Genome Design and Analytics Tools Team

As part of the Biofoundry, we have two interconnected teams developing novel computational approaches for the design and analysis of microbial platform strains.

The Genome Design team is tasked with developing novel computational tools toward the prospective design and analysis of platform strains. We have developed three computational and data analytics tools that are of primary importance for this effort: 1) Metabolic Reconstructions and Modeling, 2) Genome Sequence (Pangenome) Analysis, and 3) Transcriptional Regulatory Network (iModulon) Analysis. We have assembled these tools into a comprehensive software suite for platform strain design, termed the StrainCAD software package. We are collaborating within the Biofoundry to deploy these tools for priority platform strain design projects at the Center to accelerate experimental workflows with computational predictions. Furthermore, we have pioneered experimental tools to utilize data science principles for synthetic biology, in the form of iModulon engraftment and engineering, to accelerate experimental strain design efforts.

The Genome Analytics team is conducting large-scale biological data analysis in E. coli and other bacteria, specializing in the extraction of actionable knowledge from multi-omics datasets. The team's overarching goal is the development of StrainMD, an integrated platform for diagnosing unexpected traits of engineered strains. At the core of StrainMD is sophisticated multi-omic visualization dashboards that power a collaborative Genome Analytics studio.

Technologies 

  • Computational Genome Design
    • Metabolic modelling
    • DNA Sequence-based machine learning
  • Computational Data Analytics
    • iModulon analysis
    • Pangenome analysis
    • Structural biology
  • Experimental Genome Design
    • iModulon engraftment
    • iModulon engineering

 

Software/databases

 

Selected Publications

  1. Sastry AV, Gao Y, Szubin R, Hefner Y, Xu S, Kim D, Choudhary KS, Yang L, King ZA, Palsson BO. The Escherichia coli transcriptome mostly consists of independently regulated modules. Nat Commun. 2019 Dec 4;10(1):5536. doi: 10.1038/s41467-019-13483-w. PMID: 31797920; PMCID: PMC6892915.
  2. Catoiu EA, Phaneuf P, Monk J, Palsson BO. Whole-genome sequences from wild-type and laboratory-evolved strains define the alleleome and establish its hallmarks. Proc Natl Acad Sci U S A. 2023 Apr 11;120(15):e2218835120. doi: 10.1073/pnas.2218835120. Epub 2023 Apr 3. PMID: 37011218; PMCID: PMC10104531.
  3. Akbari A, Yurkovich JT, Zielinski DC, Palsson BO. The quantitative metabolome is shaped by abiotic constraints. Nat Commun. 2021 May 26;12(1):3178. doi: 10.1038/s41467-021-23214-9. PMID: 34039963; PMCID: PMC8155068.
  4. Shin J, Rychel K, Palsson BO. Systems biology of competency in Vibrio natriegens is revealed by applying novel data analytics to the transcriptome. Cell Rep. 2023 Jun 27;42(6):112619. doi: 10.1016/j.celrep.2023.112619. Epub 2023 Jun 6. PMID: 37285268.
Figure 1. iModulon engraftment provide a rapid way to reconstruct cellular traits. iModulon can be transferred to a heterologous host with single transformation, providing new cellular trait to the recipient. Adaptive laboratory evolution (ALE) induces changes required to accommodate heterologous function. A synthetic genome can be designed and build based on the genomic modules.
Figure 2 - iModulon workflow (Check out Figure 1 Nature Communications for details)

Contact

Daniel Zielinski
Head of team
dczielin@ucsd.edu