Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data

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 compiles high-level design specifications represented using a modular library of biochemical parts, mechanisms, and contexts to CRN implementations. This compilation process offers four 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 be represented succinctly with design choices propagated 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. 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. By using BioCRNpyler, users ranging from expert modelers to novice script-writers can easily build, manage, and explore sophisticated biochemical models using diverse biochemical implementations, environments, and modeling assumptions.

This paper describes a new software package BioCRNpyler (pronounced “Biocompiler”) designed to support rapid development and exploration of mathematical models of biochemical networks and circuits by computational biologists, systems biologists, and synthetic biologists. BioCRNpyler allows its users to generate large complex models using very few lines of code in a way that is modular. To do this, BioCRNpyler uses a powerful new representation of biochemical circuits which defines their parts, underlying biochemical mechanisms, and chemical context independently. BioCRNpyler was developed as a Python scripting language designed to be accessible to beginning users as well as easily extendable and customizable for advanced users. Ultimately, we see Biocrnpyler being used to accelerate computer automated design of biochemical circuits and model driven hypothesis generation in biology.

Special function methods for bursty models of transcription

We explore a Markov model used in the analysis of gene expression, involving the bursty production of pre-mRNA, its conversion to mature mRNA, and its consequent degradation. We demonstrate that the integration used to compute the solution of the stochastic system can be approximated by the evaluation of special functions. Furthermore, the form of the special function solution generalizes to a broader class of burst distributions. In light of the broader goal of biophysical parameter inference from transcriptomics data, we apply the method to simulated data, demonstrating effective control of precision and runtime. Finally, we propose and validate a non-Bayesian approach for parameter estimation based on the characteristic function of the target joint distribution of pre-mRNA and mRNA.

Numerical Investigation of Design and Operating Parameters of Thermal Gradient Continuous-Flow PCR Microreactor Using One Heater

To respond to the dire need for miniaturization and process simplification of continuous-flow PCR (CF-PCR) device, this paper represents design and operation guide of a novel metal alloy assisted hybrid microdevice (polydimethylsiloxane (PDMS) and glass) for CF-PCR employing one heater. In this research, the specific objectives are to determine whether one heater chip design will be flexible enough when the size of DNA base pair is varied and to investigate whether one heater CF-PCR device will be able to resolve the longstanding problem of thermal crosstalk. Furthermore, the parametric study is performed to determine which of the fourteen parameters have the greatest impact on the performance of one heater CF-PCR device. The main objective of this parametric study is to distinguish between the parameters that are either critical to the chip performance or can be freely specified. It is found that substrate thickness, flow rate, channel spacing, aspect ratio, channel pass length and external heat transfer coefficient are the most limiting parameters that can either improve or deteriorate the chip’s thermal performance. Overall, the impact of design and operating parameters are observed to be least on thermocycling profile at low Reynolds number (≤0.37 Re). However, in addition to the primary metric advantages of CF-PCR, one heater chip design helps in minimizing the thermal crosstalk effects by a factor of 4 in comparison to dual heater PCR while still maintaining a critical criteria of chip flexibility in terms of handling various sizes of DNA fragments. Hence, the proposed scheme paves the way for low-cost point-of-care diagnostics, system integration, and device miniaturization, realizing a portable microfluidic device applicable for on-site and direct field uses.

Computational Methods Enabling Next-Generation Bioprocesses

Synthetic biology—the engineering of cells to rewire the biomolecular networks inside them—has witnessed phenomenal progress [...]