Evolution Of Cellular Differentiation Research Articles

The Genetics of Fitness Reorganization during the Transition to Multicellularity: The Volvocine regA-like Family as a Model.

The evolutionary transition from single-celled to multicellular individuality requires organismal fitness to shift from the cell level to a cell group. This reorganization of fitness occurs by re-allocating the two components of fitness, survival and reproduction, between two specialized cell types in the multicellular group: soma and germ, respectively. How does the genetic basis for such fitness reorganization evolve? One possible mechanism is the co-option of life history genes present in the unicellular ancestors of a multicellular lineage. For instance, single-celled organisms must regulate their investment in survival and reproduction in response to environmental changes, particularly decreasing reproduction to ensure survival under stress. Such stress response life history genes can provide the genetic basis for the evolution of cellular differentiation in multicellular lineages. The regA-like gene family in the volvocine green algal lineage provides an excellent model system to study how this co-option can occur. We discuss the origin and evolution of the volvocine regA-like gene family, including regA-the gene that controls somatic cell development in the model organism Volvox carteri. We hypothesize that the co-option of life history trade-off genes is a general mechanism involved in the transition to multicellular individuality, making volvocine algae and the regA-like family a useful template for similar investigations in other lineages.

Characterization and Transformation of reg Cluster Genes in Volvox powersii Enable Investigation of Convergent Evolution of Cellular Differentiation in Volvox

The evolution of germ-soma cellular differentiation represents a key step in the evolution of multicellular individuality. Volvox carteri and its relatives, the volvocine green algae, provide a model system for studying the evolution of cellular differentiation. In V. carteri, the regA gene controls somatic cell differentiation and is found in a group of paralogs called the reg cluster, along with rlsA, rlsB, and rlsC. However, the developmental program of V. carteri is derived compared to other volvocine algae. Here we examine Volvox powersii which possesses an ancestral developmental program and independent evolution of the Volvox body plan. We sequenced the reg cluster from V. powersii wild-type and a mutant with fewer cells and altered germ-soma ratio. We found that the mutant strain's rlsB gene has a deletion predicted to cause a truncated protein product. We developed a genetic transformation procedure to insert wild-type rlsB into the mutant strain. Transformation did not result in phenotypic rescue, suggesting the rlsB mutation is insufficient for generating the mutant phenotype. The transformation techniques and sequences described here provide essential tools to study V. powersii, a species well suited for studying the evolution of cellular differentiation and convergent evolution of Volvox morphology.

Multilevel Thresholding for Image Segmentation Based on Cellular Metaheuristics

This article describes how multilevel thresholding image segmentation is a process used to partition an image into well separated regions. It has various applications such as object recognition, edge detection, and particle counting, etc. However, it is computationally expensive and time consuming. To alleviate these limitations, nature inspired metaheuristics are widely used to reduce the computational complexity of such problem. In this article, three cellular metaheuristics namely cellular genetic algorithm (CGA), cellular particle swarm optimization (CPSO) and cellular differential evolution (CDE) are adapted to solve the multilevel thresholding image segmentation problem. Experiments are conducted on different test images to assess the performance of the cellular algorithms in terms of efficiency, quality and stability based on the between-class variance and Kapur's entropy as objective functions. The experimental results have shown that the proposed cellular algorithms compete with and even outperform existing methods for multilevel thresholding image segmentation.

Modeling and Self-Learning Soft-Grasp Control for Free-Floating Space Manipulator During Target Capturing Using Variable Stiffness Method

During target capturing operation, the changes in the dynamics parameters of a free-floating space manipulator degrade the performance of the base attitude stabilization. This paper presents a new self-learning soft-grasp control algorithm based on the variable stiffness technology. First, the dynamic model of variable stiffness joint space manipulator system is established. Simultaneously, the detailed dynamic analysis of pre-impact and post-impact stages is carried out. Second, a new soft-grasp control strategy utilizing cellular differential evolution algorithm combined opposition-based learning with orthogonal crossover is employed to minimize the base angular momentum. Its principle is to solve the optimal stiffness value of the variable stiffness joint to realize desired buffering. Thereafter, we put forward an adaptive backstepping sliding mode control method to track the actual joint stiffness. Finally, the proposed method is applied to a two-degree of freedom planar free-floating space manipulator and the simulation results verify the effectiveness.

The Cellular Differential Evolution Based on Chaotic Local Search

To avoid immature convergence and tune the selection pressure in the differential evolution (DE) algorithm, a new differential evolution algorithm based on cellular automata and chaotic local search (CLS) or ccDE is proposed. To balance the exploration and exploitation tradeoff of differential evolution, the interaction among individuals is limited in cellular neighbors instead of controlling parameters in the canonical DE. To improve the optimizing performance of DE, the CLS helps by exploring a large region to avoid immature convergence in the early evolutionary stage and exploiting a small region to refine the final solutions in the later evolutionary stage. What is more, to improve the convergence characteristics and maintain the population diversity, the binomial crossover operator in the canonical DE may be instead by the orthogonal crossover operator without crossover rate. The performance of ccDE is widely evaluated on a set of 14 bound constrained numerical optimization problems compared with the canonical DE and several DE variants. The simulation results show that ccDE has better performances in terms of convergence rate and solution accuracy than other optimizers.

A fool to do your dirty work?

Much of life on Earth owes its spectacular success to a rather important evolutionary transition—from single-celled organisms to multicellularity—which has occurred independently in many lineages, enabling the differentiation of cells to perform the highly specialized functions that we see in living fungi, plants, and animals. However, whereas all clones of single-celled organisms have a relatively equal chance of dividing and propagating their genes, most multicellular organisms entrust the propagation of their genes to a few select germline cells amidst a sea of non-reproductive somatic cells. At this point, the fitness of individual cells and the fitness of the entire organism become decoupled. Anytime complexity increases through evolution, one must ask how selection at the lower level of organization (i.e., the individual cell) doesn’t disrupt the integration at higher levels of organization (i.e., a multicellular organism) by favoring selfishness. There are some general evolutionary hypotheses that have been offered to explain why and how multicellularity and the division of labor between somatic and germline cells evolved, as well as the conditions under which these developments would be expected. Clearly, organisms with differentiated cells can experience many fitness advantages, such as the ability to grow larger and exploit novel resources. And along with these advantages come costs, such as the energy and materials that must be allocated towards growth and maintenance, rather than reproduction. However, there are more subtle, but no less important, constraints on an organism’s ability to acquire resources, grow, metabolize, and reproduce that might also influence the evolution of cellular differentiation. One idea that has been suggested, but not yet fully developed, is that the evolution of multicellular organisms with separate somatic and reproductive cells might be influenced by constraints on the preservation of genetic information. Most of the ‘‘work’’ performed by a cell—that is, the production and use of energy—takes place in the mitochondria and chloroplasts (in eukaryotes) or across membranes (in prokaryotes). As a byproduct of this work, reactive oxygen species such as hydrogen peroxide are generated. In turn, these byproducts can create oxidative stress in a cell, one result of which can be mutations to that cell’s DNA. Here, the idea of the so-called ‘‘dirty work’’ hypothesis is that the advent of cellular differentiation allows the organism to separate the energetically costly and potentially mutagenic processes into their somatic cells, while protecting their genomes within germline cells that need perform little work. While this and other theories about the evolution of multicellularity and cellular differentiation are intriguing, empirical evidence is less forthcoming. Some studies in yeasts and cellular slime molds, among others, have provided a few clues. But the time scales necessary to observe and manipulate the processes driving the evolution of cellular differentiation are typically prohibitive. Unless, that is, one could reproduce the evolutionary process in a realistic, but tractable way. That is just what Goldsby et al. did in this issue of PLOS Biology. To explore the role of the dirty work hypothesis in the differentiation of somatic and germline cells, Goldsby et al. performed a series of evolutionary experiments on populations of digital organisms. What’s a digital organism? In this case, digital organisms have a genome that comprises a fully functional computer program. These genetic programs can process numbers that flow into and out of their habitat to perform computational logic functions (e.g., AND, NOT, XOR) which order to gain resources. These genomes mutate at some defined probability, and the organisms differentially survive and reproduce as a function of their ability to acquire resources (i.e., when enough functions are executed). With this basic framework in hand, any number of evolutionary questions can be investigated simply by defining the parameters in which the digital organisms interact. In this case, the authors explored whether and how these simple multicellular individuals make the transition to having non-reproducing somatic cells and reproductive germline cells. They established a series of evolutionary experiments

Cellular Differential Evolution for Optimization

This paper is to illustrate the Cellular Differential Evolution with the cellular structure originated from Cellular automata. Cellular neighbor local search has been designed; base vector or global best in mutation operator is substituted by neighborhood-best, which overcomes the weakness of single selection relating to global best, and balances the contradiction of local and global search, and improves the diversity of population. In addition, cellular structure ensures information exchange, inheritance and diffusion. Finally, a specific algorithm has been implemented: compared with similar variants of DE, the simulation results on 9 benchmark functions demonstrate that cellular differential evolutions are provided with obvious advantages in the solution-quality, stability and speed. <b></b>

Artificial life VII: looking backward, looking forward (editor's introduction to the special issue).

This issue contains highlights from the Artificial Life VII conference, held at Reed College in August 2000 [1]. A few authors who presented papers at the conference were invited to revise and expand their papers for publication in this special issue. Freed from the artificial space limits of conference proceedings, their papers provide additional details and situate their work in a broader context. One additional paper on open problems in artificial life grew out of discussion at the end of the conference. All the papers show the breadth and depth of the work presented at the conference. All address very deep questions about living systems, and most build bridges to concrete biological data and generate experimentally testable predictions. The origin of multicellularity is one of the major transitions in the evolution of life, and differentiated multicellular life has apparently evolved independently many times. The developmental processes which create these multicellular organizations share three notable features. First, development starts with a homogeneous cell (or set of cells) that are multipotent, that is, have the ability to differentiate into many different types of specialized cells. Second, the developmental process has an intrinsic arrow of time because the differentiated cells that result are not multipotent. Third, the developmental process is stable in the face of perturbations that destroy clusters of cells. In “Complex Organization in Multicellularity as a Necessity in Evolution,” Chikara Furusawa and Kunihiko Kaneko provide a minimal model of cellular dynamics that explains these universal features of multicellular differentiation. Furusawa and Kaneko numerically simulated a one-dimensional chain of cells governed by randomly generated biochemical reaction networks. They found that, while competition for resources prevents resources from flowing through chains of undifferentiated cells, cellular differentiation allows cells to share resources throughout the cellular chain. Thus, although individual cells taken from undifferentiated chains can grow more quickly than cells from differentiated chains, chains of differentiated cells can grow more quickly than chains of undifferentiated cells. These results lead to a number of experimentally testable predictions about multicellularity in biological organisms and provide a mechanism by which multicellularity inevitably arises in the evolution of cellular differentiation. The genetic code is a nearly universal feature of life on earth, although it does have a few exceptions such as mitochondrial DNA. It is difficult to understand at first how the genetic code could evolve at all; a mutation at a given codon might be a selective advantage, of course, but any change in the code would entail wholesale changes at a vast proportion of codons. How could this but be disastrous? Hiroaki Takagi, Kunihiko Kaneko, and Tetsuya Yomo propose a solution to this problem in