1. Ayşe Erzan, (İTÜ, İstanbul, Turkey): “An ensemble approach to evolution and the emergence of complexity”
2. Murat Tuğrul, (IST-Austria, Vienna): “Population genetics of transcription binding sites in regulatory sequences”
3. Mehmet Somel,  (U. of California., Berkeley, USA): “Relaxation of constraints in humans and in cows”
4. Erol Akçay, (Princeton Univ., USA): “Evolution of cooperation under imperfect information”
5. Hande Acar, (IST-Austria, Vienna): “Experimental Characterisation of Selective Barriers to Horizontal Gene Transfer”
6. Zachary Adam, (Montana State University, USA): “Two paths, one destination: Integrating fossil data into genetic sequence models to investigate the origin of eukaryotes”
7. Melis Akman, (U. of California, Davis, USA): “Genetic basis of local adaptations in South African Protea repens“
8. B. Duygu Özpolat, (U. of Maryland, USA): “piwi expression during regeneration in the annelid Pristina leidyi“
9. Cesim Erten, (Kadir Has U., İstanbul, Turkey): “Alignment of Biological Networks From Different Species: Problem Formulations and Algorithmic Aspects“
10. Hannes Svardal, (Gregor Mendel Institute, Vienna, Austria): “Population genomics of widely distributed African primates“
11. Arpat Özgül,  (Zurih University, Switzerland): “Coupled dynamics of traits and populations in response to environmental change“
12. Betül Kacar, (Georgia Institute of Technology, USA): “Time travel with the methods of experimental evolution”
13. Ayşegül Birand,  (ODTÜ, Ankara, Turkey): “Ecological co-optation in birds” 
14. İsmail K. Sağlam, (Hacettepe Univ., Ankara, Turkey): “Tackling the tempo and mode of speciation using phylogenetic comparative methods”
15. Osman Canko, (Erciyes University, Kayseri, Turkey): “Emergence of network properties in the tangled nature model“
16. Rodrigo Redondo, (IST-Austria, Vienna): “Epistasis and the evolution of the capsid of the bacteriophage PhiX-174.”
17. Aykut Kence, (ODTÜ, Ankara, Turkey): “Models in Development and Mating Behaviour in Dipterans“

Abstracts:

1. A. Erzan: “A thermodynamic approach to the emergence of complexity in non-equilibrium systems is still to be formulated. Various extremal principles have been enunciated but shown to be non-universal. For ecological systems the difficulty of actually calculating such functions as the entropy or the free energy, possible goal functions, is prohibitive.  Nevertheless, I would like to introduce the idea of the cost of information flow and information processing to provide certain useful constraints. The evolution of a population of Boolean graphs under such constraints yield topologies that are commonly encountered in regulatory networks“.
2. M Tuğrul: “Variation in protein coding regions cannot explain the extensive phenotypic diversity between and among populations. It is believed that variation in transcriptome levels which are regulated by non-coding DNA are responsible for natural variation. However, lack of a regulatory code prevents us from understanding the evolution of regulatory sequences. We would like to know what different patterns of regulation evolve in nature. For example, we would like to understand why we observe fuzzy promoters (i.e. many but not so strong protein bindings), or what turn-over rates are expected between functional regulatory sequences. We need to understand microscopic molecular mechanisms to entangle macroscopic evolutionary behaviors. In our study, we integrate a biophysical model of transcription into population genetics framework in order to understand the evolution of regulatory sequences. The biophysical model takes into account energy interactions among protein, RNA-polymerase and DNA. It approximates the gene expression level with a thermodynamic approach. We consider finite haploid populations evolving under mutation and selection. We analyze both stationary and dynamical properties of evolved regulatory sequences. We show what microscopic (e.g. protein binding size) and macroscopic (e.g. population size) parameters affect the results. We discuss applications of our model to existing biological data.“
3. M. Somel: “Changes in a species’ environment can lead to the evolution of novel traits through positive selection. It can also lead to relaxed negative selection, or constraint, on previously essential functions, leading to increased within-species polymorphism. Here I present a simple framework to test such relaxation in the context of human and bovine evolution, using comparative genomic and population genomic data. Population genetic simulations indicate moderate power to detect such change in coding sequence. Our analyses identify a number of processes showing evidence for relaxed selection in both taxa: in humans, including olfactory transduction and the proteasome, and in cows, energy production, muscle development, and RNA processing.“
4. E. Akçay: “The division of reproduction among the members of a breeding group has been one of the persistent problems in evolutionary biology. Despite numerous models and empirical studies, there seems to be no completely satisfying theory for how the division of reproduction –termed reproductive skew– evolves. I will present a new hypothesis for the evolution of reproductive skew. This hypothesis extends the spirit of reproductive transactions theory in assuming that the division of reproduction is determined to incentivize individuals to stay in the group and cooperate. However, it departs from previous theory in positing that individuals have private information about themselves and the state of the world. Applying the economic theory of mechanism design to this problem we determine what outcomes can be implemented in evolutionarily stable strategies, and what outcomes cannot. More fundamentally, this approach can pave the way for a theory of how the reproductive transaction game itself evolves. I will discuss how our results can help make sense of existing empirical patterns and what directions might be profitable for future empirical and theoretical research on reproductive skew.“
5. H. Acar: “During the process of HGT, there are many different physical barriers depending on the mode of transfer (either natural transformation or conjugation or transduction). However, if we eliminate all those barriers and put the gene directly in the cell and express it, then we should observe the selective barriers acting on it, in terms of its effect on the fitness of the
   organism. These surely depend on the gene’s function, the organism itself, the conditions of the experiment and many other things. But still, through this way we should obtain many interesting new information about bacterial genes. Also, our knowledge about HGT mostly rely on in silico methods and those are giving information only about the genes which have probably been selected for, i.e. success stories for the transferred genes but none of the failures. Therefore we cannot extrapolate, for example, the rate of HGT from that information. But if we do the above mentioned experiment in a systematic way and obtain a distribution for the fitness effects of many transferred genes on the host, then that distribution should tell us about the original rate of HGT.”
6. Z. Adam: “The fossil record and genetic sequences of living organisms are the two datasets that are used to study the origin of eukaryotes. While the fossil record provides morphological information over billions of years of evolution, comparative analyses of genetic sequences provide insights into the ancestral states of molecules. Molecular clock models are built to estimate the timing of diversification of different organisms in the past, but these models rely on a complex and ultimately subjective integration of both fossil and genetic sequence datasets. As a result, extrapolated dates for the origin of eukaryotes commonly differ by hundreds of millions of years, even when using the exact same datasets and genetic sequence variance models. In this talk I will describe how fossil data covering 3 billion years of evolution are collected from the field, analyzed, interpreted and then incorporated into quantitative models of genetic sequence variance rates over time to study early eukaryote evolution.”
7. M. Akman: “Environmental gradients along species distribution can lead to functional trait evolution and shape local adaptations. A strong aridity gradient is present in natural habitats of Protea repens, a dominant and widespread shrub in the highly diverse genus Protea in the Greater Cape Floristic Region of South Africa. Accordingly, the heritable trait variation we detect among populations of P. repens suggests the presence of local adaptations evolved as a response to environmental differences. Nevertheless, the genetic basis of these adaptations is unknown. By using population level next-gen sequencing of transcriptomes for SNP recovery and differential expression analysis, we aim to reveal and quantify genetic variation among populations corresponding to local adaptations. We are focusing on plants grown in a common garden from seeds collected from 20 populations across the species’ distribution. We have measured morphological and physiological traits of these plants as well as characteristics of their source environments. In order to find the genetic basis of these adaptations, we will perform association studies on several hundred of these plants for selecting candidate genes for trait evolution.“
8. B.D. Ozpolat: “Identifying the cell types that participate in regeneration and understanding their molecular characteristics is crucial for dissecting the mechanisms of regeneration. Annelid worms have long been used in regeneration research and early studies identified putative stem cells called neoblasts believed to migrate to the wound and participate in regeneration. While there is a substantial early literature on the histology and cytology of migratory cells such as neoblasts, only recently have tools become available to investigate their molecular and behavioral characteristics. Pristina leidyi, a freshwater annelid, is able to regenerate both anteriorly and posteriorly and recent time-lapse imaging studies in our lab provide definitive evidence for widespread cell migration towards cut sites. In this study, we investigate the expression of several stem cell markers during regeneration and ask whether there are distinct populations of migratory cells that express these markers differentially. Here we focus on the gene piwi, which is required for regeneration and/or germline function in diverse metazoans. We find that piwi is dynamically and strongly expressed in the regeneration blastema and primordial gonads, as well as fission zone and posterior growth zone. In addition, piwi is expressed in a population of ventral cells that appear to be migrating along the length of the body between the gut and ventral nerve cord. Interestingly, no piwi expression is detected in the other migratory cell populations identified by time-lapse imaging. Thus, there are at least two distinct populations of cells that migrate after injury. Future studies are aimed at identifying additional molecular markers for these distinct populations and investigating their function during regeneration.“
9. C. Erten: “A common method for comparative analysis of biological networks in general is through network alignment. Given a pair of biological networks from different species, the goal of network alignment is to map components in one of the networks to their similar counterparts in the other. We present mathematical models employed in formalizingthe notion of biological network alignment in two settings: that of metabolic pathways and protein-protein interaction (PPI) networks. Metabolic pathways consisting of metabolites, biochemical reactions 
   transforming a set of metabolites to others, and enzymes catalyzing these reactions provide valuable information regarding material processing centers of a functioning cell and cellular metabolism in general. A comparative analysis of pathways from different organisms provides insights for understanding evolution, speciation, phylogenic reconstruction and drug target discovery. With regards to alignments applied on PPI networks, functional orthology is an important application that serves as the main motivation to study the alignment problems as part of a comparative analysis of such networks; a successful alignment could provide a basis for deciding the proteins that have similar functions across species. Such information may further be used in predicting functions of proteins with unknown functions or in verifying those with known functions, in detecting common orthologous pathways between species or in reconstructing the evolutionary dynamics of various species. Before the introduction of network alignment as a model, common methods to detect orthologous groups of proteins have been solely 
   based on measures of evolutionary relationships usually in the form of sequence similarities. Network alignment algorithms on the other hand incorporate the interaction data as well as the evolutionary relationships represented possibly in the form of sequence data. “
10. H.Svardal: “With their abundance in savannahs and riverine forests of sub-Saharan Africa, vervet monkeys (genus Chlorocebus) are amongst the most widespread non-human primates and show considerable phenotypic diversity. Different classifications alternatively rank them as 4-6 species or as one single species with several sub-groups. We use whole genome sequencing data from 130 monkeys sampled over the whole continent to infer genetic relationships and evolutionary history. Identifying more than 50 million single nucleotide polymorphisms, this unique data set allows us to draw conclusions about the joint demographic history of the (sub)species and the speciation process. We identify signatures of past admixture events and ongoing migration, and characterise regions of genomic introgression. A genomic scan for selection reveals several  regions of putative divergent evolution that are in accordance with models of local adaptation. Besides unraveling the evolutionary process in this species, our results are valuable due to the role of vervet monkeys in medical research. Notably, they are the most common natural host of the Simian immunodeficiency virus (SIV).”
11. A. Özgül: “A major goal in population ecology is to predict how populations will respond to environmental change. Recent studies have highlighted rapid phenotypic changes accompanying and often preceding demographic regime shifts, indicating that a trait-based demographic approach can improve our ability to predict a population’s response to environmental change. 
    Furthermore, these studies show that some species can adapt to environmental change more easily than others through a range of evolutionary and ecological mechanisms; understanding these differences is crucial for identifying species that are more susceptible.  

    In this talk, I will present the links between the abiotic environment, population density, individual traits (such as body size) and demography using long-term data from two mammalian systems: Soay sheep and yellow-bellied marmots, and address the following questions: “How do populations respond demographically, ecologically and evolutionarily to environmental perturbations?“
12. B. Kacar: “Stephen Jay Gould famously proposed that because of the stochastic aspects of evolution, were one to “replay the tape of life” the result would lead to a very different living world; while others have countered that the evolutionary routes are many, but the destinations are limited. Both views were formulated in terms of entire organisms, potentially extending to the level of the biosphere. The availability of accumulated DNA sequence data, and advances in molecular and evolutionary biology combined with increased computational power now allow us to build a time travel system in the laboratory that permits these hypotheses to be tested with a degree of precision at the molecular level. In this presentation I will describe an experimental model system that allows us to effectively go back in time through the history of life by reconstructing genes that once existed in the distant past, and then to examine the resurrected genesʼ adaptation to modern environments by experimentally evolving them. Molecular time travel was accomplished by replacing an essential component of ribosomal machinery, Elongation Factor (EF-Tu) protein, with its 500 million year old ancestor in modern bacteria. We then subjected six replicates of this synthetically modified organism to 2000 generations of laboratory evolution and identified the changes in both phenotypic and genotypic levels as the resurrected genes adapted to modern environments. Results show the role of historical and physical constraints in shaping evolutionary trajectories and demonstrate, for the first time, parallel evolutionary histories observed at the level of protein interaction networks. This approach therefore allows us to carry out Gouldʼs experiment of “replaying the tape of life” at a molecular level, allowing a rare opportunity to directly assess the role of chance and necessity in biological evolution and provides new insights in our attempts to understand how biological systems, particularly complex networks have evolved to be the way they are.”
13. A. Birand: “Natural selection is commonly thought as the engine of ecological diversification, where sexual selection has a secondary role in promoting speciation. Sexual selection is also attributed a primary role in the origin of species, where it produces divergence not in ecological traits, but in sexually selected traits. Ecological co-optation suggests an alternative to these prevailing ideas. Sexual selection alone could drive ecological diversification, where a sexually selected trait is co-opted for a novel viability trait. Such an ecological co-optation will then enable species with newly co-opted trait to exploit a novel niche. In the present study, we test the prediction of ecological co-optation in antbirds, tanagers, and blackbirds. We use sexually selected plumage coloration in these groups, and check whether the birds with colorful plumage differ in their niche use (i.e. habitat range, altitudinal range, and distributional range), by using phylogenetically independent contrasts method, and sister taxa comparisons. Our results show that increasing plumage coloration produces changes in niche uses. Similarly, increasing plumage color differences between sexes leads to changes in niche width, which is a trend consistent with ecological co-optation hypothesis.”
14. İ. Sağlam: “The phylogenetic comparative method can be a powerful method for detecting the tempo and mode of trait evolution especially in the absence of direct experimental manipulation or data. By using natural variation within and between populations of species the comparative method has emerged as an effective tool for testing ecological and evolutionary processes. Initially phylogenetic signals were regarded as a nuisance parameter in comparative data sets and therefore most models aimed to eliminate phylogenetic signals on correlative analysis of biological traits. However a host of new models incorporating phylogenetic signals into analysis of comparative trait evolution have been developed. These new models can now infer the presence and effect of different selective regimes; detect and compare changes in rates of evolution of traits; detect correlated evolution among pairs of traits and shifts in trait correlation through evolutionary time. In this lecture I will briefly outline several methods and models frequently used in phylogenetic comparative analysis such as likelihood, Brownian motion and Ornstein-Uhlenbeck and try to show how these models can help us in understanding the evolutionary processes leading to character divergence and hence speciation in a group of mountain crickets.“
15. O. Canko: “The tangled nature model is a similar version of well-known Kauffmann’s NK model. The Tangled Nature model (TaNa) deals with individuals interactions and their offspring. Each species gives two offspring before dies and these children are exposed to constant mutation rate and killing probability. The model provides the power law extinction data seen in the fossil records. The model found a quasi Evolutionary Stable Strategies (qESS) and these long generation time step is suddenly interrupted by evolutionarily very active hectic periods. In order to fully understand the emerging properties of the TaNa, we have investigated the network structure of communities of the model. We have focused on the mostly studied network properties such as degree distribution, path length, clustering coefficient and modularity. Whether the model represents a similar structure to the one seen in the real world deserves to be investigate. We have also examined the response of model to the the external perturbations. However, we have not yet found a concluding remark about model due to the dynamical properties of model so highly complex. We will present our primary results.“
16. R. Redondo: “Viral capsids are structurally constrained by molecular interactions amongst the components of the constituting biomolecules. Therefore, significant epistasis amongst the sites due to physical interactions is expected. This implies that the rates of evolution of different nucleotides or amino acids are influenced by these interactions. Consequently, multiple substitutions that are compensatory might show significant epistasis, whilst the capsid evolves in a nearly-neutral basin of optimal structural stability, assuming it might be in a energetic
    minimum. In order to study the distribution of structural epistastic factors, we modeled in silico the capsid of 19 species of the PhiX-174
    family, including the wild-type. We found that there are nearly 40 variable amino acid sites across the 19 species in the main capsid protein. For each haplotype, we studied the distribution of free energies,
    relative to that of the wild type. To measure epistasis, we also studied hypothetical single mutants, and calculated the difference between the
    free energies of each species and the sum of the free energy of the constituting single-mutants. The difference revealed several significant
    epistatic interactions, mostly of positive sign. To study how epistasis evolved in this family, we reconstructed the ancestral sequences using
    Bayesian phylogenetic methods. The ancestral states includes 8 variable amino acids, for a total of 256 possible haplotypes. The dN/dS ratio is
    low, suggesting the strong action of purifying selection on the capsid, consistent with the idea that the structure is constrained. We took a dual
    approach: first, we constructed in silico models for each of the 256 structures and studied epistasis as in the extant species. Most of these
    ancestral haplotypes show negative epistasis, indicating an increase in evolutionary time. Second, we constructed a DNA library of a subsample of
    10 of these haplotypes (each single mutant, the 8-mutant, and the “ancestral wild type”). With this library, we substituted each ancestral
    haplotype in the extant wild type. We tested viability and growth-rate experiments (comparative fitness assessment) of these ancestral forms in
    order to quantify the effects of the mutations and compared the results with the in silico experiments.“
17. A. Kence: “A  matematical  model on the development  of flies were constructed to investigate reason for the bimodality of the develoment period at higher population densities. Model, however simple could predict the trends in the survival, weight changes, and development period, as well  as bimodality of developmental period in response to the density changes in the population. The model could simulate the interstrain competiton withn the dipteran populations. Another model was constructed to simulate the mating behaviour of flies. This model also seemed to explain distribution of mating speed  in  flies rather well. model could also predict  the results of interstrain  mating experiments. Besides  the reason of rare male advantage observed in dipteran experiments  which was used as one the reasons  to explain the maintanance of the genetic variation in populations  could also  be  accounted  by the model. The common  theme of the both models was variation of the individuals within the populations, each individual receiving different set of  values generated  randomly out of  distributions of each parameter of the model.”