1. Deploying Plant Tissue Culture Simulation Use Case for E-Infrastructures
Collins N. Udanor
Florence I Akaneme
Emmanuel Ukekwe
University of Nigeria Nsukka - Nigeria
Sci-GaIA Final Event, Pretoria 1

2. First Version of Plantisc Why Plant Tissue Culture?
Presentation Outline
Scientific problem
First Version of Plantisc
Why Plant Tissue Culture?
Factors affecting Plant Tissue Culture
Results from the simulation application
Deployment of the App on e-infrastructures
Future plans
Acknowledgements
References 2

3. Because plant tissue culture is still in its empirical stage,
Scientific problem
Plant Tissue culture is a method for plant propagation under in vitro conditions.
Different types and parts of plants (known as explants) may be cultivated in vitro.
Because plant tissue culture is still in its empirical stage,
it is time-consuming,
cost-intensive and
man-power demanding.
These necessitated the design and development of a plant tissue culture simulation application that predicts explant yields using multiple regression models. 3

4. Plantisc1
The initial version that was developed during the BGI project with a prediction accuracy of about 67%,
Unfortunately was not deployable on an e-infrastructure like the grid or the cloud.
Hence, during the Sci-GaIA project ( ), a use case for the development of a newer version, Plantisc2, that is deployable on e-infrastructure was proposed. 4

5. Lineberger [7] observed that the advantages of micropropagation are:
Why Plant Tissue Culture is Important
Lineberger [7] observed that the advantages of micropropagation are:
Multiplication of a plant into several thousand plants in less than one year
Plant multiplication can continue throughout the year irrespective of the season
With most species, the excision of the ex-plant does not destroy the parent plant
The technique is widely used for large scale plant multiplication (mass propagation), for efficient disease elimination and for production of secondary metabolites [8]. 5

6. Why Plant Tissue Culture is Important
One of the drawbacks of this technique is that there is no one protocol that could be used for the propagation of all kinds of plants.
This fact has been reiterated recently by [9] who reported that cultural requirements for the process of plant tissue culture differ from species to species.
The most appropriate conditions for a given species must always be evolved out of experimentation. 6

7. Factors affecting Plant Tissue Culture
One of the most variable or critical factors in plant tissue culture media are growth regulators or hormones especially auxins and cytokinins which are usually used in various combinations.
The growth regulators are important in determining the developmental pathways of plant cells.
Screening of these hormonal combinations is time and material intensive running into several months of laboratory efforts in trying to develop a protocol that will be best for mass propagation of a particular species.
Modeling or computer simulation will readily be of great help in reducing the time needed to screen the numerous hormonal combinations.
Due to the potentials of plant tissue culture technique, innovative approaches to reduce labour requirements and costs are being developed. 7

8. The system accepts input from two sources which are:
Design of the Plant Tissue Culture Simulation Software
In this section, we present the design and implementation for the predictive and simulation application.
Predictive Module
The system is designed to first predict the desired concentration mixture of auxin and cytokinin before simulating for other concentrations.
Input Data
The system accepts input from two sources which are:
a. Users’ input data pertaining to their experiment
b. Data downloaded from the site’s repository.
The two sources make use of an excel template (data.xls) through which the input is made into the system. 8

9. Design of Plantisc2: Prediction using Regression model9

10. Design of Plantisc2: Simulation Module
The system computes the line of fit for the given data and uses it to simulate for other concentration mixture which are not in the data set.
The essence of this is to ensure a deeper search having established the model of fit using the input data.
The simulation is achieved by taking the range between the lowest and the highest concentration of auxin and cytokinin respectively and using the range to generate and predict other concentration data sets which were not included in the original input data.
The simulation ensures that each auxin concentration exhausts all possible cytokinin concentration within the input data set range.
This is almost impossible in the laboratory due to cost, time and labour. 10

11. Plantisc2 Simulation Software sample outputs
Fig 1. Existing data Download
Fig 2. Simulation Window 11

12. Plantisc2 Simulation Software sample outputs
Fig 3. Prediction Window
Fig 4: Output Window 12

13. Deployment architecture of Plantisc2 application
Plantisc2 Architecture and Orchestration on e-infrastructure
Lab Experiment
Plantisc2 on Lion Cloud UNN
Lab data (.xls)
Plantisc2 Client UI (.php)
Plantisc2 data set on gLibrary repo (.csv)
Plantisc2 server on Future Gateway (.py) 13

14. Deployment architecture of Plantisc2 application on Catania FG Cloud14

15. Authentication of User on the Catania FG15

16. Deployment architecture of Plantisc2 application on the UNN Cloud16

17. Lion Cloud is built on Synnefo cloud stack with Ganeti clusters.
Deployment architecture of Plantisc2 application
The Plantisc2 app is deployed on the Catania Future Gateway using Ansible Playbook.
FutureGateway is a project to develop an API and related services which will allow web applications to interact with back-end distributed infrastructure of almost any kind.
Deploying it on the FG ensures the app is available always and leverages on the features of the FG
Plantisc2 is also deployed on the newly built Lion Cloud, University of Nigeria Cloud Computing Infrastructure (
Lion Cloud is built on Synnefo cloud stack with Ganeti clusters.
Being on the Lion Cloud may not guaranty availability for now but it provides better response time for those within our region. 17

18. Publish our results in reputable journals in conferences
Future Plans
In the near future we hope to do the following with respect to this research:
Do a performance evaluation between Plantisc1 and plantisc2 in terms of prediction accuracy
Take the result of the prediction to the lab to test the validity of our claim
Conclude deployment and testing of Plantisc2 on the University of Nigeria e-infrastructure (the Lion Cloud)
Publish our results in reputable journals in conferences
Seek funding and partnerships to increase the computing power of the UNN Cloud and improve power backup 18

19. We will not forget to mention EKOKonnect.
Acknowledgements
We acknowledge the financial support of the Tertiary Education Trust Fund (TETFund) through the TET Fund IBR programme.
We also appreciate the opportunities offered to us for training by the Sci-GaIA project through Prof. Roberto Barbera, Dr, Simon Taylor.
Dr. Bruce Becker and Mario Torissi are also thanked for their technical support at any time.
We appreciate the WACREN for their encouragement over the years and funding various training programmes.
We will not forget to mention EKOKonnect. 19

20. References
[7] Lineberger, R.D “The many dimensions of plant tissue culture research”, Accessed on 5th February 2017.
[8] Altaf Hussain, Iqbal Ahmed Qarshi, HummeraNazir and IkramUllah (2012). Plant Tissue Culture: Current Status and Opportunities In: Recent Advances in Plant in vitro Culture, Dr. Annarita Leva (Ed.), InTech, DOI: / Available from:
[9] Sathyanarayanan, (2010) “Understanding Plant Tissue Culture” Accessed on 10th October, 2010.
[10] Bhojwani, S. S and Razdan, M. K. (1983) Plant Tissue Culture: Theory and Practice, Developments in Crop Science (5), Elsevier Science Publishers, New York, 502pp.
[16] Arnold, S and Eriksson, T (1977) Induction of adventitious buds on embryos of Norway Spruce grown in vitro Physiologiaplantarum, 44: 283 – 287.
[17] Future Gateway API Deployment. Available at: 20

21. Thank you!
sci-gaia.eu 21