Hardware, Software, and Wetware Codesign Environment for Synthetic Biology

Synthetic biology is the process of forward engineering living systems. These systems can be used to produce biobased materials, agriculture, medicine, and energy. One approach to designing these systems is to employ techniques from the design of embedded electronics. These techniques include abstraction, standards, modularity, automated design, and formal semantic models of computation. Together, these elements form the foundation of "biodesign automation," where software, robotics, and microfluidic devices combine to create exciting biological systems of the future. This paper describes a "hardware, software, wetware" codesign vision where software tools can be made to act as "genetic compilers" that transform high-level specifications into engineered "genetic circuits" (wetware). This is followed by a process where automation equipment, well-defined experimental workflows, and microfluidic devices are explicitly designed to house, execute, and test these circuits (hardware). These systems can be used as either massively parallel experimental platforms or distributed bioremediation and biosensing devices. Next, scheduling and control algorithms (software) manage these systems' actual execution and data analysis tasks. A distinguishing feature of this approach is how all three of these aspects (hardware, software, and wetware) may be derived from the same basic specification in parallel and generated to fulfill specific cost, performance, and structural requirements.

The life and death of RNA across temperatures

Temperature is an environmental condition that has a pervasive effect on cells along with all the molecules and reactions in them. The mechanisms by which prototypical RNA molecules sense and withstand heat have been identified mostly in bacteria and archaea. The relevance of these phenomena is, however, broader, and similar mechanisms have been recently found throughout the tree of life, from sex determination in reptiles to adaptation of viral RNA polymerases, to genetic disorders in humans. We illustrate the temperature dependence of RNA metabolism with examples from the synthesis to the degradation of mRNAs, and review recently emerged questions. Are cells exposed to greater temperature variations and gradients than previously surmised? How do cells reconcile the conflicting thermal stability requirements of primary and tertiary structures of RNAs? To what extent do enzymes contribute to the temperature compensation of the reaction rates in mRNA turnover by lowering the energy barrier of the catalyzed reactions? We conclude with the ecological, forensic applications of the temperature-dependence of RNA degradation and the biotechnological aspects of mRNA vaccine production.

Engineering of lysine cyclodeaminase conformational dynamics for relieving substrate and product inhibitions in the biosynthesis of l-pipecolic acid

Efficient biocatalytic process construction by relieving substrate and product inhibitions via identification and engineering of enzyme conformational dynamics.

Temperature-Dependent Model of Multi-step Transcription Initiation in Escherichia coli Based on Live Single-Cell Measurements

Transcription kinetics is limited by its initiation steps, which differ between promoters and with intra- and extracellular conditions. Regulation of these steps allows tuning both the rate and stochasticity of RNA production. We used time-lapse, single-RNA microscopy measurements in live Escherichia coli to study how the rate-limiting steps in initiation of the P_lac/ara-1 promoter change with temperature and induction scheme. For this, we compared detailed stochastic models fit to the empirical data in maximum likelihood sense using statistical methods. Using this analysis, we found that temperature affects the rate limiting steps unequally, as nonlinear changes in the closed complex formation suffice to explain the differences in transcription dynamics between conditions. Meanwhile, a similar analysis of the P_tetA promoter revealed that it has a different rate limiting step configuration, with temperature regulating different steps. Finally, we used the derived models to explore a possible cause for why the identified steps are preferred as the main cause for behavior modifications with temperature: we find that transcription dynamics is either insensitive or responds reciprocally to changes in the other steps. Our results suggests that different promoters employ different rate limiting step patterns that control not only their rate and variability, but also their sensitivity to environmental changes.

Temperature affects the behavior of cells, such as their growth rate. However, it is not well understood how these changes result from the changes at the single molecule level. We observed the production of individual RNA molecules in live cells under a wide range of temperatures. This allowed us to determine not only how fast they are produced, but also how much variability there is in this process. Next, we fit a stochastic model to the data to identify which rate-limiting steps during RNA production are responsible for the observed differences between conditions. We found that genes differ in how their RNA production is limited by different steps and in how these are affected by the temperature, which explains why different genes respond differently to temperature fluctuations.

A Statistical Approach Reveals Designs for the Most Robust Stochastic Gene Oscillators

The engineering of transcriptional networks presents many challenges due to the inherent uncertainty in the system structure, changing cellular context, and stochasticity in the governing dynamics. One approach to address these problems is to design and build systems that can function across a range of conditions; that is they are robust to uncertainty in their constituent components. Here we examine the parametric robustness landscape of transcriptional oscillators, which underlie many important processes such as circadian rhythms and the cell cycle, plus also serve as a model for the engineering of complex and emergent phenomena. The central questions that we address are: Can we build genetic oscillators that are more robust than those already constructed? Can we make genetic oscillators arbitrarily robust? These questions are technically challenging due to the large model and parameter spaces that must be efficiently explored. Here we use a measure of robustness that coincides with the Bayesian model evidence, combined with an efficient Monte Carlo method to traverse model space and concentrate on regions of high robustness, which enables the accurate evaluation of the relative robustness of gene network models governed by stochastic dynamics. We report the most robust two and three gene oscillator systems, plus examine how the number of interactions, the presence of autoregulation, and degradation of mRNA and protein affects the frequency, amplitude, and robustness of transcriptional oscillators. We also find that there is a limit to parametric robustness, beyond which there is nothing to be gained by adding additional feedback. Importantly, we provide predictions on new oscillator systems that can be constructed to verify the theory and advance design and modeling approaches to systems and synthetic biology.

Single-cell kinetics of a repressilator when implemented in a single-copy plasmid

Synthetic genetic clocks, such as the Elowitz-Leibler repressilator, will be key regulatory components of future synthetic circuits. We constructed a single-copy repressilator (SCR) by implementing the original repressilator circuit on a single-copy F-plasmid. After verifying its functionality, we studied its behaviour as a function of temperature and compared it with that of the original low-copy-number repressilator (LCR). Namely, we compared the period of oscillations, functionality (the fraction of cells exhibiting oscillations) and robustness to internal fluctuations (the fraction of expected oscillations that would occur). We found that, under optimal temperature conditions, the dynamics of the two systems differs significantly, although qualitatively they respond similarly to temperature changes. Exception to this is in the functionality, in which the SCR is higher at lower temperatures but lower at higher temperatures. Next, by adding IPTG to the medium at low and high concentrations during microscopy sessions, we showed that the functionality of the SCR is more robust to external perturbations, which indicates that the oscillatory behaviour of the LCR can be disrupted by affecting only a few of the copies in a cell. We conclude that the SCR, the first functional, synthetic, single-copy, ring-type genetic clock, is more robust to lower temperatures and to external perturbations than the original LCR. The SCR will be of use in future synthetic circuits, since it complements the array of tasks that the LCR can perform.

Multi-stable dynamics of the non-adiabatic repressilator

The assumption of the fast binding of transcription factors (TFs) to promoters is a typical point in studies of synthetic genetic circuits functioning in bacteria. Although the assumption is effective for simplifying the models, it becomes questionable in the light of in vivo measurements of the times TF spends searching for its cognate DNA sites. We investigated the dynamics of the full idealized model of the paradigmatic genetic oscillator, the repressilator, using deterministic mathematical modelling and stochastic simulations. We found (using experimentally approved parameter values) that decreases in the TF binding rate changes the type of transition between steady state and oscillation. As a result, this gives rise to the hysteresis region in the parameter space, where both the steady state and the oscillation coexist. We further show that the hysteresis is persistent over a considerable range of the parameter values, but the presence of the oscillations is limited by the low rate of TF dimer degradation. Finally, the stochastic simulation of the model confirms the hysteresis with switching between the two attractors, resulting in highly skewed period distributions. Moreover, intrinsic noise stipulates trains of large-amplitude modulations around the stable steady state outside the hysteresis region, which makes the period distributions bimodal.

Estimation of GFP-tagged RNA numbers from temporal fluorescence intensity data

MOTIVATION

MS2-GFP-tagging of RNA is currently the only method to measure intervals between consecutive transcription events in live cells. For this, new transcripts must be accurately detected from intensity time traces.

RESULTS

We present a novel method for automatically estimating RNA numbers and production intervals from temporal data of cell fluorescence intensities that reduces uncertainty by exploiting temporal information. We also derive a robust variant, more resistant to outliers caused e.g. by RNAs moving out of focus. Using Monte Carlo simulations, we show that the quantification of RNA numbers and production intervals is generally improved compared with previous methods. Finally, we analyze data from live Escherichia coli and show statistically significant differences to previous methods. The new methods can be used to quantify numbers and production intervals of any fluorescent probes, which are present in low copy numbers, are brighter than the cell background and degrade slowly.

AVAILABILITY

Source code is available under Mozilla Public License at http://www.cs.tut.fi/%7ehakkin22/jumpdet/.