Synthetic biology is one facet of Artificial Life which designs novel biological components, e.g. DNA, RNA, membranes, to produce new behaviours. Here, we are interested in DNA “circuits”: DNA engineered to have particular computational properties. During gene transcription, the DNA double-helix undergoes supercoiling changes, which affects transcription of nearby genes. There is limited mathematical, as opposed to physical, modelling of DNA circuits, and supercoiling is not considered. In many current synthetic circuits, supercoiling has to be carefully removed, particularly in in vivo systems, to prevent unmodelled side effects. However, supercoiling is an intrinsic property of DNA that impacts gene expression, and could be exploited if included in models. Here, we present a new π -calculus formalism for modelling DNA circuits with supercoiling, and demonstrate its use on a simple genetic circuit. The state transition diagrams normally associated with π -calculus are not accessible when the number of states becomes large. We present a new circular visualisation of the π -calculus circuit components that is more intuitive and readable for biologists familiar with the circular visualisations of plasmids.

Whenever applicable, the Stochastic Gradient Descent (SGD) has shown itself to be unreasonably effective. Instead of underperforming and getting trapped in local minima due to the batch noise, SGD leverages it to learn to generalize better and find minima that are good enough for the entire dataset. This led to numerous theoretical and experimental investigations, especially in the context of Artificial Neural Networks (ANNs), leading to better machine learning algorithms. However, SGD is not applicable in a non-differentiable setting, leaving all that prior research off the table. In this paper, we show that a class of evolutionary algorithms (EAs) inspired by the Gillespie-Orr Mutational Landscapes model for natural evolution is formally equivalent to SGD in certain settings and, in practice, is well adapted to large ANNs. We refer to such EAs as Gillespie-Orr EA class (GO-EAs) and empirically show how an insight transfer from SGD can work for them. We then show that for ANNs trained to near-optimality or in the transfer learning setting, the equivalence also allows transferring the insights from the Mutational Landscapes model to SGD. We then leverage this equivalence to experimentally show how SGD and GO-EAs can provide mutual insight through examples of minima flatness, transfer learning, and mixing of individuals in EAs applied to large models.

As digital evolution systems grow in scale and complexity, observing and interpreting their evolutionary dynamics will become increasingly challenging. Distributed and parallel computing, in particular, introduce obstacles to maintaining the high level of observability that makes digital evolution a powerful experimental tool. Phylogenetic analyses represent a promising tool for drawing inferences from digital evolution experiments at scale. Recent work has introduced promising techniques for decentralized phylogenetic inference in parallel and distributed digital evolution systems. However, foundational phylogenetic theory necessary to apply these techniques to characterize evolutionary dynamics is lacking. Here, we lay the groundwork for practical applications of distributed phylogenetic tracking in three ways: 1) we present an improved technique for reconstructing phylogenies from tunably-precise genome annotations, 2) we begin the process of identifying how the signatures of various evolutionary dynamics manifest in phylogenetic metrics, and 3) we quantify the impact of reconstruction-induced imprecision on phylogenetic metrics. We find that selection pressure, spatial structure, and ecology have distinct effects on phylogenetic metrics, although these effects are complex and not always intuitive. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully.

PCs, being generally more effective, have replaced typewriters in our everyday lives; but, at the same time, introduce a lot of complexity. As a result, many of us are left wondering at PCs as if they were mysterious ghosts in the machine : entities with powers we cannot explain or control, almost supernatural. We analyze this increase in technological complexity at two levels in our society, one economic and one scientific, and we discuss how the field of Artificial Life (ALife) can attempt to rescue our society. At the economic level, there is evidence that computers, being so much more complex, slow labor productivity down rather than increasing it (e.g., maintenance, malware, distractions). Computers are also the subject of debate surrounding technological unemployment. We advocate for ALife to focus on developments that, like the xenobots, are minimally intrusive to our everyday work and occupy unfilled economic niches. At the scientific level, the surge in Artificial Intelligence (AI) has begotten a plethora of complex algorithms that mimic the cognition happening in animal brains: they are usually not interpretable and even their creators struggle to make sense of them. We advocate for ALife to focus more on basal forms of cognition— cognition that requires as little “brain” as possible, potentially none; algorithms that think through their bodies, stripped of any superfluous complexity, just like typewriters.

The problem of identifying conditions that enable major evolutionary transitions, in which distinct units come together to form a new higher level unit, is a complex and difficult topic spanning many disciplines. Here, we approach this problem from the perspective of the origin of life, which allows us to make the simplifying assumption that the lower-level units are not also evolving. This assumption lets us focus on identifying environmental factors that promote egalitarian major transitions in general and the origin of life specifically. To study this question, we build a simple artificial ecology model. We quantify major-transition-like dynamics using a maximum likelihood approach and a set of null models predicting the behavior of our system under various dynamics. Ultimately, we find that, even in a maximally simple artificial ecology model, we are able to observe evidence of community-level selection and thus the beginnings of a major evolutionary transition. The regions of parameter space that promote community-level selection vary based on species interactions but we observe consistent trends.

This study investigates the relationship between sparse computation and evolution in various models using a simple function we call sparsify . We use the sparsify function to alter the sparsity of arbitrary matrices during evolutionary search. The sparsify function is tested on a recurrent neural network, a gene interaction matrix, and a gene regulatory network in the context of four different optimization problems. We demonstrate that the function positively affects evolutionary adaptation. Furthermore, this study shows that the sparsify function enables automatic meta-adaptation of sparsity for the discovery of better solutions. Overall, the findings suggest that the sparsify function can be a valuable tool to improve the optimization of complex systems.

Integrated information and variational inference provide influential mathematical frameworks in neuroscience. Yet, the understanding of the connection between the two is limited. Here, we study a minimal model to show how variational inference displays large integrated information for highly correlated target distributions, in contrast with alternative inference approaches like maximum likelihood estimation.

Life’s origin and chemical evolution requires continuous and substantial selective processes at the molecular scale. However, the spontaneous emergence of selection, its mechanism and system-level influence are still insufficiently explored. To address this, an automated experimental framework has been devised to identify selection in a recursive system of oligomerizing molecules with closed-loop analytics. The approach is based on Assembly Theory, using Molecular Assembly (MA) index as an inherent complexity measure of molecules and molecular networks. A string-based MA model was developed to assist in the efficient analysis of diverse lengthy oligomers and to allow string information procedures. Coupled with smart algorithmic decision-making, the system will attempt to maximize the molecular network’s complexity in the reactor over recursive cycles. Following patterns of increasing chemical complexity in the molecular system could reveal definite traces of selection and determine the conditions and agents that promote it. This work elucidates why improbable complex states emerge, pertinent to life’s origin and its major evolutionary transitions.

Under what conditions will an organism remain viable as numerous forces threaten its self-construction, and what does this abstract space of possibilities look like? A growing body of work has begun to confront this question by imposing viability limits on dynamical system models to separate sets of viable and nonviable states. Since the viability limits are not implicit in the equations that govern the dynamics, there is no guaranteed equivalence between the phase portrait and the basins of initial conditions that will remain viable. This means that the topology of a dynamical system model with imposed viability limits demands richer analyses, which we refer to as characterizing viability space . In this paper, we set the groundwork for such techniques using a protocell model governed by nonlinear ordinary differential equations, including the development of novel criteria for bifurcations so that entire classes of systems can be studied.

Unambiguous identification of the rewards driving behaviours of entities operating in complex open-ended real-world environments is typically not possible. Nonetheless, goals and associated behaviours do emerge and are dynamically updated. Reproducing such dynamics in models would be highly desirable in many domains. Simulation experiments described here assess a candidate mechanism for dynamic reward updating through learning and inheritance, and successfully demonstrate the abandonment of an initially rewarded but ultimately detrimental behaviour.

The relationship between reaction-diffusion (RD) systems, characterized by continuous spatiotemporal states, and cellular automata (CA), marked by discrete spatiotemporal states, remains poorly understood. This paper delves into this relationship through an examination of a recently developed CA known as Lenia. We demonstrate that asymptotic Lenia, a variant of Lenia, can be comprehensively described by differential equations, and, unlike the original Lenia, it is independent of time-step ticks. Further, we establish that this formulation is mathematically equivalent to a generalization of the kernel-based Turing model (KT model). Stemming from these insights, we establish that asymptotic Lenia can be replicated by an RD system composed solely of diffusion and spatially local reaction terms, resulting in the simulated asymptotic Lenia based on an RD system, or “RD Lenia”. However, our RD Lenia cannot be construed as a chemical system since the reaction term fails to satisfy mass-action kinetics.

We propose asynchronous cellular automata fashioned model of true slime mold Physarum polycephalum plasmodium equipped with a dynamic feedback mechanism based on Bayesian and inverse Bayesian inference. These are implemented as feedback from dynamical protoplasmic flow into local tubular structures in slime mold. Because inverse Bayesian inference replaces conditional probabilities with empirical ones and relaxes the probability space, the model can behave robustly and adaptively. We describe a brief overview of our model in this paper.

Various models have been developed to shed light on neuronal mechanisms of homeostatic plasticity (HP). We focus on one such model implemented on continuous-time-recurrent neural networks. Though this HP mechanism encourages oscillatory dynamics by preventing node saturation, it was curiously detrimental to behavioral fitness when compared to non-plastic networks on several tasks (Williams, 2004, 2005). When we set out to explain this result, we discovered a type of oscillation that depends on HP’s continued regulation of circuit parameters. If HP is turned off, oscillation stops. This suggests that HP can play an enabling role in central pattern generation which has not been explored in modelling or experimental contexts. We first situate this phenomenon within the space of possibilities for HP’s involvement in oscillation. Then, we show that these “HP-enabled” oscillations are extraordinarily common in random circuits of various sizes. Finally, we describe how the degree of timescale separation between HP and neural dynamics affects HP-enabled oscillation. This analysis suggests promising avenues for dialogue between modeling and experiment.

Behavior has an understated role in the genesis of complex ecologies. Discussion of ecological regulation describes the phenomenon in terms of coupled feedbacks which have been connected by Harvey (2004) to rein control as introduced by Clynes (1969). These descriptions have motivated the question of how communities that instantiate such feedbacks can evolve in the first place, especially with respect to global regulatory effects such as those supposed in Lovelock and Margulis’ Gaia theory (1974). While Gaian regulation is not incompatible with evolution, it appears there are intermediate steps that are necessary for its establishment, and likely the establishment of coupled ecological regulation at any scale. Here we present a series of dynamical models that show how simple dormancy behavior can help account for that differential survival across a variety of seasonal conditions. Furthermore, the combination of that behavior and a traditional rein control mechanism lead to a significant increase in survivable conditions, providing a hypothesis for how ecological regulation may be scaffolded. Further discussion suggests that effective behavior of pioneer species is a requirement for the establishment of robust ecosystems.

When sizing a multi-robot swarm, a key quantity to be considered is the swarm’s agent density. In the field of multi-robot and multi-agent systems, it has been acknowledged that there is a minimum agent density to ensure the emergence of cooperative behaviors, implying that too few agents within a swarm would yield an ineffective system. However, too large a swarm may result in the agents interfering with each other’s actions, again resulting in subpar swarm performances. There is therefore a range of densities where swarm operations are optimal. In this study, we investigate the factors that determine this range for collective target-tracking tasks. Specifically, we show how the use of agent-based memory can reduce the density at which swarms are able to start tracking. We also show that besides strategy design, other environmental factors affect the range of densities over which swarms can operate efficaciously, such as a target’s movement policy, its velocity, and the number of targets to be tracked.

Through the combination of artificial components and living organisms, we can develop a novel methodology for aquatic monitoring. By observing the responses of organisms to changes in their environment, a broad-spectrum sensor was created. One of the organisms broadly used as a biosensor is Daphnia . Its broad distribution and well-studied biology make it a promising element for incorporating into a biohybrid. This Daphnia -based sensor was calibrated against increasing salinity as a preliminary experiment. The swimming behaviour (spinning and movement inhibition) was observed for different salinities. The results showcase significant and observable differences. This and other calibration experiments will be used here as bases for the behavioural results interpretation.

The study of evolutionary development (evo-devo) is frequently challenged by the scales of space and time complexity inherent to its study. This has led to the creation of abstract models to allow for the exploration of evo-devo in a manner that is both more computationally feasible and more general, without ties to the specific biological processes of a single organism. Our work expands upon these previous models by introducing an indirect encoding for developmental mechanisms, dynamic fitness landscapes, and a phenotypic structure that allows for the exploration of new interactions between the developmental and evolutionary processes. Introducing these changes allows us to conduct a more thorough study of factors impacting evo-devo. Our experimental results suggest a number of parallels to biological systems. These include representing the synergy of evolutionary and developmental processes, the evolution of adaptable features, and highly conserved regulatory genes. We also discuss the opportunities for exploration opened by this new model. These possibilities include the study of developmental exaptations and the robustness of developmental strategies.

Neural networks are often chosen as controllers in evolutionary robotics. In all but a few cases, neural networks are evolved from scratch. In this study, we investigate the effect of pretraining neural networks using a biologically inspired walking gait. We first generate joint angles for a walking gait using an inverse kinematics model. We then train a conventional feed-forward neural network to reproduce these joint angles. The pretrained model is used to seed an initial population of neural networks, which are coevolved along with the morphology of a quadrupedal robot using Lexicase selection. Our initial results show that while pretraining does not necessarily lead to higher fitness at the end of evolution, it does lead to more consistent performance and more lifelike final behaviors. This exploration has left us with many questions about the importance and process of pretraining in evolutionary robotics, and we believe our results suggest the technique is worth further investigation.