Automating the search of molecular motor templates by evolutionary methods

The first author is supported by a FPU grant (AP2007-03704) from the Ministerio de Educación of the Spanish Government, and has been supported by the BioEmergences project (code 28892) of the Sixth Framework Programme of the European Union. Our research group has been partially supported by the local government (Junta de Andalucía) through a grant for the GENEX project (P09-TIC-5123).Biological molecular motors are nanoscale devices capable of transforming chemical energy into mechanical work, which are being researched in many scientific disciplines. From a computational point of view, the characteristics and dynamics of these motors are studied at multiple time scales, ranging from very detailed and complex molecular dynamics simulations spanning a few microseconds, to extremely simple and coarse-grained theoretical models of their working cycles. However, this research is performed only in the (relatively few) instances known from molecular biology. In this work, results from elastic network analysis and behaviour-finding methods are applied to explore a subset of the configuration space of template molecular structures that are able to transform chemical energy into directed movement, for a fixed instance of working cycle. While using methods based on elastic networks limits the scope of our results, it enables the implementation of computationally lightweight methods, in a way that evolutionary search techniques can be applied to discover novel molecular motor templates. The results show that molecular motion can be attained from a variety of structural configurations, when a functional working cycle is provided. Additionally, these methods enable a new computational way to test hypotheses about molecular motors

The Evolution of Controller-Free Molecular Motors from Spatial Constraints

Locomotion of robotic and virtual agents is a challenging task requiring the control of several degrees of freedom as well as the coordination of multiple subsystems. Traditionally, it is engineered by top-down design and finetuning of the agent’s morphology and controller. A relatively recent trend in fields such as evolutionary robotics, computer animation and artificial life has been the coevolution and mutual adaptation of the morphology and controller in computational agent models. However, the controller is generally modeled as a complex system, often a neural or gene regulatory network. In the present study, inspired by molecular biology and based on normal modal analysis, we formulate a behavior-finding framework for the design of bipedal agents that are able to walk along a filament and have no explicit control system. Instead, agents interact with their environment in a purely reactive way. A simple mutation operator, based on physical relaxation, is used to drive the evolutionary search. Results show that gait patterns can be evolutionarily engineered from the spatial interaction between precisely tuned morphologies and the environment.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Behavior finding: Morphogenetic Designs Shaped by Function

Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding

NetPyNE, a tool for data-driven multiscale modeling of brain circuits

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena

NetPyNE, a tool for data-driven multiscale modeling of brain circuits.

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena

The evolution of diversity in the structure and function of artificial organisms

Life on Earth has been shaped by evolutionary processes into a marvelous diversity of form and function, at all levels from melecules to ecosystems. It can be expected that no single conceptual framework ca encompass all the aspects of the evolution of diversity. This thesis explores this question from three different points of view: the role of developmental processes, the role of evolutionary dynamics, and the interplay between the body's control system

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

Summer Research Fellowship Project Descriptions 2022

A summary of research done by Smith College’s 2021 Summer Research Fellowship (SURF) Program participants. Ever since its 1967 start, SURF has been a cornerstone of Smith’s science education. Supervised by faculty mentor-advisors drawn from the Clark Science Center and connected to its eighteen science, mathematics, and engineering departments and programs and associated centers and units. At summer’s end, SURF participants were asked to summarize their research experiences for this publication.https://scholarworks.smith.edu/clark_womeninscience/1012/thumbnail.jp

A computational theory for the generation of solutions during early conceptual design

Advancement in technology is usually made by building on previous experiences and learning from past successes and failures. However, knowledge transfer in the broad field of product design is often difficult to accomplish. Research has shown that successful component configurations, observed from existing products, can be dissected and stored for reuse; but few computational tools exist to assist designers during the conceptual phase of design. Many well-known manual methods (e.g. brainstorming, intrinsic and extrinsic searches, and morphological analysis) rely heavily on individual bias and experience and are often time intensive, laborious tasks that may not catch solutions that are functionally analogous, but seemingly unrelated. This research presents an automated concept generation tool that augments traditional activities during the conceptual phase of design --Abstract, page iii