TY - CHAP AB - Genetic factors might be largely responsible for the development of autism spectrum disorder (ASD) that alone or in combination with specific environmental risk factors trigger the pathology. Multiple mutations identified in ASD patients that impair synaptic function in the central nervous system are well studied in animal models. How these mutations might interact with other risk factors is not fully understood though. Additionally, how systems outside of the brain are altered in the context of ASD is an emerging area of research. Extracerebral influences on the physiology could begin in utero and contribute to changes in the brain and in the development of other body systems and further lead to epigenetic changes. Therefore, multiple recent studies have aimed at elucidating the role of gene-environment interactions in ASD. Here we provide an overview on the extracerebral systems that might play an important associative role in ASD and review evidence regarding the potential roles of inflammation, trace metals, metabolism, genetic susceptibility, enteric nervous system function and the microbiota of the gastrointestinal (GI) tract on the development of endophenotypes in animal models of ASD. By influencing environmental conditions, it might be possible to reduce or limit the severity of ASD pathology. AU - Hill Yardin, Elisa AU - Mckeown, Sonja AU - Novarino, Gaia AU - Grabrucker, Andreas ED - Schmeisser, Michael ED - Boekers, Tobias ID - 623 SN - 03015556 T2 - Translational Anatomy and Cell Biology of Autism Spectrum Disorder TI - Extracerebral dysfunction in animal models of autism spectrum disorder VL - 224 ER - TY - JOUR AB - Our focus here is on the infinitesimal model. In this model, one or several quantitative traits are described as the sum of a genetic and a non-genetic component, the first being distributed within families as a normal random variable centred at the average of the parental genetic components, and with a variance independent of the parental traits. Thus, the variance that segregates within families is not perturbed by selection, and can be predicted from the variance components. This does not necessarily imply that the trait distribution across the whole population should be Gaussian, and indeed selection or population structure may have a substantial effect on the overall trait distribution. One of our main aims is to identify some general conditions on the allelic effects for the infinitesimal model to be accurate. We first review the long history of the infinitesimal model in quantitative genetics. Then we formulate the model at the phenotypic level in terms of individual trait values and relationships between individuals, but including different evolutionary processes: genetic drift, recombination, selection, mutation, population structure, …. We give a range of examples of its application to evolutionary questions related to stabilising selection, assortative mating, effective population size and response to selection, habitat preference and speciation. We provide a mathematical justification of the model as the limit as the number M of underlying loci tends to infinity of a model with Mendelian inheritance, mutation and environmental noise, when the genetic component of the trait is purely additive. We also show how the model generalises to include epistatic effects. We prove in particular that, within each family, the genetic components of the individual trait values in the current generation are indeed normally distributed with a variance independent of ancestral traits, up to an error of order 1∕M. Simulations suggest that in some cases the convergence may be as fast as 1∕M. AU - Barton, Nicholas H AU - Etheridge, Alison AU - Véber, Amandine ID - 626 JF - Theoretical Population Biology SN - 00405809 TI - The infinitesimal model: Definition derivation and implications VL - 118 ER - TY - CHAP AB - In the analysis of reactive systems a quantitative objective assigns a real value to every trace of the system. The value decision problem for a quantitative objective requires a trace whose value is at least a given threshold, and the exact value decision problem requires a trace whose value is exactly the threshold. We compare the computational complexity of the value and exact value decision problems for classical quantitative objectives, such as sum, discounted sum, energy, and mean-payoff for two standard models of reactive systems, namely, graphs and graph games. AU - Chatterjee, Krishnendu AU - Doyen, Laurent AU - Henzinger, Thomas A ED - Aceto, Luca ED - Bacci, Giorgio ED - Ingólfsdóttir, Anna ED - Legay, Axel ED - Mardare, Radu ID - 625 SN - 0302-9743 T2 - Models, Algorithms, Logics and Tools TI - The cost of exactness in quantitative reachability VL - 10460 ER - TY - JOUR AB - Bacteria adapt to adverse environmental conditions by altering gene expression patterns. Recently, a novel stress adaptation mechanism has been described that allows Escherichia coli to alter gene expression at the post-transcriptional level. The key player in this regulatory pathway is the endoribonuclease MazF, the toxin component of the toxin-antitoxin module mazEF that is triggered by various stressful conditions. In general, MazF degrades the majority of transcripts by cleaving at ACA sites, which results in the retardation of bacterial growth. Furthermore, MazF can process a small subset of mRNAs and render them leaderless by removing their ribosome binding site. MazF concomitantly modifies ribosomes, making them selective for the translation of leaderless mRNAs. In this study, we employed fluorescent reporter-systems to investigate mazEF expression during stressful conditions, and to infer consequences of the mRNA processing mediated by MazF on gene expression at the single-cell level. Our results suggest that mazEF transcription is maintained at low levels in single cells encountering adverse conditions, such as antibiotic stress or amino acid starvation. Moreover, using the grcA mRNA as a model for MazF-mediated mRNA processing, we found that MazF activation promotes heterogeneity in the grcA reporter expression, resulting in a subpopulation of cells with increased levels of GrcA reporter protein. AU - Nikolic, Nela AU - Didara, Zrinka AU - Moll, Isabella ID - 624 IS - 9 JF - PeerJ SN - 21678359 TI - MazF activation promotes translational heterogeneity of the grcA mRNA in Escherichia coli populations VL - 2017 ER - TY - CONF AB - We consider the problem of developing automated techniques for solving recurrence relations to aid the expected-runtime analysis of programs. The motivation is that several classical textbook algorithms have quite efficient expected-runtime complexity, whereas the corresponding worst-case bounds are either inefficient (e.g., Quick-Sort), or completely ineffective (e.g., Coupon-Collector). Since the main focus of expected-runtime analysis is to obtain efficient bounds, we consider bounds that are either logarithmic, linear or almost-linear (O(log n), O(n), O(n · log n), respectively, where n represents the input size). Our main contribution is an efficient (simple linear-time algorithm) sound approach for deriving such expected-runtime bounds for the analysis of recurrence relations induced by randomized algorithms. The experimental results show that our approach can efficiently derive asymptotically optimal expected-runtime bounds for recurrences of classical randomized algorithms, including Randomized-Search, Quick-Sort, Quick-Select, Coupon-Collector, where the worst-case bounds are either inefficient (such as linear as compared to logarithmic expected-runtime complexity, or quadratic as compared to linear or almost-linear expected-runtime complexity), or ineffective. AU - Chatterjee, Krishnendu AU - Fu, Hongfei AU - Murhekar, Aniket ED - Majumdar, Rupak ED - Kunčak, Viktor ID - 628 SN - 978-331963386-2 TI - Automated recurrence analysis for almost linear expected runtime bounds VL - 10426 ER -