Biology subjects

Bashor, C. J.

Publications and source records attributed to Bashor, C. J..

6 recordsLinked to original sources

bioRxiv2026

Optogenetic BMP4 models symmetry breaking and primitive streak formation in hPSCs

Gastrulation begins when the epiblast epithelium breaks symmetry to form the primitive streak, but current in vitro models neither fully capture this process nor separate its mechanical and chemical inputs. Here, we introduce LightBMP, an optogenetic platform that enables spatiotemporally programmable BMP4 expression in human pluripotent stem cells. Dose and duration control reveal a sustained low-BMP regime, inaccessible to recombinant ligand, that specifies mesoderm. Spatially programmed BMP4 sources in uniform cultures mimic the radial organization of 2D gastruloids, showing that mechan-ical context and edge sensing are not required for patterning. In micropatterned colo-nies, light-defined sources break symmetry to generate a stripe-shaped, primitive-streak-like domain where cells acquire multiple mesodermal fates and undergo directed migration. Together, these results show that spatial control of a single morphogen source is sufficient to drive symmetry breaking and primitive-streak-like patterning, independent of edge constraints.

developmental biology↗

bioRxiv2023

Engineering synthetic phosphorylation signaling networks in human cells

Protein phosphorylation signaling networks play a central role in how cells sense and respond to their environment. Here, we describe the engineering of artificial phosphorylation networks in which "push-pull" motifs--reversible enzymatic phosphorylation cycles consisting of opposing kinase and phosphatase activities--are assembled from modular protein domain parts and then wired together to create synthetic phosphorylation circuits in human cells. We demonstrate that the composability of our design scheme enables model-guided tuning of circuit function and the ability to make diverse network connections; synthetic phosphorylation circuits can be coupled to upstream cell surface receptors to enable fast-timescale sensing of extracellular ligands, while downstream connections can regulate gene expression. We leverage these capabilities to engineer cell-based cytokine controllers that dynamically sense and suppress activated T cells. Our work introduces a generalizable approach for designing and building phosphorylation signaling circuits that enable user-defined sense-and- respond function for diverse biosensing and therapeutic applications.

synthetic biology↗

bioRxiv2023

Ultra-high throughput mapping of genetic design space

Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements. However, because these approaches only interrogate short sequences, it remains challenging to perform high throughput (HT) assays on constructs containing combinations of multiple sequence elements arranged across multi-kb length scales. Overcoming this barrier could accelerate synthetic biology; by screening diverse gene circuit designs and learning "composition-to-function" mappings that reveal genetic part composability rules and enable rapid identification of behavior-optimized variants. Here, we introduce CLASSIC, a genetic screening platform that combines long- and short-read next-generation sequencing (NGS) modalities to quantitatively assess pools of constructs of arbitrary length containing diverse part compositions. We show that CLAS-SIC can measure expression profiles of >105 gene circuit designs (from 5-20 kb) in a single experiment in human cells. The resulting datasets can be used to train ML models that accurately predict circuit behavior across expansive circuit design landscapes, revealing part composability rules that govern circuit performance. Our work shows that by expanding the throughput of each design-build-test-learn (DBTL) cycle, CLASSIC enhances the pace and scale of synthetic biology and establishes an experimental basis for data-driven design of complex genetic systems.

synthetic biology↗

bioRxiv2022

Cooperative assembly confers regulatory specificity and long-term genetic circuit stability

In eukaryotes, links in gene regulatory networks are often maintained through cooperative self-assembly between transcriptional regulators (TRs) and DNA cis-regulatory motifs, a strategy widely thought to enable highly specific regulatory connections to be formed between otherwise weakly-interacting, low-specificity molecular components. Here, we directly test whether this regulatory strategy can be used to engineer regulatory specificity in synthetic gene circuits constructed in yeast. We show that circuits composed of artificial zinc-finger TRs can be effectively insulated from aberrant misregulation of the host cell genome by using cooperative multivalent TR assemblies to program circuit connections. As we demonstrate in experiments and mathematical models, assembly-mediated regulatory connections enable mitigation of circuit-driven fitness defects, resulting in genetic and functional stability of circuits in long-term continuous culture. Our naturally-inspired approach offers a simple, generalizable means for building evolutionarily robust gene circuits that can be scaled to a wide range of host organisms and applications.

synthetic biology↗

bioRxiv2018

The strength of protein-protein interactions controls the information capacity and dynamical response of signaling networks

Eukaryotic cells transmit information by signaling through complex networks of interacting proteins. Here we develop a theoretical and computational framework that relates the biophysics of protein-protein interactions (PPIs) within a signaling network to its information processing properties. To do so, we generalize statistical physics-inspired models for protein binding to account for interactions that depend on post-translational state (e.g. phosphorylation). By combining these models with information-theoretic methods, we find that PPIs are a key determinant of information transmission within a signaling network, with weak interactions giving rise to \"noise\" that diminishes information transmission. While noise can be mitigated by increasing interaction strength, the accompanying increase in transmission comes at the expense of a slower dynamical response. This suggests that the biophysics of signaling protein interactions give rise to a fundamental \"speed-information\" trade-off. Surprisingly, we find that cross-talk between pathways in complex signaling networks do not significantly alter information capacity-an observation that may partially explain the promiscuity and ubiquity of weak PPIs in heavily interconnected networks. We conclude by showing how our framework can be used to design synthetic biochemical networks that maximize information transmission, a procedure we dub \"InfoMax\" design.

systems biology↗

bioRxiv2018

A generalizable experimental framework for automated cell growth and laboratory evolution

In the post-genomics era, exploration of phenotypic adaptation is limited by our ability to experimentally control selection conditions, including multi-variable and dynamic pressure regimes. While automated cell culture systems offer real-time monitoring and fine control over liquid cultures, they are difficult to scale to high-throughput, or require cumbersome redesign to meet diverse experimental requirements. Here we describe eVOLVER, a multipurpose, scalable DIY framework that can be easily configured to conduct a wide variety of growth fitness experiments at scale and cost. We demonstrate eVOLVERs versatility by configuring it for diverse growth and selection experiments that would be otherwise challenging for other systems. We conduct high-throughput evolution of yeast across different population density niches. We perform growth selection on a yeast knockout library under temporally varying temperature regimes. Finally, inspired by large-scale integration in electronics and microfluidics, we develop novel millifluidic multiplexing modules that enable complex fluidic routines including multiplexed media routing, cleaning, vial-to-vial transfers, and automated yeast mating. We propose eVOLVER to be a versatile design framework in which to study, characterize, and evolve biological systems.

systems biology↗