The Localization Hypothesis and Machines

In a recent article in 'Artificial Life', Chu and Ho suggested that Rosen's central result about the simulability of living systems might be flawed. This argument was later declared ''null and void'' by Louie. In this article the validity of Louie's objections are examined

Evolutionary pressures on the yeast transcriptome

Codon usage bias (CUB) is the well known phenomenon that the frequency of synonymous codons is unequal. This is presumably the result of adaptive pressures favouring some codons over others. The underlying reason for this pressure is unknown, although a large number of possible driver mechanisms have been proposed; one of them is the decoding time. The standard model to calculate decoding time is the Gromadski- Rodnina model. Yet, recently, there have been a number of studies arguing to the effect that this conventional speed-model is not relevant to understand the dynamics of translation. However, results remain inconclusive so far. This contribution takes a novel approach to address this issue based on comparing mRNA with random synonymous variants to estimate the evolutionary pressures that have acted on the transcriptome. It emerges that over 70%of ORFs have been subject to a strong selection pressure for translation speed and that there is also a strong selection pressure for the avoidance of traffic jams. Finally, it is also shown that both homogeneous and very heterogeneous transcripts are over-represented. These results corroborate the validity of the Gromadski-Rodnina model

A Category Theoretical Argument Against the Possibility of Artificial Life

One of Robert Rosen's main contributions to the scientific community is summarized in his book 'Life itself'. There Rosen presents a theoretical framework to define living systems; given this definition, he goes on to show that living systems are not realisable in computational universes. Despite being well known and often cited, Rosen's central proof has so far not been evaluated by the scientific community. In this article we review the essence of Rosen's ideas leading up to his rejection of the possibility of real artificial life in silico. We also evaluate his arguments and point out that some of Rosen's central notions are ill- defined. The conclusion of this article is that Rosen's central proof is wrong

Walking, hopping and jumping: a model of transcription factor dynamics on DNA

We present a model of how transcription factors scan DNA to find their specific binding sites. Following the classical work of Winter et al. (1981), our model assumes two modes of transcription factor dynamics. Adjacent moves, where the proteins make a single step movement to one side, or short walks where the transcription factors slide along the DNA several binding sites at a time. The purpose of this article is twofold. Firstly, we discuss how such a system can be efficiently modeled computationally. Secondly, we analyse how the mean first binding times of transcription factors to their specific time depends on key parameters of the system

Evolving Biological Systems: Evolutionary Pressure to Inefficiency

The evolution of quantitative details (i.e. “parameter values”) of biological systems is highly under-researched. We use evolutionary algorithms to co-evolve parameters for a generic but biologically plausible topological differential equation model of nutrient uptake. In our model, evolving cells compete for a finite pool of nutrient resources. From our investigations it emerges that the choice of values is very important for the properties of the biological system. Our analysis also shows that clonal populations that are not subject to competition from other species best grow at a very slow rate. However, if there is co-evolutionary pressure, that is, if a population of clones has to compete with other cells, then the fast growth is essential, so as not to leave resources to the competitor. We find that this strategy, while favoured evolutionarily, is inef- ficient from an energetic point of view, that is less growth is achieved per unit of input nutrient. We conclude, that competition can lead to an evolutionary pressure towards inefficiency

Evolving strategies for single-celled organisms in multi-nutrient environments

When micro-organisms are in environments with multiple nutrients, they often preferentially utilise one first. A second is only utilised once the first is exhausted. Such a two-phase growth pattern is known as diauxic growth. Experimentally, this manifests itself through two distinct exponential growth phases separated by a lag phase of arrested growth. The dura- tion of the lag phase can be quite substantial. From an evolu- tionary point of view the existence of a lag phase is somewhat puzzling because it implies a substantial loss of growth op- portunity. Mutants with shorter lag phases would be prone to outcompete those with longer phases. Yet in nature, diauxic growth with lag phases appears to be a robust phenomenon. We introduce a model of the evolution of diauxic growth that captures the basic interactions regulating it in bacteria. We observe its evolution without a lag phase. We conclude that the lag phase is an adaptation that is only beneficial when fit- ness is averaged over a large number of environments

Dynamical Hierarchies

<Guest Editor's Introduction&gt

Random Feedback Alignment Algorithms to train Neural Networks: Why do they Align?

Feedback alignment algorithms are an alternative to backpropagation to train neural networks, whereby some of the partial derivatives that are required to compute the gradient are replaced by random terms. This essentially transforms the update rule into a random walk in weight space. Surprisingly, learning still works with those algorithms, including training of deep neural networks. This is generally attributed to an alignment of the update of the random walker with the true gradient - the eponymous gradient alignment -- which drives an approximate gradient descend. The mechanism that leads to this alignment remains unclear, however. In this paper, we use mathematical reasoning and simulations to investigate gradient alignment. We observe that the feedback alignment update rule has fixed points, which correspond to extrema of the loss function. We show that gradient alignment is a stability criterion for those fixed points. It is only a necessary criterion for algorithm performance. Experimentally, we demonstrate that high levels of gradient alignment can lead to poor algorithm performance and that the alignment is not always driving the gradient descend