Bashor Lab

We are pleased to share a paper from our lab out in this week’s issue of @Nature, where we show that HT + ML can dramatically speed up the synbio DBTL cycles, profiling gene circuit design spaces at unprecedented scale: nature.com/articles/s4158… (1/16)

An autonomous system for multi-objective continuous evolution at scale biorxiv.org/content/10.648… #biorxiv_bioeng

Finding new medicines is getting more and more expensive, and AI won't help much unless we can generate physiological data at scale. In our new preprint, @GordianBio extends the progress of the functional genomics community to run pooled in vivo screens at scale, in a way that answers questions about physiology and therapeutic potential. We show screens in mice and horses, fibrotic and degenerative disease, with a framework for physiological predictions validated in human ex vivo tissues. Very proud of @v_sontake, @vkartha88, Neety and the rest of the team. Tweetorial follows:

Evolution navigated billions of challenges to get to us to where we are today. Directed evolution compresses this to 1D axis. Imagine if you could sample 200 dimensions at once, with data to boot 📈 First @chorylab PACE preprint on our new system to tackle this: TurboPRANCE👇

Very happy to see this work from Fabian Rehm, Jason Chin, and team on the development of a high error-rate orthogonal DNA replication system in E. coli, thus supporting continuous hypermutation and evolution of target genes in vivo. The system is based on a protein-primed linear plasmid replication mechanism, which has become a reliable way of realizing the orthogonal replication concept through which hypermutation is durably targeted to an orthogonal plasmid while sparing the genome. Our original orthogonal DNA replication (OrthoRep) system in yeast, Rongzhen Tian's and Jason Chin's BacORep and EcORep systems in bacteria, and now this promising new system all repurpose protein-primed replication to achieve orthogonality. I look forward to seeing how these systems continue to be applied to gene and biomolecular evolution at scale! nature.com/articles/s4158…

New lab preprint: if ML struggles with extrapolation, let's expand the diversity of training data with gene synth, DNA shuffling, and ML gen.... also lots of #FluorescentProteins #ProteinEngineering #MachineLearning #SyntheticBiology 1/n

Shoutout to the authors, members of the @bashorlab, and our collaborators and friends, Pankaj Mehta @BU_Tweets, @traingene, and Ankit Patel @bcmhouston for helpful discussions, contributions and support, which led to CLASSIC becoming a reality! (16/16)

We expanded on some of these thoughts on combining ML/AI with synbio DBTL cycles in our review last year, led by @kshitij_raiguy, in collaboration with Ankit Patel’s lab @bcmhouston: sciencedirect.com/science/articl… (15/16)

We are pleased to share a paper from our lab out in this week’s issue of @Nature, where we show that HT + ML can dramatically speed up the synbio DBTL cycles, profiling gene circuit design spaces at unprecedented scale: nature.com/articles/s4158… (1/16)