InSpace

Interstellar iN-Situ Predictive Algorithm for Cellular Engineering (InSpace), made by UW students, aims to predict relative viability of bacteria-of-interest (BOI) to spaceflight conditions (data sourced from NASA GeneLab).

Current Functionality:

• Retrieve protein sequence from NCBI (GI number)
• Obtain properties of a protein sequence
  • MW, polarity, isoelectricity, aromaticity
• Predict microbial log2FC (viability metric) for space environment
  • Non-linear regression (Bagging Regressor)

Justification

As spaceflight technologies advance and mission lengths increase, there is an interest to develop technologies that decrease cost and reduce risks associated with providing biologically relevant resources to longer-term space missions [1]. A potential solution is in-situ resource utilization or ISRU, coupled with the process of in-situ manufacturing, where materials located on-site can be utilized as precursors to create other space-based commodities [2].

In regards to the design of these complex processes, it is crucial to understand the effects on gene expression and protein dynamics in response to two of the largest environmental stressors: space radiation and gravity [3]. Our tool aims to provide researchers with a machine learning model to predict gene expression of important gene clusters based on previously flown model organisms (Escherichia coli, Pseudomonas aeruginosa, and Bacillus subtilis).

[1] Menezes A. A.; Cumbers J.; Hogan J. A.; & Arkin A. P. (2015). Towards synthetic biological approaches to resource utilization on space missions. ​Journal of The Royal Society Interface​​12​(102), 20140715. https://doi.org/10.1098/rsif.2014.0715

[2] Mahoney, E. In-Situ Resource Utilization http://www.nasa.gov/isru (accessed Jan 23, 2021).

[3] Simpson, J. A. (1983). Elemental and isotopic composition of the galactic cosmic rays. Annual Review of Nuclear and Particle Science​, ​33​(1), 323-382.

Dependencies

1. BioPython
   • Bio.Entrez, Bio.SeqUtils.ProtParam packages
   • Retrieving amino acid sequences and feature generation for predictive model
2. Numpy
   • Performing mathematical operations
3. Pandas
   • Manipulating and handling dataframes
4. Scikit-Learn
   • Building machine learning models (Bagging regressor, ensemble)
5. Seaborn
   • Visualizing selected gene clusters in respect to predicted log2FC