Biology subjects

Flesch, T.

Publications and source records attributed to Flesch, T..

3 recordsLinked to original sources

bioRxiv2024

A scalable method for modulating plant gene expression using a multispecies genomic model and protoplast-based massively parallel reporter assay

Precision breeding tools such as CRISPR-Cas genome editing can speed up innovation in plant biotechnology and boost crop yields. The challenge remains to efficiently apply precision breeding methods to plant gene regulation. Endogenous gene regulatory sequences are subject to complex transcriptional control, a bottleneck in altering gene expression patterns in a predictable way. Here we present the CRE.AI.TIVE platform, enabling upregulation of plant gene activity without a priori knowledge of individual cis-regulatory elements or their specific location. A predictive machine learning model underpinning the platform has been trained on a wide range of tissue specific transcriptomic and epigenomic coverage datasets from DNA sequence of 12 plant species, showing competitive performance on RNA-seq coverage prediction across all species. Our platform further combines in silico DNA sequence mutagenesis and a protoplast-based massively parallel reporter assay (MPRA). We demonstrate the platforms functionality by mutagenesis of a proximal promoter of the tomato gene SlbHLH96 which yields predictions of variant gene activity in silico. 2,000 sequence candidates with varying predicted gene expression strength were validated with MPRA in plant protoplasts, identifying variants with significantly upregulated gene activity. A portion of functional sequence variants were further individually evaluated with a fluorescence reporter assay and were observed to contain a new order of known cis-regulatory elements. The CRE.AI.TIVE platform offers a first-of-its-kind scalable method of gene upregulation in plants with native DNA sequences without the need for CRE cataloguing and rational promoter design.

plant biology↗

bioRxiv2021

Rich and lazy learning of task representations in brains and neural networks

How do neural populations code for multiple, potentially conflicting tasks? Here, we used computational simulations involving neural networks to define "lazy" and "rich" coding solutions to this multitasking problem, which trade off learning speed for robustness. During lazy learning the input dimensionality is expanded by random projections to the network hidden layer, whereas in rich learning hidden units acquire structured representations that privilege relevant over irrelevant features. For context-dependent decision-making, one rich solution is to project task representations onto low-dimensional and orthogonal manifolds. Using behavioural testing and neuroimaging in humans, and analysis of neural signals from macaque prefrontal cortex, we report evidence for neural coding patterns in biological brains whose dimensionality and neural geometry are consistent with the rich learning regime.

neuroscience↗

bioRxiv2018

Focused learning promotes continual task performance in humans

Humans can learn to perform multiple tasks in succession over the lifespan (\"continual\" learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans. Unlike neural networks, humans that were trained on temporally autocorrelated task objectives (focussed training) learned to perform new tasks more effectively, and performed better on a later test involving randomly interleaved tasks. Analysis of error patterns suggested that focussed learning permitted the formation of factorised task representations that were protected from mutual interference. Furthermore, individuals with a strong prior tendency to represent the task space in a factorised manner enjoyed greater benefit of focussed over interleaved training. Building artificial agents that learn to factorise tasks appropriately may be a promising route to solving continual task performance in machine learning.\n\nSignificance StatementHumans learn to perform many different tasks over the lifespan, such as speaking both French and Spanish. The brain has to represent task information without mutual interference. In machine learning, this \"continual learning\" is a major unsolved challenge. Here, we studied the patterns of errors made by humans and state-of-the-art deep networks whilst they learned new tasks from scratch and without instruction. Humans, but not machines, seem to benefit from training regimes that focussed on one task at a time, especially when they had a prior bias to represent stimuli in a way that facilitated task separation. Machines trained to exhibit the same prior bias suffered less interference between tasks, suggesting new avenues for solving continual learning in artificial systems.

neuroscience↗