Biology subjects

Miro-Bueno, J.

Publications and source records attributed to Miro-Bueno, J..

3 recordsLinked to original sources

bioRxiv2024

Building a syntrophic Pseudomonas putida consortium with reciprocal substrate processing of lignocellulosic disaccharides

Synthetic microbial consortia can leverage their expanded enzymatic reach to tackle biotechnological challenges too complex for single strains, such as lignocellulose valorisation. The benefit of metabolic cooperation comes with a catch - installing stable interactions between consortium members. We constructed a syntrophic consortium of Pseudomonas putida strains for lignocellulosic disaccharide processing. Two strains were engineered to hydrolyse and metabolise lignocellulosic sugars: one grows on xylose and hydrolyses cellobiose to produce glucose, while the other grows on glucose and cleaves xylobiose to produce xylose. This specialisation allows each strain to provide essential growth substrate to its partner, establishing a stable mutualistic interaction, which we term reciprocal substrate processing. Key enzymes from Escherichia coli (xylose isomerase pathway) and Thermobifida fusca (glycoside hydrolases) were introduced into P. putida to broaden its carbohydrate utilisation capabilities and arranged in a way to install the strain cross-dependency. A mathematical model of the consortium assisted in predicting the effects of substrate composition, strain ratios, and protein expression levels on population dynamics. Our results demonstrated that modulating extrinsic factors such as substrate concentration can optimise growth and balance fitness disparities between the strains, but achieving this by altering intrinsic factors such as glycoside hydrolase expression levels is much more challenging. This study underscores the potential of synthetic microbial consortia to facilitate the bioconversion of lignocellulosic sugars and offers insights into overcoming the challenges of establishing synthetic microbial cooperation.

synthetic biology↗

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↗