life inspired

"What is the relationship between the complexity and the fitness of evolved organisms, whether natural or artificial? It has been asserted, primarily based on empirical data, that the complexity of plants and animals increases as their fitness within a particular environment increases via evolution by natural selection. We simulate the evolution of the brains of simple organisms living in a planar maze that they have to traverse as rapidly as possible. Their connectome evolves over 10,000s of generations. We evaluate their circuit complexity, using four information-theoretical measures, including one that emphasizes the extent to which any network is an irreducible entity. We find that their minimal complexity increases with their fitness." Full article @ PLOS Computational Biology

Labels: adaptation, complexity, connectome, information

"Santa Fe Institute's Introduction to Complexity course is now enrolling!
This free online course is open to anyone, and has no prerequisites. Watch the Intro Video to learn what this course is about and how to take it"

Labels: complex systems, complexity, course

"During development, cells undergo a unidirectional course of differentiation that progressively decreases the number of cell types they can potentially become. Stem cells, however, keep their potential to both proliferate and differentiate. A very important issue then is to understand the characteristics that distinguish stem cells from other cell types and allow them to conduct stable proliferation and differentiation. Here, we review relevant dynamical-systems approaches to describe the state transition between stem and differentiated cells, with an emphasis on fluctuating and oscillatory gene expression levels, as these represent the specific properties of stem cells. Relevance between recent experimental results and dynamical-systems descriptions of stem cell differentiation is also discussed." Full perspective @ Science

Labels: attractor behavior, complexity, development, dynamical systems, epigenetic landscape, modeling

"we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focusing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance." Full article @ Complexity

Labels: complexity, information, self-organization

"Scientists have engineered bacteria that can communicate with each other in a synchronized manner, lighting up in waves of fluorescent green [...] The engineered cells also offer a new model system for studying emergent systems, or how complex properties -- such as synchronized oscillation -- arise from simpler starting materials -- a two genes network." Full article @ The Scientist.

Labels: bacteria, complexity, dynamical systems

These maps are always interesting even if no two-dimensional map can do justice to such an interdisciplinary field---at leas it makes a coolScience t-shirt...

Labels: complex systems, complexity

"The known world of RNA is expanding faster than that of any other cellular building block. The latest additions are types of long and short non-coding RNAs formed by bidirectional transcription and unusual processing. The relationship between DNA, RNA and protein is no longer as simple as we once thought — that specific genomic sequences are transcribed into messenger RNAs, which are then translated into proteins. In recent years, a flurry of studies has reported the existence of a large and growing family of non-protein-coding RNAs (ncRNAs), describing the different mechanisms of their transcription and their role in regulating gene expression." Full article @ Nature

Labels: complexity, gene regulation, RNA

"How studying biological interactions and evolution yields techniques for predicting the outcome of complex interactions". By John Holland @ The Scientist

Labels: biology, complexity, genetic algorithms