Facing bioprocess modeling: a review of the methodologies of modeling

This article presents a detailed review of different approaches for process modeling, indicating their deficiencies and limitations when applied to bioprocesses modeling. As a result of the analysis it is concluded that these methodologies fail in bioprocesses modeling because they do not explicitly take into account the interaction between environment and cellular material, at least descriptively. It is noted that so far the way to bring these two worlds has been through purely predictive functions. Finally, the trends in bioprocess model are described; concluding that the approach is oriented to phenomenological based mathematical models with descriptive or explanatory features, to represent the relationship between the cell and its environment.En este artículo se presenta una revisión detallada de las diferentes metodologías para el modelado de procesos, señalando sus deficiencias y limitaciones al aplicarlas al modelado de bioprocesos. Como resultado del análisis se encuentra que, al aplicar esas metodologías a los bioprocesos, todas fallan porque no consideran explícitamente la interacción existente entre el medio ambiente y el material celular, al menos de forma descriptiva. Se resalta que hasta ahora la forma de unir estos dos mundos ha sido a través de funciones puramente predictivas. Finalmente, se describen las tendencias en el modelado de bioprocesos, concluyéndose que el enfoque está orientado al planteamiento de modelos matemáticos de base fenomenológica, con rasgos descriptivos o explicativos, para representar la relación existente entre la célula y su medio ambiente.Neste artigo apresenta-se uma revisão detalhada das diferentes metodologias para a modelagem de processos, assinalando suas deficiências e limitações ao aplicar à modelagem de bioprocessos. Como resultado da análise se encontra que ao aplicar essas metodologias de bioprocessos, todos falham porque não consideram explicitamente a interação existente entre o meio ambiente e o material celular, ao menos de forma descritiva. Ressalta-se que até agora a forma de unir estes dois mundos tem sido  através de funções puramente preditivas. Finalmente, descrevem-se as tendências na modelagem de bioprocessos, concluindo-se que o enfoque está orientado à proposta de modelos matemáticos de base fenomenológica, com características descritivas ou explicativas, para representar a relação entre a célula e seu meio ambiente. &nbsp

Компьютерное моделирование двух моделей хемостата для одного питательного ресурса

CHICHURIN A.V., SHVYCHKINA A.N. Computer simulation of two models of the chemostat nutrient resourceРассматриваются динамические модели хемостата Михаэлиса Ментена, когда константы Михаэлиса–Ментена для обеих конкурирующих популяций микроорганизмов равны. Построены программные модули, использующие численные процедуры, которые позволяют осуществить моделирование процессов хемостатного культивирования при изменяющихся параметрах системы, а также визуализировать динамику процесса развития для каждого из микроорганизмов. Проведен сравнительный анализ некоторых численных методов, которые использовались для интегрирования результирующего нелинейного дифференциального уравнения первого порядка

Systems and control : 21th Benelux meeting, 2002, March 19-21, Veldhoven, The Netherlands

Book of abstract

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic