Seminar by Esteban Moro
Eduard Fontserè
2023-02-27 14:00:00
Title: Understanding urban networks resilience through behavioral mobility data.
Abstract: The economic and social progress of our urban areas, our institutions, and our jobs depend on the diversity and resilience of the social fabric in cities. Despite their importance, several major forces erode the diversity and strength of those social connections: from income or racial segregation to differences in education and job access. In this talk, I will present our recent work to understand the fragility of the complex network of social connections and interactions in cities through the analysis of behavioral mobility data and its relationship with networked inequalities in experienced segregation, access to healthy food, labor markets, or adaptation to the recent pandemic.
Material:
* Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19 [NatHumBehavior].
* Universal resilience patterns in labor markets [NatCommunications]
* Mobility patterns are associated with experienced income segregation in large US cities [NatCommunications].
* You are where you eat: Effect of mobile food environments on fast food visits [medRxiv]
Bio:
Esteban Moro is a researcher, data scientist, and professor at MIT Connection Science and Universidad Carlos III (UC3M) in Spain. He has published extensively throughout his career (more than 100 articles) and has led many projects funded by government agencies and private companies. Esteban's work lies in the intersection of big data and computational social science, with particular attention to human dynamics, collective intelligence, social networks, and urban mobility in problems like viral marketing, natural disaster management, or economic segregation in cities. He has received numerous awards for his research, including the “Shared University Award” from IBM in 2007 for his research in modeling viral marketing in social networks and the “Excellence in Research” Awards in 2013 and 2015 from UC3M. Esteban’s work appeared in major journals, including Nature, PNAS, and Science Advances, and is regularly covered by media outlets The Atlantic, The Washington Post, The Wall Street Journal, and El País (Spain).
Seminar "Dynamics on networks through the lens of spectral and information theories"
aula 3.20 Dpt. Materia Fisica Condensada (Fisica UB)
2022-07-07 12:00:00
Dr. Antoine Allard, Université Laval, Québec, Canada
Dijous 7 a les 12:00, aula 3.20
Title: Dynamics on networks through the lens of spectral and information theories
A central topic in network science is the study of the interplay between the global behavior of complex systems and the structure of the local interactions among their constituents. In this talk, I will briefly present two recent projects in which we develop methods to study this structure-behavior relationship in complex systems. The first project uses spectral theory to effectively reduce the dimension of the system of coupled ODEs describing the evolution of a dynamical process taking place on a network [1]. The second project exploits the tools of information theory to unveil a non-reciprocal relation between how much knowing about the network structure informs us about the evolution of a dynamical process (predictability), and how much knowing about the dynamical process (i.e. time series) informs us about the network it is evolving on [2].
[1]: https://arxiv.org/abs/2206.11230
[2]: https://arxiv.org/abs/2206.04000
Consolider Seminar by Rubén Pérez-Carrasco: Effects of cell cycle variability on stochastic gene expression
Sala Eduard Fontseré (Facultat Física)
2022-03-29 12:00:00
Title: Effects of cell cycle variability on stochastic gene expression
Date: 29 March 2022, 12h
Speaker: Rubén Pérez-Carrasco (Imperial College London)
Abstract:
Many models of stochastic gene expression do not incorporate a cell cycle description. I will show how this can be tackled analytically by studying how mRNA fluctuations are influenced by DNA replication for a prescribed cell cycle duration stochasticity. Results show that omitting cell cycle details can introduce significant errors in the predicted mean and variance of gene expression for prokaryotic and eukaryotic organisms, reaching a 25% error in the variance for mouse fibroblasts. Furthermore, we can derive a negative binomial approximation to the mRNA distribution, indicating that cell cycle stochasticity introduces similar fluctuations to bursty transcription. Finally, I will show how disregarding cell cycle stochasticity can introduce inference errors in transcription rates bigger than 10%.
Seminar by Juan Fernández-Gracia (IFISC): Characterising and modeling of the ocean microbiome… and twitter?
Aula 3.20 Departament Física de la Matèria Condensada
2022-03-28 12:00:00
Abstract: Microorganisms like bacteria, archaea and eukaryotes coexist in large and complex ecosystems. Actually, microbial communities form the largest and more diverse ecosystems on the planet. Understanding their composition and which mechanisms lead to and maintain those compositions is of crucial importance for example if we want to understand associated changes in function of distinct microbiomes. Furthermore the interactions among their individuals are diverse, encompassing mutualism, commensalism, or competition. Measuring these interactions in direction and strength at a large scale is a challenging process that requires a combination of data analysis and modeling, which shouldn’t ignore the dynamic nature of the abundances of different species. Here we use data on microbial species abundances first to characterize the microbiome composition in the global ocean and estimate the total microbial richness, for which we derive some scaling relations with the sampling effort and relate them to the form of the abundance distributions. Then we will jump to the question of inferring interactions among different species and we will present two methods, namely a static one based on non-linear correlation measures of abundances across samples; and a dynamic one, based on a generalized Lotka-Volterra set of equations. Finally, we will conclude with a glimpse on how to apply ecological theories to data on social activities, in particular to conversations in twitter.