Information processing in biology

To survive, organisms must respond appropriately to a variety of challenges posed by a dynamic and uncertain environment. The mechanisms underlying such responses can in general be framed as input-output devices which map environment states (inputs) to associated responses (output. In this light, it is appealing to attempt to model these systems using information theory, a well developed mathematical framework to describe input-output systems. Under the information theoretical perspective, an organism’s behavior is fully characterized by the repertoire of its outputs under different environmental conditions. Due to natural selection, it is reasonable to assume this input-output mapping has been fine tuned in such a way as to maximize the organism’s fitness. If that is the case, it should be possible to abstract away the mechanistic implementation details and obtain the general principles that lead to fitness under a certain environment. These can then be used inferentially to both generate hypotheses about the underlying implementation as well as predict novel responses under external perturbations. In this work I use information theory to address the question of how biological systems generate complex outputs using relatively simple mechanisms in a robust manner. In particular, I will examine how communication and distributed processing can lead to emergent phenomena which allow collective systems to respond in a much richer way than a single organism could

Simulation of stochastic network dynamics via entropic matching

The simulation of complex stochastic network dynamics arising, for instance, from models of coupled biomolecular processes remains computationally challenging. Often, the necessity to scan a models' dynamics over a large parameter space renders full-fledged stochastic simulations impractical, motivating approximation schemes. Here we propose an approximation scheme which improves upon the standard linear noise approximation while retaining similar computational complexity. The underlying idea is to minimize, at each time step, the Kullback-Leibler divergence between the true time evolved probability distribution and a Gaussian approximation (entropic matching). This condition leads to ordinary differential equations for the mean and the covariance matrix of the Gaussian. For cases of weak nonlinearity, the method is more accurate than the linear method when both are compared to stochastic simulations.Comment: 23 pages, 6 figures; significantly revised versio

Information processing in biology

To survive, organisms must respond appropriately to a variety of challenges posed by a dynamic and uncertain environment. The mechanisms underlying such responses can in general be framed as input-output devices which map environment states (inputs) to associated responses (output. In this light, it is appealing to attempt to model these systems using information theory, a well developed mathematical framework to describe input-output systems. Under the information theoretical perspective, an organism’s behavior is fully characterized by the repertoire of its outputs under different environmental conditions. Due to natural selection, it is reasonable to assume this input-output mapping has been fine tuned in such a way as to maximize the organism’s fitness. If that is the case, it should be possible to abstract away the mechanistic implementation details and obtain the general principles that lead to fitness under a certain environment. These can then be used inferentially to both generate hypotheses about the underlying implementation as well as predict novel responses under external perturbations. In this work I use information theory to address the question of how biological systems generate complex outputs using relatively simple mechanisms in a robust manner. In particular, I will examine how communication and distributed processing can lead to emergent phenomena which allow collective systems to respond in a much richer way than a single organism could

O individualismo em Max Stirner

info:eu-repo/semantics/publishedVersio

Gestão de projetos de formação e consultadoria : avaliação do impacto de intervenções

O objetivo deste relatório é um estudo de caso que assenta numa nova abordagem à temática da avaliação da formação e na sua aplicação a uma intervenção formativa constante do plano de formação interno de um grupo de empresas de consultoria de Coimbra. O trabalho deriva de um período de estágio de 6 meses, onde o investigador desenvolveu as suas teorias com base na observação participante durante o estágio na empresa e na ação de formação em estudo, além da análise de vários documentos disponibilizados pela empresa. A condução de entrevistas aos intervenientes da ação foi também importante, principalmente na fase conclusiva. Alicerçado na literatura, é proposto um novo modelo de avaliação da formação para uso futuro por parte da empresa, onde estão contemplados 7 níveis de avaliação e dois instrumentos de apoio (mapa de impacto e grelha final de avaliação). É de salientar o resultado bastante satisfatório da avaliação realizada. Na parte final, depois de retiradas as conclusões principais do estudo, está incluído um espaço com sugestões para ajudar a empresa em intervenções futuras e são elencadas as limitações do trabalho para total conhecimento das potencialidades do mesmo.The objetive of this report is a study case that sito n a new training evaluation approach and its application on a training intervention that belongs to the internal training plan of a consultant company in Coimbra. This work arises from a 6 month internship where the investigator developed his theories based on a participant observation of the field and the intervention, and also through the analysis of various documents made available by the company. The interviews that were conducted were important, especially for the concluding parts. Supported on literature, a new training evaluation model is proposed for future use by the company, where there are 7 levels contemplated and two support instruments (impact map and final evaluation grid). The great results of the evaluation should be pointed out. In the end, after drawn conclusions, there is a space with suggestions to help the company on future interventions and the limitations of the work are described to provide full knowledge of its potencial