The impact of cellular characteristics on the evolution of shape homeostasis

The importance of individual cells in a developing multicellular organism is well known but precisely how the individual cellular characteristics of those cells collectively drive the emergence of robust, homeostatic structures is less well understood. For example cell communication via a diffusible factor allows for information to travel across large distances within the population, and cell polarisation makes it possible to form structures with a particular orientation, but how do these processes interact to produce a more robust and regulated structure? In this study we investigate the ability of cells with different cellular characteristics to grow and maintain homeostatic structures. We do this in the context of an individual-based model where cell behaviour is driven by an intra-cellular network that determines the cell phenotype. More precisely, we investigated evolution with 96 different permutations of our model, where cell motility, cell death, long-range growth factor (LGF), short-range growth factor (SGF) and cell polarisation were either present or absent. The results show that LGF has the largest positive impact on the fitness of the evolved solutions. SGF and polarisation also contribute, but all other capabilities essentially increase the search space, effectively making it more difficult to achieve a solution. By perturbing the evolved solutions, we found that they are highly robust to both mutations and wounding. In addition, we observed that by evolving solutions in more unstable environments they produce structures that were more robust and adaptive. In conclusion, our results suggest that robust collective behaviour is most likely to evolve when cells are endowed with long range communication, cell polarisation, and selection pressure from an unstable environment

How morphology of artificial organisms influences their evolution

International audienceThe principle of natural selection implies that variations are transmitted from parents to offsprings. The individuals with advantageous variations have better fitness. Consequently, such variations spread in the population and influence its evolution. This schematic description is conventionally accepted but it jumps over an important step: how variations are related to fitness. In the other words, how the phenotype is related to the reproduction and mortality rates. It is important to note that this relation will not be imposed by the assumptions of the model but it should follow from the morphology of the artificial organisms. In order to study this question, we will introduce in this work virtual populations of artificial organisms and will observe their behavior. The main idea of this study is that we prescribe individual characteristics of the organisms (size, form) but not their behavior in the search for resources. The model presented below will allow us to study on a simple example the interaction between morphology and natural selection, or, in a more general formulation, the evolution of the phenotype. 1.1. Artificial life models Artificial life models are largely used to study behavior of biological organisms at the individual level, their collective behavior and evolution. We will consider a complete life cycle model which includes the genotype of the organisms in its relation to the phenotype, the mechanism of motion and food search determined by the morphology of the organisms, and reproduction (Fig. 1). A B S T R A C T The purpose of this work is to study virtual populations of artificial organisms with their genotype, morphology, mechanism of motion, search and competition for food, reproduction, mutations. The genotype determines the phenotype (morphology), while morphology determines efficiency of motion and success in the search for food in the competition with other individuals; sufficient amount of food allows reproduction. Ensemble of these elements constitutes the minimal model to study natural selection of artificial organisms. Considering only some of them, as it is often the case in artificial life models, can be used for the optimization of some properties (for example, robot's gait or embryo's form) but not to study natural selection in the evolutionary context. Artificial organisms are considered in this work in the form of polygons (triangles) on the plane. Their genotype is given by three positive numbers associated to the vertices and their morphology is determined by the lengths of the sides equal the sum of the numbers in the adjacent vertices. Behavior of the individuals and their success in the search for food depend on their morphology. More efficient individuals will reproduce more than the others and will transmit their advantageous variations to their offsprings. Hence we can observe how natural selection chooses more efficient morphology and how it evolves due to random mutations. We develop an individual based model where the individuals recognize food and move to it with the speed determined by their morphology (and not prescribed in the algorithm). If they have enough food, they survive and reproduce. Therefore morphology and evolution are tightly interconnected and should be studied together. Dynamics of such populations appears to be different from the dynamics described by conventional models of competition and evolution of species. In particular, a new phenotype can emerge due to a different strategy of foraging (related to a different morphology) and not only due to a difference in consumed resources with the existing phenotype. We also observe that realization of Cope's rule (increase of body size in the process of evolution) can depend on parameters of the model.

Modeling Planarian Regeneration: A Primer for Reverse-Engineering the Worm

A mechanistic understanding of robust self-assembly and repair capabilities of complex systems would have enormous implications for basic evolutionary developmental biology as well as for transformative applications in regenerative biomedicine and the engineering of highly fault-tolerant cybernetic systems. Molecular biologists are working to identify the pathways underlying the remarkable regenerative abilities of model species that perfectly regenerate limbs, brains, and other complex body parts. However, a profound disconnect remains between the deluge of high-resolution genetic and protein data on pathways required for regeneration, and the desired spatial, algorithmic models that show how self-monitoring and growth control arise from the synthesis of cellular activities. This barrier to progress in the understanding of morphogenetic controls may be breached by powerful techniques from the computational sciences—using non-traditional modeling approaches to reverse-engineer systems such as planaria: flatworms with a complex bodyplan and nervous system that are able to regenerate any body part after traumatic injury. Currently, the involvement of experts from outside of molecular genetics is hampered by the specialist literature of molecular developmental biology: impactful collaborations across such different fields require that review literature be available that presents the key functional capabilities of important biological model systems while abstracting away from the often irrelevant and confusing details of specific genes and proteins. To facilitate modeling efforts by computer scientists, physicists, engineers, and mathematicians, we present a different kind of review of planarian regeneration. Focusing on the main patterning properties of this system, we review what is known about the signal exchanges that occur during regenerative repair in planaria and the cellular mechanisms that are thought to underlie them. By establishing an engineering-like style for reviews of the molecular developmental biology of biomedically important model systems, significant fresh insights and quantitative computational models will be developed by new collaborations between biology and the information sciences

Behavior finding: Morphogenetic Designs Shaped by Function

Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding

Redundancy creates opportunity in developmental representations

This paper investigates the influence of redundancy on the evolutionary performance of a gene regulatory network governing a cellular growth process. Redundancy is believed to play a key role in robustness and evolvability of biological systems. We use a cellular model controlled by a gene regulatory network to evolve elongated morphologies. We show that removing the redundancy in the genome during the evolution decreases the performance of the evolution strategy. A comparing run with few parameters and therefore no redundancy performs worst, which supports the hypothesis that redundancy improves evolvability. © 2011 IEEE

At the Biological Modeling and Simulation Frontier

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine

Modelado de un sistema celular artificial para generación de formas y procesado de información

[Resumen] En el ámbito de la informática se han modelado distintos procesos naturales para adaptar sus fundamentos en la resolución de problemas. En los últimos años algunos investigadores han centrado su atención en el comportamiento de las células no nerviosas. El motivo de este interés se debe a las características que presentan dichas células en cuanto a autoorganización y procesado de señales. Las células naturales de un organismo son capaces de autoorganizarse usando unas pocas señales y la información contenida en el ADN de las mismas. Además, si se piensa en una célula, esta recibe múltiples señales de distintas fuentes y referidas a varios problemas, y, la célula, es capaz de dar una respuesta coordinada a todos, procesando la información en paralelo con otras células. Adaptar este comportamiento en un modelo artificial supondría una nueva herramienta que facilitaría afrontar problemas como los multiobjetivo. El objetivo de esta tesis es realizar un nuevo paso para la consecución de ese objetivo. Así se busca estudiar e identificar los mecanismos más útiles del modelo biológico y crear un modelo artificial que los incluya. Para comprobar el comportamiento de ese nuevo modelo, se plantea realizar algunas pruebas clásicas que se basan en la generación y autoorganización de distintas formas geométricas. Además, también se hace una primera incursión en el estudio de la aplicación de este tipo de modelos a la resolución de problemas de clasificación de entradas, que no se había hecho anteriormente con ningún otro modelo dentro de la Embriogénesis Artificial.[Abstract] Fundamentals of different natural processes in the field of Computer Science have been modelled in order to apply them in problem-solving situations. In recent years, the behaviour of non-nervous cells has been the focus of attention of some researchers. The main reason of this attention consists in the features shown by these cells in terms of self-organisation and signal processing capacities. Natural cells of an organism are able to self-organise themselves by using a few signals and the information contained in their DNA. Moreover, cells receive many signals from different sources which are associated with several problems and they are able to process all those signals and coordinate their response at the same time as their neighbours, processing the signals and giving a coordinate response. The Artificial Models, which can adapt that behaviour, are the new tools facing challenges such as multi-objective problems. This thesis is aimed at making another step towards this objective. Thus, the main focus of this work is to study and identify the most relevant mechanisms of the biological model and develop an artificial model by adapting these mechanisms. In order to check the behavior of the development model, some standard assays based in the generation and self-organization of different geometrical forms were performed. Furthermore, the model presented herein is the first one of this kind of models applied to a new area such as the resolution of pattern classification problems, where no other Artificial Embryogeny model was applied before