Daniel Goodman

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Wyss Institute / Harvard University
Goodman, Daniel

Daniel Bryan Goodman is a PhD candidate in the Harvard-MIT Division of Health Sciences and Technology and in Professor George Church’s lab at the Wyss Institute of Biologically Inspired Engineering.  Daniel is using microarray-based DNA synthesis technologies to design, assemble, and test thousands of genetic elements, genes, and gene networks in parallel.

Prior to joining the Wyss Institute, he was a Whitaker Bioengineering Fellow at The University of Cambridge in the United Kingdom. Daniel received his B.S. in Bioengineering from the University of California at San Diego, where he worked on comparative genomics and proteomics with Prof. Pavel Pevzner and Prof. Phil Bourne.

Tue July 9 | 2:00 - 4:00
ABSTRACT: Composability of regulatory sequences controlling transcription and translation in E. coli.

The unpredictability of gene expression hinders our ability to engineer biological systems. A standardized library of well-characterized regulatory elements offers a potential solution only if such elements behave predictably when combined. To answer this question, we synthesized 27,000 combinations of common promoters, ribosome binding sites, and N-terminal coding sequences and simultaneously measured DNA, RNA, and protein levels from the entire library. Using a simple additive model, we found that RNA and protein expression were within 2x of predicted levels 80% and 64% of the time respectively. This large dataset allowed quantitation of global effects. Surprisingly, we found that the use of rare codons at the N-terminus strongly increases expression compared to common codons. Additionally, we measured how GC content and secondary structure affect translation efficiency and how translation rate alters mRNA stability. Even after accounting for the effects of these variables, unexplained severe outliers remain that hinder the use of prediction and standardization in large-scale genetic engineering projects. The ease and scale of our DNA synthesis approach indicates that it is feasible to design and screen large libraries for desired behavior.