Identifying the potential oligogenic causes of rare diseases remains a challenge, notwithstanding the advancements made in the last decade. While a variety of predictive and ranking approaches have been proposed, their precision remains limited, as only a small number of high-quality training cases are available and it remains difficult to know which features may be most relevant for the design of new predictors. We hypothesize here that structured biological information, which provides an integration of various relevant biological networks and ontologies in a single heterogeneous knowledge graph, can make a difference as it allows for learning a relevant genetic representation through KGE methods. An exhaustive benchmarking is performed here wherein we assess the performance of various state-of-the-art embedding models for the task of identifying potentially pathogenic gene pairs. The results obtained show that these KGE provide highly accurate predictions, leading to an Area Under the Precision-Recall Curve of up to $0.93$, representing also a significant advancement over previous approaches for predicting gene pairs involved in oligogenic diseases. We show nonetheless that care needs to be taken in the cross-validation when using embeddings, as data leakage between folds in embedding space will reveal overly optimistic results. The further evaluation of the methods on a holdout set as well as on a group of new male infertility cases show that three Translational Distance models (TransE, MurE, and RotatE) and two of the Semantic Matching models (DisMult and QuatE) provide the better results. The analysis is concluded by comparing all known gene combinations for these top-ranking models, examining their similarities and differences. Overall, KGE provide a predictive advancement but new steps will need to be taken generate explanations as to why the pairs are relevant for oligogenic diseases.

Adversarial attacks pose a significant threat to data-driven systems, and researchers have devoted considerable effort to studying them. Despite its economic relevance, credit card fraud detection has received comparatively little attention. To address this gap, we propose a novel threat model that highlights the limitations of existing attacks and motivates new approaches. We introduce Fraud-RLA, an adversarial attack against credit card Fraud Detection Systems that leverages Reinforcement Learning to evade detection. Fraud-RLA is designed to maximize the amount stolen by optimizing the exploration-exploitation trade-off while requiring substantially less prior knowledge than competing methods. Our experiments on a realistic Fraud Detection System show that Fraud-RLA is effective, even under the severe limitations imposed by our threat model.

Despite their performance, large language models (LLMs) can inadvertently perpetuate biases found in the data they are trained on. By analyzing LLM responses to bias-eliciting headlines, we find that these models often mirror human biases. To address this, we explore crowd-based strategies for mitigating bias through response aggregation. We first demonstrate that simply averaging responses from multiple LLMs, intended to leverage the ``wisdom of the crowd", can exacerbate existing biases due to the limited diversity within LLM crowds. In contrast, we show that locally weighted aggregation methods more effectively leverage the wisdom of the LLM crowd, achieving both bias mitigation and improved accuracy. Finally, recognizing the complementary strengths of LLMs (accuracy) and humans (diversity), we demonstrate that hybrid crowds containing both significantly enhance performance and further reduce biases across ethnic and gender-related contexts.

When sufficient experience to make informed decisions is unavailable, expert advice can help us navigate uncertainty. As expertise evolves, driven by continuous learning in human experts or model updates in artificial experts, it is crucial to adopt adaptive approaches. Existing methods for exploiting non-stationary experts focus on competing with the single best expert. In contrast, this work harnesses the power of collective intelligence to facilitate better decision-making in the face of evolving expertise or dynamic environments. To achieve this, we propose the novel CORVAL approach which optimally combines the insights of multiple experts. By adapting to drifts in expertise, our novel approach can surpass the performance of the single best expert as well as previous approaches. Empirical evaluations on a diverse range of non-stationary problems, including active learning applications, showcase the improved performance of our approach in collective decision-making scenarios.

MOTIVATION: Whole exome sequencing (WES) has emerged as a powerful tool for genetic research, enabling the collection of a tremendous amount of data about human genetic variation. However, properly identifying which variants are causative of a genetic disease remains an important challenge, often due to the number of variants that need to be screened. Expanding the screening to combinations of variants in two or more genes, as would be required under the oligogenic inheritance model, simply blows this problem out of proportion. RESULTS: We present here the High-throughput oligogenic prioritizer (Hop), a novel prioritization method that uses direct oligogenic information at the variant, gene and gene pair level to detect digenic variant combinations in WES data. This method leverages information from a knowledge graph, together with specialized pathogenicity predictions in order to effectively rank variant combinations based on how likely they are to explain the patient's phenotype. The performance of Hop is evaluated in cross-validation on 36 120 synthetic exomes for training and 14 280 additional synthetic exomes for independent testing. Whereas the known pathogenic variant combinations are found in the top 20 in approximately 60% of the cross-validation exomes, 71% are found in the same ranking range when considering the independent set. These results provide a significant improvement over alternative approaches that depend simply on a monogenic assessment of pathogenicity, including early attempts for digenic ranking using monogenic pathogenicity scores. AVAILABILITY AND IMPLEMENTATION: Hop is available at https://github.com/oligogenic/HOP.

Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance. In practice, this may lead to sub-optimal or discriminatory decisions against minority cases. In this work, we model such changes in depth and breadth of knowledge as a partitioning of the problem space into regions of differing expertise. We provide here new algorithms that explicitly consider and adapt to the relationship between problem instances and experts’ knowledge. We first propose and highlight the drawbacks of a naive approach based on nearest neighbor queries. To address these drawbacks we then introduce a novel algorithm — expertise trees — that constructs decision trees enabling the learner to select appropriate models. We provide theoretical insights and empirically validate the improved performance of our novel approach on a range of problems for which existing methods proved to be inadequate.

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

BACKGROUND: Understanding the impact of gene interactions on disease phenotypes is increasingly recognised as a crucial aspect of genetic disease research. This trend is reflected by the growing amount of clinical research on oligogenic diseases, where disease manifestations are influenced by combinations of variants on a few specific genes. Although statistical machine-learning methods have been developed to identify relevant genetic variant or gene combinations associated with oligogenic diseases, they rely on abstract features and black-box models, posing challenges to interpretability for medical experts and impeding their ability to comprehend and validate predictions. In this work, we present a novel, interpretable predictive approach based on a knowledge graph that not only provides accurate predictions of disease-causing gene interactions but also offers explanations for these results. RESULTS: We introduce BOCK, a knowledge graph constructed to explore disease-causing genetic interactions, integrating curated information on oligogenic diseases from clinical cases with relevant biomedical networks and ontologies. Using this graph, we developed a novel predictive framework based on heterogenous paths connecting gene pairs. This method trains an interpretable decision set model that not only accurately predicts pathogenic gene interactions, but also unveils the patterns associated with these diseases. A unique aspect of our approach is its ability to offer, along with each positive prediction, explanations in the form of subgraphs, revealing the specific entities and relationships that led to each pathogenic prediction. CONCLUSION: Our method, built with interpretability in mind, leverages heterogenous path information in knowledge graphs to predict pathogenic gene interactions and generate meaningful explanations. This not only broadens our understanding of the molecular mechanisms underlying oligogenic diseases, but also presents a novel application of knowledge graphs in creating more transparent and insightful predictors for genetic research.

BACKGROUND: The prediction of potentially pathogenic variant combinations in patients remains a key task in the field of medical genetics for the understanding and detection of oligogenic/multilocus diseases. Models tailored towards such cases can help shorten the gap of missing diagnoses and can aid researchers in dealing with the high complexity of the derived data. The predictor VarCoPP (Variant Combinations Pathogenicity Predictor) that was published in 2019 and identified potentially pathogenic variant combinations in gene pairs (bilocus variant combinations), was the first important step in this direction. Despite its usefulness and applicability, several issues still remained that hindered a better performance, such as its False Positive (FP) rate, the quality of its training set and its complex architecture. RESULTS: We present VarCoPP2.0: the successor of VarCoPP that is a simplified, faster and more accurate predictive model identifying potentially pathogenic bilocus variant combinations. Results from cross-validation and on independent data sets reveal that VarCoPP2.0 has improved in terms of both sensitivity (95% in cross-validation and 98% during testing) and specificity (5% FP rate). At the same time, its running time shows a significant 150-fold decrease due to the selection of a simpler Balanced Random Forest model. Its positive training set now consists of variant combinations that are more confidently linked with evidence of pathogenicity, based on the confidence scores present in OLIDA, the Oligogenic Diseases Database ( https://olida.ibsquare.be ). The improvement of its performance is also attributed to a more careful selection of up-to-date features identified via an original wrapper method. We show that the combination of different variant and gene pair features together is important for predictions, highlighting the usefulness of integrating biological information at different levels. CONCLUSIONS: Through its improved performance and faster execution time, VarCoPP2.0 enables a more accurate analysis of larger data sets linked to oligogenic diseases. Users can access the ORVAL platform ( https://orval.ibsquare.be ) to apply VarCoPP2.0 on their data.

The development of high-throughput next-generation sequencing technologies and large-scale genetic association studies produced numerous advances in the biostatistics field. Various aggregation tests, i.e. statistical methods that analyze associations of a trait with multiple markers within a genomic region, have produced a variety of novel discoveries. Notwithstanding their usefulness, there is no single test that fits all needs, each suffering from specific drawbacks. Selecting the right aggregation test, while considering an unknown underlying genetic model of the disease, remains an important challenge. Here we propose a new ensemble method, called Excalibur, based on an optimal combination of 36 aggregation tests created after an in-depth study of the limitations of each test and their impact on the quality of result. Our findings demonstrate the ability of our method to control type I error and illustrate that it offers the best average power across all scenarios. The proposed method allows for novel advances in Whole Exome/Genome sequencing association studies, able to handle a wide range of association models, providing researchers with an optimal aggregation analysis for the genetic regions of interest.

Rapid technological advancements in Artificial Intelligence (AI), as well as the growing deployment of intelligent technologies in new application domains, have generated serious anxiety and a fear of missing out among different stake-holders, fostering a racing narrative. Whether real or not, the belief in such a race for domain supremacy through AI, can make it real simply from its consequences, as put forward by the Thomas theorem. These consequences may be negative, as racing for technological supremacy creates a complex ecology of choices that could push stake-holders to underestimate or even ignore ethical and safety procedures. As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements, contemplating the use of the precautionary principle in AI innovation and research. Yet, given the breadth and depth of AI and its advances, it is difficult to assess which technology needs regulation and when. As there is no easy access to data describing this alleged AI race, theoretical models are necessary to understand its potential dynamics, allowing for the identification of when procedures need to be put in place to favour outcomes beneficial for all. We show that, next to the risks of setbacks and being reprimanded for unsafe behaviour, the time-scale in which domain supremacy can be achieved plays a crucial role. When this can be achieved in a short term, those who completely ignore the safety precautions are bound to win the race but at a cost to society, apparently requiring regulatory actions. Our analysis reveals that imposing regulations for all risk and timing conditions may not have the anticipated effect as only for specific conditions a dilemma arises between what is individually preferred and globally beneficial. Similar observations can be made for the long-term development case. Yet different from the short-term situation, conditions can be identified that require the promotion of risk-taking as opposed to compliance with safety regulations in order to improve social welfare. These results remain robust both when two or several actors are involved in the race and when collective rather than individual setbacks are produced by risk-taking behaviour. When defining codes of conduct and regulatory policies for applications of AI, a clear understanding of the time-scale of the race is thus required, as this may induce important non-trivial effects. This article is part of the special track on AI and Society.

A race for technological supremacy in AI could lead to serious negative consequences, especially whenever ethical and safety procedures are underestimated or even ignored, leading potentially to the rejection of AI in general. For all to enjoy the benefits provided by safe, ethical and trustworthy AI systems, it is crucial to incentivise participants with appropriate strategies that ensure mutually beneficial normative behaviour and safety-compliance from all parties involved. Little attention has been given to understanding the dynamics and emergent behaviours arising from this AI bidding war, and moreover, how to influence it to achieve certain desirable outcomes (e.g. AI for public good and participant compliance). To bridge this gap, this paper proposes a research agenda to develop theoretical models that capture key factors of the AI race, revealing which strategic behaviours may emerge and hypothetical scenarios therein. Strategies from incentive and agreement modelling are directly applicable to systematically analyse how different types of incentives (namely, positive vs. negative, peer vs. institutional, and their combinations) influence safety-compliant behaviours over time, and how such behaviours should be configured to ensure desired global outcomes, studying at the same time how these mechanisms influence AI development. This agenda will provide actionable policies, showing how they need to be employed and deployed in order to achieve compliance and thereby avoid disasters as well as loosing confidence and trust in AI in general.

Many real-world decision problems are characterized by multiple conflicting objectives which must be balanced based on their relative importance. In the dynamic weights setting the relative importance changes over time and specialized algorithms that deal with such change, such as a tabular Reinforcement Learning (RL) algorithm by Natarajan and Tadepalli (2005), are required. However, this earlier work is not feasible for RL settings that necessitate the use of function approximators. We generalize across weight changes and high-dimensional inputs by proposing a multi-objective Q-network whose outputs are conditioned on the relative importance of objectives and we introduce Diverse Experience Replay (DER) to counter the inherent non-stationarity of the Dynamic Weights setting. We perform an extensive experimental evaluation and compare our methods to adapted algorithms from Deep Multi-Task/Multi-Objective Reinforcement Learning and show that our proposed network in combination with DER dominates these adapted algorithms across weight change scenarios and problem domains.

Paroxysmal nocturnal hemoglobinuria (PNH) is an acquired clonal blood disorder characterized by hemolysis and a high risk of thrombosis, that is due to a deficiency in several cell surface proteins that prevent complement activation. Its origin has been traced to a somatic mutation in the PIG-A gene within hematopoietic stem cells (HSC). However, to date the question of how this mutant clone expands in size to contribute significantly to hematopoiesis remains under debate. One hypothesis posits the existence of a selective advantage of PIG-A mutated cells due to an immune mediated attack on normal HSC, but the evidence supporting this hypothesis is inconclusive. An alternative (and simpler) explanation attributes clonal expansion to neutral drift, in which case selection neither favours nor inhibits expansion of PIG-A mutated HSC. Here we examine the implications of the neutral drift model by numerically evolving a Markov chain for the probabilities of all possible outcomes, and investigate the possible occurrence and evolution, within this framework, of multiple independently arising clones within the HSC pool. Predictions of the model agree well with the known incidence of the disease and average age at diagnosis. Notwithstanding the slight difference in clonal expansion rates between our results and those reported in the literature, our model results lead to a relative stability of clone size when averaging multiple cases, in accord with what has been observed in human trials. The probability of a patient harbouring a second clone in the HSC pool was found to be extremely low ([Formula: see text]). Thus our results suggest that in clinical cases of PNH where two independent clones of mutant cells are observed, only one of those is likely to have originated in the HSC pool.

Evolutionary game theory focuses on the fitness differences between simple discrete or probabilistic strategies to explain the evolution of particular decision-making behavior within strategic situations. Although this approach has provided substantial insights into the presence of fairness or generosity in gift-giving games, it does not fully resolve the question of which cognitive mechanisms are required to produce the choices observed in experiments. One such mechanism that humans have acquired, is the capacity to anticipate. Prior work showed that forward-looking behavior, using a recurrent neural network to model the cognitive mechanism, are essential to produce the actions of human participants in behavioral experiments. In this paper, we evaluate whether this conclusion extends also to gift-giving games, more concretely, to a game that combines the dictator game with a partner selection process. The recurrent neural network model used here for dictators, allows them to reason about a best response to past actions of the receivers (reactive model) or to decide which action will lead to a more successful outcome in the future (anticipatory model). We show for both models the decision dynamics while training, as well as the average behavior. We find that the anticipatory model is the only one capable of accounting for changes in the context of the game, a behavior also observed in experiments, expanding previous conclusions to this more sophisticated game.

Various social situations entail a collective risk. A well-known example is climate change, wherein the risk of a future environmental disaster clashes with the immediate economic interest of developed and developing countries. The collective-risk game operationalizes this kind of situations. The decision process of the participants is determined by how good they are in evaluating the probability of future risk as well as their ability to anticipate the actions of the opponents. Anticipatory behavior contrasts with the reactive theories often used to analyze social dilemmas. Our initial work can already show that anticipative agents are a better model to human behavior than reactive ones. All the agents we studied used a recurrent neural network, however, only the ones that used it to predict future outcomes (anticipative agents) were able to account for changes in the context of games, a behavior also observed in experiments with humans. This extended abstract aims to explain how we wish to investigate anticipation within the context of the collective-risk game and the relevance these results may have for the field of hybrid socio-technical systems.

Before engaging in a group venture agents may seek commitments from other members in the group and, based on the level of participation (i.e. the number of actually committed participants), decide whether it is worth joining the venture. Alternatively, agents can delegate this costly process to a (beneficent or non-costly) third-party, who helps seek commitments from the agents. Using methods from Evolutionary Game Theory, this paper shows that, in the context of Public Goods Game, much higher levels of cooperation can be achieved through such centralized commitment management. It provides a more efficient mechanism for dealing with commitment free-riders, those who are not willing to bear the cost of arranging commitments whilst enjoying the benefits provided by the paying commitment proposers. We show that the participation level plays a crucial role in the decision of whether an agreement should be formed; namely, it needs to be more strict in terms of the level of participation required from players of the centralized system for the agreement to be formed; however, once it is done right, it is much more beneficial in terms of the level of cooperation and social welfare achieved. In short, our analysis provides important insights for the design of multi-agent systems that rely on commitments to monitor agents' cooperative behavior.

MOTIVATION: Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions. RESULTS: Here we present Rigapollo, a highly flexible pairwise alignment method based on a pairwise HMM-SVM that can use any type of information to build alignments. Rigapollo lets the user decide the optimal features to align their protein class of interest. It outperforms current state of the art methods on two well-known benchmark datasets when aligning highly divergent sequences. AVAILABILITY AND IMPLEMENTATION: A Python implementation of the algorithm is available at http://ibsquare.be/rigapollo. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: There are now many predictors capable of identifying the likely phenotypic effects of single nucleotide variants (SNVs) or short in-frame Insertions or Deletions (INDELs) on the increasing amount of genome sequence data. Most of these predictors focus on SNVs and use a combination of features related to sequence conservation, biophysical, and/or structural properties to link the observed variant to either neutral or disease phenotype. Despite notable successes, the mapping between genetic variants and their phenotypic effects is riddled with levels of complexity that are not yet fully understood and that are often not taken into account in the predictions, despite their promise of significantly improving the prediction of deleterious mutants. RESULTS: We present DEOGEN, a novel variant effect predictor that can handle both missense SNVs and in-frame INDELs. By integrating information from different biological scales and mimicking the complex mixture of effects that lead from the variant to the phenotype, we obtain significant improvements in the variant-effect prediction results. Next to the typical variant-oriented features based on the evolutionary conservation of the mutated positions, we added a collection of protein-oriented features that are based on functional aspects of the gene affected. We cross-validated DEOGEN on 36 825 polymorphisms, 20 821 deleterious SNVs, and 1038 INDELs from SwissProt. The multilevel contextualization of each (variant, protein) pair in DEOGEN provides a 10% improvement of MCC with respect to current state-of-the-art tools. AVAILABILITY AND IMPLEMENTATION: The software and the data presented here is publicly available at http://ibsquare.be/deogen CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Src Homology 3 domains are ubiquitous small interaction modules known to act as docking sites and regulatory elements in a wide range of proteins. Prior experimental NMR work on the SH3 domain of Src showed that ligand binding induces long-range dynamic changes consistent with an induced fit mechanism. The identification of the residues that participate in this mechanism produces a chart that allows for the exploration of the regulatory role of such domains in the activity of the encompassing protein. Here we show that a computational approach focusing on the changes in side chain dynamics through ligand binding identifies equivalent long-range effects in the Src SH3 domain. Mutation of a subset of the predicted residues elicits long-range effects on the binding energetics, emphasizing the relevance of these positions in the definition of intramolecular cooperative networks of signal transduction in this domain. We find further support for this mechanism through the analysis of seven other publically available SH3 domain structures of which the sequences represent diverse SH3 classes. By comparing the eight predictions, we find that, in addition to a dynamic pathway that is relatively conserved throughout all SH3 domains, there are dynamic aspects specific to each domain and homologous subgroups. Our work shows for the first time from a structural perspective, which transduction mechanisms are common between a subset of closely related and distal SH3 domains, while at the same time highlighting the differences in signal transduction that make each family member unique. These results resolve the missing link between structural predictions of dynamic changes and the domain sectors recently identified for SH3 domains through sequence analysis.

This special issue pays tribute to 20 years of artificial life research in Europe. Since its inception around a coffee table in Paris in the summer of 1990, the European Conference on Artificial Life (ECAL) has grown and evolved to incorporate multiple research disciplines that aim to combine the natural and computational sciences. In addition to extending a long and rich tradition in theoretical biology present at that time in Europe toward the computational sciences and engineering, ECAL has also provided a platform for different innovative ideas that have produced a significant impact on other research domains or have resulted in novel research directions with their own workshops and conferences. This influence continues today, with the consideration of the different models and methods that are currently extensively used to answer biological questions in domains such as computational and systems biology. This evolution will benefit the field and will ensure that our models continue to push the envelope as they have been doing for the last two decades.Together with the reviewers and conference chairs of ECAL 2011 in Paris, we selected a small number of especially interesting contributions among the conference's 128 accepted submissions. Their authors were asked to extend their manuscripts for a wider audience and to provide more details than what was allowed by the conference format. The reader can find below a brief description of these articles, which range from artificial chemistry to evolutionary robotics and computational biology. We hope that this selection will provide an incentive for researchers from related fields to pay closer attention to the innovations created within the artificial life community and to motivate researchers already interested in this field to make further advances in the realism and influence of their models. In the following we summarize the topics discussed in this special issue.Xabier Barandiaran and Matthew Egbert study how norms become established from an organism-centered perspective. They give an in-depth analysis and evaluation of a minimal model capable of establishing norms dynamically. The model clearly opens a novel road toward answering questions associated to the notion of minimal agency, which is essential to a wide variety of artificial life models.The work of Navneet Bhalla, Peter Bentley, Peter Vize, and Christian Jacob focuses on the use of staging in the context of physical self-assembling systems made of magnetic shapes. They investigate whether replacing a full mix with gradual addition of morphological information manages to produce more stable constructs for systems composed of elements with heterogeneous properties. They show that this staging methodology can solve complex self-assembly problems by reducing the possibilities of errors in the shape-matching process.Tom Froese, Nathaniel Virgo, and Takashi Ikegami argue in the third article that time scales should play a central role in all models aiming to answer questions linked to the origin of life. Using a reaction-diffusion model, they assert that adaptive behavior realized at intermediate time scales by spatiotemporally localized self-producing systems may have been already present in the beginnings, and conclude with the feasibility of a movement-first approach to the origin of life.In the fourth contribution, Heiko Hamann, Thomas Schmickl, and Karl Crailsheim investigate the relevance of statistical mechanics, and more specifically of the fluctuation theorem, to understanding the collective behavior of simulated swarms. They are able to show that an inverted fluctuation theorem is present in this type of systems, which they claim may be generally applicable to different self-organizing systems studied in artificial life.Tsutomu Oohashi, Tadao Maekawa, Osamu Ueno, Norie Kawai, Emi Nishina, and Manabu Honda investigate a completely different question: Can organisms evolve to give up their immortality? Using an artificial-chemistry-based model called SIVA, they examine the evolution of programmed self-decomposition. Interestingly, they identify the conditions for mortal organisms emerging from immortal ones to outcompete them, which they associate with the emergence of an altruistic activity in terrestrial life.As mentioned earlier, artificial life modeling has a prominent role to play in computational and systems biology. The manuscript of Joshua Payne, Jason Moore, and Andreas Wagner is an example of this cross-fertilization. The authors investigate the space of signal-integration functions, which are defined as functions that map regulatory signals to gene expression states. Their aim is to understand the relationship between the robustness and evolvability of gene expression states when genetic perturbations modify these functions. They show, among other things, that the majority of signal-integration functions are resistant to perturbation.José Pereira, Porfírio Silva, Pedro Lima, and Alcherio Martinoli propose in their work a new framework to coordinate the actions of robot teams using concepts from the field of institutional economics. They first introduce the notion of executable Petri nets (EPNs), which can be run directly on a robot. These EPNs are then used to define institutions and individual robot behaviors, resulting in what they call an institutional agent controller. To validate this new approach, they design some swarm-related experiments in which robots need to coordinate to remain connected over a wireless network, showing that their novel institutional robotics framework can perform as well as some of the other approaches that aim to design swarm behavior, while offering a higher potential for modular control design and consideration of social behavior in robot teams.In the eighth contribution to this special issue, John Rieffel, Davis Knox, Schuyler Smith, and Barry Trimmer provide insight into four years of research into soft robotics. They particularly focus on the question of how to coevolve soft robot morphology and control. They argue that the evolution of soft robots corresponds to solving a problem of three tightly interdependent variables, namely material, morphology and control. In their study, they provide three approaches to address this problem, showing a way to achieve physically embodied soft robots.Within the 20 years of the European Conference of Artificial Life, numerous articles have addressed new evolutionary algorithms designed to solve particular problems or to overcome specific issues in the model originally proposed by John Holland. The ninth and last article, by Nicholas Tomko, Inman Harvey, Nathaniel Virgo, and Andrew Philippides, focuses here on the question of how to improve on prior work about niching and speciation. They suggest a new genetic algorithm (GA), called the group GA, which is based on principles of group or multilevel selection. They show that this new algorithm outperforms some of the earlier works in the same context, and that the group size need not be preset but can itself be evolved.

Background: Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that e↵ectively counter potentially resistant mutants. Results: We propose a simple relational learning approach for mutant prediction where the input consists of mutation data with drug-resistance information, either as sets of mutations conferring resistance to a certain drug, or as sets of mutants with information on their susceptibility to the drug. The algorithm learns a set of relational rules characterizing drug-resistance and use them to generate a set of potentially resistant mutants. Conclusions: Promising results were obtained in generating resistant mutations for both nucleoside and nonnucleoside HIV reverse transcriptase inhibitors. The approach can be generalized quite easily to learning mutants characterized by more complex rules correlating multiple mutations. Background HIV is a pandemic cause of lethal pathologies in more than 33 million people. Its horizontal transmission trough mucosae is di cult to control and treat

Experimental NMR relaxation studies have shown that peptide binding induces dynamical changes at the side-chain level throughout the second PDZ domain of PTP1e, identifying as such the collection of residues involved in long-range communication. Even though different computational approaches have identified subsets of residues that were qualitatively comparable, no quantitative analysis of the accuracy of these predictions was thus far determined. Here, we show that our information theoretical method produces quantitatively better results with respect to the experimental data than some of these earlier methods. Moreover, it provides a global network perspective on the effect experienced by the different residues involved in the process. We also show that these predictions are consistent within both the human and mouse variants of this domain. Together, these results improve the understanding of intra-protein communication and allostery in PDZ domains, underlining at the same time the necessity of producing similar data sets for further validation of thses kinds of methods.

Most tissues in multicellular organisms are maintained by continuous cell renewal processes. However, high turnover of many cells implies a large number of error-prone cell divisions. Hierarchical organized tissue structures with stem cell driven cell differentiation provide one way to prevent the accumulation of mutations, because only few stem cells are long lived. We investigate the deterministic dynamics of cells in such a hierarchical multi compartment model, where each compartment represents a certain stage of cell differentiation. The dynamics of the interacting system is described by ordinary differential equations coupled across compartments. We present analytical solutions for these equations, calculate the corresponding extinction times and compare our results to individual based stochastic simulations. Our general compartment structure can be applied to different tissues, as for example hematopoiesis, the epidermis, or colonic crypts. The solutions provide a description of the average time development of stem cell and non stem cell driven mutants and can be used to illustrate general and specific features of the dynamics of mutant cells in such hierarchically structured populations. We illustrate one possible application of this approach by discussing the origin and dynamics of PIG-A mutant clones that are found in the bloodstream of virtually every healthy adult human. From this it is apparent, that not only the occurrence of a mutant but also the compartment of origin is of importance.

Humans are inclined to engage in long-lasting relationships whose stability does not only rely on cooperation, but often also on loyalty - our tendency to keep interacting with the same partners even when better alternatives exist. Yet, what is the evolutionary mechanism behind such irrational behavior? Furthermore, under which conditions are individuals tempted to abandon their loyalty, and how does this affect the overall level of cooperation? Here, we study a model in which individuals interact along the edges of a dynamical graph, being able to adjust both their behavior and their social ties. Their willingness to sever interactions is determined by an individual characteristic and subject to evolution. We show that defectors ultimately loose any commitment to their social contacts, a result of their inability to establish any social tie under mutual agreement. Ironically, defectors' constant search for new partners to exploit leads to heterogeneous networks in which cooperation survives more easily. Cooperators, on the other hand, develop much more stable and long-term relationships. Their loyalty to their partners only decreases when the competition with defectors becomes fierce. These results indicate how our innate commitment to partners is related to mutual agreement among cooperators and how this commitment is evolutionary disadvantageous in times of conflict, both from an individual and a group perspective.

A coevolutionary model is discussed that incorporates the logical structure of constitutional chemistry and its kinetics on the one hand and the topological evolution of the chemical reaction network on the other hand. The motivation for designing this model is twofold. First, experiments that are to provide insight into chemical problems should be expressed in a syntax that remains as close as possible to real chemistry. Second, the study of physical properties of the complex chemical reaction networks requires growing models that incorporate features realistic from a biochemical perspective. In this article the theory and algorithms underlying the coevolutionary model are explained, and two illustrative examples are provided. These examples show that one needs to be careful in making general claims concerning the structure of chemical reaction networks.

Gánti's chemoton model is an illustrious example of a minimal cell model. It is composed of three stoichiometrically coupled autocatalytic subsystems: a metabolism, a template replication process, and a membrane enclosing the other two. Earlier studies on chemoton dynamics yield inconsistent results. Furthermore, they all appealed to deterministic simulations, which do not take into account the stochastic effects induced by small population sizes. We present, for the first time, results of a chemoton simulation in which these stochastic effects have been taken into account. We investigate the dynamics of the system and analyze in depth the mechanisms responsible for the observed behavior. Our results suggest that, in contrast to the most recent study by Munteanu and Solé, the stochastic chemoton reaches a unique stable division time after a short transient phase. We confirm the existence of an optimal template length and show that this is a consequence of the monomer concentration, which depends on the template length and the initiation threshold. Since longer templates imply shorter division times, these results motivate the selective pressure toward longer templates observed in nature.

The evolution of complexity has been a central theme for Biology [2] and Artificial Life research [1]. It is generally agreed that complexity has increased in our universe, giving way to life, multi-cellularity, societies, and systems of higher complexities. However, the mechanisms behind the complexification and its relation to evolution are not well understood. Moreover complexification can be used to mean different things in different contexts. For example, complexification has been interpreted as a process of diversification between evolving units [2] or as a scaling process related to the idea of transitions between different levels of complexity [7]. Understanding the difference or overlap between the mechanisms involved in both situations is mandatory to create acceptable synthetic models of the process, as is required in Artificial Life research. (...)

As modeling of changes in backbone conformation still lacks a computationally efficient solution, we developed a discretisation of the conformational states accessible to the protein backbone similar to the successful rotamer approach in side chains. The BriX fragment database, consisting of fragments from 4 to 14 residues long, was realized through identification of recurrent backbone fragments from a non-redundant set of high-resolution protein structures. BriX contains an alphabet of more than 1,000 frequently observed conformations per peptide length for 6 different variation levels. Analysis of the performance of BriX revealed an average structural coverage of protein structures of more than 99% within a root mean square distance (RMSD) of 1 Angstrom. Globally, we are able to reconstruct protein structures with an average accuracy of 0.48 Angstrom RMSD. As expected, regular structures are well covered, but, interestingly, many loop regions that appear irregular at first glance are also found to form a recurrent structural motif, albeit with lower frequency of occurrence than regular secondary structures. Larger loop regions could be completely reconstructed from smaller recurrent elements, between 4 and 8 residues long. Finally, we observed that a significant amount of short sequences tend to display strong structural ambiguity between alpha helix and extended conformations. When the sequence length increases, this so-called sequence plasticity is no longer observed, illustrating the context dependency of polypeptide structures.

Conventional evolutionary game theory predicts that natural selection favours the selfish and strong even though cooperative interactions thrive at all levels of organization in living systems. Recent investigations demonstrated that a limiting factor for the evolution of cooperative interactions is the way in which they are organized, cooperators becoming evolutionarily competitive whenever individuals are constrained to interact with few others along the edges of networks with low average connectivity. Despite this insight, the conundrum of cooperation remains since recent empirical data shows that real networks exhibit typically high average connectivity and associated single-to-broad-scale heterogeneity. Here, a computational model is constructed in which individuals are able to self-organize both their strategy and their social ties throughout evolution, based exclusively on their self-interest. We show that the entangled evolution of individual strategy and network structure constitutes a key mechanism for the sustainability of cooperation in social networks. For a given average connectivity of the population, there is a critical value for the ratio W between the time scales associated with the evolution of strategy and of structure above which cooperators wipe out defectors. Moreover, the emerging social networks exhibit an overall heterogeneity that accounts very well for the diversity of patterns recently found in acquired data on social networks. Finally, heterogeneity is found to become maximal when W reaches its critical value. These results show that simple topological dynamics reflecting the individual capacity for self-organization of social ties can produce realistic networks of high average connectivity with associated single-to-broad-scale heterogeneity. On the other hand, they show that cooperation cannot evolve as a result of "social viscosity" alone in heterogeneous networks with high average connectivity, requiring the additional mechanism of topological co-evolution to ensure the survival of cooperative behaviour.

This paper proposes an evolutionary approach for the composition of solutions in an incremental way. The approach is based on the metaphor of transitions in complexity discussed in the context of evolutionary biology. Partially defined solutions interact and evolve into aggregations until a full solution for the problem at hand is found. The impact of the initial population on the outcome and the dynamics of the process is evaluated using the domain of binary constraint satisfaction problems.

This paper proposes an incremental approach for building solutions using evolutionary computation. It presents a simple evolutionary model called a Transition model in which partial solutions are constructed that interact to provide larger solutions. An evolutionary process is used to merge these partial solutions into a full solution for the problem at hand. The paper provides a preliminary study on the evolutionary dynamics of this model as well as an empirical comparison with other evolutionary techniques on binary constraint satisfaction.

The DNA of some naturally competent species of bacteria contains a large number of evenly distributed copies of a short sequence. This highly overrepresented sequence is believed to be an uptake signal sequence (USS) that helps bacteria to take up DNA selectively from (dead) members of their own species. For some time it has been assumed that the USS evolved in order to enable bacteria to distinguish between conspecific and nonconspecific DNA fragments (the preference-first hypothesis). Recently, Redfield suggested that this hypothesis is not in fact realistic, as it would require biologically implausible group selection. In this article we present a model designed to demonstrate the emergence of similar USSs in a population of simulated evolving agents. We use this model to examine the conditions under which a USS will emerge in a preference-first scenario.

October 01 2005 Dynamical Hierarchies (Guest Editors' Introduction) In Special Collection: CogNet Tom Lenaerts, Tom Lenaerts IRIDIA, Université Libre de Bruxelles, CP 194/6 1050 Brussels, Belgium Search for other works by this author on: This Site Google Scholar Dominique Chu, Dominique Chu Senter for Vitskapsteori, Universitetet i Bergen 5020, Bergen, Norway Search for other works by this author on: This Site Google Scholar Richard Watson Richard Watson School of Electronics and Computer Science, University of Southampton, Southampton, UK Search for other works by this author on: This Site Google Scholar Author and Article Information Tom Lenaerts IRIDIA, Université Libre de Bruxelles, CP 194/6 1050 Brussels, Belgium Dominique Chu Senter for Vitskapsteori, Universitetet i Bergen 5020, Bergen, Norway Richard Watson School of Electronics and Computer Science, University of Southampton, Southampton, UK Online Issn: 1530-9185 Print Issn: 1064-5462 © 2005 Massachusetts Institute of Technology2005 Artificial Life (2005) 11 (4): 403–405. https://doi.org/10.1162/106454605774270606 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Tom Lenaerts, Dominique Chu, Richard Watson; Dynamical Hierarchies (Guest Editors' Introduction). Artif Life 2005; 11 (4): 403–405. doi: https://doi.org/10.1162/106454605774270606 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 2005 Massachusetts Institute of Technology2005 Article PDF first page preview Close Modal You do not currently have access to this content.