Dynamic Influence Networks for Rule-based Models

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres

Cortical synchronization as a neural basis for visual perception

Cortical synchronization has been suggested as a neural mechanism that is able to solve the feature binding problem. This idea has been intensively studied at neurophysiological, psychophysical and computational level. In this paper, arguments for and against the role of cortical synchronization in visual perception are critically examined. Initial neurophysiological findings of correlated neural activity in the primary visual cortex have been questioned by studies which reveal enhanced firing rate to the figure region compared to the background. Computational investigations reveal that synchronization has capacity limit. At the behavioural level, change blindness has been used as the evidence for capacity limit of visual perception. However, further examination of this issue showed that detailed visual representation exists but it is obscured by limitation of attention and visual working memory. Other behavioural phenomena, such as perceptual asynchrony, also point to the fact that there is dissociation between correlated neural activity and perception. Therefore, at present there is no sufficient evidence to support the conclusion that cortical synchronization plays a crucial role in visual perception

In Synch but Not in Step: Circadian Clock Circuits Regulating Plasticity in Daily Rhythms

The suprachiasmatic nucleus (SCN) is a network of neural oscillators that program daily rhythms in mammalian behavior and physiology. Over the last decade much has been learned about how SCN clock neurons coordinate together in time and space to form a cohesive population. Despite this insight, much remains unknown about how SCN neurons communicate with one another to produce emergent properties of the network. Here we review the current understanding of communication among SCN clock cells and highlight a collection of formal assays where changes in SCN interactions provide for plasticity in the waveform of circadian rhythms in behavior. Future studies that pair analytical behavioral assays with modern neuroscience techniques have the potential to provide deeper insight into SCN circuit mechanisms

Progress toward an understanding of cortical computation

The additional data, perspectives, questions, and criticisms contributed by the commentaries strengthen our view that local cortical processors coordinate their activity with the context in which it occurs using contextual fields and synchronized population codes. We therefore predict that whereas the specialization of function has been the keynote of this century the coordination of function will be the keynote of the next

Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity

Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of β-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for ‘synthetic cellular heterogeneity’ that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a ‘phenotypic sensitivity analysis’ method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.National Institutes of Health (U.S.) (NIH NIGMS grant R01GM086881)National Science Foundation (U.S.) (NSF Award #1001092)National Science Foundation (U.S.) (NSF Graduate Research Fellowship Program)Swiss National Science Foundation (SystemsX.ch grant

Embodied Cognitive Science of Music. Modeling Experience and Behavior in Musical Contexts

Recently, the role of corporeal interaction has gained wide recognition within cognitive musicology. This thesis reviews evidence from different directions in music research supporting the importance of body-based processes for the understanding of music-related experience and behaviour. Stressing the synthetic focus of cognitive science, cognitive science of music is discussed as a modeling approach that takes these processes into account and may theoretically be embedded within the theory of dynamic systems. In particular, arguments are presented for the use of robotic devices as tools for the investigation of processes underlying human music-related capabilities (musical robotics)