 1 are Input
on Stage 2
Used at Stage
№
Column name Data Type
Filtered by
Easy NN Plus,
Field Type
1 & 2 Cultivation days Integer Included Emperical data
1 & 2 Percentage Real Included Emperical data
1 & 2 Callus Boolean Included Emperical data
1 & 2 Compact Boolean Included Emperical data
1 & 2 Green Boolean Included Emperical data
1 & 2 Leaf rosette Boolean Included Emperical data
1 & 2 Plants Boolean Included Emperical data

10. Output columns on Stage 2
Used at Stage №
Column
name
Data Type
Filtered by
Easy NN Plus,
Field Type
2 Necrotic Tissue Real Included Emperical data
2 Calus.2 Boolean Included Emperical data
2 Soft Boolean Included Emperical data
2 Pale Boolean Included Emperical data
2 Compact.2 Boolean Included Emperical data
2 Liquidy Boolean Included Emperical data
2 Green.2 Boolean Included Emperical data

11. Some notes about data
 The excluded once are GA3, IBA, Sucrose and Agar. Because
they are phytoregulators we took the decision to include other
calculated columns, which represent weighted value of the
media such as Medium AC ratio and Medium price. This step
was right as it is evident by the Importance &Sensitivity Analysis.
On the Stage 2, because of the less data than that on Stage
1, we were forced to exclude the TDZ phytoregulator used for
prediction on Stage 1, and also Kinetin and Casein included in
the CM.
 The output columns are the initial response of the experiments
using IPM. We defined most of them as Boolean because of the
experiment itself – there are cases that show that we could
obtain more than one type of response, i.e. for example we
could find at the same time plants and leaf rosettes. That is why
it is not a good idea to make only one text category column for
them.

12. Some notes about data
 The interest subject is constructing the media
classification by IPM.AC ratio. As it is known a high ratio
of cytokinine to auxin favors shoot production, whereas
a high auxin to cytokinine ratio favors root production.
Therefore we established the criteria as follows:
 Interval [0; 0,5] - root apical meristem formation
medium, type 1
 Interval (0,5; 1) – shoot and root apical meristem formation
and calli formation medium, type 2
 Interval [1; 24] - shoot apical meristem formation
medium, type 0
 According to this we created a calculated field, named
MC type. It shows whether the Medium Combination is
clear type 0/1/2 or types 10, 11, 12, 20, 21, 22.
 The formula is: MC Type = 10 * IPM type + CM type

13. Media Combination types
Nutrient Media
Type
IPM.type = 0 IPM.type = 1 IPM.type = 2
CM.type = 0
0 – both
media invoke
shoots
10 – (roots,
shoots)
20 – (all,
shoots)
CM.type = 1
1 – (shoots,
roots)
11 – both media
invoke roots
21 – (all, roots)
CM.type = 2
2 – (shoots,
all)
12 – (roots, all)
22 – both media
invoke shoots,
roots, or calli
(all, all)

14. RESULTS

15. Error rates
The obtained maximum error rate of both stages about learning process is under
0.00001 according to the criteria. We believe that such kind of error does not
have the effect of overtraining because we escaped of using validating
mechanism. After complete analysis we found a small portion query rows about 5
or 7 which could be defined as calli on the stage 1 but they were not. This
produced an error about 0.0028 but such an error is small enough and
acceptable if we want to be not over trained. The main opportunity of not over
trained ANN is that they can predict even for values out of ranges. It is helpful tip
knowing that we have no a big quantity of data, so our data could not be
defined as comprehensive.

16. Importance &
Sensitivity
Analysis
Instead of the standard column graphics
interpretation about Importance and Sensitivity
we use Scatter Plot. It is a good decision
because it shows how the input data are
distributed by Importance and Sensitivity.
We examine high level of sensitivity about
cultivation days as a factor. This means that a
change from 30 to 40 days could be fatal and
will produce necrotic tissues. That is why most of
the query rows include preferably cultivation
days=30.
IPM Value has the highest level of importance,
but the lowest sensitivity, which means that our
hypothesis about weighted value of the media
is true, but to take an effect in the results we
need bigger change. And it is correct, because
of the low price of most of the phytoregulators.
Almost the same is valid for the IPM.AC ratio.
The other group of interest is (Casein,
Glutamine, and Explant). Casein and Glutamine
are common used for inducing growth, no
matter of shoots or roots. That is why they have
good level of importance and high level of
sensitivity according to all the others.

17. Importance &
Sensitivity
Analysis
It is definitely that the predictors in green have
not more than average ratio of importance and
low level of sensitivity which means that there
are more important and more sensitive
parameters than them. So we should not
expect much different results if we make slightly
changes in their values. On the other hand we
can see that the predicted values depend on
much more of the predicted results and
predictors from the first stage, and also they are
high sensitive, which means that if we find
media which produce something else, this will
generally affect the predicted result at the
second stage. Anyway there are some of them
that are in the first quadrant (less important, less
sensitive). These predictors are the columns with
names: Glutamine, Zeatin, IAA, IPM.AC ratio
and IPM.Value. It is easy to view that there is
direct proportional dependence between
importance and sensitivity about Red and Blue
groups, i.e. opposite of the dependences if we
see them at Stage 1. This means that the data
from the first stage are more stable, than the
data from the second - and it is normal,
because at first stage we have more training
examples than the second.

18. Analysis of the IPM, CM and
Media Combinations (MC)
between IPM and CM

19. Explant development in IPM

20. Explant development in MC
(IPM; CM)

21. Continue…
It is evident from the last two figures
that if we want to have success in
process of rooting we do need nutrient
media which favors rooting plants and
leaf rosettes, mostly from apical buds
and rhizome segments, as there are
very good possibilities also to use
explants from the other types. Even if
the ―Calli Like‖ percentage is high, the
number of nutrient media favoring this
might be 5 only.
It is not recommended to try to induce
rooting process for calli as far as almost
100 % of them necrotize. At this point
we defined the objects for analysis of
effectiveness, i.e. now we need to test
all of these Media combinations and
to see their price values. Thus the next
step is to provide analysis about price
ranges of MC that meet the criteria
―first: plant or leaf rosettes growth –
second: successful rooting process‖.

22. Analysis of
effectiveness
The next 3 figures show that
there are much more MCs of
high price level that could
support plant growth followed
by rooting.
There are several options for
choice between:
16 % => 7-8 €/l or 14-15 €/l
13 %=> 10-11 €/l

23. Analysis of effectiveness
One of the aims of this study is to find such media combinations which will provide results at less cost. That is
why we decided to take only the MCs with price range between [0; 6] euro per liter. However, the number
of MCs corresponding to this interval is not such a big, i.e. we have only 22 media combinations that meet
the criteria (from 119), or it is about 18 % of all combinations. The current figure indicates that we did not
found any MC for explant type ―rhizome segment‖ meeting our criteria. This is important in case of limited
resources trying to avoid a great number of explants of rhizome segments.

24. Analysis of
effectiveness
/roots from leaf rosettes/
In the price range [0; 5] euro per liter
there are 5 groups with similar
percentages, as the average is about
16 %. Positively the number of MCs
corresponding to this interval is big
enough i.e. we have 71 media
combinations out of 74 which meet
the criteria comprising about 96 % of
all the combinations.

25. Analysis of effectiveness
There is no limitation by the explant type and it is quite easy to find MCs meeting our criteria,
when it is about to produce rooted leaf rosettes. This is important from biotechnological point of
view because rooted leaf rosette easily could become in vitro cultivated ―plants‖ with roots,
which are ready to transfer into natural conditions.

26. Analysis of MCs which meet
criteria of effectiveness

27. Structure of the table about
measuring the effectiveness
This table was constructed with the purpose of graphical analysis of effectiveness. Two graphics
of ―rose of wind‖ type were made estimating them as the most proper way to illustrate the
results. The price of the MC made a shell formation, because the tables are sorted by it by
ascending.
Column name Data Type Field Type
IPM response Text Filter Field
Explant Text Emperical data
Media Combination's name Text Calculated field
Media Combination's Type Integer Calculated field
Media Combination's Value Real Calculated field
Roots Boolean Filter Field
Leaf rosette Boolean Filter Field
Plants Boolean Filter Field

28. Analysis of
effectiveness
/rose of wind/
In the price range [0; 5] euro per liter
there are 5 groups with similar
percentages, as the average is about
16 %. Positively the number of MCs
corresponding to this interval is big
enough i.e. we have 71 media
combinations out of 74 which meet
the criteria comprising about 96 % of
all the combinations.

29. …rose of wind conclusions
 There are 4-5 MCs where media combinations are a variation with MC type =1, with different explants
and prices. These media are from the same price level with minimal changes. This supports our thesis
that MCs in closed price levels should have similar effects and compounds.
 There are 13 media combinations of type 0 where the price level for plants is between 4 and 5, but
such media are mostly exceptions.
 There are 31 MCs type 1 – i.e. IPM supplies shoot growing, and the CM supplies roots growing. This
mostly occurs in leaf rosette and explant type – rhizome segments. The price levels distribution about
is from 1.62 euro/l to 4.43 euro/l.
 There are 2 MCs type 2 – i.e. IPM supplies shoot growing while the CM – shoots, roots and calli
growing. Initial response type leaf rosette price level is about 2.80 euro/l
 There are 2 MCs type 10 – it is non standard type of MC, as IPM provides roots growing while the CM –
shoots growing. It occurs when the explants are leaf node and stem segments have initial response
―plant‖.
MC’s name Price MC’s type
M(MSP;N477) 3,29 1
M(N151;N132) 3,55 0
M(N351;N332) 3,7 0
M(N352;N333) 4,43 0
M(N353;N334) 5,15 0
M(N354;N335) 5,89 0
No relation to the type of the explant was obvious, but it was found that:
o There are MCs that have the same productivity both, for the plants and for the leaf rosettes. They are 6
and they have the next coding:
i.e. these are media combinations strongly inducing
growth of shoots.
 There is a group of media combinations of 12
that has type 11, extremely low cost up to 10 euro
cents. These MCs could be used only to explants
from leaf rosettes. Type 11 supposed that the MC
is strongly roots growing media.

30. Acknowledgment
Research was supported by National
Science Fund of Bulgaria—Project for Junior
Scientists DMU 03/55
(leader Dr. Krasimira Tasheva).

31. THANK YOU !
E-mail:
v.n.simeonova@me.com
BIOMATH 2012
Sofia, Bulgaria
YOUNG SCHOOL
SCIENTISTS