Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts
by Jaron T. Colas, John P. O ’Doherty, Scott T. Grafton
Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis...
Source: PLoS Computational Biology - March 29, 2024 Category: Biology Authors: Jaron T. Colas Source Type: research
Parameter estimation in behavioral epidemic models with endogenous societal risk-response
by Ann Osi, Navid Ghaffarzadegan
Behavioral epidemic models incorporating endogenous societal risk-response, where changes in risk perceptions prompt adjustments in contact rates, are crucial for predicting pandemic trajectories. Accurate parameter estimation in these models is vital for validation and precise projections. However, few studies have examined the problem of identifiability in models where disease and behavior parameters must be jointly estimated. To address this gap, we conduct simulation experiments to assess the effect on parameter estimation accuracy of a) delayed risk response, b) neglecting behavioral ...
Source: PLoS Computational Biology - March 29, 2024 Category: Biology Authors: Ann Osi Source Type: research
Amplifiers of selection for the Moran process with both Birth-death and death-Birth updating
by Jakub Svoboda, Soham Joshi, Josef Tkadlec, Krishnendu Chatterjee
Populations evolve by accumulating advantageous mutations. Every population has some spatial structure that can be modeled by an underlying network. The network then influences the probability that new advantageous mutations fixate. Amplifiers of selection are networks that increase the fixation probability of advantageous mutants, as compared to the unstructured fully-connected network. Whether or not a network is an amplifier depends on the choice of the random process that governs the evolutionary dynamics. Two popular choices are Moran process with Bi...
Source: PLoS Computational Biology - March 29, 2024 Category: Biology Authors: Jakub Svoboda Source Type: research
Raising awareness of uncertain choices in empirical data analysis: A teaching concept toward replicable research practices
We present a seminar course intended for advanced undergraduates and beginning graduate students of data analysis fields such as statistics, data science, or bioinformatics that aims to increase the awareness of uncertain choices in the analysis of empirical data and present ways to deal with these choices through theoretical modules and practical hands-on sessions. (Source: PLoS Computational Biology)
Source: PLoS Computational Biology - March 28, 2024 Category: Biology Authors: Maximilian M. Mandl Source Type: research
Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects
We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and lesioning these neurons by setting their output to zero or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to repr...
Source: PLoS Computational Biology - March 28, 2024 Category: Biology Authors: Peng Liu Source Type: research
The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model
by B üsra Atamer Balkan, You Chang, Martijn Sparnaaij, Berend Wouda, Doris Boschma, Yangfan Liu, Yufei Yuan, Winnie Daamen, Mart C. M. de Jong, Colin Teberg, Kevin Schachtschneider, Reina S. Sikkema, Linda van Veen, Dorine Duives, Quirine A. ten Bosch
SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor space...
Source: PLoS Computational Biology - March 28, 2024 Category: Biology Authors: B üsra Atamer Balkan Source Type: research
Self-replicating artificial neural networks give rise to universal evolutionary dynamics
We present a new deep-learning based computational model, theself-replicating artificial neural network (SeRANN). We train it to (i) copy its own genotype, like a biological organism, which introduces endogenous spontaneous mutations; and (ii) simultaneously perform a classification task that determines its fertility. Evolving 1,000 SeRANNs for 6,000 generations, we observed various evolutionary phenomena such as adaptation, clonal interference, epistasis, and evolution of both the mutation rate and the distribution of fitness effects of new mutations. Our results demonstrate that universal evolutionary phenomena can natur...
Source: PLoS Computational Biology - March 28, 2024 Category: Biology Authors: Boaz Shvartzman Source Type: research
Bias in the arrival of variation can dominate over natural selection in Richard Dawkins ’s biomorphs
by Nora S. Martin, Chico Q. Camargo, Ard A. Louis
Biomorphs, Richard Dawkins ’s iconic model of morphological evolution, are traditionally used to demonstrate the power of natural selection to generate biological order from random mutations. Here we show that biomorphs can also be used to illustrate how developmental bias shapes adaptive evolutionary outcomes. In particula r, we find that biomorphs exhibit phenotype bias, a type of developmental bias where certain phenotypes can be many orders of magnitude more likely than others to appear through random mutations. Moreover, this bias exhibits a strong preference for si...
Source: PLoS Computational Biology - March 27, 2024 Category: Biology Authors: Nora S. Martin Source Type: research
A dual computational and experimental strategy to enhance TSLP antibody affinity for improved asthma treatment
This study focused on enhancing the affinity of the T6 antibody, which specifically targets TSLP, by integrating computational and experimental methods. The initial affinity of the T6 antibody for TSLP was lower than the benchmark antibody AMG157. To improve this, we utilized alanine scanning, molecular docking, and computational tools including mCSM-PPI2 and GEO-PPI to identify critical amino acid residues for site-directed mutagenesis. Subsequent mutations and experimental validations resulted in an antibody with significantly enhanced blocking capacity against TSLP. Our findings demonstrate the potential of computer-ass...
Source: PLoS Computational Biology - March 27, 2024 Category: Biology Authors: Yitong Lv Source Type: research
PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration
by Cecilia Wieder, Juliette Cooke, Clement Frainay, Nathalie Poupin, Russell Bowler, Fabien Jourdan, Katerina J. Kechris, Rachel PJ Lai, Timothy Ebbels
As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets base...
Source: PLoS Computational Biology - March 25, 2024 Category: Biology Authors: Cecilia Wieder Source Type: research
Enrichment on steps, not genes, improves inference of differentially expressed pathways
by Nicholas Markarian, Kimberly M. Van Auken, Dustin Ebert, Paul W. Sternberg
Enrichment analysis is frequently used in combination with differential expression data to investigate potential commonalities amongst lists of genes and generate hypotheses for further experiments. However, current enrichment analysis approaches on pathways ignore the functional relationships between genes in a pathway, particularly OR logic that occurs when a set of proteins can each individually perform the same step in a pathway. As a result, these approaches miss pathways with large or multiple sets because of an inflation of pathway size (...
Source: PLoS Computational Biology - March 25, 2024 Category: Biology Authors: Nicholas Markarian Source Type: research
Modelling and analysis of cAMP-induced mixed-mode oscillations in cortical neurons: Critical roles of HCN and M-type potassium channels
by Matteo Martin, Morten Gram Pedersen
Cyclic AMP controls neuronal ion channel activity. For example hyperpolarization-activated cyclic nucleotide –gated (HCN) and M-type K+ channels are activated by cAMP. These effects have been suggested to be involved in astrocyte control of neuronal activity, for example, by controlling the action potential firing frequency. In cortical neurons, cAMP can induce mixed-mode oscillations (MMOs) consisting of small-amplitude, subthreshold oscillations separating complete action potentials, which lowers the firing frequency greatly. We extend a model of neuronal activity by including HC...
Source: PLoS Computational Biology - March 22, 2024 Category: Biology Authors: Matteo Martin Source Type: research
Detecting changes in generation and serial intervals under varying pathogen biology, contact patterns and outbreak response
by Rachael Pung, Timothy W. Russell, Adam J. Kucharski
The epidemiological characteristics of SARS-CoV-2 transmission have changed over the pandemic due to emergence of new variants. A decrease in the generation or serial intervals would imply a shortened transmission timescale and, hence, outbreak response measures would need to expand at a faster rate. However, there are challenges in measuring these intervals. Alongside epidemiological changes, factors like varying delays in outbreak response, social contact patterns, dependence on the growth phase of an outbreak, and effects of exposure to multiple infectors can also ...
Source: PLoS Computational Biology - March 22, 2024 Category: Biology Authors: Rachael Pung Source Type: research
Learning environment-specific learning rates
by Jonas Simoens, Tom Verguts, Senne Braem
People often have to switch back and forth between different environments that come with different problems and volatilities. While volatile environments require fast learning (i.e., high learning rates), stable environments call for lower learning rates. Previous studies have shown that people adapt their learning rates, but it remains unclear whether they can also learn about environment-specific learning rates, and instantaneously retrieve them when revisiting environments. Here, using optimality simulations and hierarchical Bayesian analyses across three experiments, we show ...
Source: PLoS Computational Biology - March 22, 2024 Category: Biology Authors: Jonas Simoens Source Type: research
Ethical frameworks should be applied to computational modelling of infectious disease interventions
by Cameron Zachreson, Julian Savulescu, Freya M. Shearer, Michael J. Plank, Simon Coghlan, Joel C. Miller, Kylie E. C. Ainslie, Nicholas Geard
This perspective is part of an international effort to improve epidemiological models with the goal of reducing the unintended consequences of infectious disease interventions. The scenarios in which models are applied often involve difficult trade-offs that are well recognised in public health ethics. Unless these trade-offs are explicitly accounted for, models risk overlooking contested ethical choices and values, leading to an increased risk of unintended consequences. We argue ...
Source: PLoS Computational Biology - March 21, 2024 Category: Biology Authors: Cameron Zachreson Source Type: research
