Development of a SGW-based Plant Tissue Culture Micropropagation Yield Forecasting Application, Plantisc2 Collins Udanor – University of Nigeria Nsukka - Nigeria (collins.udanor@unn.edu.ng) WACREN e-Research Hackfest – Lagos (Nigeria)

Scientific problem area Starting point Technology stack Outline Scientific problem area Starting point Technology stack Computational and data model Implementation strategy

Scientific problem Abstract: Plant tissue culture is a collection of techniques used to maintain or grow plant cells, tissues or organs under sterile conditions on a nutrient culture medium of known composition. Plant tissue culture is widely used to produce clones of a plant in a method known as micropropagation. During the UNESCO-HP Brain Gain Initiative (BGI) project (2009-2013), the University of Nigeria team conducted series of plant tissue culture experiments and developed a stand-alone application, Plantisc. A Plant Tissue Culture micro propagation simulation software, which achieved over 67% predication accuracy whose result was published in a peer-reviewed journal . [http://elvedit.com/journals/IJACSIT/wp-content/uploads/2014/06/Gridsim-Paper-2.pdf]

What scientific domain is this application addressing ? Plant Science and Biotechnology What are the identified problems that this application tries to solve ? Time taken to perform the experiment is much the experiment is cost intensive and it is still in an empirical stage. What do you see as some of the benefits of using the web for this application? Availability of the application to a wider research community Reduction in cost of experiment Reduction in time of experiment

Fig 1. Tissue Culture experiments in the Lab

Workflow The various workflows that users of the application will undertake will include: The application will have a user input GUI for the user to upload his/her data The data is read from a .csv file or other format and stored as an array in a Python function The user selects what part of the plant tissue he/she wants to forecast the yield, e.g. root, shoot, leaf The user selects different combinations of auxins at various quantities The user submits the job The user checks the status of the job The user retrieves the result when completed The user analyzes the results The cycle is repeated as the need may arise.

Figure 2: Input capture form

Figure 3: Output

Please describe : Data model The various workflows that users of the application will undertake Described in the last slide (4) Data origins, ingestion, management : From experiments in Plant Tissue culture labs Where are you getting data from ? Data can be stored in repositories anywhere, PC or cloud repos Where should it be moved to ? Data may be moved to application database or cached during runtime Where should it finally be stored ? In a cloud repository for a specific number of days

Which of these models apply to you ? Computing model Which of these models apply to you ? High throughput computing (grid/cloud/p2p) High-performance computing (grid/HPC) The above types

Implementation strategy We need you to develop a project plan Strategy: - Develop Regression models - Modular development - Unit testing - Share on Github Mid-hack checkpoint - Should have Created user interface - Upload user data - Implemented Regression model End-of hack checkpoint Full functional application Deployable on the cloud - Upload user data by mid December - Implemented Regression model - by end of December - Full functional application - By mid January - Deployable on the cloud - By end of January 2017

Envisaged challenges may include: Risks and unknowns What do you think will get in your way ? List anything, and try to estimate the negative impact that it will have Envisaged challenges may include: Time factor - Official work hours may slow the speed of development Lack of support from other programmers - most of my programmers are outside the country on study

Thank you! sci-gaia.eu info@sci-gaia.eu