Data-Theoretical Synthesis of the Early Developmental Process.

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 min 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.

Morphogenetic processes as data: Quantitative structure in the Drosophila eye imaginal disc.

We can improve our understanding of biological processes through the use of computational and mathematical modeling. One such morphogenetic process (ommatidia formation in the Drosophila eye imaginal disc) provides us with an opportunity to demonstrate the power of this approach. We use a high-resolution image that catches the spatially- and temporally-dependent process of ommatidia formation in the act. This image is converted to quantitative measures and models that provide us with new information about the dynamics and geometry of this process. We approach this by addressing four computational hypotheses, and provide a publicly-available repository containing data and images for further analysis. Potential spatial patterns in the morphogenetic furrow and ommatidia are summarized, while the ommatidia cells are projected to a spherical map in order to identify higher-level spatiotemporal features. In the conclusion, we discuss the implications of our approach and findings for developmental complexity and biological theory.

A maze learning comparison of Elman, long short-term memory, and Mona neural networks.

This study compares the maze learning performance of three artificial neural network architectures: an Elman recurrent neural network, a long short-term memory (LSTM) network, and Mona, a goal-seeking neural network. The mazes are networks of distinctly marked rooms randomly interconnected by doors that open probabilistically. The mazes are used to examine two important problems related to artificial neural networks: (1) the retention of long-term state information and (2) the modular use of learned information. For the former, mazes impose a context learning demand: at the beginning of the maze, an initial door choice forms a context that must be remembered until the end of the maze, where the same numbered door must be chosen again in order to reach the goal. For the latter, the effect of modular and non-modular training is examined. In modular training, the door associations are trained in separate trials from the intervening maze paths, and only presented together in testing trials. All networks performed well on mazes without the context learning requirement. The Mona and LSTM networks performed well on context learning with non-modular training; the Elman performance degraded as the task length increased. Mona also performed well for modular training; both the LSTM and Elman networks performed poorly with modular training.