Metastable Evolutionary Dynamics: Crossing Fitness Barriers or Escaping via Neutral Paths?

We analytically study the dynamics of evolving populations that exhibit metastability on the level of phenotype or fitness. In constant selective environments, such metastable behavior is caused by two qualitatively different mechanisms. One the one hand, populations may become pinned at a local fitness optimum, being separated from higher-fitness genotypes by a {\em fitness barrier} of low-fitness genotypes. On the other hand, the population may only be metastable on the level of phenotype or fitness while, at the same time, diffusing over {\em neutral networks} of selectively neutral genotypes. Metastability occurs in this case because the population is separated from higher-fitness genotypes by an {\em entropy barrier}: The population must explore large portions of these neutral networks before it discovers a rare connection to fitter phenotypes. We derive analytical expressions for the barrier crossing times in both the fitness barrier and entropy barrier regime. In contrast with ``landscape'' evolutionary models, we show that the waiting times to reach higher fitness depend strongly on the width of a fitness barrier and much less on its height. The analysis further shows that crossing entropy barriers is faster by orders of magnitude than fitness barrier crossing. Thus, when populations are trapped in a metastable phenotypic state, they are most likely to escape by crossing an entropy barrier, along a neutral path in genotype space. If no such escape route along a neutral path exists, a population is most likely to cross a fitness barrier where the barrier is {\em narrowest}, rather than where the barrier is shallowest.Comment: 32 pages, 7 figures, 1 table; http://www.santafe.edu/projects/evca/med.ps.g

The Evolutionary Unfolding of Complexity

We analyze the population dynamics of a broad class of fitness functions that exhibit epochal evolution---a dynamical behavior, commonly observed in both natural and artificial evolutionary processes, in which long periods of stasis in an evolving population are punctuated by sudden bursts of change. Our approach---statistical dynamics---combines methods from both statistical mechanics and dynamical systems theory in a way that offers an alternative to current ``landscape'' models of evolutionary optimization. We describe the population dynamics on the macroscopic level of fitness classes or phenotype subbasins, while averaging out the genotypic variation that is consistent with a macroscopic state. Metastability in epochal evolution occurs solely at the macroscopic level of the fitness distribution. While a balance between selection and mutation maintains a quasistationary distribution of fitness, individuals diffuse randomly through selectively neutral subbasins in genotype space. Sudden innovations occur when, through this diffusion, a genotypic portal is discovered that connects to a new subbasin of higher fitness genotypes. In this way, we identify innovations with the unfolding and stabilization of a new dimension in the macroscopic state space. The architectural view of subbasins and portals in genotype space clarifies how frozen accidents and the resulting phenotypic constraints guide the evolution to higher complexity.Comment: 28 pages, 5 figure

The evolution of domain-content in bacterial genomes

BACKGROUND: Across all sequenced bacterial genomes, the number of domains nc in different functional categories c scales as a power-law in the total number of domains n, i.e. nc proportional n(alpha)c, with exponents alpha(c) that vary across functional categories. Here we investigate the implications of these scaling laws for the evolution of domain-content in bacterial genomes and derive the simplest evolutionary model consistent with these scaling laws. RESULTS: We show that, using only an assumption of time invariance, the scaling laws uniquely determine the relative rates of domain additions and deletions across all functional categories and evolutionary lineages. In particular, the model predicts that the rate of additions and deletions of domains of category c is proportional to the number of domains nc currently in the genome and we discuss the implications of this observation for the role of horizontal transfer in genome evolution. Second, in addition to being proportional to nc, the rate of additions and deletions of domains of category c is proportional to a category-dependent constant rho(c), which is the same for all evolutionary lineages. This 'evolutionary potential' rho(c) represents the relative probability for additions/deletions of domains of category c to be fixed in the population by selection and is predicted to equal the scaling exponent alpha(c). By comparing the domain content of 93 pairs of closely-related genomes from all over the phylogenetic tree of bacteria, we demonstrate that the model's predictions are supported by available genome-sequence data. CONCLUSION: Our results establish a direct quantitative connection between the scaling of domain numbers with genome size, and the rate of addition and deletions of domains during short evolutionary time intervals.of domain numbers with genome size, and the rate of addition and deletions of domains during short evolutionary time intervals

The Genomic Context and Corecruitment of SP1 Affect ERRα Coactivation by PGC-1α in Muscle Cells

The peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α) coordinates the transcriptional network response to promote an improved endurance capacity in skeletal muscle, eg, by coactivating the estrogen-related receptor-α (ERRα) in the regulation of oxidative substrate metabolism. Despite a close functional relationship, the interaction between these 2 proteins has not been studied on a genomic level. We now mapped the genome-wide binding of ERRα to DNA in a skeletal muscle cell line with elevated PGC-1α and linked the DNA recruitment to global PGC-1α target gene regulation. We found that, surprisingly, ERRα coactivation by PGC-1α is only observed in the minority of all PGC-1α recruitment sites. Nevertheless, a majority of PGC-1α target gene expression is dependent on ERRα. Intriguingly, the interaction between these 2 proteins is controlled by the genomic context of response elements, in particular the relative GC and CpG content, monomeric and dimeric repeat-binding site configuration for ERRα, and adjacent recruitment of the transcription factor specificity protein 1. These findings thus not only reveal a novel insight into the regulatory network underlying muscle cell plasticity but also strongly link the genomic context of DNA-response elements to control transcription factor-coregulator interactions

Improved analysis of (e)CLIP data with RCRUNCH yields a compendium of RNA-binding protein binding sites and motifs

We present RCRUNCH, an end-to-end solution to CLIP data analysis for identification of binding sites and sequence specificity of RNA-binding proteins. RCRUNCH can analyze not only reads that map uniquely to the genome but also those that map to multiple genome locations or across splice boundaries and can consider various types of background in the estimation of read enrichment. By applying RCRUNCH to the eCLIP data from the ENCODE project, we have constructed a comprehensive and homogeneous resource of in-vivo-bound RBP sequence motifs. RCRUNCH automates the reproducible analysis of CLIP data, enabling studies of post-transcriptional control of gene expression

Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

Accurate and large-scale prediction of protein–protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners