Plants are integral to our lives, providing food, shelter and the air we breathe. The shapes that plants take are central to their functionality, tailoring each for its particular place in the ecosystem. Given the relatively large and static forms of plants, it may not be immediately apparent that chemical kinetics is involved in, for example, distinguishing the form of a spruce tree from that of a fern. But plants share the common feature that their shapes are continuously being generated, and this largely occurs in localized regions of cell division and expansion, such as the shoot and root apical meristems at either end of a plant’s main axis; these regions remain essentially embryonic throughout the life cycle. The final regular structure of a plant, such as the arrangement of leaves along the main stalk, may seem to follow an overall spatial template; but in reality the spatial patterning is occurring at relatively short range, and it is the temporal unfolding of this small scale patterning which generates the plant’s form. A key part of understanding plant morphogenesis, or shape generation, therefore, is to understand how the molecular determinants of cell type, cell division and cell expansion are localized to and patterned within the actively growing regions. At this scale, transport processes such as diffusion and convection are obvious components of localization, for moving molecules to the correct places; but the reaction kinetics for molecular creation, destruction and interaction are also critical to maintaining the molecular identity and the size regulation of the active regions., Book chapter, Published. Submission date: 04. October, 2011; Review date: 13. November, 2011; Published online: 29. February, 2012.
Gene recruitment or co-option is defined as the placement of a new gene under a foreign regulatory system. Such re-arrangement of pre-existing regulatory networks can lead to an increase in genomic complexity. This reorganization is recognized as a major driving force in evolution. We simulated the evolution of gene networks by means of the Genetic Algorithms (GA) technique. We used standard GA methods of point mutation and multi-point crossover, as well as our own operators for introducing or withdrawing new genes on the network. The starting point for our computer evolutionary experiments was a 4-gene dynamic model representing the real genetic network controlling segmentation in the fruit fly Drosophila. Model output was fit to experimentally observed gene expression patterns in the early fly embryo. We compared this to output for networks with more and less genes, and with variation in maternal regulatory input. We found that the mutation operator, together with the gene introduction procedure, was sufficient for recruiting new genes into pre-existing networks. Reinforcement of the evolutionary search by crossover operators facilitates this recruitment, but is not necessary. Gene recruitment causes outgrowth of an evolving network, resulting in redundancy, in the sense that the number of genes goes up, as well as the regulatory interactions on the original genes. The recruited genes can have uniform or patterned expressions, many of which recapitulate gene patterns seen in flies, including genes which are not explicitly put in our model. Recruitment of new genes can affect the evolvability of networks (in general, their ability to produce the variation to facilitate adaptive evolution). We see this in particular with a 2-gene subnetwork. To study robustness, we have subjected the networks to experimental levels of variability in maternal regulatory patterns. The majority of networks are not robust to these perturbations. However, a significant subset of the networks do display very high robustness. Within these networks, we find a variety of outcomes, with independent control of different gene expression boundaries. Increase in the number and connectivity of genes (redundancy) does not appear to correlate with robustness. Indeed, removal of recruited genes tends to give a worse fit to data than the original network; new genes are not freely disposable once they acquire functions in the network., Book chapter, Published.
The field of Evolutionary Computation (EC) has been inspired by ideas from the classical theory of biological evolution, with, in particular, the components of a population from which reproductive parents are chosen, a reproductive protocol, a method for altering the genetic information of offspring, and a means for testing the fitness of offspring in order to include them in the population. In turn, impressive progress in EC - understanding the reasons for efficiencies in evolutionary searches - has begun to influence scientific work in the field of molecular evolution and in the modeling of biological evolution (Stemmer, 1994a,b; van Nimwegen et al. 1997; 1999; Crutchfield & van Nimwegen, 2001). In this chapter, we will discuss how developments in EC, particularly in the area of crossover operators for Genetic Algorithms (GA), provide new understanding of evolutionary search efficiencies, and the impacts this can have for biological molecular evolution, including directed evolution in the test tube. GA approaches have five particular elements: encoding (the ‘chromosome’); a population; a method for selecting parents and making a child chromosome from the parents' chromosomes; a method for altering the child’s chromosomes (mutation and crossover/recombination); criteria for fitness; and rules, based on fitness, by which offspring are included into the population (and parents retained). We will discuss our work and others’ on each of these aspects, but our focus is on the substantial efficiencies that can be found in the alteration of the child chromosome step. For this, we take inspiration from real biological reproduction mechanisms., Book chapter, Published.
Thee aim of this chapter is to provide the reader with a general overview of the different phenomena and factors involved with durability of repair and suggest the major aspects which the repair design should focus on. However, it is important to keep in mind that any repair problem needs to be addressed as a unique problem ﬁrst, and the designer, by developing awareness of the several challenges involved, should be able to make mindful choices, adequate to the specific application that is being considered., Book chapter, Published.
This chapter surveys recent developments in simulating the evolution of GRNs in developmental biology. Over the past two decades, computational biologists have developed a number of approaches to study how developmental GRNs evolve. This has led to a number of breakthroughs in understanding the mechanisms of how species maintain their body plans, and how they evolve or speciate in response to environmental perturbations. EA uses the general evolutionary processes of repeated mutation, reproduction and selection in optimization problems. The progress in computational biology described here has deepened and refined understanding of the biological principles underlying these processes. Our aim is for this chapter to provide some inspiration to computer scientists in EA to incorporate new biologically inspired techniques. We feel this offers a large potential for improving EA efficiency. In turn, computational biology could greatly benefit from EA research, for instance in multi-objective optimization, coding of multiscale problems, and efficiencies in solution techniques. Following a brief survey of the major trends in the computational biology approaches, we discuss the refinements these have made to understanding evolutionary mechanisms. In particular, we discuss the factors affecting GRN evolvability and robustness; the effect different genetic alteration mechanisms (e.g. types of mutation) have on evolutionary speed and robustness; the role of network growth; modelling co-evolution; modelling multi-factor control of gene expression; and applying these techniques to the evolution of GRNs controlling spatially-dependent gene expression (underlying embryonic tissue differentiation). We finish with a brief summary of how these might be incorporated into and improve EA searches., Book chapter, Published.