Showing papers by "Ron Weiss published in 2012"

Scikit-learn: Machine Learning in Python

Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.

Formation and optogenetic control of engineered 3D skeletal muscle bioactuators

Abstract: Densely arrayed skeletal myotubes are activated individually and as a group using precise optical stimulation with high spatiotemporal resolution. Skeletal muscle myoblasts are genetically encoded to express a light-activated cation channel, Channelrhodopsin-2, which allows for spatiotemporal coordination of a multitude of skeletal myotubes that contract in response to pulsed blue light. Furthermore, ensembles of mature, functional 3D muscle microtissues have been formed from the optogenetically encoded myoblasts using a high-throughput device. The device, called “skeletal muscle on a chip”, not only provides the myoblasts with controlled stress and constraints necessary for muscle alignment, fusion and maturation, but also facilitates the measurement of forces and characterization of the muscle tissue. We measured the specific static and dynamic stresses generated by the microtissues and characterized the morphology and alignment of the myotubes within the constructs. The device allows testing of the effect of a wide range of parameters (cell source, matrix composition, microtissue geometry, auxotonic load, growth factors and exercise) on the maturation, structure and function of the engineered muscle tissues in a combinatorial manner. Our studies integrate tools from optogenetics and microelectromechanical systems (MEMS) technology with skeletal muscle tissue engineering to open up opportunities to generate soft robots actuated by a multitude of spatiotemporally coordinated 3D skeletal muscle microtissues.

Foundations for the design and implementation of synthetic genetic circuits.

Abstract: Synthetic gene circuits are designed to program new biological behaviour, dynamics and logic control. For all but the simplest synthetic phenotypes, this requires a structured approach to map the desired functionality to available molecular and cellular parts and processes. In other engineering disciplines, a formalized design process has greatly enhanced the scope and rate of success of projects. When engineering biological systems, a desired function must be achieved in a context that is incompletely known, is influenced by stochastic fluctuations and is capable of rich nonlinear interactions with the engineered circuitry. Here, we review progress in the provision and engineering of libraries of parts and devices, their composition into large systems and the emergence of a formal design process for synthetic biology.

An End-to-End Workflow for Engineering of Biological Networks from High-Level Specifications

Abstract: We present a workflow for the design and production of biological networks from high-level program specifications. The workflow is based on a sequence of intermediate models that incrementally translate high-level specifications into DNA samples that implement them. We identify algorithms for translating between adjacent models and implement them as a set of software tools, organized into a four-stage toolchain: Specification, Compilation, Part Assignment, and Assembly. The specification stage begins with a Boolean logic computation specified in the Proto programming language. The compilation stage uses a library of network motifs and cellular platforms, also specified in Proto, to transform the program into an optimized Abstract Genetic Regulatory Network (AGRN) that implements the programmed behavior. The part assignment stage assigns DNA parts to the AGRN, drawing the parts from a database for the target cellular platform, to create a DNA sequence implementing the AGRN. Finally, the assembly stage computes an optimized assembly plan to create the DNA sequence from available part samples, yielding a protocol for producing a sample of engineered plasmids with robotics assistance. Our workflow is the first to automate the production of biological networks from a high-level program specification. Furthermore, the workflow's modular design allows the same program to be realized on different cellular platforms simply by swapping workflow configurations. We validated our workflow by specifying a small-molecule sensor-reporter program and verifying the resulting plasmids in both HEK 293 mammalian cells and in E. coli bacterial cells.

Generating a playlist

Abstract: Generating a playlist may include designating a seed track in an audio library; identifying audio tracks in the audio library having constructs that are within a range of a corresponding construct of the seed track, where the constructs for the audio tracks are derived from frequency representations of the audio tracks, and the corresponding construct for the seed track is derived from a frequency representation of the seed track; and generating the playlist using at least some of the audio tracks that were identified.

Latent Collaborative Retrieval

Abstract: Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user's preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query × user × item tensor for training instead of the more traditional user × item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user's profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines.

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

Abstract: 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.

Latent Collaborative Retrieval

Abstract: Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user's preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query x user x item tensor for training instead of the more traditional user x item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user's profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines.

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

Abstract: 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.

A Method for Fast, High-Precision Characterization of Synthetic Biology Devices

Abstract: Engineering biological systems with predictable behavior is a foundational goal of synthetic biology. To accomplish this, it is important to accurately characterize the behavior of biological devices. Prior characterization efforts, however, have generally not yielded enough high-quality information to enable compositional design. In the TASBE (A Tool-Chain to Accelerate Synthetic Biological Engineering) project we have developed a new characterization technique capable of producing such data. This document describes the techniques we have developed, along with examples of their application, so that the techniques can be accurately used by others. 10 1 10 10 10 10 10 10 1 10 10 10 10 10 [Dox] IF P M EF L/ pl as m id Normalized Dox transfer curve, colored by plasmid bin 10 10 10 10 10 10 10 10 10 10 10 IFP MEFL O FP M EF L/ pl as m id Normalized Tal1 transfer curve, colored by plasmid count Work partially sponsored by DARPA; the views and conclusions contained in this document are those of the authors and not DARPA or the U.S. Government.

Method for recommending musical entities to a user

Abstract: Methods, systems, and computer programs are presented for recommending music entities to a user. One method includes defining a set of labels with each label identifying a music concept and constructing at least vector for each of a plurality of entities based on source data. Each vector includes the set of define labels and each label is assigned with a label score. Two vectors respectively associated with two of the plurality of entities are compared. The method further includes generating a recommendation action based on comparison result of the two vectors and transmitting the data for the recommendation action to a device of the user. In one example, the comparisons can be pre-computed and used for the recommendation action.

Understanding Signaling Dynamics Through Synthesis

Abstract: Tissue-scale organization emerges from the action of sophisticated multiscale developmental programs. But the design rules for composing elementary signaling and information processing modules into such functional systems and for integrating them into the noisy and convoluted living context remain incompletely addressed. The construction of a synthetic gene circuit encoding contact-dependent signal propagation demonstrates one broadly applicable approach to this problem. The circuit comprises orthogonal signaling through the Delta ligand and the Notch receptor, multicellular positive feedback, and transcriptional signal amplification. Positive feedback and contact signaling proved sufficient for bistability and signal propagation across a population of mammalian cells, but only when combined with signal amplification. Thus, construction and characterization of synthetic gene circuits have made it possible to establish mechanistic sufficiency and the minimal requirements for the phenotype of interest.

Formation and optogenetic control of engineered 3D skeletal muscle bioactuators

Abstract: National Science Foundation (U.S.) (Science and Technology Center—Emergent Behaviors of Integrated Cellular Systems (EBICS) grant No. CBET-0939511)

Methods of evaluating gene expression levels

Abstract: Described herein are methods of evaluating the expression levels of DNA parts encoding proteins in test circuits. In particular, the methods disclosed herein are useful to evaluate the expression of an output protein regulated by a regulatory protein-genetic element pair.

Methods of evaluating gene expression levels produced by a regulatory molecule -genetic element pair

Abstract: Described herein are methods of evaluating the expression levels of DNA parts encoding proteins in test circuits. In particular, the methods disclosed herein are useful to evaluate the expression of an output protein regulated by a regulatory protein-genetic element pair.