Bradly Alicea

Virtual reality in neuroscience research and therapy

Virtual reality (VR) environments are increasingly being used by neuroscientists to simulate natural events and social interactions. VR creates interactive, multimodal sensory stimuli that offer unique advantages over other approaches to neuroscientific research and applications. VRs compatibility with imaging technologies such as functional MRI allows researchers to present multimodal stimuli with a high degree of ecological validity and control while recording changes in brain activity. Therapists, too, stand to gain from progress in VR technology, which provides a high degree of control over the therapeutic experience. Here we review the latest advances in VR technology and its applications in neuroscience research.

Toy models for macroevolutionary patterns and trends

Many models have been used to simplify and operationalize the subtle but complex mechanisms of biological evolution. Toy models are gross simplifications that nevertheless attempt to retain major essential features of evolution, bridging the gap between empirical reality and formal theoretical understanding. In this paper, we examine thirteen models which describe evolution that also qualify as such toy models, including the tree of life, branching processes, adaptive ratchets, fitness landscapes, and the role of nonlinear avalanches in evolutionary dynamics. Such toy models are intended to capture features such as evolutionary trends, coupled evolutionary dynamics of phenotype and genotype, adaptive change, branching, and evolutionary transience. The models discussed herein are applied to specific evolutionary contexts in various ways that simplify the complexity inherent in evolving populations. While toy models are overly simplistic, they also provide sufficient dynamics for capturing the fundamental mechanism(s) of evolution. Toy models might also be used to aid in high-throughput data analysis and the understanding of cultural evolutionary trends. This paper should serve as an introductory guide to the toy modeling of evolutionary complexity.

Quantifying Mosaic Development: Towards an Evo-Devo Postmodern Synthesis of the Evolution of Development via Differentiation Trees of Embryos.

Embryonic development proceeds through a series of differentiation events. The mosaic version of this process (binary cell divisions) can be analyzed by comparing early development of Ciona intestinalis and Caenorhabditis elegans. To do this, we reorganize lineage trees into differentiation trees using the graph theory ordering of relative cell volume. Lineage and differentiation trees provide us with means to classify each cell using binary codes. Extracting data characterizing lineage tree position, cell volume, and nucleus position for each cell during early embryogenesis, we conduct several statistical analyses, both within and between taxa. We compare both cell volume distributions and cell volume across developmental time within and between single species and assess differences between lineage tree and differentiation tree orderings. This enhances our understanding of the differentiation events in a model of pure mosaic embryogenesis and its relationship to evolutionary conservation. We also contribute several new techniques for assessing both differences between lineage trees and differentiation trees, and differences between differentiation trees of different species. The results suggest that at the level of differentiation trees, there are broad similarities between distantly related mosaic embryos that might be essential to understanding evolutionary change and phylogeny reconstruction. Differentiation trees may therefore provide a basis for an Evo-Devo Postmodern Synthesis.

Using Polysome Isolation with Mechanism Alteration to Uncover Transcriptional and Translational Dynamics in Key Genes

What does it mean when we say a cell’s biochemistry is regulated during changes to the phenotype? While there are a plethora of potential mechanisms and contributions to the final outcome, a more tractable approach is to examine the dynamics of mRNA. This way, we can assess the contributions of both known and unknown decay and aggregation processes for maintaining levels of gene product on a gene-by-gene basis. In this extended protocol, drug treatments that target specific cellular functions (termed mechanism disruption) can be used in tandem with mRNA extraction from the polysome to look at the dynamics of mRNA levels associated with transcription and translation at multiple stages during a physiological perturbation. This is accomplished through validating the polysome isolation method in human cells and comparing fractions of mRNA for each experimental treatment at multiple points in time. First, three different drug treatments corresponding to the arrest of various cellular processes are administered to populations of human cells. For each treatment, the transcriptome and translatome are compared directly at different time points by assaying both cell-type specific and non-specific genes. There are two findings of note. First, extraction of mRNA from the polysome and comparison with the transcriptome can yield interesting information about the regulation of cellular mRNA during a functional challenge to the cell. In addition, the conventional application of such drugs to assess mRNA decay is an incomplete picture of how severely challenged or senescent cells regulate mRNA in response. This extended protocol demonstrates how the gene- and process-variable degradation of mRNA might ultimately require investigations into the course-grained dynamics of cellular mRNA, from transcription to ribosome.

Contextual Geometric Structures: modeling the fundamental components of cultural behavior

The structural complexity of culture cannot be characterized by simply modeling cultural beliefs or inherited ideas. Formal computational and algorithmic models of culture have focused on the inheritance of discrete cultural units, which can be hard to define and map to practical contexts. In cultural anthropology, research involving structuralist and post-structuralist perspectives have helped us better understand culturally-dependent classification systems and oppositional phenomena (e.g. light-dark, hot-cold, good-evil). Contemporary research in cognitive neuroscience suggests that complementary sets may be represented dynamically in the brain, but no model for the evolution of these sets has of yet been proposed. To fill this void, a method for simulating cultural or other highly symbolic behaviors called contextual geometric structures will be introduced. The contextual geometric structures approach is based on a hybrid model that approximates both individual/group cultural practice and a fluctuating environment. The hybrid model consists of two components. The first is a set of discrete automata with a soft classificatory structure. These automata are then embedded in a Lagrangian-inspired particle simulation that defines phase space relations and environmental inputs. The concept of conditional features and equations related to diversity, learning, and forgetting are used to approximate the goal-directed and open-ended features of cultural-related emergent behavior. This allows cultural patterns to be approximated in the context of both stochastic and deterministic evolutionary dynamics. This model can yield important information about multiple structures and social relationships, in addition to phenomena related to sensory function and higher-order cognition observed in neural systems.

OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans

The adoption of powerful software tools and computational methods from the software industry by the scientific research community has resulted in a renewed interest in integrative, large-scale biological simulations. These typically involve the development of computational platforms to combine diverse, process-specific models into a coherent whole. The OpenWorm Foundation is an independent research organization working towards an integrative simulation of the nematode Caenorhabditis elegans, with the aim of providing a powerful new tool to understand how the organisms behaviour arises from its fundamental biology. In this perspective, we give an overview of the history and philosophy of OpenWorm, descriptions of the constituent sub-projects and corresponding open-science management practices, and discuss current achievements of the project and future directions. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

Genotype-specific developmental plasticity shapes the timing and robustness of reproductive capacity in Caenorhabditis elegans

The effects of environmental stress on developmental phenotypes allows us to observe the adaptive capacity of various genotypes. In single-genotype populations, this allows for observing the effects of standing variation on the process of developmental plasticity. Typically, such efforts do not allow us to distinguish the effect of genotype from the effects of environment. In the Nematode Caenorhabditis elegans, however, the L1 stage of development allows for precise and controlled imposition of starvation on a synchronized population of individuals. This allows for systematic comparisons within and between defined genotypes. This study provides a number of innovations in the study of Nematode developmental plasticity. The first is to demonstrate systems-level mechanisms of adaptive response for various genetic mutant phenotypes to extended L1 arrest. The effects of developmental deprivation are quantifiable by applying statistical modeling techniques to reproductive time-series. This is characterized for both a wildtype strain (N2) and a host of genetic mutant strains. A further contribution is investigating the effects of starvation at multi-generational timescales. This includes both two generations post-starvation and multiple starvation experiences with selection for fecundity over 11 generations. Statistical techniques such as Kernel Density Estimation (KDE), fold-change analysis, and Area Under the Curve (AUC) analysis reveal differences from baseline in the form of shifts in reproductive timing and kurtosis in peak reproductive capacity characterize adaptive responses that are semi-independent of environment. Extending these patterns of response to ad hoc comparisons with multi-generational contexts reveals that different genotypes characterized by mutations of specific genetic loci lead to vastly different outcomes.

Information Isometry Technique Reveals Organizational Features in Developmental Cell Lineages

In this paper, we will introduce a method for calculating and visualizing the information content of embryogenesis called the information isometry technique. We treat cell lineage trees as directed acyclic graphs (DAGs) that can be subject to reordering using various criteria. When we compare alternative orderings of these graphs, they reveal subtle patterns of information. We use one such alternative criteria (e.g. a differentiation code) to sort cells at each level of the tree. Both axial- and differentiation-based orderings can by characterized using a binary classifier to quantify the order of particular cells at each level of a given tree. We calculate a Hamming distance to compare these orderings and reveal differences that result from these ordering criteria. We also introduce a method of visualization through the construction of isometric graphs, or a series of colored points forming isometric lines with each representing a level of the original lineage tree. We show that these graphs reveal biologically significant patterns through comparisons between randomly generated lineage/differentiation trees and the Caenorhabditis elegans lineage/differentiation tree. As a collective indicator of distance between various cell lineage orderings, isometric graphs can reveal a number of emergent patterns within cell lineages and between sublineages, including the relative information content of specific subtrees. These patterns of information content reveal the informative nature of alternative ordering criteria with respect to important trends in development.

Modeling Cellular Information Processing Using a Dynamical Approximation of Cellular mRNA

How does the regulatory machinery of an animal cell ensure its survival during large-scale biochemical and phenotypic transitions? When a cell is strongly perturbed by an environmental stimulus, it can either die or persist with compensatory changes. But what do the dynamics of individual genes look like during this process of adaptation? In a previous technical paper, two approaches (drug treatments and polysome isolation) were used in tandem to demonstrate the effects of perturbation on cellular phenotype. In this paper, we can use these data in tandem with a discrete, first-order feedback model that incorporates leaky components to better characterize adaptive responses of mRNA regulation related to information processing in the cell. By evaluating the dynamic relationship between mRNA associated with transcription (translatome) and mRNA associated with the polysome (transcriptome) at multiple timepoints, hypothetical conditions for decay and aggregation are found and discussed. Our feedback model allows for the approximation of fluctuations and other aspects of cellular information processing, in addition to the derivation of three information processing principles. These results will lead us to a better understanding of how mRNA provides variable information over time to the complex intracellular environment, particularly in the context of large-scale phenotypic change.

DevoWorm: data-theoretical synthesis of C. elegans development.

The DevoWorm group adds an important dimension to the OpenWorm Foundation9s goal of creating a digital nematode. Compared with the great diversity and plasticity found across the tree of life, Caenorhabditis elegans development is a rather unique model system. C. elegans biology provides us with a highly-deterministic developmental cell lineage, and a clear linkage from zygote to cells of the adult phenotype. This paper provides an example of the DevoWorm approach, merging computational modeling and insights from data science. The first part introduces alternative ways of understanding the embryo, including the role of hierarchical differentiation and whole-embryo pattern generation. We suggest that systematic decomposition of embryo feature space is just as important to understanding the embryo as single-gene and molecular studies. The second half of this paper focuses on the process of developmental cell terminal differentiation, and how terminally-differentiated cells contribute to structure and function of the adult phenotype. An analysis is conducted for cells that were present during discrete time intervals covering 200 to 400 minutes of embryogenesis, providing us with basic statistics on the tempo of the embryogenetic process in addition to the appearance of specific cell types and their order relative to embryogenetic time. As with ideas presented in the first section, these data may also provide clues as to the timing for the initial onset of stereotyped and autonomic behaviors of the developing animal. Taken together, these overlapping approaches can provide critical links across life-history, anatomy and function to reveal the essential components needed to create a complex digital organism, where artificial life imitates real life.Biological development is often described as a dynamic, emergent process. This is evident across a variety of phenomena, from the temporal organization of cell types in the embryo to compounding trends that affect large-scale differentiation. To better understand this, we propose combining quantitative investigations of biological development with theory-building techniques. This provides an alternative to the gene-centric view of development: namely, the view that developmental genes and their expression determine the complexity of the developmental phenotype. Using the model system Caenorhabditis elegans, we examine time-dependent properties of the embryonic phenotype and utilize the unique life-history properties to demonstrate how these emergent properties can be linked together by data analysis and theory-building. We also focus on embryogenetic differentiation processes, and how terminally-differentiated cells contribute to structure and function of the adult phenotype. Examining embryogenetic dynamics from 200 to 400 minutes post-fertilization provides basic quantitative information on developmental tempo and process. To summarize, theory construction techniques are summarized and proposed as a way to rigorously interpret our data. Our proposed approach to a formal data representation that can provide critical links across life-history, anatomy and function.