Modeling across multiple scales is a current challenge in Systems Biology, especially when applied to multicellular organisms. In this paper, we present an approach to model at different spatial scales, using the new concept of Hierarchically Colored Petri Nets (HCPN). We apply HCPN to model a tissue comprising multiple cells hexagonally packed in a honeycomb formation in order to describe the phenomenon of Planar Cell Polarity (PCP) signaling in Drosophila wing. We have constructed a family of related models, permitting different hypotheses to be explored regarding the mechanisms underlying PCP. In addition our models include the effect of well-studied genetic mutations. We have applied a set of analytical techniques including clustering and model checking over time series of primary and secondary data. Our models support the interpretation of biological observations reported in the literature.

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Twitter data offers an unprecedented opportunity to study demographic differences in public opinion across a virtually unlimited range of subjects. Whilst demographic attributes are often implied within user data, they are not always easily identified using computational methods. In this paper, we present a semi-automatic solution that combines automatic classification methods with a user interface designed to enable rapid resolution of ambiguous cases. TweetClass employs a two-step, interactive process to support the determination of gender and age attributes. At each step, the user is presented with feedback on the confidence levels of the automated analysis and can choose to refine ambiguous cases by examining key profile and content data. We describe how a user-centered design approach was used to optimise the interface and present the results of an evaluation which suggests that TweetClass can be used to rapidly boost demographic sample sizes in situations where high accuracy is required.

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In the last few years researchers have dedicated several efforts to the definition of Genetic Programming (GP) [?] systems based on the semantics of the solutions, where by semantics we generally intend the behavior of a program once it is executed on a set of inputs, or more particularly the set of its output values on input training data (this definition has been used, among many others, for instance in [?, ?, ?, ?]). In particular, new genetic operators, called geometric semantic operators, have been proposed by Moraglio et al. [?]. They are defined s follows: DEFINITION 1. (Geometric Semantic Crossover). Given two parent functions T1, T2 : R n → R, the geometric semantic crossover returns the real function TXO = (T1 • TR) + ((1 − TR) • T2), where TR is a random real function whose output values range in the interval [0, 1]. DEFINITION 2. (Geometric Semantic Mutation). Given a parent function T : R n → R, the geometric semantic mutation with mutation step ms returns the real function TM = T + ms • (TR1 − TR2), where TR1 and TR2 are random real functions.As Moraglio et al. demonstrate in [?], these operators have interesting properties: crossover produces and offpring whose semantic vector is on the line that joins the two semantic vectors of the parents; mutation induces a unimodal fitness landscape on any problem consisting in finding the match between a set of input data andCopyright is held by the author/owner(s).