Since 2001, the NSF ADVANCE program has funded organizational change projects promoting gender equity in academic science, technology, engineering, and mathematics (STEM) fields. The connections between institutions and individuals involved in the …
Many biological networks are modeled with multivariate discrete dynamical systems. Current theory suggests that the network of interactions captures salient features of system dynamics, but it misses a key aspect of these networks: some interactions are more important than others due to dynamical redundancy and nonlinearity. This unequivalence leads to a canalized dynamics that differs from constraints inferred from network structure alone. To capture the redundancy present in biochemical regulatory and signaling interactions, we present the effective graph, an experimentally validated mathematical framework that synthesizes both structure and dynamics in a weighted graph representation of discrete multivariate systems. Our results demonstrate the ubiquity of redundancy in biology and provide a tool to increase causal explainability and control of biochemical systems.
The 20th anniversary of the publication of the first draft of the human genome1,2 offers an opportunity to track how the project has empowered research into the genetic roots of human disease, changed drug discovery and helped to revise the idea of …
By studying the publication careers of over 1.5 million scientists, we take a career-focused persepective to identify the factors inhibiting gender equity in STEM. We find that despite an improved trend towards population equality, the gender differences in career-wise productivity and impact have been growing over the last 70 years. Yet, the research outcomes of men and women year to year are essentially equivalent. Third, and most importantly, women are ending their publishing careers at higher rates than men, and this is happening across every stage of their careers.
Logical models offer a simple but powerful means to understand the complex dynamics of biochemical regulation, without the need to estimate kinetic parameters. However, even simple automata components can lead to collective dynamics that are …
Clustering similarity measures can be classified based on the cluster types: i) partitions that group elements into non-overlapping clusters, ii) hierarchical clusterings that group elements into a nested series of partitions (a.k.a. dendrogram), or …
The study of network structure has uncovered signatures of the organization of complex systems. Using Boolean network ensembles, we demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.
Clustering is one of the most universal approaches for understanding complex data. A pivotal aspect of clustering analysis is quantitatively comparing clusterings; clustering comparison is the basis for many tasks such as clustering evaluation, …
How knowledge informs and alters disciplines is itself an enlightening, and vibrant field. This type of meta research into new findings, insights, conceptual frameworks and techniques is important, among other things, for policymakers who fund research in the hope of tackling society’s most pressing challenges, which inevitably span disciplines.
We derive corrected variants of two clustering similarity measures (the Rand index and Mutual Information) in the context of two random clustering ensembles in which the number and sizes of clusters vary. In addition, we study the impact of one-sided comparisons in the scenario with a reference clustering. We demonstrate that the choice of random model can have a drastic impact on the ranking of similar clustering pairs, and the evaluation of a clustering method with respect to a random baseline; thus, the choice of random clustering model should be carefully justified.