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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.09233">arXiv:2410.09233</a> <span> [<a href="https://arxiv.org/pdf/2410.09233">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> Recovering complex ecological dynamics from time series using state-space universal dynamic equations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Buckner%2C+J+H">Jack H. Buckner</a>, <a href="/search/q-bio?searchtype=author&query=Meunier%2C+Z+D">Zechariah D. Meunier</a>, <a href="/search/q-bio?searchtype=author&query=Arroyo-Esquivel%2C+J">Jorge Arroyo-Esquivel</a>, <a href="/search/q-bio?searchtype=author&query=Fitzpatrick%2C+N">Nathan Fitzpatrick</a>, <a href="/search/q-bio?searchtype=author&query=Greiner%2C+A">Ariel Greiner</a>, <a href="/search/q-bio?searchtype=author&query=McManus%2C+L+C">Lisa C. McManus</a>, <a href="/search/q-bio?searchtype=author&query=Watson%2C+J+R">James R. Watson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.09233v1-abstract-short" style="display: inline;"> Ecological systems often exhibit complex nonlinear dynamics like oscillations, chaos, and regime shifts. Universal dynamic equations have shown promise in modeling complex dynamics by combining known functional forms with neural networks that represent unknown relationships. However, these methods do not yet accommodate the forms of uncertainty common to ecological datasets. To address this limita… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09233v1-abstract-full').style.display = 'inline'; document.getElementById('2410.09233v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.09233v1-abstract-full" style="display: none;"> Ecological systems often exhibit complex nonlinear dynamics like oscillations, chaos, and regime shifts. Universal dynamic equations have shown promise in modeling complex dynamics by combining known functional forms with neural networks that represent unknown relationships. However, these methods do not yet accommodate the forms of uncertainty common to ecological datasets. To address this limitation, we developed state-space universal dynamic equations by combining universal differential equations with a state-space modeling framework, accounting for uncertainty. We tested this framework on two simulated and two empirical case studies and found that this method can recover nonlinear biological interactions that produce complex behaviors, including chaos and regime shifts. Their forecasting performance is context-dependent, with the best performance being achieved on chaotic and oscillating time series. This new approach leveraging both ecological theory and data-driven machine learning offers a promising new way to make accurate and useful predictions of ecosystem change. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09233v1-abstract-full').style.display = 'none'; document.getElementById('2410.09233v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.13254">arXiv:2409.13254</a> <span> [<a href="https://arxiv.org/pdf/2409.13254">pdf</a>, <a href="https://arxiv.org/format/2409.13254">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Emergent Collective Reproduction via Evolving Neuronal Flocks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Le%2C+N+H">Nam H. Le</a>, <a href="/search/q-bio?searchtype=author&query=Watson%2C+R">Richard Watson</a>, <a href="/search/q-bio?searchtype=author&query=Levin%2C+M">Mike Levin</a>, <a href="/search/q-bio?searchtype=author&query=Buckley%2C+C">Chrys Buckley</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.13254v1-abstract-short" style="display: inline;"> This study facilitates the understanding of evolutionary transitions in individuality (ETIs) through a novel artificial life framework, named VitaNova, that intricately merges self-organization and natural selection to simulate the emergence of complex, reproductive groups. By dynamically modelling individual agents within an environment that challenges them with predators and spatial constraints,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13254v1-abstract-full').style.display = 'inline'; document.getElementById('2409.13254v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13254v1-abstract-full" style="display: none;"> This study facilitates the understanding of evolutionary transitions in individuality (ETIs) through a novel artificial life framework, named VitaNova, that intricately merges self-organization and natural selection to simulate the emergence of complex, reproductive groups. By dynamically modelling individual agents within an environment that challenges them with predators and spatial constraints, VitaNova elucidates the mechanisms by which simple agents evolve into cohesive units exhibiting collective reproduction. The findings underscore the synergy between self-organized behaviours and adaptive evolutionary strategies as fundamental drivers of ETIs. This approach not only contributes to a deeper understanding of higher-order biological individuality but also sets a new precedent in the empirical investigation of ETIs, challenging and extending current theoretical frameworks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13254v1-abstract-full').style.display = 'none'; document.getElementById('2409.13254v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 10 figures, conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.03842">arXiv:2310.03842</a> <span> [<a href="https://arxiv.org/pdf/2310.03842">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> PepMLM: Target Sequence-Conditioned Generation of Therapeutic Peptide Binders via Span Masked Language Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+T">Tianlai Chen</a>, <a href="/search/q-bio?searchtype=author&query=Dumas%2C+M">Madeleine Dumas</a>, <a href="/search/q-bio?searchtype=author&query=Watson%2C+R">Rio Watson</a>, <a href="/search/q-bio?searchtype=author&query=Vincoff%2C+S">Sophia Vincoff</a>, <a href="/search/q-bio?searchtype=author&query=Peng%2C+C">Christina Peng</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+L">Lin Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Hong%2C+L">Lauren Hong</a>, <a href="/search/q-bio?searchtype=author&query=Pertsemlidis%2C+S">Sarah Pertsemlidis</a>, <a href="/search/q-bio?searchtype=author&query=Shaepers-Cheu%2C+M">Mayumi Shaepers-Cheu</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+T+Z">Tian Zi Wang</a>, <a href="/search/q-bio?searchtype=author&query=Srijay%2C+D">Divya Srijay</a>, <a href="/search/q-bio?searchtype=author&query=Monticello%2C+C">Connor Monticello</a>, <a href="/search/q-bio?searchtype=author&query=Vure%2C+P">Pranay Vure</a>, <a href="/search/q-bio?searchtype=author&query=Pulugurta%2C+R">Rishab Pulugurta</a>, <a href="/search/q-bio?searchtype=author&query=Kholina%2C+K">Kseniia Kholina</a>, <a href="/search/q-bio?searchtype=author&query=Goel%2C+S">Shrey Goel</a>, <a href="/search/q-bio?searchtype=author&query=DeLisa%2C+M+P">Matthew P. DeLisa</a>, <a href="/search/q-bio?searchtype=author&query=Truant%2C+R">Ray Truant</a>, <a href="/search/q-bio?searchtype=author&query=Aguilar%2C+H+C">Hector C. Aguilar</a>, <a href="/search/q-bio?searchtype=author&query=Chatterjee%2C+P">Pranam Chatterjee</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.03842v3-abstract-short" style="display: inline;"> Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation. The computational design of protein-based… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03842v3-abstract-full').style.display = 'inline'; document.getElementById('2310.03842v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.03842v3-abstract-full" style="display: none;"> Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation. The computational design of protein-based binders presents unique opportunities to access "undruggable" targets, but have often relied on stable 3D structures or structure-influenced latent spaces for effective binder generation. In this work, we introduce PepMLM, a target sequence-conditioned generator of de novo linear peptide binders. By employing a novel span masking strategy that uniquely positions cognate peptide sequences at the C-terminus of target protein sequences, PepMLM fine-tunes the state-of-the-art ESM-2 pLM to fully reconstruct the binder region, achieving low perplexities matching or improving upon validated peptide-protein sequence pairs. After successful in silico benchmarking with AlphaFold-Multimer, outperforming RFDiffusion on structured targets, we experimentally verify PepMLM's efficacy via fusion of model-derived peptides to E3 ubiquitin ligase domains, demonstrating endogenous degradation of emergent viral phosphoproteins and Huntington's disease-driving proteins. In total, PepMLM enables the generative design of candidate binders to any target protein, without the requirement of target structure, empowering downstream therapeutic applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03842v3-abstract-full').style.display = 'none'; document.getElementById('2310.03842v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.07342">arXiv:2101.07342</a> <span> [<a href="https://arxiv.org/pdf/2101.07342">pdf</a>, <a href="https://arxiv.org/format/2101.07342">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1039/D1AN00075F">10.1039/D1AN00075F <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Doherty%2C+T">Trevor Doherty</a>, <a href="/search/q-bio?searchtype=author&query=McKeever%2C+S">Susan McKeever</a>, <a href="/search/q-bio?searchtype=author&query=Al-Attar%2C+N">Nebras Al-Attar</a>, <a href="/search/q-bio?searchtype=author&query=Murphy%2C+T">Tiarnan Murphy</a>, <a href="/search/q-bio?searchtype=author&query=Aura%2C+C">Claudia Aura</a>, <a href="/search/q-bio?searchtype=author&query=Rahman%2C+A">Arman Rahman</a>, <a href="/search/q-bio?searchtype=author&query=O%27Neill%2C+A">Amanda O'Neill</a>, <a href="/search/q-bio?searchtype=author&query=Finn%2C+S+P">Stephen P Finn</a>, <a href="/search/q-bio?searchtype=author&query=Kay%2C+E">Elaine Kay</a>, <a href="/search/q-bio?searchtype=author&query=Gallagher%2C+W+M">William M. Gallagher</a>, <a href="/search/q-bio?searchtype=author&query=Watson%2C+R+W+G">R. William G. Watson</a>, <a href="/search/q-bio?searchtype=author&query=Gowen%2C+A">Aoife Gowen</a>, <a href="/search/q-bio?searchtype=author&query=Jackman%2C+P">Patrick Jackman</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.07342v1-abstract-short" style="display: inline;"> The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer st… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.07342v1-abstract-full').style.display = 'inline'; document.getElementById('2101.07342v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.07342v1-abstract-full" style="display: none;"> The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.07342v1-abstract-full').style.display = 'none'; document.getElementById('2101.07342v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">19 pages, 8 tables, 18 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1612.05955">arXiv:1612.05955</a> <span> [<a href="https://arxiv.org/pdf/1612.05955">pdf</a>, <a href="https://arxiv.org/format/1612.05955">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> Resolving the paradox of evolvability with learning theory: How evolution learns to improve evolvability on rugged fitness landscapes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Kounios%2C+L">Loizos Kounios</a>, <a href="/search/q-bio?searchtype=author&query=Clune%2C+J">Jeff Clune</a>, <a href="/search/q-bio?searchtype=author&query=Kouvaris%2C+K">Kostas Kouvaris</a>, <a href="/search/q-bio?searchtype=author&query=Wagner%2C+G+P">G眉nter P. Wagner</a>, <a href="/search/q-bio?searchtype=author&query=Pavlicev%2C+M">Mihaela Pavlicev</a>, <a href="/search/q-bio?searchtype=author&query=Weinreich%2C+D+M">Daniel M. Weinreich</a>, <a href="/search/q-bio?searchtype=author&query=Watson%2C+R+A">Richard A. Watson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1612.05955v1-abstract-short" style="display: inline;"> It has been hypothesized that one of the main reasons evolution has been able to produce such impressive adaptations is because it has improved its own ability to evolve -- "the evolution of evolvability". Rupert Riedl, for example, an early pioneer of evolutionary developmental biology, suggested that the evolution of complex adaptations is facilitated by a developmental organization that is itse… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1612.05955v1-abstract-full').style.display = 'inline'; document.getElementById('1612.05955v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1612.05955v1-abstract-full" style="display: none;"> It has been hypothesized that one of the main reasons evolution has been able to produce such impressive adaptations is because it has improved its own ability to evolve -- "the evolution of evolvability". Rupert Riedl, for example, an early pioneer of evolutionary developmental biology, suggested that the evolution of complex adaptations is facilitated by a developmental organization that is itself shaped by past selection to facilitate evolutionary innovation. However, selection for characteristics that enable future innovation seems paradoxical: natural selection cannot favor structures for benefits they have not yet produced, and favoring characteristics for benefits that have already been produced does not constitute future innovation. Here we resolve this paradox by exploiting a formal equivalence between the evolution of evolvability and learning systems. We use the conditions that enable simple learning systems to generalize, i.e., to use past experience to improve performance on previously unseen, future test cases, to demonstrate conditions where natural selection can systematically favor developmental organizations that benefit future evolvability. Using numerical simulations of evolution on highly epistatic fitness landscapes, we illustrate how the structure of evolved gene regulation networks can result in increased evolvability capable of avoiding local fitness peaks and discovering higher fitness phenotypes. Our findings support Riedl's intuition: Developmental organizations that "mimic" the organization of constraints on phenotypes can be favored by short-term selection and also facilitate future innovation. Importantly, the conditions that enable the evolution of such surprising evolvability follow from the same non-mysterious conditions that permit generalization in learning systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1612.05955v1-abstract-full').style.display = 'none'; document.getElementById('1612.05955v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 December, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2016. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1508.06854">arXiv:1508.06854</a> <span> [<a href="https://arxiv.org/pdf/1508.06854">pdf</a>, <a href="https://arxiv.org/format/1508.06854">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> How Evolution Learns to Generalise: Principles of under-fitting, over-fitting and induction in the evolution of developmental organisation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Kouvaris%2C+K">Kostas Kouvaris</a>, <a href="/search/q-bio?searchtype=author&query=Clune%2C+J">Jeff Clune</a>, <a href="/search/q-bio?searchtype=author&query=Kounios%2C+L">Louis Kounios</a>, <a href="/search/q-bio?searchtype=author&query=Brede%2C+M">Markus Brede</a>, <a href="/search/q-bio?searchtype=author&query=Watson%2C+R+A">Richard A. Watson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1508.06854v1-abstract-short" style="display: inline;"> One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments which is crucial for evolvability. Recent work showed that when selective environments vary in a systematic manner, it is possible that development can constrain the phenotypic space in regions that are evolutionarily more advantageous. Yet, the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1508.06854v1-abstract-full').style.display = 'inline'; document.getElementById('1508.06854v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1508.06854v1-abstract-full" style="display: none;"> One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments which is crucial for evolvability. Recent work showed that when selective environments vary in a systematic manner, it is possible that development can constrain the phenotypic space in regions that are evolutionarily more advantageous. Yet, the underlying mechanism that enables the spontaneous emergence of such adaptive developmental constraints is poorly understood. How can natural selection, given its myopic and conservative nature, favour developmental organisations that facilitate adaptive evolution in future previously unseen environments? Such capacity suggests a form of \textit{foresight} facilitated by the ability of evolution to accumulate and exploit information not only about the particular phenotypes selected in the past, but regularities in the environment that are also relevant to future environments. Here we argue that the ability of evolution to discover such regularities is analogous to the ability of learning systems to generalise from past experience. Conversely, the canalisation of evolved developmental processes to past selective environments and failure of natural selection to enhance evolvability in future selective environments is directly analogous to the problem of over-fitting and failure to generalise in machine learning. We show that this analogy arises from an underlying mechanistic equivalence by showing that conditions corresponding to those that alleviate over-fitting in machine learning enhance the evolution of generalised developmental organisations under natural selection. This equivalence provides access to a well-developed theoretical framework that enables us to characterise the conditions where natural selection will find general rather than particular solutions to environmental conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1508.06854v1-abstract-full').style.display = 'none'; document.getElementById('1508.06854v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 August, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2015. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1506.06374">arXiv:1506.06374</a> <span> [<a href="https://arxiv.org/pdf/1506.06374">pdf</a>, <a href="https://arxiv.org/format/1506.06374">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> What can ecosystems learn? Expanding evolutionary ecology with learning theory </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Power%2C+D+A">Daniel A. Power</a>, <a href="/search/q-bio?searchtype=author&query=Watson%2C+R+A">Richard A. Watson</a>, <a href="/search/q-bio?searchtype=author&query=Szathm%C3%A1ry%2C+E">E枚rs Szathm谩ry</a>, <a href="/search/q-bio?searchtype=author&query=Mills%2C+R">Rob Mills</a>, <a href="/search/q-bio?searchtype=author&query=Powers%2C+S+T">Simon T Powers</a>, <a href="/search/q-bio?searchtype=author&query=Doncaster%2C+C+P">C Patrick Doncaster</a>, <a href="/search/q-bio?searchtype=author&query=Czapp%2C+B">B艂a偶ej Czapp</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1506.06374v1-abstract-short" style="display: inline;"> Understanding how the structure of community interactions is modified by coevolution is vital for understanding system responses to change at all scales. However, in absence of a group selection process, collective community behaviours cannot be organised or adapted in a Darwinian sense. An open question thus persists: are there alternative organising principles that enable us to understand how co… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1506.06374v1-abstract-full').style.display = 'inline'; document.getElementById('1506.06374v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1506.06374v1-abstract-full" style="display: none;"> Understanding how the structure of community interactions is modified by coevolution is vital for understanding system responses to change at all scales. However, in absence of a group selection process, collective community behaviours cannot be organised or adapted in a Darwinian sense. An open question thus persists: are there alternative organising principles that enable us to understand how coevolution of component species creates complex collective behaviours exhibited at the community level? We address this issue using principles from connectionist learning, a discipline with well-developed theories of emergent behaviours in simple networks. We identify conditions where selection on ecological interactions is equivalent to 'unsupervised learning' (a simple type of connectionist learning) and observe that this enables communities to self organize without community-level selection. Despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal organisation that habituates to past environmental conditions and actively recalling those conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1506.06374v1-abstract-full').style.display = 'none'; document.getElementById('1506.06374v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 June, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">37 pages 11 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1208.0520">arXiv:1208.0520</a> <span> [<a href="https://arxiv.org/pdf/1208.0520">pdf</a>, <a href="https://arxiv.org/ps/1208.0520">ps</a>, <a href="https://arxiv.org/format/1208.0520">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> How to measure group selection in real-world populations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Powers%2C+S+T">Simon T. Powers</a>, <a href="/search/q-bio?searchtype=author&query=Heys%2C+C">Christopher Heys</a>, <a href="/search/q-bio?searchtype=author&query=Watson%2C+R+A">Richard A. Watson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1208.0520v1-abstract-short" style="display: inline;"> Multilevel selection and the evolution of cooperation are fundamental to the formation of higher-level organisation and the evolution of biocomplexity, but such notions are controversial and poorly understood in natural populations. The theoretic principles of group selection are well developed in idealised models where a population is neatly divided into multiple semi-isolated sub-populations. Bu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1208.0520v1-abstract-full').style.display = 'inline'; document.getElementById('1208.0520v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1208.0520v1-abstract-full" style="display: none;"> Multilevel selection and the evolution of cooperation are fundamental to the formation of higher-level organisation and the evolution of biocomplexity, but such notions are controversial and poorly understood in natural populations. The theoretic principles of group selection are well developed in idealised models where a population is neatly divided into multiple semi-isolated sub-populations. But since such models can be explained by individual selection given the localised frequency-dependent effects involved, some argue that the group selection concepts offered are, even in the idealised case, redundant and that in natural conditions where groups are not well-defined that a group selection framework is entirely inapplicable. This does not necessarily mean, however, that a natural population is not subject to some interesting localised frequency-dependent effects -- but how could we formally quantify this under realistic conditions? Here we focus on the presence of a Simpson's Paradox where, although the local proportion of cooperators decreases at all locations, the global proportion of cooperators increases. We illustrate this principle in a simple individual-based model of bacterial biofilm growth and discuss various complicating factors in moving from theory to practice of measuring group selection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1208.0520v1-abstract-full').style.display = 'none'; document.getElementById('1208.0520v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 August, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">pp. 672-679 in Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems (Advances in Artificial Life, ECAL 2011). Edited by Tom Lenaerts, Mario Giacobini, Hugues Bersini, Paul Bourgine, Marco Dorigo and Ren茅 Doursat. MIT Press (2011). http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12760. 8 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1208.0518">arXiv:1208.0518</a> <span> [<a href="https://arxiv.org/pdf/1208.0518">pdf</a>, <a href="https://arxiv.org/ps/1208.0518">ps</a>, <a href="https://arxiv.org/format/1208.0518">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> The efficacy of group selection is increased by coexistence dynamics within groups </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Powers%2C+S+T">Simon T. Powers</a>, <a href="/search/q-bio?searchtype=author&query=Penn%2C+A+S">Alexandra S. Penn</a>, <a href="/search/q-bio?searchtype=author&query=Watson%2C+R+A">Richard A. Watson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1208.0518v1-abstract-short" style="display: inline;"> Selection on the level of loosely associated groups has been suggested as a route towards the evolution of cooperation between individuals and the subsequent formation of higher-level biological entities. Such group selection explanations remain problematic, however, due to the narrow range of parameters under which they can overturn within-group selection that favours selfish behaviour. In princi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1208.0518v1-abstract-full').style.display = 'inline'; document.getElementById('1208.0518v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1208.0518v1-abstract-full" style="display: none;"> Selection on the level of loosely associated groups has been suggested as a route towards the evolution of cooperation between individuals and the subsequent formation of higher-level biological entities. Such group selection explanations remain problematic, however, due to the narrow range of parameters under which they can overturn within-group selection that favours selfish behaviour. In principle, individual selection could act on such parameters so as to strengthen the force of between-group selection and hence increase cooperation and individual fitness, as illustrated in our previous work. However, such a process cannot operate in parameter regions where group selection effects are totally absent, since there would be no selective gradient to follow. One key parameter, which when increased often rapidly causes group selection effects to tend to zero, is initial group size, for when groups are formed randomly then even moderately sized groups lack significant variance in their composition. However, the consequent restriction of any group selection effect to small sized groups is derived from models that assume selfish types will competitively exclude their more cooperative counterparts at within-group equilibrium. In such cases, diversity in the migrant pool can tend to zero and accordingly variance in group composition cannot be generated. In contrast, we show that if within-group dynamics lead to a stable coexistence of selfish and cooperative types, then the range of group sizes showing some effect of group selection is much larger. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1208.0518v1-abstract-full').style.display = 'none'; document.getElementById('1208.0518v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 August, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">pp. 498-505 in Bullock S., Noble J., Watson R.A., Bedau M.A. (eds.) Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems. MIT Press (2008). 8 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1208.0482">arXiv:1208.0482</a> <span> [<a href="https://arxiv.org/pdf/1208.0482">pdf</a>, <a href="https://arxiv.org/ps/1208.0482">ps</a>, <a href="https://arxiv.org/format/1208.0482">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1111/j.1558-5646.2011.01250.x">10.1111/j.1558-5646.2011.01250.x <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The concurrent evolution of cooperation and the population structures that support it </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Powers%2C+S+T">Simon T. Powers</a>, <a href="/search/q-bio?searchtype=author&query=Penn%2C+A+S">Alexandra S. Penn</a>, <a href="/search/q-bio?searchtype=author&query=Watson%2C+R+A">Richard A. Watson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1208.0482v1-abstract-short" style="display: inline;"> The evolution of cooperation often depends upon population structure, yet nearly all models of cooperation implicitly assume that this structure remains static. This is a simplifying assumption, because most organisms possess genetic traits that affect their population structure to some degree. These traits, such as a group size preference, affect the relatedness of interacting individuals and hen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1208.0482v1-abstract-full').style.display = 'inline'; document.getElementById('1208.0482v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1208.0482v1-abstract-full" style="display: none;"> The evolution of cooperation often depends upon population structure, yet nearly all models of cooperation implicitly assume that this structure remains static. This is a simplifying assumption, because most organisms possess genetic traits that affect their population structure to some degree. These traits, such as a group size preference, affect the relatedness of interacting individuals and hence the opportunity for kin or group selection. We argue that models that do not explicitly consider their evolution cannot provide a satisfactory account of the origin of cooperation, because they cannot explain how the prerequisite population structures arise. Here, we consider the concurrent evolution of genetic traits that affect population structure, with those that affect social behavior. We show that not only does population structure drive social evolution, as in previous models, but that the opportunity for cooperation can in turn drive the creation of population structures that support it. This occurs through the generation of linkage disequilibrium between socio-behavioral and population-structuring traits, such that direct kin selection on social behavior creates indirect selection pressure on population structure. We illustrate our argument with a model of the concurrent evolution of group size preference and social behavior. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1208.0482v1-abstract-full').style.display = 'none'; document.getElementById('1208.0482v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 August, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Post-print of accepted manuscript, 6 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Evolution 65(6), pp. 1527-1543, June 2011 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" 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