Sylvain Cussat-Blanc

Y-chromosome descent clusters and male differential reproductive success: young lineage expansions dominate Asian pastoral nomadic populations

High-frequency microsatellite haplotypes of the male-specific Y-chromosome can signal past episodes of high reproductive success of particular men and their patrilineal descendants. Previously, two examples of such successful Y-lineages have been described in Asia, both associated with Altaic-speaking pastoral nomadic societies, and putatively linked to dynasties descending, respectively, from Genghis Khan and Giocangga. Here we surveyed a total of 5321 Y-chromosomes from 127 Asian populations, including novel Y-SNP and microsatellite data on 461 Central Asian males, to ask whether additional lineage expansions could be identified. Based on the most frequent eight-microsatellite haplotypes, we objectively defined 11 descent clusters (DCs), each within a specific haplogroup, that represent likely past instances of high male reproductive success, including the two previously identified cases. Analysis of the geographical patterns and ages of these DCs and their associated cultural characteristics showed that the most successful lineages are found both among sedentary agriculturalists and pastoral nomads, and expanded between 2100 BCE and 1100 CE. However, those with recent origins in the historical period are almost exclusively found in Altaic-speaking pastoral nomadic populations, which may reflect a shift in political organisation in pastoralist economies and a greater ease of transmission of Y-chromosomes through time and space facilitated by the use of horses.

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.

A Synthesis of the Cell2Organ Developmental Model

Over the past two decades, many techniques have been elaborated to simulate artificial, robotic creatures at different scales. After behavioral models in the 1990s, researchers made the robot morphologies evolvable to be better adapted to their environment. More recently, developmental mechanisms of living beings have inspired “artificial embryogeny” and generated smaller creatures composed of tens to thousands of cells. Yet, there is no encompassing “transversal” model that covers multiple scales at once. To address this challenge, our project consists of growing a complete creature with various organs and high-level functionalities from a single cell. For this, we propose a developmental model, Cell2Organ, based on three simulation layers. The first layer represents a chemical solution, in which cells can divide and process substrates through chemical reactions. Its purpose is to develop a metabolism adapted to the environment and allow organisms to perform actions by using accumulated energy. We also present an alternative model for the chemical layer that replaces molecular morphogens with a generative process based on L-systems. The second layer is a hydrodynamic medium, in which cells interact with simulated substrate flows so that they can impact the whole environment. Finally, we describe our plan to extend Cell2Organ with a third, physical layer, which would allow creatures to exhibit motion in a Newtonian world. There, cells will be able to modify their individual shape and affect the overall morphology of the organism.

Controlling cooperative and conflicting continuous actions with a Gene Regulatory Network

Artificial Gene Regulatory Networks (GRN) usually simulate cell behavior in developmental models. However, since 2003, GRN based controllers have been applied to robots to solve problems with few sensors and actuators. In this paper, we present our first steps toward an effective GRN-based controller for intelligent agents in video games. We will also introduce an experiment, the Radbot, where a robot has to handle and manage simultaneously four conflicting and cooperative continuous actions. Finally, we will show how a GRN-based controller can be evolved to solve the Radbot experiment.

Cell2Organ: Self-repairing artificial creatures thanks to a healthy metabolism

For living organisms, the robustness property is capital. For almost all of them, robustness rhymes with self-repairing. Indeed, organisms are subject to various injuries brought by the environment. To maintain their integrity, organisms are able to regenerate dead parts of themselves. This mechanism, commonly named self-repairing, is interesting to reproduce. Many works exist about self-repairing in robotics and electronics but fewer are in our domain of interest, artificial embryogenesis. In this paper, we show the self-repairing abilities of our model, Cell2Organ, designed to generate artificial creatures for artificial worlds. This model has previously been presented in [1].

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.

Using Pictures to Visualize the Complexity of Gene Regulatory Networks

This paper proposes a new method to evaluate the complexity of a Gene Regulatory Network (GRN). It is based on the generation of pictures. In addition to being visually interesting, the pictures shows the capacity of the GRN to produce smooth and/or sudden transitions, fractal-like complexity and regularities. We also have studied the influence of the size of the GRN on the complexity of pictures generated.

Gene regulated car driving: using a gene regulatory network to drive a virtual car

This paper presents a virtual racing car controller based on an artificial gene regulatory network. Usually used to control virtual cells in developmental models, recent works showed that gene regulatory networks are also capable to control various kinds of agents such as foraging agents, pole cart, swarm robots, etc. This paper details how a gene regulatory network is evolved to drive on any track through a three-stages incremental evolution. To do so, the inputs and outputs of the network are directly mapped to the car sensors and actuators. To make this controller a competitive racer, we have distorted its inputs online to make it drive faster and to avoid opponents. Another interesting property emerges from this approach: the regulatory network is naturally resistant to noise. To evaluate this approach, we participated in the 2013 simulated racing car competition against eight other evolutionary and scripted approaches. After its first participation, this approach finished in third place in the competition.

Cracking the egg: Virtual embryogenesis of real robots

All multicellular living beings are created from a single cell. A developmental process, called embryogenesis, takes this first fertilized cell down a complex path of reproduction, migration, and specialization into a complex organism adapted to its environment. In most cases, the first steps of the embryogenesis take place in a protected environment such as in an egg or in utero. Starting from this observation, we propose a new approach to the generation of real robots, strongly inspired by living systems. Our robots are composed of tens of specialized cells, grown from a single cell using a bio-inspired virtual developmental process. Virtual cells, controlled by gene regulatory networks, divide, migrate, and specialize to produce the robots body plan (morphology), and then the robot is manually built from this plan. Because the robot is as easy to assemble as Lego, the building process could be easily automated.

Simultaneous cooperative and conflicting behaviors handled by a gene regulatory network

In many current developmental models, artificial Gene Regulatory Networks (GRN) simulate cell behavior. More specifically, GRN can determine and regulate cell behaviors using collected external signals through protein sensors. In this paper, we propose to use the GRN properties to control an agent using external perception. More precisely, we will try to evaluate how a GRN can handle and manage simultaneously four conflicting and cooperative continuous actions to solve a new experiment, the Radbot.