16,375 research outputs found
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
Three-dimensional morphology and gene expression in the Drosophila blastoderm at cellular resolution II: dynamics.
BackgroundTo accurately describe gene expression and computationally model animal transcriptional networks, it is essential to determine the changing locations of cells in developing embryos.ResultsUsing automated image analysis methods, we provide the first quantitative description of temporal changes in morphology and gene expression at cellular resolution in whole embryos, using the Drosophila blastoderm as a model. Analyses based on both fixed and live embryos reveal complex, previously undetected three-dimensional changes in nuclear density patterns caused by nuclear movements prior to gastrulation. Gene expression patterns move, in part, with these changes in morphology, but additional spatial shifts in expression patterns are also seen, supporting a previously proposed model of pattern dynamics based on the induction and inhibition of gene expression. We show that mutations that disrupt either the anterior/posterior (a/p) or the dorsal/ventral (d/v) transcriptional cascades alter morphology and gene expression along both the a/p and d/v axes in a way suggesting that these two patterning systems interact via both transcriptional and morphological mechanisms.ConclusionOur work establishes a new strategy for measuring temporal changes in the locations of cells and gene expression patterns that uses fixed cell material and computational modeling. It also provides a coordinate framework for the blastoderm embryo that will allow increasingly accurate spatio-temporal modeling of both the transcriptional control network and morphogenesis
Systems approaches and algorithms for discovery of combinatorial therapies
Effective therapy of complex diseases requires control of highly non-linear
complex networks that remain incompletely characterized. In particular, drug
intervention can be seen as control of signaling in cellular networks.
Identification of control parameters presents an extreme challenge due to the
combinatorial explosion of control possibilities in combination therapy and to
the incomplete knowledge of the systems biology of cells. In this review paper
we describe the main current and proposed approaches to the design of
combinatorial therapies, including the empirical methods used now by clinicians
and alternative approaches suggested recently by several authors. New
approaches for designing combinations arising from systems biology are
described. We discuss in special detail the design of algorithms that identify
optimal control parameters in cellular networks based on a quantitative
characterization of control landscapes, maximizing utilization of incomplete
knowledge of the state and structure of intracellular networks. The use of new
technology for high-throughput measurements is key to these new approaches to
combination therapy and essential for the characterization of control
landscapes and implementation of the algorithms. Combinatorial optimization in
medical therapy is also compared with the combinatorial optimization of
engineering and materials science and similarities and differences are
delineated.Comment: 25 page
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
Conservation and co-option in developmental programmes: the importance of homology relationships
One of the surprising insights gained from research in evolutionary developmental biology (evo-devo) is that increasing diversity in body plans and morphology in organisms across animal phyla are not reflected in similarly dramatic changes at the level of gene composition of their genomes. For instance, simplicity at the tissue level of organization often contrasts with a high degree of genetic complexity. Also intriguing is the observation that the coding regions of several genes of invertebrates show high sequence similarity to those in humans. This lack of change (conservation) indicates that evolutionary novelties may arise more frequently through combinatorial processes, such as changes in gene regulation and the recruitment of novel genes into existing regulatory gene networks (co-option), and less often through adaptive evolutionary processes in the coding portions of a gene. As a consequence, it is of great interest to examine whether the widespread conservation of the genetic machinery implies the same developmental function in a last common ancestor, or whether homologous genes acquired new developmental roles in structures of independent phylogenetic origin. To distinguish between these two possibilities one must refer to current concepts of phylogeny reconstruction and carefully investigate homology relationships. Particularly problematic in terms of homology decisions is the use of gene expression patterns of a given structure. In the future, research on more organisms other than the typical model systems will be required since these can provide insights that are not easily obtained from comparisons among only a few distantly related model species
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
In silico transitions to multicellularity
The emergence of multicellularity and developmental programs are among the
major problems of evolutionary biology. Traditionally, research in this area
has been based on the combination of data analysis and experimental work on one
hand and theoretical approximations on the other. A third possibility is
provided by computer simulation models, which allow to both simulate reality
and explore alternative possibilities. These in silico models offer a powerful
window to the possible and the actual by means of modeling how virtual cells
and groups of cells can evolve complex interactions beyond a set of isolated
entities. Here we present several examples of such models, each one
illustrating the potential for artificial modeling of the transition to
multicellularity.Comment: 21 pages, 10 figures. Book chapter of Evolutionary transitions to
multicellular life (Springer
Artificial Gene Regulatory Networks and Spatial Computation: A Case Study
International audienceThis paper explores temporal and spatial dynamics of a population of Genetic Regulatory Networks (GRN). In order to so, a GRN model is spatially distributed to solve a multi-cellular Artificial Embryogeny problem, and Evolutionary Computation is used to optimize the developmental sequences. An in-depth analysis is provided and show that such a population of GRN display strong spatial synchronization as well as various kind of behavioral patterns, ranging from smooth diffusion to abrupt transition patterns
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