Dynamical Modularity in Automata Models of Biochemical Networks

Given the large size and complexity of most biochemical regulation and signaling networks, there is a non-trivial relationship between the micro-level logic of component interactions and the observed macro-dynamics. Here we address this issue by formalizing the existing concept of pathway modules, which are sequences of state updates that are guaranteed to occur (barring outside interference) in the dynamics of automata networks after the perturbation of a subset of driver nodes. We present a novel algorithm to automatically extract pathway modules from networks and we characterize the interactions that may take place between modules. This methodology uses only the causal logic of individual node variables (micro-dynamics) without the need to compute the dynamical landscape of the networks (macro-dynamics). Specifically, we identify complex modules, which maximize pathway length and require synergy between their components. This allows us to propose a new take on dynamical modularity that partitions complex networks into causal pathways of variables that are guaranteed to transition to specific states given a perturbation to a set of driver nodes. Thus, the same node variable can take part in distinct modules depending on the state it takes. Our measure of dynamical modularity of a network is then inversely proportional to the overlap among complex modules and maximal when complex modules are completely decouplable from one another in the network dynamics. We estimate dynamical modularity for several genetic regulatory networks, including the Drosophila melanogaster segment-polarity network. We discuss how identifying complex modules and the dynamical modularity portrait of networks explains the macro-dynamics of biological networks, such as uncovering the (more or less) decouplable building blocks of emergent computation (or collective behavior) in biochemical regulation and signaling.Comment: 42 pages, 7 figure

Evolution of the Informational Complexity of Contemporary Western Music

We measure the complexity of songs in the Million Song Dataset (MSD) in terms of pitch, timbre, loudness, and rhythm to investigate their evolution from 1960 to 2010. By comparing the Billboard Hot 100 with random samples, we find that the complexity of popular songs tends to be more narrowly distributed around the mean, supporting the idea of an inverted U-shaped relationship between complexity and hedonistic value. We then examine the temporal evolution of complexity, reporting consistent changes across decades, such as a decrease in average loudness complexity since the 1960s, and an increase in timbre complexity overall but not for popular songs. We also show, in contrast to claims that popular songs sound more alike over time, that they are not more similar than they were 50 years ago in terms of pitch or rhythm, although similarity in timbre shows distinctive patterns across eras and similarity in loudness has been increasing. Finally, we show that musical genres can be differentiated by their distinctive complexity profiles.Comment: 8 pages, 6 figure

Igniting a SPARK in Dead-Eyed Youth

Far too many youths, known as being “high-risk,” are not faring well at life and in school. To accompany this problem, most educators lack the ability to effectively respond to these youths. Motivated by a desire to provide educators and those working with youth at risk with a much-needed alternative to emotionally draining conventional classroom cultures, our intent is to move beyond the deforming rhetoric of behaviorism and lay claim to moral and spiritual foundations that bring both groups together in a setting that promotes a culture of dialogue, openness, trust, and caring

Dynamical methods for target control of biological networks

Estimating the influence that individual nodes have on one another in a Boolean network is essential to predict and control the system’s dynamical behaviour, for example, detecting key therapeutic targets to control pathways in models of biological signalling and regulation. Exact estimation is generally not possible due to the fact that the number of configurations that must be considered grows exponentially with the system size. However, approximate, scalable methods exist in the literature. These methods can be divided into two main classes: (i) graph-theoretic methods that rely on representations of Boolean dynamics into static graphs and (ii) mean-field approaches that describe average trajectories of the system but neglect dynamical correlations. Here, we compare systematically the performance of these state-of-the-art methods on a large collection of real-world gene regulatory networks. We find comparable performance across methods. All methods underestimate the ground truth, with mean-field approaches having a better recall but a worse precision than graph-theoretic methods. Computationally speaking, graph-theoretic methods are faster than mean-field ones in sparse networks, but are slower in dense networks. The preference of which method to use, therefore, depends on a network’s connectivity and the relative importance of recall versus precision for the specific application at hand

Healing Trauma in our At-Risk Youth

Trauma in our at-risk youth community is a growing concern and must be addressed if we are to expect positive transformations. We have developed a mentoring program that is specifically designed to bring healing that is transforming our at-risk youth that are on probation with the justice system here in Georgia. The outcomes are humbling and remarkable