Alexander J. Gates

Designs for Ultra-Tiny, Special-Purpose Nanoelectronic Circuits

Designs and simulation results are given for two small, special-purpose nanoelectronic circuits. The area of special-purpose nanoelectronics has not been given much consideration previously, though much effort has been devoted to the development of general-purpose nanoelectronic systems, i.e., nanocomputers. This paper demonstrates via simulation that the nanodevices and nanofabrication techniques developed recently for general-purpose nanocomputers also might be applied with substantial benefit to implement less complex nanocircuits targeted at specific applications. Nanocircuits considered here are a digital controller for the leg motion on an autonomous millimeter-scale robot and an analog nanocircuit for amplification of signals in a tiny optoelectronic sensor or receiver. Simulations of both nanocircuit designs show significant improvement over microelectronic designs in metrics such as footprint area and power consumption. These improvements are obtained from designs employing nanodevices and nanofabrication techniques that already have been demonstrated experimentally. Thus, the results presented here suggest that such improvements might be realized in the near term for important, special-purpose applications.

Control of complex networks requires both structure and dynamics

The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies to revert a diseased cell to a healthy state, or a mature cell to a pluripotent state. Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. 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.

Exploring the space of viable configurations in a model of metabolism-boundary co-construction

We introduce a spatial model of concentration dynamics that supports the emergence of spatiotemporal inhomogeneities that engage in metabolism–boundary co-construction. These configurations exhibit disintegration following some perturbations, and self-repair in response to others. We define robustness as a viable configurations tendency to return to its prior configuration in response to perturbations, and plasticity as a viable configurations tendency to change to other viable configurations. These properties are demonstrated and quantified in the model, allowing us to map a space of viable configurations and their possible transitions. Combining robustness and plasticity provides a measure of viability as the average expected survival time under ongoing perturbation, and allows us to measure how viability is affected as the configuration undergoes transitions. The framework introduced here is independent of the specific model we used, and is applicable for quantifying robustness, plasticity, and viability in any computational model of artificial life that demonstrates the conditions for viability that we promote.

CANA: A python package for quantifying control and canalization in Boolean Networks

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 computationally intractable when aggregated into networks. In previous work we demonstrated that automata network models of biochemical regulation are highly canalizing, whereby many variable states and their groupings are redundant (Marques-Pita and Rocha, 2013). The precise charting and measurement of such canalization simplifies these models, making even very large networks amenable to analysis. Moreover, canalization plays an important role in the control, robustness, modularity and criticality of Boolean network dynamics, especially those used to model biochemical regulation (Gates and Rocha, 2016; Gates et al., 2016; Manicka, 2017). Here we describe a new publicly-available Python package that provides the necessary tools to extract, measure, and visualize canalizing redundancy present in Boolean network models. It extracts the pathways most effective in controlling dynamics in these models, including their effective graph and dynamics canalizing map, as well as other tools to uncover minimum sets of control variables.

The Impact of Random Models on Clustering Similarity

Clustering is a central approach for unsupervised learning. After clustering is applied, the most fundamental analysis is to quantitatively compare clusterings. Such comparisons are crucial for the evaluation of clustering methods as well as other tasks such as consensus clustering. It is often argued that, in order to establish a baseline, clustering similarity should be assessed in the context of a random ensemble of clusterings. The prevailing assumption for the random clustering ensemble is the permutation model in which the number and sizes of clusters are fixed. However, this assumption does not necessarily hold in practice; for example, multiple runs of K-means clustering returns clusterings with a fixed number of clusters, while the cluster size distribution varies greatly. Here, 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. The consequences of different random models are illustrated using synthetic examples, handwriting recognition, and gene expression data. 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.

The structure of ontogenies in a model protocell

Emergent individuals are often characterized with respect to their viability: their ability to maintain themselves and persist in variable environments. As such individuals interact with an environment, they undergo sequences of structural changes that correspond to their ontogenies. Ultimately, individuals that adapt to their environment, and increase their chances of survival, persist. This article provides an initial step towards a more formal treatment of these concepts. A network of possible ontogenies is uncovered by subjecting a model protocell to sequential perturbations and mapping the resulting structural configurations. The analysis of this network reveals trends in how the protocell can move between configurations, how its morphology changes, and how the role of the environment varies throughout. Viability is defined as expected life span given an initial configuration. This leads to two notions of adaptivity: a local adaptivity that addresses how viability changes in plastic transitions, and a global adaptivity that looks at longer-term tendencies for increased viability. To demonstrate how different protocell-environment pairings produce different patterns of ontogenic change, we generate and analyze a second ontogenic network for the same protocell in a different environment. Finally, the mechanisms of a minimal adaptive transition are analyzed, and it is shown that these rely on distributed spatial processes rather than an explicit regulatory mechanism. The combination of this model and analytical techniques provides a foundation for studying the emergence of viability, ontogeny, and adaptivity in more biologically realistic systems.

Ontogeny and adaptivity in a model protocell.

Viability, ontogeny, and adaptivity have been widely discussed within the context of emergent individuality. This paper provides an initial step towards a more formal treatment of these concepts. A network of possible ontogenies is uncovered by subjecting a model protocell to sequential perturbations, and mapping the resulting structural configurations. The analysis of this network reveals trends in how the protocell can move between configurations, how its morphology changes, and how the role of the environment varies throughout. Viability is defined as expected lifespan given an initial configuration. This leads to two notions of adaptivity: a local adaptivity that addresses how viability changes in plastic transitions, and a global adaptivity that looks at longerterm tendencies for increased viability. The mechanisms of a minimal adaptive transition are analyzed, and it is shown that these rely on distributed spatial processes rather than an explicit regulatory mechanism.

Structure and Dynamics Affect the Controlability of Complex Systems: A Preliminary Study

Complex systems are typically understood as large nonlinear multivariate systems. Their organization and behavior are commonly modeled by representations such as graphs and automata networks. Graphs, where nodes representing variables lack intrinsic dynamics, capture the structure or organization of complex systems. The simplest way to study multi-variate dynamics, is to allow network nodes to have states and update them with automata; for instance, Boolean networks (BN) are canonical models of complex systems and exhibit a wide range of dynamical behaviors [1].