Linge Bai

Automated shape composition based on cell biology and distributed genetic programming

Motivated by the ability of living cells to form specific shapes and structures, we present a computational approach using distributed genetic programming to discover cell-cell interaction rules for automated shape composition. The key concept is to evolve local rules that direct virtual cells to produce a self-organizing behavior that leads to the formation of a macroscopic, user-de.ned shape. The interactions of the virtual cells, called Morphogenic Primitives (MPs), are based on chemotaxis-driven aggregation behaviors exhibited by actual living cells. Cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. MPs, though, do not attempt to completely mimic the behavior of real cells. The chemical fields are explicitly defined as mathematical functions and are not necessarily physically accurate. The functions are derived via a distributed genetic programming process. A fitness measure, based on the shape that emerges from the chemical-field-driven aggregation, determines which functions will be passed along to later generations. This paper describes the cell interactions of MPs and a distributed genetic programming method to discover the chemical fields needed to produce macroscopic shapes from simple aggregating primitives.

An Emergent System for Self-Aligning and Self-Organizing Shape Primitives

Motivated by the natural phenomenon of living cells self-organizing into specific shapes and structures, we present an emergent system that utilizes evolutionary computing methods for designing and simulating self-aligning and self-organizing shape primitives.Given the complexity of the emergent behavior, genetic programming is employed to control the evolution of our emergent system. The system has two levels of description. At the macroscopic level, a user-specified, pre-defined shape is given as input to the system. The system outputs local interaction rules that direct morphogenetic primitives (MP) to aggregate into the shape. At the microscopic level, MPs follow interaction rules based only on local interactions. All MPs are identical and do not know the final shape to be formed. The aggregate is then evaluated at the macroscopic level for its similarity to the user-defined shape. In this paper, we present (1) an emergent system that discovers local interaction rules that direct MPs to form user-defined shapes, (2) the simulation system that implements these rules and causes MPs to self-align and self-organize into a user-defined shape, and (3) the robustness and scalability qualities of the overall approach.

Chemotaxis-based sorting of self-organizing heterotypic agents

Cell sorting is a fundamental phenomenon in morphogenesis, which is the process that leads to shape formation in living organisms. The sorting of heterotypic cell populations is produced by a variety of inter-cellular actions, e.g. differential chemotactic response, adhesion and motility. Via a process called chemotaxis, living cells respond to chemicals released by other cells into the environment. Each cell can respond to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. Inspired by the biological phenomena of chemotaxis and cell sorting in heterotypic cell aggregates, we propose a chemotaxis-based algorithm for the sorting of self- organizing heterotypic agents. In our algorithm two types of agents are initially randomly placed in a toroidal environment. Agents emit a chemical signal and interact with nearby agents. Given the appropriate parameters, the two kinds of agents self-organize into a complex aggregate consisting of a group of one type of agents surrounded by agents of the second type. This paper describes the chemotaxis- based sorting algorithm, the behaviors of our self-organizing heterotypic agents, evaluation of the final aggregates and parametric studies of the results.

Self-organized sorting of heterotypic agents via a chemotaxis paradigm

Cell sorting is a fundamental phenomenon in morphogenesis, a process that leads to shape formation in living organisms. The sorting of heterotypic cell populations is produced by a variety of inter-cellular actions, e.g. differential chemotactic response, adhesion, rigidity, and motility. Via a process called chemotaxis, living cells respond to chemicals released by other cells into the environment. Inspired by the biological phenomena of chemotaxis and cell sorting in heterotypic cell aggregates, we propose a chemotaxis-based algorithm that sorts self-organizing heterotypic agents. In our algorithm, two types of agents are initially randomly placed in a toroidal environment. Agents emit a chemical signal and interact with nearby agents. Given the appropriate parameters, the two kinds of agents self-organize into a complex aggregate consisting of a single group of one type of agent surrounded by agents of the second type. This paper describes the chemotaxis-based sorting algorithm, the behaviors of our self-organizing heterotypic agents, evaluation of the final aggregates and parametric studies of the algorithm.

Self-organizing primitives for automated shape composition

Motivated by the ability of living cells to form into specific shapes and structures, we present a new approach to shape modeling based on self-organizing primitives whose behaviors are derived via genetic programming. The key concept of our approach is that local interactions between the primitives direct them to come together into a macroscopic shape. The interactions of the primitives, called morphogenic primitives (MP), are based on the chemotaxis-driven aggregation behaviors exhibited by actual living cells. Here, cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. MPs, though, do not attempt to completely mimic the behavior of real cells. The chemical fields are explicitly defined as mathematical functions and are not necessarily physically accurate. The explicit mathematical form of the chemical field functions are derived via genetic programming (GP), an evolutionary computing process that evolves a population of functions. A fitness measure, based on the shape that emerges from the chemical-field-driven aggregation, determines which functions will be passed along to later generations. This paper describes the cell interactions of MPs and the GP-based method used to define the chemical field functions needed to produce user- specified shapes from simple aggregating primitives.

Chemotaxis-Inspired Cellular Primitives for Self-Organizing Shape Formation

Motivated by the ability of living cells to form specific shapes and structures, we are investigating chemotaxis-inspired cellular primitives for self-organizing shape formation. This chapter details our initial effort to create Morphogenetic Primitives (MPs), software agents that may be programmed to self-organize into user-specified 2D shapes. The interactions of MPs are inspired by chemotaxis-driven aggregation behaviors exhibited by actual living cells. Cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. The artificial chemical fields of individual MPs are explicitly defined as mathematical functions. Genetic programming is used to discover the chemical field functions that produce an automated shape formation capability. We describe the cell-based behaviors of MPs and a distributed genetic programming method that discovers the chemical fields needed to produce macroscopic shapes from simple aggregating primitives. Several examples of aggregating MPs demonstrate that chemotaxis is an effective paradigm for spatial self-organization algorithms.

Epithelial cell reconstruction and visualization of the developing Drosophila wing imaginal disc

Quantifying and visualizing the shape of developing biological tissues provide information about the morphogenetic processes in multicellular organisms. The size and shape of biological tissues depend on the number, size, shape, and arrangement of the constituting cells. To better understand the mechanisms that guide tissues into their final shape, it is important to investigate and measure the cellular arrangements within tissues. Here we present a set of techniques that produces detailed 3D models of the individual cells in an epithelial sheet. The inputs to the techniques are a volumetric model of an epithelium and a mesh model of the cell boundaries lying on its apical surface. The techniques include: definition of a Region of Interest (ROI), projection of the ROI vertices first to the basal surface then to the apical surface, projection of apical cell faces to the basal surface, creation of 3D epithelial cell models, and calculation and visualization of length and volume for each cell. In their first utilization we have applied these techniques to construct the individual epithelial cells of the wing imaginal disc of Drosophila melanogaster. To date, 3D epithelial cell models have been created, allowing for the calculation and visualization of cell parameters. The results show position-dependent patterns of cell shape in the wing imaginal disc. Our procedures should offer a general data processing pipeline for the construction of detailed 3D models of a wide variety of epithelial tissues.

3D surface reconstruction and visualization of the Drosophila wing imaginal disc at cellular resolution

Quantifying and visualizing the shape of developing biological tissues provide information about the morphogenetic processes in multicellular organisms. The size and shape of biological tissues depend on the number, size, shape, and arrangement of the constituting cells. To better understand the mechanisms that guide tissues into their final shape, it is important to investigate the cellular arrangement within tissues. Here we present a data processing pipeline to generate 3D volumetric surface models of epithelial tissues, as well as geometric descriptions of the tissues’ apical cell cross-sections. The data processing pipeline includes image acquisition, editing, processing and analysis, 2D cell mesh generation, 3D contourbased surface reconstruction, cell mesh projection, followed by geometric calculations and color-based visualization of morphological parameters. In their first utilization we have applied these procedures to construct a 3D volumetric surface model at cellular resolution of the wing imaginal disc of Drosophila melanogaster. The ultimate goal of the reported effort is to produce tools for the creation of detailed 3D geometric models of the individual cells in epithelial tissues. To date, 3D volumetric surface models of the whole wing imaginal disc have been created, and the apicolateral cell boundaries have been identified, allowing for the calculation and visualization of cell parameters, e.g. apical cross-sectional area of cells. The calculation and visualization of morphological parameters show position-dependent patterns of cell shape in the wing imaginal disc. Our procedures should offer a general data processing pipeline for the construction of 3D volumetric surface models of a wide variety of epithelial tissues.

Self-organizing primitives for shape composition based on chemotaxis and genetic programming

In living things, cells aggregate and grow to create complicated structures. This process, called morphogenesis, is one of the fundamental components involved in the development of all complex organisms. One of the essential processes involved in morphogenesis is chemotaxis. Chemotaxis is the phenomenon where cells interact with other cells by emitting a chemical that diffuses into the surrounding environment. Neighboring cells detect the overall chemical concentration at their surfaces and respond to the chemical stimulus by moving either towards or away from the source. The motions induced by chemotaxis may then produce patterns or sortings of cells, or even large-scale structures, e.g. cavities or vessels. These phenomena have motivated us to look to developmental biology for concepts that lead to a more organic, living-cell-inspired approach to shape composition.