Biology subjects

Sechkar, K.

Publications and source records attributed to Sechkar, K..

6 recordsLinked to original sources

bioRxiv2025

Automatic feedback control for resource-aware characterisation of genetic circuits

Many applications of engineered cells are enabled by genetic circuits - networks of genes regulating each other to process signals. Complex circuits are built by combining standardised modular components with different functions. Nonetheless, genes in a cell compete for the same limited pool of cellular resources, causing unintended interactions that violate modularity. Thus, circuit components behave differently when combined versus when observed in isolation, which can compromise a biotechnologys predictability and reliability. To forecast steady-state interactions between modules, experimental protocols for characterising their resource competition properties have been proposed. However, they rely on open-loop batch culture techniques in which dynamic control signals cannot be applied to cells. Consequently, these experimental methods have limited predictive power, as they may fail to capture all possible steady states, such as repelling equilibria that would not be approached by a system without external forcing. In contrast, we propose a novel, comprehensive protocol for characterising the resource-dependence of genetic modules performance. Based on the control-based continuation technique, it captures both stable and unstable steady states by applying stabilising cybergenetic feedback with an automated cell culturing platform. Using several models with different degrees of complexity, we simulate applying our pipeline to a self-activating genetic switch. This case study illustrates how informative characterisation of a genetic module with automatic feedback control enables reliable forecasting of its performance when combined with any other circuit component. Hence, our protocol promises to restore predictability to the design of genetic circuits from standardised components.

synthetic biology↗

bioRxiv2024

Quantum Correlations in Engineered Magneto-Sensitive Fluorescent Proteins Enables Multi-Modal Sensing in Living Cells

Quantum mechanical phenomena have been identified as fundamentally significant to an increasing number of biological processes. Simultaneously, quantum sensing is emerging as a cutting-edge technology for diverse applications across materials and biological science. However, until recently, biological based candidates for quantum sensors have been limited to in vitro systems, were prone to light induced degradation, and the experimental setups involved are typically not amenable to high-throughput study as would enable further engineering e.g. via directed evolution. We recently created a new class of magneto-sensitive fluorescent proteins (MFPs), which we show overcome these challenges and represent a new form of engineered biological quantum sensors that function both at physiological conditions and in living cells. Through directed evolution, we demonstrate the possibility of engineering these proteins to alter properties of their response to magnetic fields and radio frequencies. These effects are explained in terms of the radical pair mechanism (RPM), involving the protein backbone and a bound flavin cofactor. Using this engineered system we demonstrate the first observation of a fluorescent protein exhibiting Optically Detected Magnetic Resonance (ODMR) in living bacterial cells at room temperature, at sufficiently high signal-to-noise to be detected in a single cell. These magnetic resonance and magnetic field effects measured via fluorescence enable novel technologies; examples we demonstrate include spatial localisation of fluorescence signals using gradient fields (i.e. Magnetic Resonance Imaging (MRI) using a genetically encoded probe), sensing of the molecular microenvironment, multiplexing of bio-imaging, and lock-in detection, overcoming typical fluorescence imaging challenges of light scattering and autofluorescence. Taken together, our results represent a new range of sensing modalities for engineered biological systems, based on and designed around understanding the quantum mechanical properties of MFPs.

bioengineering↗

bioRxiv2024

Model-guided gene circuit design for engineering genetically stable cell populations in diverse applications

Maintaining engineered cell populations genetic stability is a key challenge in synthetic biology. Synthetic genetic constructs compete with a host cells native genes for expression resources, burdening the cell and impairing its growth. This creates a selective pressure favouring mutations which alleviate this growth defect by removing synthetic gene expression. Non-functional mutants thus spread in cell populations, eventually making them lose engineered functions. Past work has attempted to limit mutation spread by coupling synthetic gene expression to survival. However, these approaches are highly context-dependent and must be tailor-made for each particular synthetic gene circuit to be retained. In contrast, we develop and analyse a biomolecular controller which depresses mutant cell growth independently of the mutated synthetic genes identity. Modelling shows how our design can be deployed alongside various synthetic circuits without any re-engineering of its genetic components, outperforming extant gene-specific mutation spread mitigation strategies. Our controllers performance is evaluated using a novel simulation approach which leverages resource-aware cell modelling to directly link a circuits design parameters to its population-level behaviour. Our designs adaptability promises to mitigate mutation spread in an expanded range of applications, whilst our analyses provide a blueprint for using resource-aware cell models in circuit design.

synthetic biology↗

bioRxiv2024

Combining positive and negative regulation for modular and robust biomolecular control architectures

Engineered biotechnologies are powered by synthetic gene regulation and control systems, known as genetic circuits, which must be modular and robust to disturbances if they are to perform reliably. An emerging family of regulatory mechanisms is mediated by clustered interspaced palindromic repeats (CRISPR) that can both interfere with (downregulate) or activate (upregulate) a given genes expression. However, all CRSIPR regulation relies on a shared resource pool of dCas9 proteins. Hence, a circuits components can indirectly affect one another via resource competition - even without any intended interactions between them - which compromises the modularity of synthetic biological designs. Using a resourceaware model of CRISPR regulation, we find that circuit modules which simultaneously subject a gene to CRISPR interference and activation are rendered robust to resource competition crosstalk. Evaluating this architectures simulated performance, we identify the scenarios where it can be advantageous over the extant resource competition mitigation strategies. We then consider different feedback architectures to demonstrate that combining opposite regulatory interactions overcomes the trade-off in robustness to perturbations of different nature. The motif of combined positive and negative regulation may therefore give rise to more robust and modular biomolecular controllers, as well as hint at the characteristics of natural systems that possess it.

synthetic biology↗

bioRxiv2023

A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits

Synthetic genes compete among themselves and with the host cells genes for expression machinery, exhibiting resource couplings that affect the dynamics of cellular processes. The modeling of such couplings can be facilitated by simplifying the kinetics of resource-substrate binding. Model-guided design allows to counter unwanted indirect interactions by using biomolecular controllers or tuning the biocircuits parameters. However, resource-aware biocircuit design in bacteria is complicated by the interdependence of resource availability and cell growth rate, which significantly affects biocircuit performance. This phenomenon can be captured by coarse-grained models of the whole bacterial cell. The level of detail in these models must balance accurate representation of metabolic regulation against model simplicity and interpretability. We propose a coarse-grained E. coli cell model that combines the ease of simplified resource coupling analysis with the appreciation of bacterial growth regulation mechanisms. Reliably capturing known growth phenomena, it enables numerical prototyping of biocircuits and derivation of analytical relations which can guide the design process. By reproducing several distinct empirical laws observed in prior studies, our model provides a unifying framework for previously disjoint experimental observations. Finally, we propose a novel biomolecular controller that achieves near-perfect adaptation of cell-wide ribosome availability to changes in synthetic gene expression. Showcasing our models usefulness, we use it to determine the controllers setpoint and operation range from its constituent genes parameters.

synthetic biology↗

bioRxiv2021

A linear programming-based strategy to save pipette tips in automated DNA assembly

Laboratory automation and mathematical optimisation are key to improving the efficiency of synthetic biology research. While there are algorithms optimising the construct designs and synthesis strategies for DNA assembly, the optimisation of how DNA assembly reaction mixes are prepared remains largely unexplored. Here, we focus on reducing the pipette tip consumption of a liquid-handling robot as it delivers DNA parts across a multi-well plate where several constructs are being assembled in parallel. We propose a linear programming formulation of this problem based on the capacitated vehicle routing problem, along with an algorithm which applies a linear programming solver to our formulation, hence providing a strategy to prepare a given set of DNA assembly mixes using fewer pipette tips. The algorithm performed well in randomly generated and real-life scenarios concerning several modular DNA assembly standards, proving capable of reducing the pipette tip consumption by up to 61% in large-scale cases. Combining automatic process optimisation and robotic liquid-handling, our strategy promises to greatly improve the efficiency of DNA assembly, either used alone or in combination with other algorithmic methods.

synthetic biology↗