Fall 2020 PROGRAM (University of Washington)

Bio: Dina Mistry, PhD, is a network scientist currently working in the Epidemiology team at the Institute for Disease Modeling, a part of the Bill & Melinda Gates Foundation. She earned her Ph.D. under the supervision of Professor Alessandro Vespignani at the Network Science Institute at Northeastern University. Her research focuses on characterizing and modeling the heterogeneity of social contact networks and mobility networks.

Abstract: Epidemiological models have long been used to gain insight into the dynamics of how infectious diseases spread within populations. Of these, mechanistic models are becoming important tools to retrospectively identify factors contributing to disease transmission, nowcast ongoing outbreaks, forecast epidemics, and highlight the potential of intervention strategies. As the complexity of modeling goals increases, often the choice of appropriate models changes and more complex modeling approaches are needed. In some respect, the use of models to address public health questions is only as powerful as the data they are built upon. Complex models aimed at evaluating interventions designed to suppress transmission by reducing interaction call for increased realism and accuracy of the descriptions in how people interact in their everyday lives. This requires both data and an understanding of interaction patterns relevant to disease transmission. Network epidemiology provides the right framework for us to integrate rich, detailed data on these patterns and ask complex questions about disease transmission, behaviour, and the impact of individual based interventions applied in diverse and heterogeneous populations. In this talk, I'll introduce the kinds of modeling network epidemiology is best poised to address and discuss the ongoing research my team has been doing to apply network modeling to real world questions about reopening strategies during the current COVID-19 pandemic.

Paper: Controlling COVID-10 via test-trace-quarantine

Website: https://dinacmistry.github.io/

Bio: Nitika Sharma, PhD, is a postdoctoral scholar at UCLA who has started working on multilayer networks of tropical paper wasps and vultures recently. She did her PhD from the Indian Institute of Science in Bangalore, India on the spatial organization of tropical paper wasps within their nests.

Abstract: Reproduction provides direct fitness benefits, therefore, it is important to determine why in some societies certain individuals have disproportionate access to reproductive opportunities. In many social systems, reproductive hierarchy is determined by aggression, age, genetics, or physiology. The social interactions that underly reproductive hierarchies can occur in multiple situations, yet they are rarely studied in unison. The reproductive heir in the social wasp, Ropalidia marginata, remains cryptic until the queen dies or disappears. To determine if a reproductive heir can be identified through her behavior, we examined four types of social interactions: aggression, spatial overlap, and exchange of solid or liquid food. We asked if accounting for all four social situations in a multilayer network provides more information about the structure of the society than examining each situation on its own, or in an aggregate network that does not distinguish between social situations. We found that the reproductive heir had the most social interactions in the multilayer network, but not in each of the social situations when considered separately, or when all situations were lumped together. Our work demonstrates that multilayer networks can uncover new insights on social organization by explicitly considering the links between multiple situations of social interactions.

Bio: Mari Kawakatsu is a PhD candidate in the Program in Applied and Computational Mathematics at Princeton University. In her research as a mathematical biologist, she uses tools from evolutionary game theory, dynamical systems, and network science to explore mathematical and computational models of collective and emergent behavior in social systems.

Abstract: Many social and biological systems are characterized by enduring hierarchies, including those organized around prestige in academia, dominance in animal groups, and desirability in online dating. Despite their ubiquity, the general mechanisms that explain the creation and endurance of such hierarchies are not well understood. We introduce a generative model for the dynamics of hierarchies using time-varying networks in which new links are formed based on the preferences of nodes in the current network and old links are forgotten over time. The model produces a range of hierarchical structures, ranging from egalitarianism to bistable hierarchies, and we derive critical points that separate these regimes in the limit of long system memory. Importantly, our model supports statistical inference, allowing for a principled comparison of generative mechanisms using data. We apply the model to study hierarchical structures in empirical data on hiring patterns among mathematicians, dominance relations among parakeets, and friendships among members of a fraternity, observing several persistent patterns as well as interpretable differences in the generative mechanisms favored by each. Our work contributes to the growing literature on statistically grounded models of time-varying networks.

Paper: Emergence of Hierarchy in Networked Endorsement Dynamics

Website: www.marikawakatsu.com

Bio: Leonie Neuhäuser is a 1st year PhD student at RWTH Aachen. She holds a Master of Science in Mathematical Modelling and Scientific Computing from the University of Oxford. Previously, she studied mathematics and psychology in Germany at Universität Bonn and in Mexico at Benemérita Universidad Autónoma de Puebla.

Abstract: Multibody interactions can reveal higher-order dynamical effects that are not captured by traditional two-body network models. In this work, we derive and analyze models for consensus dynamics on hypergraphs, where nodes interact in groups rather than in pairs. Our work reveals that multibody dynamical effects that go beyond rescaled pairwise interactions can appear only if the interaction function is nonlinear, regardless of the underlying multibody structure. As a practical application, we introduce a specific nonlinear function to model three-body consensus, which incorporates reinforcing group effects such as peer pressure. Unlike consensus processes on networks, we find that the resulting dynamics can cause shifts away from the average system state. The nature of these shifts depends on a complex interplay between the distribution of the initial states, the underlying structure, and the form of the interaction function. By considering modular hypergraphs, we discover state-dependent, asymmetric dynamics between polarized clusters where multibody interactions make one cluster dominate the other.

Paper: Multibody interactions and nonlinear consensus dynamics on networked systems

Bio: Maria Ramos, PhD, received her Ph.D. in Sociology at Duke University in Spring 2020. She is currently a Visiting Research Fellow at Duke Network Analysis Center. Maria's research combines network analysis and computational methods to investigate mental models of beliefs about politics and the self.

Abstract: A long-standing sociological puzzle is how multiple identities, categories of people individuals use to describe themselves and others, are organized within the self. Sociologists have conceptualized identities as organized around central elements that shape the selection and enactment of multiple other identities. While this organization has been implicit or explicit in the literature, little work has directly examined this pattern of organization. In this article, I map identity structures using network analysis techniques. I also investigate the claim that values, abstract ideals that guide action, bridge identities from different domains to give the self a sense of cohesion. I use data from the European Social Survey 2002 to construct networks where nodes are values, identities, and sociodemographic characteristics. Network ties are given by measures of statistical association between nodes. Results suggest identities are organized around content domains (e.g., family, religion, and occupation). Country of citizenship emerges as the most central node in the between-nations network. Both across and within countries, a few nodes (e.g., age and religious denomination) act as hubs, connecting distinct domain regions (e.g., family, religion, occupation). Meanwhile, values occupy a peripheral position in most within-country networks.

Website: www.mariacramos.com

Bio: Sarah is a 4th year PhD candidate in the Graduate Program in Neuroscience at the University of Minnesota. She was first inducted in the network neuroscience world when she attended the 2019 Complex Network Winter Workshop in Quebec City. Outside of science, she enjoys playing Dungeons and Dragons with friends and training her two dogs in agility, dogsledding, and scent detection.

Abstract: Behavior results in widespread activation of the cerebral cortex. To fully understanding the cerebral cortex's role in behavior therefore requires a mesoscopic level description of the cortical regions engaged and their functional interactions. Mesoscopic imaging of Ca2+ fluorescence through transparent polymer skulls implanted on transgenic Thy1-GCaMP6f mice reveals widespread activation of the cerebral cortex during locomotion, including not just primary motor and somatosensory regions but also premotor, auditory, retrosplenial, and visual cortices. To understand these patterns of activation, we used spatial Independent Component Analysis (sICA) that segmented the dorsal cortex of individual mice into 20-22 Independent Components (ICs). The resulting ICs are highly consistent across imaging sessions and animals. Using the time series of Ca2+ fluorescence in each IC, we examined the changes in functional connectivity from rest to locomotion. Compared to rest, functional connectivity increases prior to and at the onset of locomotion. During continued walking, a global decrease in functional connectivity develops compared to rest that uncovers a distinct, sparser network in which ICs in secondary motor areas increase their correlations with more posterior ICs in somatosensory, motor, visual, and retrosplenial cortices. Eigenvector centrality analysis demonstrates that ICs located in premotor areas increase their influence on the network during locomotion while ICs in other regions, including somatosensory and primary motor, decrease in importance. We observed sequential changes in functional connectivity across transitions between rest and locomotion, with premotor areas playing an important role in coordination of computations across cortical brain regions.

Paper: Wide-Field Calcium Imaging of Dynamic Cortical Networks During Locomotion 

Bio: Diana García Cortés is a PhD student in Biomedical sciences at the Theoretical Oncology lab at the National Institute of Genomic Medicine in Mexico. My main research focus are co-expression cancer networks and she aims to promote participation of mexican students, especially women, into network science.

Abstract: In any type of cancer, healthy cells traverse through a transition where they acquire a set of traits that promote tumorigenesis. During this transition there is an accumulation of genomic alterations that disrupt transcriptional regulatory mechanisms, modify gene expression and alter key cellular processes. We have studied gene co-expression profiles and gene co-expression networks from RNA-seq samples from TCGA (4237 samples from colon, lung, kidney, thyroid, and uterus cancer and 609 healthy samples from the same tissues). In cancer, there is a loss of long-range co-expression, however, each manifestation displays particularities. Cancer co-expression net-works present an imbalance in the proportion of intra- and inter-chromosomal interactions, meaning that the majority of high co-expression interactions connect gene-pairs in the same chromosome. Those features are not present in the healthy phenotype. The analysis was also performed for breast cancer molecular subtypes. This locally biased co-expression in cancer cells was not previously reported. To determine its importance and its relationship with the traits that promote tumorigenesis is a matter of our current research.

Papers: Gene Co-expression Is Distance-Dependent in Breast Cancer, Eight years of homicide evolution in Monterrey, Mexico: a network approach

Bio: Sara D. Williams, PhD in Ecology, Evolution, and Marine Biology from Northeastern University, uses network science and modeling combined with fieldwork and physiological experiments to attack problems facing coral reefs from multiple angles. She studies the connections among polyps in a coral colony, the associations of coral species and their symbiotic algae and microbiome, and the spread of coral diseases - all under the lens of global climate change. Williams is currently a Postdoctoral researcher in the Coral Health and Disease lab at Mote Marine Laboratory in Sarasota, FL.

Abstract: As coral reefs struggle to persist under a multitude of threats, understanding the resistance and resilience of these complex systems is a central goal of coral reef biology and ecology. Different coral species associate with different species of symbiotic algae (Symbiodiniaceae), creating a complex network of symbiotic associations on a reef. This symbiotic relationship is vulnerable to increasing temperatures. Coral bleaching is the breakdown of the association between the coral host and its endosymbiotic algae in response to stressful temperatures. We analyzed a global network of coral-symbiont associations for resistance to temperature stress and robustness to various perturbations. Our novel bleaching model determined resistance of the networks to increasing temperature by removing links when the environmental temperature surpassed their weight, a temperature threshold for individual host-symbiont pairs based on known physiological responses. Ecological robustness, defined by how much perturbation is needed to decrease the number of nodes by 50%, was determined for multiple removal models that considered traits of the hosts, symbionts, and their associations. We show that the global network of associations between corals and Symbiodiniaceae and its distribution of thermal tolerances are non-random, and the evolution of this architecture has led to higher sensitivity to environmental perturbations. By limiting our spatial scale and expanding our temporal scale, we can start to answer questions about reef resilience. To do this, I repetitively monitored and sampled a coral-Symbiodiniaceae network in Bocas del Toro, Panama from January 2017 to January 2018, during which the reef experienced two high-temperature bleaching events. We explored how Symbiodiniaceae communities varied across host species, depth, and time. Temporal networks of the symbiotic associations were used to assess differences in association patterns that led to structural and/or functional resilience to repeat heat stress events. I define structural resilience as a system's ability to either resist changing the structure of associations or return to the initial structure after a disturbance. Functional resilience is a system's ability to recover in relation to its health and function, but it may also undergo structural changes during that recovery. Structures of Symbiodiniaceae co-occurrence networks and coral-symbiont networks varied through time indicating that on a reef scale, the coral-symbiont associations responded to changing environmental conditions.

Paper: Resistance and robustness of the global coral-symbiont network 

Website: https://saradellwilliams.wordpress.com

Bio: Heather Zinn Brooks, PhD, Assistant Professor of Mathematics, specializes in mathematical modeling of complex systems. Brooks earned both her Bachelor's degree and Ph.D. in Mathematics from the University of Utah. Before joining the faculty at Harvey Mudd, she held a CAM postdoctoral position in the Mathematics department at UCLA. She strives to create and communicate mathematics in a way that is exciting, relevant, and inclusive. 

Abstract: Online social media networks have become extremely influential sources of news and information. Given the large audience and the ease of sharing content online, the content that spreads on online social networks can have important consequences on public opinion, policy, and voting. To better understand the online content spread, mathematical modeling of opinion dynamics is becoming an increasingly popular field of study. In this talk, I will introduce you to a special class of models of opinion dynamics on networks called bounded-confidence models. I will then discuss some of the applications and theory that my collaborators and I have been developing with these models, including the impact of media, opinion dissemination, mean-field dynamics, and extensions to hypergraphs and multilayer networks. This talk will also include some unsolved questions for future work. 

Website: https://math.hmc.edu/hzinnbrooks/