Scalable Co-Optimization of Morphology and Control in Embodied Machines

Evolution sculpts both the body plans and nervous systems of agents together over time. In contrast, in AI and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge. In psychology, the theory of embodied cognition posits that behavior arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioral performance. Here, we further examine this hypothesis and demonstrate a technique for "morphological innovation protection", which temporarily reduces selection pressure on recently morphologically-changed individuals, thus enabling evolution some time to "readapt" to the new morphology with subsequent control policy mutations. We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioral training -- while simultaneously providing a testbed to investigate the theory of embodied cognition

The Genomic HyperBrowser: inferential genomics at the sequence level

The immense increase in the generation of genomic scale data poses an unmet analytical challenge, due to a lack of established methodology with the required flexibility and power. We propose a first principled approach to statistical analysis of sequence-level genomic information. We provide a growing collection of generic biological investigations that query pairwise relations between tracks, represented as mathematical objects, along the genome. The Genomic HyperBrowser implements the approach and is available at http://hyperbrowser.uio.no

Mathematical modeling of the metastatic process

Mathematical modeling in cancer has been growing in popularity and impact since its inception in 1932. The first theoretical mathematical modeling in cancer research was focused on understanding tumor growth laws and has grown to include the competition between healthy and normal tissue, carcinogenesis, therapy and metastasis. It is the latter topic, metastasis, on which we will focus this short review, specifically discussing various computational and mathematical models of different portions of the metastatic process, including: the emergence of the metastatic phenotype, the timing and size distribution of metastases, the factors that influence the dormancy of micrometastases and patterns of spread from a given primary tumor.Comment: 24 pages, 6 figures, Revie

Divergent mutational processes distinguish hypoxic and normoxic tumours.

Many primary tumours have low levels of molecular oxygen (hypoxia), and hypoxic tumours respond poorly to therapy. Pan-cancer molecular hallmarks of tumour hypoxia remain poorly understood, with limited comprehension of its associations with specific mutational processes, non-coding driver genes and evolutionary features. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we quantify hypoxia in 1188 tumours spanning 27 cancer types. Elevated hypoxia associates with increased mutational load across cancer types, irrespective of underlying mutational class. The proportion of mutations attributed to several mutational signatures of unknown aetiology directly associates with the level of hypoxia, suggesting underlying mutational processes for these signatures. At the gene level, driver mutations in TP53, MYC and PTEN are enriched in hypoxic tumours, and mutations in PTEN interact with hypoxia to direct tumour evolutionary trajectories. Overall, hypoxia plays a critical role in shaping the genomic and evolutionary landscapes of cancer

Localization of adaptive variants in human genomes using averaged one-dependence estimation.

Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios

Simulation of genome-wide evolution under heterogeneous substitution models and complex multispecies coalescent histories

Genomic evolution can be highly heterogeneous. Here, we introduce a new framework to simulate genome-wide sequence evolution under a variety of substitution models that may change along the genome and the phylogeny, following complex multispecies coalescent histories that can include recombination, demographics, longitudinal sampling, population subdivision/species history, and migration. A key aspect of our simulation strategy is that the heterogeneity of the whole evolutionary process can be parameterized according to statistical prior distributions specified by the user. We used this framework to carry out a study of the impact of variable codon frequencies across genomic regions on the estimation of the genome-wide nonsynonymous/synonymous ratio. We found that both variable codon frequencies across genes and rate variation among sites and regions can lead to severe underestimation of the global dN/dS values. The program SGWE—Simulation of Genome-Wide Evolution—is freely available from http://code.google.com/p/sgwe-project/, including extensive documentation and detailed examples.Ministerio de Ciencia e Innovación | Ref. JCI-2011-1045

Extensive local adaptation within the chemosensory system following Drosophila melanogaster's global expansion.

How organisms adapt to new environments is of fundamental biological interest, but poorly understood at the genetic level. Chemosensory systems provide attractive models to address this problem, because they lie between external environmental signals and internal physiological responses. To investigate how selection has shaped the well-characterized chemosensory system of Drosophila melanogaster, we have analysed genome-wide data from five diverse populations. By couching population genomic analyses of chemosensory protein families within parallel analyses of other large families, we demonstrate that chemosensory proteins are not outliers for adaptive divergence between species. However, chemosensory families often display the strongest genome-wide signals of recent selection within D. melanogaster. We show that recent adaptation has operated almost exclusively on standing variation, and that patterns of adaptive mutations predict diverse effects on protein function. Finally, we provide evidence that chemosensory proteins have experienced relaxed constraint, and argue that this has been important for their rapid adaptation over short timescales

Local adaptation drives the diversification of effectors in the fungal wheat pathogen Parastagonospora nodorum in the United States

Filamentous fungi rapidly evolve in response to environmental selection pressures in part due to their genomic plasticity. Parastagonospora nodorum, a fungal pathogen of wheat and causal agent of septoria nodorum blotch, responds to selection pressure exerted by its host, influencing the gain, loss, or functional diversification of virulence determinants, known as effector genes. Whole genome resequencing of 197 P. nodorum isolates collected from spring, durum, and winter wheat production regions of the United States enabled the examination of effector diversity and genomic regions under selection specific to geographically discrete populations. 1,026,859 SNPs/InDels were used to identify novel loci, as well as SnToxA and SnTox3 as factors in disease. Genes displaying presence/absence variation, predicted effector genes, and genes localized on an accessory chromosome had significantly higher pN/pS ratios, indicating a higher rate of sequence evolution. Population structure analyses indicated two P. nodorum populations corresponding to the Upper Midwest (Population 1) and Southern/Eastern United States (Population 2). Prevalence of SnToxA varied greatly between the two populations which correlated with presence of the host sensitivity gene Tsn1 in the most prevalent cultivars in the corresponding regions. Additionally, 12 and 5 candidate effector genes were observed to be under diversifying selection among isolates from Population 1 and 2, respectively, but under purifying selection or neutrally evolving in the opposite population. Selective sweep analysis revealed 10 and 19 regions that had recently undergone positive selection in Population 1 and 2, respectively, involving 92 genes in total. When comparing genes with and without presence/absence variation, those genes exhibiting this variation were significantly closer to transposable elements. Taken together, these results indicate that P. nodorum is rapidly adapting to distinct selection pressures unique to spring and winter wheat production regions by rapid adaptive evolution and various routes of genomic diversification, potentially facilitated through transposable element activity