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Research

Programming Biology

The What, Why, and How of our work

The focus of our lab is to program biological behaviors by designing de novo proteins that encode information and collectively carry out user-defined computations in test tubes and mammalian cells. Research projects in the lab span from fundamental questions to real-world applications. The field of synthetic biology has largely been focusing on using DNA, RNA, or genetic circuits for bioprogramming, we choose to use proteins instead because:

  1. Proteins interact directly with endogenous protein-level pathways in cells to sense and manipulate cell functions
  2. Proteins respond more rapidly than gene regulation, which requires slow and stochastic steps of transcription and translation
  3. Proteins operate across distinct cellular compartments, including the cytoplasm, nucleus, mitochondrion, and plasma membrane, among others.

Molecular Computing with Protein Circuits

We design protein circuits that can programmably and robustly carry out computations both inside and outside of cells. Such circuits allow one to predictably control cell functions. Circuit components are proteins designed from scratch using Rosetta, which enables full customization of their functionalities at the single molecule level. Key topics include:

  • Discover design principles that allow arbitrary molecular computations to be carried out by a set of interacting proteins
  • Scale protein circuits so they accommodate a large number of inputs and outputs
  • Interface protein circuits with biological processes to turn cells into living therapeutics

Related Publications

  • Programmable protein circuit designChen, Z., Elowitz, M.B. (4/29/2021). Cell.

  • De novo design of protein logic gatesChen, Z., Kibler, R.D., Hunt, A., Busch, F., Pearl, J., Jia, M., VanAernum, Z.L., Wicky, B.I.M., Dods, G., Liao, H., Wilken, M.S., Ciarlo, C., Green, S., El-Samad, H., Stamatoyannopoulos, J., Wysocki, V.H., Jewett, M.C., Boyken, S.E., Baker, D. (4/3/2020). Science.

  • Programmable design of orthogonal protein heterodimersChen, Z., Boyken, S.E., Jia, M., Busch, F., Flores-Solis, D., Bick, M.J., Lu, P., Van Aernum, Z.L., Sahasrabuddhe, A., Langan, R.A., Bermeo, S., Brunette, T., Mulligan, V.K., Carter, L.P., DiMaio, F., Sgourakis, N.G., Wysocki, V.H., Baker, D. (1/3/2019). Nature.

  • A synthetic protein-level neural network in mammalian cellsChen, Z., Linton, J.M., Xia, S., Fan, X., Yu, D., Wang, J., Zhu, R., Elowitz, M.B. (12/13/2024). Science.

Programmable Self-assembly of Proteins

Proteins in nature self-assemble into cages, fibers, sheets, and crystals that are critical to cellular functions. In most cases, the algorithms governing such assemblies are embedded in local interactions between adjacent proteins, allowing complex structures to autonomously arise from simple building blocks. We use Rosetta to design information-bearing proteins that programmably assemble into desired shapes, which can provide versatile supramolecular structure motifs to study and alter cellular functions. Key topics include:

  • Create arbitrary assembly shapes, both structured and unstructured, from a pool of orthogonally interacting building blocks
  • Optimize protein binding kinetics to minimize assembly byproducts
  • Control reversible assemblies by environmental cues and protein ligands
  • Functionalize protein assemblies both inside and outside of cells for basic studies and therapeutic applications

Related Publications

  • Self-assembling 2D arrays with de novo protein building blocksChen, Z., Johnson, M.C., Chen, J., Bick, M.J., Boyken, S.E., Lin, B., De Yoreo, J.J., Kollman, J.M., Baker, D., DiMaio, F. (5/3/2019). J. Am. Chem. Soc..

  • Creating the protein version of DNA base pairingChen, Z. (11/22/2019). Science.

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