A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology

An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment

Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters

To relax the homogeneity assumption of classical dynamic Bayesian networks (DBNs), various recent studies have combined DBNs with multiple changepoint processes. The underlying assumption is that the parameters associated with time series segments delimited by multiple changepoints are a priori independent. Under weak regularity conditions, the parameters can be integrated out in the likelihood, leading to a closed-form expression of the marginal likelihood. However, the assumption of prior independence is unrealistic in many real-world applications, where the segment-specific regulatory relationships among the interdependent quantities tend to undergo gradual evolutionary adaptations. We therefore propose a Bayesian coupling scheme to introduce systematic information sharing among the segment-specific interaction parameters. We investigate the effect this model improvement has on the network reconstruction accuracy in a reverse engineering context, where the objective is to learn the structure of a gene regulatory network from temporal gene expression profiles

Korzyści relacyjne i jakość relacji - w kierunku zrozumienia powiązań nauki i biznesu

Celem artykułu jest odpowiedź na pytanie w jaki sposób marketing relacji, w szczególności koncepcja korzyści relacyjnych oraz jakości relacji może wpływać na transfer wiedzy i technologii z uczelni do biznesu. Celem jest także wskazanie istotnych przyszłych kierunków badań w tym zakresie. Integracja teorii marketingu relacji i transferu technologii może stworzyć nowe ramy dla pełniejszego zrozumienia powiazań pomiędzy nauką a biznesem. Badania w tym zakresie mogą także przyczynić się do poszerzenia i rozwoju teorii marketingu relacji, która do tej pory ograniczała się do analizy relacji w ramach jednego sektora. Wyniki przeprowadzonych badań wskazują, że powiązania o wysokim relacyjnym zaangażowaniu są powszechne, uznawane zarówno przez środowisko akademickie jak i biznesowe za cenne oraz odgrywają ważną rolę w stymulowaniu innowacji. Jakość relacji oraz korzyści relacyjne mogą odgrywać istotną rolę w budowaniu długoterminowych powiązań pomiędzy uczelniami i przemysłem. Integracja teorii behawioralnych z teorią transferu technologii może przyczynić się do lepszego zrozumienia zachowania poszczególnych uczestników transferu na poziomie indywidualnym. English abstract: The goal of this article is to answer the question in what way relational marketing and in particular, the concept of relational benefits, as well as quality of relation may influence the transfer of knowledge and technologies from universities to business. Another goal is to highlight significant, future directions of research in this area. Integration of the theory of relational marketing and technology transfer may create a new framework for fuller understanding of the ties between science and business. Research in this area may contribute to the expansion and development of the theory of relational marketing, which until now was limited to the analysis of relations within a single sector. The results of conducted research show that ties characterized by high relational engagement are common, recognized by both academic and business environment as precious and play an important role in stimulating innovations. The quality of relations and relational benefits may play an important role in building long-term ties between universities and the industry. Integration of behavioural theories with the theory of technology transfer may contribute to a better understanding of the behaviour of particular participants of the transfer on the individual level

Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes

<b>Method:</b> Dynamic Bayesian networks (DBNs) have been applied widely to reconstruct the structure of regulatory processes from time series data, and they have established themselves as a standard modelling tool in computational systems biology. The conventional approach is based on the assumption of a homogeneous Markov chain, and many recent research efforts have focused on relaxing this restriction. An approach that enjoys particular popularity is based on a combination of a DBN with a multiple changepoint process, and the application of a Bayesian inference scheme via reversible jump Markov chain Monte Carlo (RJMCMC). In the present article, we expand this approach in two ways. First, we show that a dynamic programming scheme allows the changepoints to be sampled from the correct conditional distribution, which results in improved convergence over RJMCMC. Second, we introduce a novel Bayesian clustering and information sharing scheme among nodes, which provides a mechanism for automatic model complexity tuning. <b>Results:</b> We evaluate the dynamic programming scheme on expression time series for Arabidopsis thaliana genes involved in circadian regulation. In a simulation study we demonstrate that the regularization scheme improves the network reconstruction accuracy over that obtained with recently proposed inhomogeneous DBNs. For gene expression profiles from a synthetically designed Saccharomyces cerevisiae strain under switching carbon metabolism we show that the combination of both: dynamic programming and regularization yields an inference procedure that outperforms two alternative established network reconstruction methods from the biology literature

Transfer wiedzy i technologii z organizacji naukowo-badawczych do przedsiębiorstw

Rozwój innowacyjnej gospodarki zależy od umiejętności korzystania z osiągnięć nauki i możliwości ich dystrybucji. Książka wpisuje się w niezwykle istotną dyskusję dotyczącą poprawy innowacyjności polskiej gospodarki poprzez udoskonalenie współpracy pomiędzy sektorem nauki i biznesu. Autorzy diagnozują sytuację polskich uczelni w zakresie komercjalizacji wyników badań oraz współpracy z biznesem, badają różne grupy interesariuszy biorących udział w profesjonalnych procesach transferu wiedzy i technologii w Polsce, Norwegii, Francji, Czechach, na Węgrzech, a także w USA i Kanadzie. Analizują studia dobrych praktyk – zarówno polskich, jak i zagranicznych – aby na tej podstawie zaprezentować rekomendacje niezbędnych zmian dla uczelni w obszarze kształtowania dobrych relacji z przedsiębiorstwami w celu zwiększania potencjału innowacyjnego i zrostu konkurencyjności gospodarki

The role of culture-moderated social capital in technology transfer – insights from Asia and America

This study examines the impact of cultural differences on the creation of social capital in technology transfer processes. The aim is to understand the influence of culture on relationships, particularly the structural, relational and cognitive dimensions of social capital created in relationships associated with university-industry links, specifically technology transfer (TT). The research builds on a culture-moderated social capital perspective; observing that the characteristics and usefulness of social capital are determined by cultural practices prevailing in social structures. The influence of culture on social capital in case studies of six American and ten Asian technology transfer offices and organizations involved in technology transfer has been investigated. Using university research technology transfer and commercialization as the centerpiece of the empirical work, we examine basic Hofstede's cultural characteristics and the way they influence TT practices in two different culture types. Our findings suggest cultures can influence creation and utilization of social capital in university-industry links. Culture can influence not only relationships with external stakeholders in technology transfer (industry, governmental bodies) but also internal relationships and management styles in TT offices (influences on organizational culture). We propose that the awareness of cultural characteristics and influences is important not only in cross-cultural technology transfer but also domestic operations. Using this awareness to build trust lies at the heart of interactions with internal and external stakeholders. The research results should be useful for entrepreneurs, universities and technology transfer officers in order to better understand the nature, and role, of culture-moderated social capital in technology transfer and to support effective processes for scientific research commercialization

Sample quantiles corresponding to mid p-values for zero–modification tests

Wilson and Einbeck (2015, 2016) propose a test for zero-modification relative to a stated model. The basis of the test is that the number of observed zeros follows a Poisson-binomial distribution. The decision to reject, or otherwise, the non zero–modified model is made by either (i) computing the mid p-value corresponding to the number of observed zeros, or (ii) comparing the number of observed zeros to the relevant “traditional” quantile of the appropriate Poisson–binomial distribution. In general either approach will result in the same decision, but occasionally discrepancies may occur. In this paper we investigate the use of mid-distribution quantiles in approach (ii) above, and show that this reduces the possibility of discrepancies