Functional synthesis of genetic systems

Synthetic genetic regulatory networks (or genetic circuits) can operate in complex biochemical environments to process and manipulate biological information to produce a desired behavior. The ability to engineer such genetic circuits has wide-ranging applications in various fields such as therapeutics, energy, agriculture, and environmental remediation. However, engineering multilevel genetic circuits quickly and reliably is a big challenge in the field of synthetic biology. This difficulty can partly be attributed to the growing complexity of biology. But some of the predominant challenges include the absence of formal specifications -- that describe precise desired behavior of these biological systems, as well as a lack of computational and mathematical frameworks -- that enable rapid in-silico design and synthesis of genetic circuits. This thesis introduces two major frameworks to reliably design genetic circuits. The first implementation focuses on a framework that enables synthetic biologists to encode Boolean logic functions into living cells. Using high-level hardware description language to specify the desired behavior of a genetic logic circuit, this framework describes how, given a library of genetic gates, logic synthesis can be applied to synthesize a multilevel genetic circuit, while accounting for biological constraints such as 'signal matching', 'crosstalk', and 'genetic context effects'. This framework has been implemented in a tool called Cello, which was applied to design 60 circuits for Escherichia coli, where the circuit function was specified using Verilog code and transformed to a DNA sequence. Across all these circuits, 92% of the output states functioned as predicted. The second implementation focuses on a framework to design complex genetic systems where the focus is on how the system behaves over time instead of its behavior at steady-state. Using Signal Temporal Logic (STL) -- a formalism used to specify properties of dense-time real-valued signals, biologists can specify very precise temporal behaviors of a genetic system. The framework describes how genetic circuits that are built from a well characterized library of DNA parts, can be scored by quantifying the 'degree of robustness' of in-silico simulations against an STL formula. Using formal verification, experimental data can be used to validate these in-silico designs. In this framework, the design space is also explored to predict external controls (such as approximate small molecule concentrations) that might be required to achieve a desired temporal behavior. This framework has been implemented in a tool called Phoenix.2021-02-28T00:00:00

A new encryption technique for the secured transmission and storage of text information with medical images

Modern day hospital management systems rely heavily on electronic data processing to maintain patient records. These electronic medical records (EMRs) must be maintained in an unaltered form by the creator. The need for a secure data handling method for the transmission and storage of text and digital media, comprising patient’s diagnostic history, imaging, scans, etc., is indispensible. This paper presents a novel method of text encryption by means of symmetric key encryption technique, using variable length key derived from the encrypted text itself

PROVISIONING DAY-ZERO CONFIGURATIONS THROUGH PASSIVE RADIO-FREQUENCY IDENTIFICATION

Embodiments presented herein provide a mechanism for setting the day-zero configuration of a network device without having to power on or unbox the device. Using an embedded passive radio-frequency identification (RFID) tag situated in a device, the device can be programmed at a distance using a mobile device

Metrics for Signal Temporal Logic Formulae

Signal Temporal Logic (STL) is a formal language for describing a broad range of real-valued, temporal properties in cyber-physical systems. While there has been extensive research on verification and control synthesis from STL requirements, there is no formal framework for comparing two STL formulae. In this paper, we show that under mild assumptions, STL formulae admit a metric space. We propose two metrics over this space based on i) the Pompeiu-Hausdorff distance and ii) the symmetric difference measure, and present algorithms to compute them. Alongside illustrative examples, we present applications of these metrics for two fundamental problems: a) design quality measures: to compare all the temporal behaviors of a designed system, such as a synthetic genetic circuit, with the "desired" specification, and b) loss functions: to quantify errors in Temporal Logic Inference (TLI) as a first step to establish formal performance guarantees of TLI algorithms.Comment: This paper has been accepted for presentation at, and publication in the proceedings of, the 2018 IEEE Conference on Decision and Control (CDC), to be held in Fontainebleau, Miami Beach, FL, USA on Dec. 17-19, 201

The Synthetic Biology Open Language (SBOL) Version 3:Simplified Data Exchange for Bioengineering

The Synthetic Biology Open Language (SBOL) is a community-developed data standard that allows knowledge about biological designs to be captured using a machine-tractable, ontology-backed representation that is built using Semantic Web technologies. While early versions of SBOL focused only on the description of DNA-based components and their sub-components, SBOL can now be used to represent knowledge across multiple scales and throughout the entire synthetic biology workflow, from the specification of a single molecule or DNA fragment through to multicellular systems containing multiple interacting genetic circuits. The third major iteration of the SBOL standard, SBOL3, is an effort to streamline and simplify the underlying data model with a focus on real-world applications, based on experience from the deployment of SBOL in a variety of scientific and industrial settings. Here, we introduce the SBOL3 specification both in comparison to previous versions of SBOL and through practical examples of its use

Synthetic biology open language (SBOL) version 3.0.0

Synthetic biology builds upon genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. When designing a synthetic system, synthetic biologists need to exchange information about multiple types of molecules, the intended behavior of the system, and actual experimental measurements. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, following an open community process involving both wet bench scientists and dry scientific modelers and software developers, across academia, industry, and other institutions. This document describes SBOL 3.0.0, which condenses and simplifies previous versions of SBOL based on experiences in deployment across a variety of scientific and industrial settings. In particular, SBOL 3.0.0, (1) separates sequence features from part/sub-part relationships, (2) renames Component Definition/Component to Component/Sub-Component, (3) merges Component and Module classes, (4) ensures consistency between data model and ontology terms, (5) extends the means to define and reference Sub-Components, (6) refines requirements on object URIs, (7) enables graph-based serialization, (8) moves Systems Biology Ontology (SBO) for Component types, (9) makes all sequence associations explicit, (10) makes interfaces explicit, (11) generalizes Sequence Constraints into a general structural Constraint class, and (12) expands the set of allowed constraints