Model Reduction Tools For Phenomenological Modeling of Input-Controlled Biological Circuits

We present a Python-based software package to automatically obtain phenomenological models of input-controlled synthetic biological circuits that guide the design using chemical reaction-level descriptive models. From the parts and mechanism description of a synthetic biological circuit, it is easy to obtain a chemical reaction model of the circuit under the assumptions of mass-action kinetics using various existing tools. However, using these models to guide design decisions during an experiment is difficult due to a large number of reaction rate parameters and species in the model. Hence, phenomenological models are often developed that describe the effective relationships among the circuit inputs, outputs, and only the key states and parameters. In this paper, we present an algorithm to obtain these phenomenological models in an automated manner using a Python package for circuits with inputs that control the desired outputs. This model reduction approach combines the common assumptions of time-scale separation, conservation laws, and species' abundance to obtain the reduced models that can be used for design of synthetic biological circuits. We consider an example of a simple gene expression circuit and another example of a layered genetic feedback control circuit to demonstrate the use of the model reduction procedure

An automated model reduction tool to guide the design and analysis of synthetic biological circuits

We present an automated model reduction algorithm that uses quasi-steady state approximation based reduction to minimize the error between the desired outputs. Additionally, the algorithm minimizes the sensitivity of the error with respect to parameters to ensure robust performance of the reduced model in the presence of parametric uncertainties. We develop the theory for this model reduction algorithm and present the implementation of the algorithm that can be used to perform model reduction of given SBML models. To demonstrate the utility of this algorithm, we consider the design of a synthetic biological circuit to control the population density and composition of a consortium consisting of two different cell strains. We show how the model reduction algorithm can be used to guide the design and analysis of this circuit

Model Reduction Tools For Phenomenological Modeling of Input-Controlled Biological Circuits

We present a Python-based software package to automatically obtain phenomenological models of input-controlled synthetic biological circuits that guide the design using chemical reaction-level descriptive models. From the parts and mechanism description of a synthetic biological circuit, it is easy to obtain a chemical reaction model of the circuit under the assumptions of mass-action kinetics using various existing tools. However, using these models to guide design decisions during an experiment is difficult due to a large number of reaction rate parameters and species in the model. Hence, phenomenological models are often developed that describe the effective relationships among the circuit inputs, outputs, and only the key states and parameters. In this paper, we present an algorithm to obtain these phenomenological models in an automated manner using a Python package for circuits with inputs that control the desired outputs. This model reduction approach combines the common assumptions of time-scale separation, conservation laws, and species' abundance to obtain the reduced models that can be used for design of synthetic biological circuits. We consider an example of a simple gene expression circuit and another example of a layered genetic feedback control circuit to demonstrate the use of the model reduction procedure

A two-state ribosome and protein model can robustly capture the chemical reaction dynamics of gene expression

We derive phenomenological models of gene expression from a mechanistic description of chemical reactions using an automated model reduction method. Using this method, we get analytical descriptions and computational performance guarantees to compare the reduced dynamics with the full models. We develop a new two-state model with the dynamics of the available free ribosomes in the system and the protein concentration. We show that this new two-state model captures the detailed mass-action kinetics of the chemical reaction network under various biologically plausible conditions on model parameters. On comparing the performance of this model with the commonly used mRNA transcript-protein dynamical model for gene expression, we analytically show that the free ribosome and protein model has superior error and robustness performance

High Efficiency Mid and Deep Ultraviolet Optoelectronic Devices

Ultraviolet (UV) light is a critical component of future technological products, having applications in curing polymers, sensors, medical diagnostics, as well as in the sterilization of pathogens – a need which is of prime importance to curtail the spread of diseases and possibly a future pandemic. Solid state UV devices can replace existing sources, such as mercury lamps and xenon lamps, by providing non-hazardous, scalable, easy to use, durable, compact and more efficient performance. The III-nitride material system has established itself as the basis for optoelectronic devices operating in the visible and ultraviolet (UV) wavelength range. While InGaN-based devices have already been commercialized for visible light applications, demonstrating a high external quantum efficiency (EQE) and wall-plug efficiency (WPE), the adoption of AlGaN-based UV devices has been hindered due to their correspondingly lower efficiencies. The primary reasons for the low efficiency of AlGaN LEDs include the low internal quantum efficiency because of defects and dislocations in the device active region, inadequate light extraction due to the primarily transverse-magnetic (TM) polarized light emission, and inefficient carrier injection efficiency from the poor p-type doping of the wide band-gap materials. In this work, we have investigated the design, epitaxy, fabrication and characterization of high efficiency AlGaN devices operating in the mid and deep UV wavelength regime. We used molecular beam epitaxy (MBE) to grow high-quality Mg-doped AlGaN layers under slightly Ga-rich conditions. The unique growth conditions pinned the Fermi level away from the valence band during epitaxy, which improved Mg incorporation by over an order of magnitude as compared to conventional epitaxy. We demonstrated Mg-doped AlGaN layers having Al compositions up to 90% with resistivities several orders of magnitude lower than previous reports, which is further supported by the dramatically improved EQE of LEDs with emission at 280 nm grown using this technique. Despite significantly improving the p-type doping, the disparity in the electron and hole concentrations and mobilities is large for Si-doped and Mg-doped AlGaN, respectively. The imbalance of the electron and hole injection to the active region can cause reduced injection efficiency. To address this issue, we investigated different electron-blocking layers (EBLs) and their positioning. We demonstrated that by placing the EBL before the active region as an n-type EBL, instead of a conventional p-type EBL, the flow of electrons can be impeded without hindering hole transport. We have also utilized polarization-engineered tunnel junctions to increase the hole injection from the p-contacts, which is a critical challenge for wide-bandgap AlGaN. The thickness of the critical tunnel junction layer was optimized for an LED at 265 nm, and we demonstrated a maximum EQE of 11%, the highest value ever reported for devices operating at this wavelength. We also extended this heterostructure design towards shorter wavelengths. Extensive temperature-dependent optical and electrical measurements of 245 and 255 nm LEDs indicate the pivotal role of carrier overflow on device performance and efficiency. This work provides a unique path for achieving high efficiency mid and deep UV LEDs that were not previously possible. The techniques developed here can be extended to even shorter wavelengths to maximize the efficiency of UV-C AlGaN light sources. Future work includes the development of AlGaN mid and deep UV laser diodes and UV-C and far UV-C LEDs with efficiency comparable to commercial blue LEDs.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169670/1/ayushp_1.pd

An automated model reduction tool to guide the design and analysis of synthetic biological circuits

We present an automated model reduction algorithm that uses quasi-steady state approximation based reduction to minimize the error between the desired outputs. Additionally, the algorithm minimizes the sensitivity of the error with respect to parameters to ensure robust performance of the reduced model in the presence of parametric uncertainties. We develop the theory for this model reduction algorithm and present the implementation of the algorithm that can be used to perform model reduction of given SBML models. To demonstrate the utility of this algorithm, we consider the design of a synthetic biological circuit to control the population density and composition of a consortium consisting of two different cell strains. We show how the model reduction algorithm can be used to guide the design and analysis of this circuit

Bus Stop Spacings Statistics: Theory and Evidence

Transit agencies have been removing a large number of bus stops, but discussions around the bus stop spacings exhibit a lack of clarity and data for comparison. This paper proposes new terminology and concepts for statistical consideration of stop spacings, and introduces a python package and open-source database which uses General Transit Feed Specification data to derive real-world stop spacing distributionsComment: 18 pages, 5 tables, 7 figure

Persistance du cache d’AntidoteDB : Conception et mise en œuvre d’un cache pour un datastore de CRDT

Many services, today, rely on Geo-replicated databases. Geo-replication improves performance by moving a copy of the data closer to its usage site. High availability is achieved by maintaining copies of this data in several locations. Performance is gained by distributing the data and allowing multiple requests to be served at once. But, replicating data can lead to an inconsistent global state of the database when updates compete with each other.In this work, we study how a cache is designed and implemented, for a database that prevents state inconsistencies by using CRDTs. Further, we study how this cache can be persisted into a checkpoint store and measure the performance of our design with several benchmarks. The implementation of the system is based on AntidoteDB. An additional library is implemented to realise the discussed design.De nombreux services reposent aujourd’hui sur des bases de données géo-répliquées. La géo-réplication améliore les performances en rapprochant une copie des données de leur site d’utilisation. La haute disponibilité est obtenue en maintenant des copies de ces données à plusieurs endroits. Les performances sont améliorées en distribuant les données et en permettant à plusieurs requêtes d’être servies en même temps. Cependant, la réplication des données peut conduire à un état global incohérent de la base de données lorsque les mises à jour sont en concurrence les unes avec les autres.Dans ce travail, nous étudions la conception et la mise en œuvre d'une cache, pour une base de données qui convergente utilisant les CRDTs. De plus, nous étudions comment persister le cache en en stockant des instantanés ; enfin, nous mesurons la performance du système ainsi conçu grâce à plusieurs bancs d'essai. La mise en œuvre est basée sur Antidote DB, comme une bibliothèque

BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts

Biochemical interactions in systems and synthetic biology are often modeled with Chemical Reaction Networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in python, that complies high-level design specifications to CRN representations. This compilation process offers three advantages. First, the building of the actual CRN representation is automatic and outputs Systems Biology Markup Language (SBML) models compatible with numerous simulators. Second, a library of modular biochemical components allows for different architectures and implementations of biochemical circuits to represented succinctly with design choices propogated throughout the underlying CRN automatically. This prevents the often occurring mismatch between high-level designs and model dynamics. Third, high-level design specification can be embedded into diverse biomolecular environments, such as cell-free extracts and in vivo milieus. With these advantages offered by BioCRNpyler, users can quickly build and test multitude of models in different environments. Finally, our software toolbox has a parameter database, which allows users to rapidly prototype large models using very few parameters which can be customized later