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Register a new userMaternal origins of developmental reproducibility
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Cell fate decisions in multicellular organisms are precisely coordinated, leading to highly reproducible macroscopic outcomes of developmental processes. The origins of this reproducibility can be found at the molecular level during the earliest stages of development when spatial patterns of morphogen (form-generating) molecules emerge reproducibly. However, the initial conditions for these early stages are determined by the female during oogenesis, and it is unknown whether reproducibility is passed on to the zygote or whether it is reacquired by the zygote. Here we examine the earliest reproducible pattern in the Drosophila embryo, the Bicoid protein gradient. Using a unique combination of absolute molecule counting techniques, we show that it is generated from a highly controlled source of mRNA molecules that is reproducible from embryo to embryo to within ~8%. This occurs in a perfectly linear feed-forward process: changes in the females gene dosage lead to proportional changes in the mRNA and protein counts in the embryo. In this setup, noise is kept low in the transition from one molecular species to another, allowing the female to precisely deposit the same absolute number of mRNA molecules in each embryo and therefore confer reproducibility to the Bicoid pattern. Our results indicate that the reproducibility of the morphological structures that emerge in the embryo originates during oogenesis when all initial patterning signals are controlled with precision similar to what we observe for the Bicoid pattern.
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Developmental processes in multicellular organisms occur far from equilibrium, yet produce complex patterns with astonishing reproducibility. We measure the precision and reproducibility of bilaterally symmetric fly wings across the natural range of genetic and environmental conditions and find that wing patterns are specified with identical spatial precision and are reproducible to within a single cell width. The early fly embryo operates at a similar degree of reproducibility, suggesting that the overall spatial precision of morphogenesis in Drosophila performs at the single cell level, arguably the physical limit of what a biological system can achieve.
A key step in the origin of life is the emergence of a primitive metabolism. This requires the formation of a subset of chemical reactions that is both self-sustaining and collectively autocatalytic. A generic theory to study such processes (called RAF theory) has provided a precise and computationally effective way to address these questions, both on simulated data and in laboratory studies. One of the classic applications of this theory (arising from Stuart Kauffmans pioneering work in the 1980s) involves networks of polymers under cleavage and ligation reactions; in the first part of this paper, we provide the first exact description of the number of such reactions under various model assumptions. Conclusions from earlier studies relied on either approximations or asymptotic counting, and we show that the exact counts lead to similar (though not always identical) asymptotic results. In the second part of the paper, we solve some questions posed in more recent papers concerning the computational complexity of some key questions in RAF theory. In particular, although there is a fast algorithm to determine whether or not a catalytic reaction network contains a subset that is both self-sustaining and autocatalytic (and, if so, find one), determining whether or not sets exist that satisfy certain additional constraints exist turns out to be NP-complete.
There is much to learn through synthesis of Developmental Biology, Cognitive Science and Computational Modeling. One lesson we can learn from this perspective is that the initialization of intelligent programs cannot solely rely on manipulation of numerous parameters. Our path forward is to present a design for developmentally-inspired learning agents based on the Braitenberg Vehicle. Using these agents to exemplify artificial embodied intelligence, we move closer to modeling embodied experience and morphogenetic growth as components of cognitive developmental capacity. We consider various factors regarding biological and cognitive development which influence the generation of adult phenotypes and the contingency of available developmental pathways. These mechanisms produce emergent connectivity with shifting weights and adaptive network topography, thus illustrating the importance of developmental processes in training neural networks. This approach provides a blueprint for adaptive agent behavior that might result from a developmental approach: namely by exploiting critical periods or growth and acquisition, an explicitly embodied network architecture, and a distinction between the assembly of neural networks and active learning on these networks.
The connection between brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. Particularly in artificial intelligence research, behavior is generated by a black box approximating the brain. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. This model generates outputs and behaviors from a priori associations, yet this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We will introduce our approach, which is to use Braitenberg Vehicles (BVs) to model the development of an artificial nervous system. The resulting developmental BVs will generate behaviors that range from stimulus responses to group behavior that resembles collective motion. Next, we will situate this work in the domain of artificial brain networks. Then we will focus on broader themes such as embodied cognition, feedback, and emergence. Our perspective will then be exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we will revisit concepts related to our approach and how they might guide future development.
Mosquitoes are vectors of viral diseases with epidemic potential in many regions of the world, and in absence of vaccines or therapies, their control is the main alternative. Chemical control through insecticides has been one of the conventional strategies, but induces insecticide resistance, which may affect other insects and cause ecological damage. Biological control, through the release of mosquitoes infected by the maternally inherited bacterium Wolbachia, which inhibits their vector competence, has been proposed as an alternative. The effects of both techniques may be intermingled in practice: prior insecticide spraying may debilitate wild population, so facilitating subsequent invasion by the bacterium; but the latter may also be hindered by the release of susceptible mosquitoes in an environment where the wild population became resistant, as a result of preexisting undesired exposition to insecticide. To tackle such situations, we propose here a unifying model allowing to account for the cross effects of both control techniques, and based on the latter, design release strategies able to infect a wild population. The latter are feedback laws, whose stabilizing properties are studied.
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