Reconstruction

Biology has become a big-data science; advanced computing power, methods, and analytics are now required to glean impactful insights from the massive amounts of data being generated in biology. Additionally, integrating interoperable data types is key to exploring and accurately modelling biological systems. The Reconstruction team works to develop solutions to these challenges in the form of reconstruction resources, use these same tools to learn about organisms of interest, and support the use of these tools by other research and Biofoundry teams to enhance and accelerate the engineering of strain designs.

Reconstruction resources can be generally defined as frameworks of interoperable biological information working to maximize the coverage of knowledge on organisms of interest. Examples include results and automated pipelines for comprehensive genomic collections (Pangenomes), variant gene sets (Alleleomes), biochemical network simulations (Metabolic models), and independently regulated gene modules (iModulons).

Leveraging interoperable data types has rendered broader insights and enhanced clarity on the systemic effects of industrial conditions and genome edits, proving to be an invaluable strategy toward better understanding microbial systems biology. The Reconstruction Team will continue to take advantage of the continuously growing wealth of big-biological data to comprehensively characterize our organisms of interest and take advantage of the knowledge that can be extracted from ever-larger scales of data.

Example Work

• Pipeline development: bgcflow
• Pangenome database: PanKB

Team Members

• Bernhard Ø. Palsson (Design & Learn)
• Patrick Phaneuf (Head of Reconstruction) 
• Jonathan Josephs-Spaulding, Postdoc
• Omid Ardalani, PhD Student
• Binhuan Sun, PhD Student
• Liubov Pashkova, Bioinformatics Software Engineer

Selected Publications

1. Rajput A, Chauhan SM, Mohite OS, Hyun JC, Ardalani O, Jahn LJ, Sommer MO, Palsson BO. Pangenome analysis reveals the genetic basis for taxonomic classification of the Lactobacillaceae family. Food Microbiol. 2023 Oct;115:104334. doi: 10.1016/j.fm.2023.104334. Epub 2023 Jul 8. PMID: 37567624.
2. Ardalani, O., Phaneuf, P.V., Mohie, O.S., Nielsen, L.K. and Palsson, B., 2023. Pangenome reconstruction of Lactobacillaceae metabolism predicts species-specific metabolic traits. bioRxiv, pp.2023-09.
3. Harke, A.S., Josephs-Spaulding, J., Mohite, O.S., Chauhan, S.M., Ardalani, O., Palsson, B. and Phaneuf, P.V., 2023. Genomic insights into Lactobacillaceae: Analyzing the Alleleome of core pangenomes for enhanced understanding of strain diversity and revealing Phylogroup-specific unique variants. bioRxiv, pp.2023-09. 
4. Phaneuf, P., Jarczynska, Z.D., Kandasamy, V., Chauhan, S., Feist, A.M. and Palsson, B.O., 2023. Using the E. coli Alleleome in Strain Design. bioRxiv, pp.2023-09.
5. Phaneuf, P.V., Kim, S.H., Rychel, K., Rode, C., Beulig, F., Palsson, B.O. and Yang, L., 2023. Data-Driven Strain Design Towards Mitigating Biomanufacturing Stresses. bioRxiv, pp.2023-09.
6. Phaneuf PV, Zielinski DC, Yurkovich JT, Johnsen J, Szubin R, Yang L, Kim SH, Schulz S, Wu M, Dalldorf C, Ozdemir E, Lennen RM, Palsson BO, Feist AM. Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data. ACS Synth Biol. 2021 Dec 17;10(12):3379-3395. doi: 10.1021/acssynbio.1c00337. Epub 2021 Nov 11. PMID: 34762392; PMCID: PMC8870144.
7. Nuhamunada, M., Mohite, O.S., Phaneuf, P.V., Palsson, B. and Weber, T., 2023. BGCFlow: Systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets. bioRxiv, pp.2023-06.