Biology subjects

Klumpe, H.

Publications and source records attributed to Klumpe, H..

3 recordsLinked to original sources

bioRxiv2026

Synthetic cell-cell adhesion provides benefits of proximity without diffusion-related costs

Multicellularity provides many advantages over unicellularity, but accessing those benefits with synthetic biology approaches is challenging due to potential tradeoffs. The same diffusion limitations that exclude toxins can limit nutrient transport, and small intercellular spaces that facilitate sharing also increase local competition. To isolate when and how cell aggregation, an essential feature of multicellularity, is useful, we compared yeast strains differing only in expression of a single adhesion gene. Unexpectedly, aggregates containing thousands of cells showed no signs of diffusion limitations, growing as well as unicellular strains in both rich and toxic media, likely because reversible cell adhesion allows cells to exchange positions. Aggregating cells also grew better than unicellular strains in the limiting carbon conditions expected to increase diffusion limitations. In mixtures of different genotypes, these benefits were specific to cells expressing the adhesion protein. Together, these results suggest key design principles for engineering aggregates: reformable bonds provide a mechanism to sidestep the fitness costs of increased size, and the benefits within such groups may arise more from nutrient sharing than from diffusion limitations.

synthetic biology↗

bioRxiv2023

Deep neural networks for predicting single cell responses and probability landscapes

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.

synthetic biology↗

bioRxiv2020

Ligand-receptor promiscuity enables cellular addressing

In multicellular organisms, secreted ligands selectively activate, or "address," specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors, provoking the question of how addressing specificity can emerge from molecular promiscuity. To investigate this issue, we developed a general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture. We find that promiscuously interacting ligand-receptor systems allow a small number of ligands, acting in combinations, to address a larger number of individual cell types, each defined by its receptor expression profile. Promiscuous systems outperform seemingly more specific one-to-one signaling architectures in addressing capacity. Combinatorial addressing extends to groups of cell types, is robust to receptor expression noise, grows more powerful with increasing receptor multiplicity, and is maximized by specific biochemical parameter relationships. Together, these results identify fundamental design principles governing cell addressing by ligand combinations.

systems biology↗