Towards the Evolution of Novel Vertical-Axis Wind Turbines

Renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. An artificial neural network is used as a surrogate model to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency, resulting in an important cost reduction. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.Comment: 14 pages, 11 figure

Mathematical models for chemotaxis and their applications in self-organisation phenomena

Chemotaxis is a fundamental guidance mechanism of cells and organisms, responsible for attracting microbes to food, embryonic cells into developing tissues, immune cells to infection sites, animals towards potential mates, and mathematicians into biology. The Patlak-Keller-Segel (PKS) system forms part of the bedrock of mathematical biology, a go-to-choice for modellers and analysts alike. For the former it is simple yet recapitulates numerous phenomena; the latter are attracted to these rich dynamics. Here I review the adoption of PKS systems when explaining self-organisation processes. I consider their foundation, returning to the initial efforts of Patlak and Keller and Segel, and briefly describe their patterning properties. Applications of PKS systems are considered in their diverse areas, including microbiology, development, immunology, cancer, ecology and crime. In each case a historical perspective is provided on the evidence for chemotactic behaviour, followed by a review of modelling efforts; a compendium of the models is included as an Appendix. Finally, a half-serious/half-tongue-in-cheek model is developed to explain how cliques form in academia. Assumptions in which scholars alter their research line according to available problems leads to clustering of academics and the formation of "hot" research topics.Comment: 35 pages, 8 figures, Submitted to Journal of Theoretical Biolog

Emergent diversity in an open-ended evolving virtual community

Understanding the dynamics of biodiversity has become an important line of research in theoretical ecology and, in particular, conservation biology. However, studying the evolution of ecological communities under traditional modeling approaches based on differential calculus requires speciesʼ characteristics to be predefined, which limits the generality of the results. An alternative but less standardized methodology relies on intensive computer simulation of evolving communities made of simple, explicitly described individuals. We study here the formation, evolution, and diversity dynamics of a community of virtual plants with a novel individual-centered model involving three different scales: the genetic, the developmental, and the physiological scales. It constitutes an original attempt at combining development, evolution, and population dynamics (based on multi-agent interactions) into one comprehensive, yet simple model. In this world, we observe that our simulated plants evolve increasingly elaborate canopies, which are capable of intercepting ever greater amounts of light. Generated morphologies vary from the simplest one-branch structure of promoter plants to a complex arborization of several hundred thousand branches in highly evolved variants. On the population scale, the heterogeneous spatial structuration of the plant community at each generation depends solely on the evolution of its component plants. Using this virtual data, the morphologies and the dynamics of diversity production were analyzed by various statistical methods, based on genotypic and phenotypic distance metrics. The results demonstrate that diversity can spontaneously emerge in a community of mutually interacting individuals under the influence of specific environmental conditions.This research was partially supported by a grant for the GENEX project (P09-TIC-5123) from the Consejería de Innovación y Ciencia de Andalucía. J.D.F. was supported by a FPU grant from the Spanish Ministerio de Educación. R.D. wishes to thank the Région Ile-de-France for supporting his research position at the Complex Systems Institute, Paris Ile-de-France

Taking aim at moving targets in computational cell migration

Cell migration is central to the development and maintenance of multicellular organisms. Fundamental understanding of cell migration can, for example, direct novel therapeutic strategies to control invasive tumor cells. However, the study of cell migration yields an overabundance of experimental data that require demanding processing and analysis for results extraction. Computational methods and tools have therefore become essential in the quantification and modeling of cell migration data. We review computational approaches for the key tasks in the quantification of in vitro cell migration: image pre-processing, motion estimation and feature extraction. Moreover, we summarize the current state-of-the-art for in silico modeling of cell migration. Finally, we provide a list of available software tools for cell migration to assist researchers in choosing the most appropriate solution for their needs

Multicellular Systems Biology of Development

Embryonic development depends on the precise coordination of cell fate specification, patterning and morphogenesis. Although great strides have been made in the molecular understanding of each of these processes, how their interplay governs the formation of complex tissues remains poorly understood. New techniques for experimental manipulation and image quantification enable the study of development in unprecedented detail, resulting in new hypotheses on the interactions between known components. By expressing these hypotheses in terms of rules and equations, computational modeling and simulation allows one to test their consistency against experimental data. However, new computational methods are required to represent and integrate the network of interactions between gene regulation, signaling and biomechanics that extend over the molecular, cellular and tissue scales. In this thesis, I present a framework that facilitates computational modeling of multiscale multicellular systems and apply it to investigate pancreatic development and the formation of vascular networks. This framework is based on the integration of discrete cell-based models with continuous models for intracellular regulation and intercellular signaling. Specifically, gene regulatory networks are represented by differential equations to analyze cell fate regulation; interactions and distributions of signaling molecules are modeled by reaction-diffusion systems to study pattern formation; and cell-cell interactions are represented in cell-based models to investigate morphogenetic processes. A cell-centered approach is adopted that facilitates the integration of processes across the scales and simultaneously constrains model complexity. The computational methods that are required for this modeling framework have been implemented in the software platform Morpheus. This modeling and simulation environment enables the development, execution and analysis of multi-scale models of multicellular systems. These models are represented in a new domain-specific markup language that separates the biological model from the computational methods and facilitates model storage and exchange. Together with a user-friendly graphical interface, Morpheus enables computational modeling of complex developmental processes without programming and thereby widens its accessibility for biologists. To demonstrate the applicability of the framework to problems in developmental biology, two case studies are presented that address different aspects of the interplay between cell fate specification, patterning and morphogenesis. In the first, I focus on the interplay between cell fate stability and intercellular signaling. Specifically, two studies are presented that investigate how mechanisms of cell-cell communication affect cell fate regulation and spatial patterning in the pancreatic epithelium. Using bifurcation analysis and simulations of spatially coupled differential equations, it is shown that intercellular communication results in a multistability of gene expression states that can explain the scattered spatial distribution and low cell type ratio of nascent islet cells. Moreover, model analysis shows that disruption of intercellular communication induces a transition between gene expression states that can explain observations of in vitro transdifferentiation from adult acinar cells into new islet cells. These results emphasize the role of the multicellular context in cell fate regulation during development and may be used to optimize protocols for cellular reprogramming. The second case study focuses on the feedback between patterning and morphogenesis in the context of the formation of vascular networks. Integrating a cell-based model of endothelial chemotaxis with a reaction-diffusion model representing signaling molecules and extracellular matrix, it is shown that vascular network patterns with realistic morphometry can arise when signaling factors are retained by cell-modified matrix molecules. Through the validation of this model using in vitro assays, quantitative estimates are obtained for kinetic parameters that, when used in quantitative model simulations, confirm the formation of vascular networks under measured biophysical conditions. These results demonstrate the key role of the extracellular matrix in providing spatial guidance cues, a fact that may be exploited to enhance vascularization of engineered tissues. Together, the modeling framework, software platform and case studies presented in this thesis demonstrate how cell-centered computational modeling of multi-scale and multicellular systems provide powerful tools to help disentangle the complex interplay between cell fate specification, patterning and morphogenesis during embryonic development

Tissue Competition: Interplay of Mechanics, Interfaces, and Evolution

Competitions between tissues occur frequently in living systems. Well-studied examples are the competition between different clones during development in the Drosophila wing disc and cancer, in which the tumor competes with the surrounding host tissue. The competition is affected by various biochemical and physical factors, including concentrations of nutrients and other chemicals, cell-cell communication, and geometrical constraints. In this thesis, we study the competition between different tissues regulated solely by the mechanical interactions between cells, with cancer as the biological example in mind. In particular, we focus on the role of the interface and the interactions between different cell populations including evolutionary aspects. For mechanically-regulated competition, it has been proposed that the outcome is solely determined by the homeostatic pressure, the pressure at which division and apoptosis balance. The tissue with the higher homeostatic pressure outcompetes the weaker one. Accordingly, tumorigenesis consists of subsequent rounds of takeover of the tissue by a cell population with a higher homeostatic pressure. However, experiments on growing tissue spheroids reveal that surface effects can play a dominant role in tissue growth. Cells divide preferentially at the surface and undergo apoptosis in the bulk. It turns out that similar interfacial effects play a role in the competition between cell populations and alter the evolution of a tissue. To explore the mechanics of tissue competition we employ a particle-based simulation model, in which a cell is represented by two particles which repel each other via an active growth force. Cells divide when the distance between the two particles reaches a certain threshold, while cell death occurs randomly at a constant rate. Cells interact with each other like soft sticky spheres and a dissipative particle dynamics thermostat accounts for energy dissipation and random fluctuations. First, we study the role of the adhesion between different tissues by looking at an extreme case: vanishing cross-adhesion strength. The resulting strong interfacial tension leads to segregation of the competing tissues. In a small region near the interface, the division rate of both tissues is enhanced. The enhanced division leads to a flux of cells from the interface towards the bulk, similar to growing tissue spheroids. To compensate for the influx of cells from the interface, the system pressure is always larger than each individual homeostatic pressure and both tissues undergo net apoptosis in the bulk. This results in stable coexistence between the two tissues in a variety of different structures, even for a difference in homeostatic pressure. Next, we study the evolution of a tissue under the influence of mutations which change the mechanical properties of a cell. For independent mutations, the tissue evolves towards populations with low internal adhesion and high growth-force strength, which both increase its homeostatic pressure. Motivated by the results from the previous chapter and biological evidence, we impose a coupling between the two parameters, such that a higher growth force comes at the cost of a higher adhesion strength. Interestingly, this can result in a diverging evolution in which the tissue evolves towards a very heterogeneous distribution of populations. The compartment is than occupied by cells with very different properties, coexisting in a highly dynamic state. Surprisingly, this state can be dominated by the cell population with the lowest homeostatic pressure. Competitions between two cell populations alone and a phenomenological model provide a qualitative explanation of these results. We further reveal that the rate at which mutations occur plays a minor role in the competition and only affects the evolutionary time scale. Third, we study competition on a substrate, in which we focus on the stability of the interface between the competing tissues. Cells interact with the substrate via friction, resulting in a finite stress-decay length. The interface between two identical tissues is unstable due to diffusion. Already small differences between the competing tissues suffice to arrive at a stable, almost flat interface which propagates at constant velocity. A reduced apoptosis rate results in an increased tissue viscosity. For larger viscosity of the tissue with the lower homeostatic pressure, a fingering instability emerges, reminiscent of Saffman-Taylor viscous fingering. Besides homeostatic pressure, the competition can also be driven by collective motility of one tissue directed towards the other. Small motility forces suffice to result in propagation with a stable interface. However, above a critical motility strength, protrusions of the motile tissue into the non-motile one form at a well-defined wavelength. The resulting almost sinusoidal interface pattern is remarkably stable over time, contrary to the highly dynamic fingering instability discussed before. In summary, the interplay between mechanics, evolutionary forces, and cross-interactions gives rise to interesting interfacial phenomena. This includes stable coexistence between two or many cell populations in a variety of structures and an unstable front during propagation on a substrate