Publications

Percolation theory enables the design of high capacity cation-disordered oxides for rechargeable lithium battery cathodes.

Traditional theories of electron transport in crystals are based on the Boltzmann equation and do not capture physics arising from quantum coherence. We introduce a transport formalism based on ''orbital Wigner functions'', which accurately captures quantum coherent physics in multiband fermionic systems. We illustrate the power of this approach compared to traditional semiclassical transport theory by testing it numerically against microscopic simulations of one-dimensional, non-interacting, two-band systems -- the simplest systems capable of exhibiting inter-orbital coherence. We show that orbital Wigner functions accurately capture strongly non-equilibrium features of electron dynamics that lie beyond conventional Boltzmann theory, such as the ballistic transport of a relative phase between microscopic orbitals and topological Thouless pumping of charge both at non-zero temperature and away from the adiabatic limit. Our approach is motivated in part by modern ultracold atom experiments that can prepare and measure far-from-equilibrium charge transport and phase coherence in multiband fermionic systems, calling for correspondingly precise theories of transport. The quantitative accuracy exhibited by our approach, together with its capacity to capture nontrivial physics even at the ballistic scale, establishes orbital Wigner functions as an ideal starting point for developing a fully systematic theory of transport in crystals.

Summary Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection, and amplification. While powerful, systematic searches through large sequence-spaces is a labor-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generates enzyme variants using the recently developed orthogonal replication system, OrthoRep, and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a ratelimiting enzymatic reaction of the biosynthetic pathway for cis , cis -muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae . After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers >13-fold compared to reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study, is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

Here in this paper we consider the possibility and conditions for pair photoemission whereby two incident photons emit pairs of electrons from a candidate material as a novel method to measure and visualize electronic correlations. As opposed to double photoemission - where a single photon precipitates the ejection of a pair electrons via a subsequent electron energy loss scattering process - we show that pair photoemission need not be limited to interference between initial photoelectrons and valence electrons, and moreover, can occur without the energy penalty of two work functions. This enables detection of pairs of electrons at high energy resolution that may be correlated in the same quantum many-body states.

Abstract Monoterpene indole alkaloids (MIAs) from Mitragyna speciosa (“kratom”), such as mitragynine and speciogynine, are promising novel scaffolds for opioid receptor ligands for treatment of pain, addiction, and depression. While kratom leaves have been used for centuries in South-East Asia as stimulant and pain management substance, the biosynthetic pathway of these psychoactives have only recently been partially elucidated. Here, we demonstrate the de novo production of mitragynine and speciogynine in Saccharomyces cerevisiae through the reconstruction of a five-step synthetic pathway from common MIA precursor strictosidine comprising fungal tryptamine 4-monooxygenase to bypass an unknown kratom hydroxylase. Upon optimizing cultivation conditions, a titer of ∼290 µg/L kratom MIAs from glucose was achieved. Untargeted metabolomics analysis of lead production strains led to the identification of numerous shunt products derived from the activity of strictosidine synthase (STR) and dihydrocorynantheine synthase (DCS), highlighting them as candidates for enzyme engineering to further improve kratom MIAs production in yeast. Finally, by feeding fluorinated tryptamine and expressing a human tailoring enzyme, we further demonstrate production of fluorinated and hydroxylated mitragynine derivatives with potential applications in drug discovery campaigns. Altogether, this study introduces a yeast cell factory platform for the biomanufacturing of complex natural and new-to-nature kratom MIAs derivatives with therapeutic potential.

Hierarchical feature extractors such as Convolutional Networks (ConvNets) have achieved impressive performance on a variety of classification tasks using purely feedforward processing. Feedforward architectures can learn rich representations of the input space but do not explicitly model dependencies in the output spaces, that are quite structured for tasks such as articulated human pose estimation or object segmentation. Here we propose a framework that expands the expressive power of hierarchical feature extractors to encompass both input and output spaces, by introducing top-down feedback. Instead of directly predicting the outputs in one go, we use a self-correcting model that progressively changes an initial solution by feeding back error predictions, in a process we call Iterative Error Feedback (IEF). IEF shows excellent performance on the task of articulated pose estimation in the challenging MPII and LSP benchmarks, matching the state-of-the-art without requiring ground truth scale annotation.