Biology subjects

Pearce, M. T.

Publications and source records attributed to Pearce, M. T..

10 recordsLinked to original sources

bioRxiv2026

EVEE: Interpretable variant effect prediction from genomic foundation model embeddings

Scientific foundation models learn high-dimensional representations from diverse data modalities, yet what they encode and how to extract that knowledge remain open questions. Here we show that probing the internal representations of Evo 2, a 7-billion-parameter genomic foundation model, enables accurate and interpretable genetic variant effect prediction. We introduce a covariance-based probe that captures second-order structure from Evo 2 sequence embeddings to predict variant pathogenicity across variant types and functional consequences, matching or exceeding specialized predictors within their domains. To ground these predictions in known biological mechanisms, we train a complementary panel of probes on existing annotations to detect which genomic properties are disrupted by a variant. This categorized evidence is then integrated with each variants genomic context through a language model to generate variant-specific mechanistic hypotheses. Our pathogenicity predictions correlate with experimental measures of variant function, clinical penetrance, and biobank disease associations while the mechanistic hypotheses are consistent with expert reviews, known mechanism classes, and downstream molecular readouts. We release pathogenicity scores, disruption profiles, and contextualized interpretations for 4.2 million variants from the ClinVar database as an open resource through the Evo Variant Effect Explorer (EVEE). More broadly, this structured probing approach offers a general framework for interrogating foundation models across scientific disciplines and grounding their outputs in existing domain concepts.

genomics↗

bioRxiv2023

Healthy ageing effects on implicit auditory memory: from encoding to 6-month retention

Any listening task, from sound recognition to sound-based communication, rests on auditory memory which is known to decline in healthy ageing. However, how this decline maps onto multiple components and stages of auditory memory remains poorly characterised. In an online unsupervised longitudinal study, we tested ageing effects on implicit auditory memory for rapid tone patterns. The test required participants (younger, aged 20-30, and older adults aged 60-70) to quickly respond to rapid regularly repeating patterns emerging from random sequences. Patterns were novel in most trials (REGn), but unbeknownst to the participants, a few distinct patterns reoccurred identically throughout the sessions (REGr). After correcting for processing speed, the response times (RT) to REGn should reflect the information held in echoic and short-term memory before detecting the pattern; long-term memory formation and retention should be reflected by the RT advantage (RTA) to REGr vs REGn which is expected to grow with exposure. Older participants were slower than younger adults in detecting REGn and exhibited a smaller RTA to REGr. Computational simulations using a model of auditory sequence memory indicated that these effects reflect age-related limitations both in early and long-term memory stages. In contrast to ageing-related accelerated forgetting of verbal material, here older adults maintained stable memory traces for REGr patterns up to 6 months after the first exposure. The results demonstrate that ageing is associated with reduced short-term memory and long-term memory formation for tone patterns, but not with forgetting, even over surprisingly long timescales.

neuroscience↗

bioRxiv2022

Spatiotemporal Ecological Chaos Enables Gradual Evolutionary Diversification Without Niches or Tradeoffs

Ecological and evolutionary dynamics are intrinsically entwined. On short time scales, ecological interactions determine the fate of new mutants and changes in the community they induce, while on longer time scales evolution shapes the whole community. How eco-evolutionary dynamics gives rise to the extensive coexisting diversity of strains found in many bacterial species is a major puzzle. In this paper we study the evolution of large numbers of closely related strains with generalized Lotka Volterra interactions but no niche structure. The host-pathogen-like interactions drive the ecological dynamics into a spatiotemporally chaotic state characterized by continual local blooms and busts. Upon the slow serial introduction of new strains, the community is found to diversify indefinitely, accommodating arbitrarily large numbers of strains in the absence of any kind of stabilizing niche interactions. This diversifying phase is robust to changes in evolutionary parameters, and persists even in the presence of a distribution of general, nonspecific fitness differences between individual strains, which explicitly break the assumption of tradeoffs inherent in much previous work. However, gradual increase of the general fitnesses in the ecosystem slows down the diversification. Quantitative analysis of the range of behaviors is carried out by a combination of analytical methods and simulations. Building on a dynamical-mean field-theory understanding of the ecological dynamics, an approximate effective model captures the effects of evolution on the distributions of key properties, such as strain abundances. This work establishes a potential scenario and a theoretical framework for understanding how the interplay between evolution and ecology can give rise to extensive fine-scale diversity. Future avenues for investigation are suggested, including the effects of the build-up of relatedness between strains, how conditioning on the evolutionary history affects the ecological interactions and dynamics, and application to coevolution of the diversity of a bacterial and a phage species.

evolutionary biology↗

bioRxiv2020

Long-term implicit memory for sequential auditory patterns in humans

To understand auditory scenes, listeners track and retain the statistics of sensory inputs as they unfold over time. We combined behavioural manipulation and modelling to investigate how sequence statistics are encoded into long-term memory and used to interpret incoming sensory signals. In a series of experiments, participants detected the emergence of regularly repeating patterns in novel rapid sound sequences. Unbeknownst to them, a few regular patterns reoccurred sparsely (every [~]3 minutes). Reoccurring sequences showed a rapidly growing detection time advantage over novel sequences. This effect was implicit, robust to interference, and persisted up to 7 weeks. Human performance was reproduced by a memory-constrained probabilistic model, where sequences are stored as n-grams and are subject to memory decay. Results suggest that similar psychological mechanisms may underlie integration processes over different-time scales in memory formation and flexible retrieval.

neuroscience↗

bioRxiv2020

PPM-Decay: A Computational Model of Auditory Prediction with Memory Decay

Statistical learning and probabilistic prediction are fundamental processes in auditory cognition. A prominent computational model of these processes is Prediction by Partial Matching (PPM), a variable-order Markov model that learns by internalizing n-grams from training sequences. However, PPM has limitations as a cognitive model: in particular, it has a perfect memory that weights all historic observations equally, which is inconsistent with memory capacity constraints and recency effects observed in human cognition. We address these limitations with PPM-Decay, a new variant of PPM that introduces a customizable memory decay kernel. In three studies - one with artificially generated sequences, one with chord sequences from Western music, and one with new behavioral data from an auditory pattern detection experiment - we show how this decay kernel improves the models predictive performance for sequences whose underlying statistics change over time, and enables the model to capture effects of memory constraints on auditory pattern detection. The resulting model is available in our new open-source R package, ppm (https://github.com/pmcharrison/ppm).

animal behavior and cognition↗

bioRxiv2019

Decomposing neural responses to melodic surprise in musicians and non-musicians: evidence for a hierarchy of predictions in the auditory system

Neural responses to auditory surprise are typically studied with highly unexpected, disruptive sounds. Consequently, little is known about auditory prediction in everyday contexts that are characterized by fine-grained, non-disruptive fluctuations of auditory surprise. To address this issue, we used IDyOM, a computational model of auditory expectation, to obtain continuous surprise estimates for a set of newly composed melodies. Our main goal was to assess whether the neural correlates of non-disruptive surprising sounds in a musical context are affected by musical expertise. Using magnetoencephalography (MEG), auditory responses were recorded from musicians and non-musicians while they listened to the melodies. Consistent with a previous study, the amplitude of the N1m component increased with higher levels of computationally estimated surprise. This effect, however, was not different between the two groups. Further analyses offered an explanation for this finding: Pitch interval size itself, rather than probabilistic prediction, was responsible for the modulation of the N1m, thus pointing to low-level sensory adaptation as the underlying mechanism. In turn, the formation of auditory regularities and proper probabilistic prediction were reflected in later components: the mismatch negativity (MMNm) and the P3am, respectively. Overall, our findings reveal a hierarchy of expectations in the auditory system and highlight the need to properly account for sensory adaptation in research addressing statistical learning. Highlights- In melodies, sound expectedness (modeled with IDyOM) is associated with the amplitude of the N1m. - This effect is not different between musicians and non-musicians. - Sensory adaptation related to melodic pitch intervals explains better the N1m effect. - Auditory regularities and the expectations captured by IDyOM are reflected in the MMNm and P3am. - Evidence for a hierarchy of auditory predictions during melodic listening.

neuroscience↗

bioRxiv2019

Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non-musicians

Auditory prediction error responses elicited by surprising sounds can be reliably recorded with musical stimuli that are more complex and realistic than those typically employed in EEG or MEG oddball paradigms. However, these responses are reduced as the predictive uncertainty of the stimuli increases. In this study, we investigate whether this effect is modulated by musical expertise. Magnetic mismatch negativity (MMNm) responses were recorded from 26 musicians and 24 non-musicians while they listened to low-and high-uncertainty melodic sequences in a musical multi-feature paradigm that included pitch, slide, intensity, and timbre deviants. When compared to non-musicians, musically trained participants had significantly larger pitch and slide MMNm responses. However, both groups showed comparable reductions of pitch and slide MMNm amplitudes in the high-uncertainty condition compared to the low-uncertainty condition. In a separate, behavioral deviance detection experiment, musicians were more accurate and confident about their responses than non-musicians, but deviance detection in both groups was similarly affected by the uncertainty of the melodies. In both experiments, the interaction between uncertainty and expertise was not significant, suggesting that the effect is comparable in both groups. Consequently, our results replicate the modulatory effect of predictive uncertainty on prediction error; show that it is present across different types of listeners; and suggest that expertise-related and stimulus-driven modulations of predictive precision are dissociable and independent.

neuroscience↗

bioRxiv2019

Stabilization of extensive fine-scale diversity by spatio-temporal chaos

It has become recently apparent that the diversity of microbial life extends far below species level to the finest scales of genetic differences. Remarkably, extensive fine-scale diversity can coexist spatially. How is this diversity stable on long timescales despite selective or ecological differences and other evolutionary processes? Most work has focused on stable coexistence or assumed ecological neutrality. We present an alternative: extensive diversity maintained by ecologically-driven spatio-temporal chaos, with no assumptions about niches or other specialist differences between strains. We study generalized Lotka-Volterra models with anti-symmetric correlations in the interactions inspired by multiple pathogen strains infecting multiple host strains. Generally, these exhibit chaos with increasingly wild population fluctuations driving extinctions. But the simplest spatial structure, many identical islands with migration between them, stabilizes a diverse chaotic state. Some types (sub-species) go globally extinct, but many persist for times exponentially long in the number of islands. All persistent types have episodic local blooms to high abundance, crucial for their persistence as, for many, their average population growth rate is negative. Snapshots of the distribution of abundances show a power-law at intermediate abundances that is essentially indistinguishable from the neutral theory. But the dynamics of the large populations are much faster than birth-death fluctuations. We argue that this spatio-temporally chaotic \"phase\" should exist in a wide range of models, and that even in rapidly mixed systems, longer lived spores could similarly stabilize a diverse chaotic phase.

ecology↗

bioRxiv2017

Rapid adaptation in large populations with very rare sex: scalings and spontaneous oscillations

Genetic exchange in microbes and other facultative sexuals can be rare enough that evolution is almost entirely asexual and populations almost clonal. But the benefits of genetic exchange depend crucially on the diversity of genotypes in a population. How very rare recombination together with the accumulation of new mutations shapes the diversity of large populations and gives rise to faster adaptation is still poorly understood. This paper analyzes a particularly simple model: organisms with two asexual chromosomes that can reassort during rare matings that occur at a rate r. The speed of adaptation for large population sizes, N, is found to depend on the ratio ~ log(Nr)/log(N). For larger populations, the r needed to yield the same speed deceases as a power of N. Remarkably, the population undergoes spontaneous oscillations alternating between phases when the fittest individuals are created by mutation and when they are created by reassortment, which--in contrast to conventional regimes--decreases the diversity. Between the two phases, the mean fitness jumps rapidly. The oscillatory dynamics and the strong fluctuations this induces have implications for the diversity and coalescent statistics. The results are potentially applicable to large microbial populations, especially viruses that have a small number of chromosomes. Some of the key features may be more broadly applicable for large populations with other types of rare genetic exchange.

evolutionary biology↗

bioRxiv2017

Neutral and niche dynamics in a synthetic microbial community

Ecologists debate the relative importance of niche versus neutral processes in understanding biodiversity1,2. This debate is especially pertinent to microbial communities, which play crucial roles in biogeochemical cycling3,4, food production5, industrial processes6,7, and human health and disease8. Here we created a synthetic microbial community using heritable genetic barcodes and tracked community composition over time across a range of experimental conditions. We show that a transition exists between the neutral and niche regimes, and, consistent with theory, the crossover point depends on factors including immigration, fitness, and population size. We find that diversity declined most rapidly at intermediate population sizes, which can be explained by a tradeoff between replacement by migration and duration of growth. We then ran an experiment where the community underwent abrupt or gradual changes in size, the outcome of which highlights that selecting the correct model is essential to managing diversity. Taken together these results emphasize the importance of including niche effects to obtain realistic models across a wide range of parameters, even in simple systems.

microbiology↗