Kyle Ira Harrington

Imaging analysis of clock neurons reveals light buffers the wake-promoting effect of dopamine

How animals maintain proper amounts of sleep yet remain flexible to changes in environmental conditions remains unknown. We found that environmental light suppressed the wake-promoting effects of dopamine in fly brains. The ten large lateral-ventral neurons (l-LNvs), a subset of clock neurons, are wake-promoting and respond to dopamine, octopamine and light. Behavioral and imaging analyses suggested that dopamine is a stronger arousal signal than octopamine. Notably, light exposure not only suppressed l-LNv responses, but also synchronized responses of neighboring l-LNvs. This regulation occurred by distinct mechanisms: light-mediated suppression of octopamine responses was regulated by the circadian clock, whereas light regulation of dopamine responses occurred by upregulation of inhibitory dopamine receptors. Plasticity therefore alters the relative importance of diverse cues on the basis of the environmental mix of stimuli. The regulatory mechanisms described here may contribute to the control of sleep stability while still allowing behavioral flexibility.

Asymmetric division coordinates collective cell migration in angiogenesis

The asymmetric division of stem or progenitor cells generates daughters with distinct fates and regulates cell diversity during tissue morphogenesis. However, roles for asymmetric division in other more dynamic morphogenetic processes, such as cell migration, have not previously been described. Here we combine zebrafish in vivo experimental and computational approaches to reveal that heterogeneity introduced by asymmetric division generates multicellular polarity that drives coordinated collective cell migration in angiogenesis. We find that asymmetric positioning of the mitotic spindle during endothelial tip cell division generates daughters of distinct size with discrete ‘tip’ or ‘stalk’ thresholds of pro-migratory Vegfr signalling. Consequently, post-mitotic Vegfr asymmetry drives Dll4/Notch-independent self-organization of daughters into leading tip or trailing stalk cells, and disruption of asymmetry randomizes daughter tip/stalk selection. Thus, asymmetric division seamlessly integrates cell proliferation with collective migration, and, as such, may facilitate growth of other collectively migrating tissues during development, regeneration and cancer invasion.

Gene Regulatory Network Evolution Through Augmenting Topologies

Artificial gene regulatory networks (GRNs) are biologically inspired dynamical systems used to control various kinds of agents, from the cells in developmental models to embodied robot swarms. Most recent work uses a genetic algorithm (GA) or an evolution strategy in order to optimize the network for a specific task. However, the empirical performances of these algorithms are unsatisfactory. This paper presents an algorithm that primarily exploits a network distance metric, which allows genetic similarity to be used for speciation and variation of GRNs. This algorithm, inspired by the successful neuroevolution of augmenting topologies algorithms use in evolving neural networks and compositional pattern-producing networks, is based on a specific initialization method, a crossover operator based on gene alignment, and speciation based upon GRN structures. We demonstrate the effectiveness of this new algorithm by comparing our approach both to a standard GA and to evolutionary programming on four different experiments from three distinct problem domains, where the proposed algorithm excels on all experiments.

What’s in an Evolved Name? The Evolution of Modularity via Tag-Based Reference

Programming languages provide a variety of mechanisms to associate names with values, and these mechanisms play a central role in programming practice. For example, they allow multiple references to the same storage location or function in different parts of a complex program. By contrast, the representations used in current genetic programming systems provide few if any naming mechanisms, and it is therefore generally not possible for evolved programs to use names in sophisticated ways. In this chapter we describe a new approach to names in genetic programming that is based on Holland’s concept of tags. We demonstrate the use of tag-based names, we describe some of the ways in which they may help to extend the power and reach of genetic programming systems, and we look at the ways that tag-based names are actually used in an evolved program that solves a robot navigation problem.

Eco-evolutionary dynamics of complex social strategies in microbial communities

Microbial communities abound with examples of complex social interactions that shape microbial ecosystems. One particularly striking example is microbial cooperation via the secretion of public goods. It has been suggested by theory, and recently demonstrated experimentally, that microbial population dynamics and the evolutionary dynamics of cooperative social genes take place with similar timescales, and are linked to each other via an eco-evolutionary feedback loop. We overview this recent evidence, and discuss the possibility that a third process may be also part of this loop: phenotypic dynamics. Complex social strategies may be implemented at the single-cell level by means of gene regulatory networks. Thus gene expression plasticity or stochastic gene expression, both of which may occur with a timescale of one to a few generations, can potentially lead to a three-way coupling between behavioral dynamics, population dynamics, and evolutionary dynamics

Temporal modulation of collective cell behavior controls vascular network topology

Vascular network density determines the amount of oxygen and nutrients delivered to host tissues, but how the vast diversity of densities is generated is unknown. Reiterations of endothelial-tip-cell selection, sprout extension and anastomosis are the basis for vascular network generation, a process governed by the VEGF/Notch feedback loop. Here, we find that temporal regulation of this feedback loop, a previously unexplored dimension, is the key mechanism to determine vascular density. Iterating between computational modeling and in vivo live imaging, we demonstrate that the rate of tip-cell selection determines the length of linear sprout extension at the expense of branching, dictating network density. We provide the first example of a host tissue-derived signal (Semaphorin3E-Plexin-D1) that accelerates tip cell selection rate, yielding a dense network. We propose that temporal regulation of this critical, iterative aspect of network formation could be a general mechanism, and additional temporal regulators may exist to sculpt vascular topology. DOI: http://dx.doi.org/10.7554/eLife.13212.001

Robot coverage control by evolved neuromodulation

An important connection between evolution and learning was made over a century ago and is now termed as the Baldwin effect. Learning acts as a guide for an evolutionary search process. In this study reinforcement learning agents are trained to solve the robot coverage control problem. These agents are improved by evolving neuromodulatory gene regulatory networks (GRN) that influence the learning and memory of agents. Agents trained by these neuromodulatory GRNs can consistently generalize better than agents trained with fixed parameter settings. This work introduces evolutionary GRN models into the context of neuromodulation and illustrates some of the benefits that stem from neuromodulatory GRNs.

Autoconstructive evolution for structural problems

While most hyper-heuristics search for a heuristic that is later used to solve classes of problems, autoconstructive evolution represents an alternative which simultaneously searches both heuristic and solution space. In this study we contrast autoconstructive evolution, in which intergenerational variation is accomplished by the evolving programs themselves, with a genetic programming system, PushGP, to understand the dynamics of this hybrid approach. A problem size scaling analysis of these genetic programming techniques is performed on structural problems. These problems involve fewer domain-specific features than most model problems while maintaining core features representative of program search. We use two such problems, Order and Majority, to study autoconstructive evolution in the Push programming language.

Genetically-regulated Neuromodulation Facilitates Multi-Task Reinforcement Learning

In this paper, we use a gene regulatory network (GRN) to regulate a reinforcement learning controller, the State- Action-Reward-State-Action (SARSA) algorithm. The GRN serves as a neuromodulator of SARSAs learning parame- ters: learning rate, discount factor, and memory depth. We have optimized GRNs with an evolutionary algorithm to regulate these parameters on specific problems but with no knowledge of problem structure. We show that genetically- regulated neuromodulation (GRNM) performs comparably or better than SARSA with fixed parameters. We then ex- tend the GRNM SARSA algorithm to multi-task problem generalization, and show that GRNs optimized on multi- ple problem domains can generalize to previously unknown problems with no further optimization.

The effects of tags on the evolution of honest signaling

In the study described here we examine the importance of social tags in the emergence and maintenance of signaling, using the Sir Philip Sydney Game. We use tags in the calculation of inclusive fitness for members in a finite population, and analyze their evolution under different population distributions. We support the claim that inclusive fitness theory may not be sufficient to explain the evolution of cooperation. While cooperativity through honest signaling is sometimes achieved with tag-based relatedness, we suggest that the importance of tag-based mechanisms may not simply be due to their role in kin selection.