Biology subjects

Grozinger, L.

Publications and source records attributed to Grozinger, L..

5 recordsLinked to original sources

bioRxiv2024

Genetic designs for stochastic and probabilistic biocomputing

The programming of computations in living cells can be done by manipulating information flows within genetic networks. Typically, a single bit of information is encoded by a single genes steady state expression. Expression is discretized into high and low levels that correspond to 0 and 1 logic values, analogous to the high and low voltages in electronic logic circuits. However, the processes of molecular signaling and computation in living systems challenge this computational paradigm with their dynamic, stochastic and continuous operation. Although there is a good understanding of these phenomena in genetic networks, and there are already stochastic and probabilistic models of computation which can take on these challenges, there is currently a lack of work which puts both together to implement computations tailored to these features of living matter. Here, we design genetic networks for stochastic and probabilistic computing paradigms and develop the theory behind their operation. Moving beyond the digital abstraction, we explore the concepts of bit-streams (sequences of pulses acting as time-based signals) and probabilistic-bits or p-bits (values that can be either 1 or 0 with an assigned probability), as more suitable candidates for the encoding and processing of information in genetic networks. Specifically, the conceptualization of signals as stochastic bit-streams allows for encoding information in the frequency of random expression pulses, offering advantages such as robustness to noise. Additionally, the notion of p-bit enables the design of genetic circuits with capabilities surpassing those of current genetic logic gates, including invertibility. We design several circuits to illustrate these advantages and provide mathematical models and computational simulations that demonstrate their functionality. Our approach to stochastic and probabilistic computing in living cells not only enhances and reflects understanding of information processing in biological systems but also presents promising avenues for designing genetic circuits with advanced functionalities.

synthetic biology↗

bioRxiv2024

Reprogramming genetic circuits using space

Genetic circuits confer computing abilities to living cells, performing novel transformations of input stimuli into output responses. Circuit editing often focuses on substituting DNA components, such as RBSs, regulators, or promoters, from part libraries to achieve desired performance. However, this approach is inherently limited by the availability of DNA components. Here, we show that circuit performance can be reprogrammed without altering its DNA sequence by using a library of positions: a set of physical locations within the cells volume. Using bacteria as the living chassis, we engineer 219 spatially unique genetic circuits of four different types--three regulatory cascades and a toggle switch--by either inserting the entire circuit in a specific chromosomal position or separating and distributing circuit modules. Their analysis, together with a mathematical model, reveals that spatial positioning can be used not only to optimize circuits but also to switch circuits between modes of operation, giving rise to new functions as circuit complexity increases. We provide foundational insights into leveraging intracellular space for circuit design.

synthetic biology↗

bioRxiv2022

An electrogenetic toggle switch design

Synthetic biology uses molecular biology to implement genetic circuits that perform computations. These circuits can process inputs and deliver outputs according to predefined rules that are encoded, often entirely, into genetic parts. However, the field has recently begun to focus on using mechanisms beyond the realm of genetic parts for engineering biological circuits. We analyse the use of electrogenic processes for circuit design and present a model for a merged genetic and electrogenetic toggle switch. Computational simulations explore conditions under which bistability emerges in order to identify the circuit design principles for best switch performance. The results provide a basis for the rational design and implementation of hybrid devices that can be measured and controlled both genetically and electronically.

synthetic biology↗

bioRxiv2020

FlowScatt: enabling volume-independent flow cytometry data by decoupling fluorescence from scattering

MotivationMeasuring fluorescence by flow cytometry is fundamental for characterising single-cell performance. While it is known that fluorescence and scattering values tend to positively correlate, the impact of cell volume on fluorescence is typically overlooked. This makes of fluorescence values alone an inaccurate measurement for high-precision characterisations. ResultsWe developed FlowScatt, an open-source software package that removes volume-dependency in the fluorescence channel. Using FlowScatt, flourescence values are re-calculated based on the unified volume per cell that arises from scattering decomposition. AvailabilityFlowScatt is openly available as a Python package on https://github.com/rstoof/FlowScatt. Experimental data for validation is available online. Contactangel.goni-moreno@newcastle.ac.uk

bioinformatics↗

bioRxiv2020

Contextual dependencies expand the re-usability of genetic inverters

The design and implementation of Boolean logic functions in living cells has become a very active field within synthetic biology. By controlling networks of regulatory proteins, novel genetic circuits are engineered to generate predefined output responses. Although many current implementations focus solely on the genetic components of the circuit, the host context in which the circuit performs is crucial for its outcome. Here, we characterise 20 genetic NOT logic gates (inverters) in up to 7 bacterial-based contexts each, to finally generate 135 different functions. The contexts we focus on are particular combinations of four plasmid backbones and three hosts, two Escherichia coli and one Pseudomonas putida strains. Each NOT logic gate shows seven different logic behaviours, depending on the context. That is, gates can be reconfigured to fit response requirements by changing only contextual parameters. Computational analysis shows that this range of behaviours improves the compatibility between gates, because there are considerably more possibilities for combination than when considering a unique function per genetic construct. Finally, we address the issue of interoperability and portability by measuring, scoring, and comparing gate performance across contexts. Rather than being a limitation, we argue that the effect of the genetic background on synthetic constructs expand the scope of the functions that can be engineered in complex cellular environments, and advocate for considering context as a fundamental design parameter for synthetic biology.

synthetic biology↗