Biology subjects

Giordano, G.

Publications and source records attributed to Giordano, G..

11 recordsLinked to original sources

bioRxiv2026

Establishing label-free quantitative X-ray dose-response profiling by Holo-Tomographic Flow Cytometry

Flow cytometry (FC) offers multiparametric analysis capabilities that can quantify cellular damage after exposure to cytotoxic agents. Here, we present a comprehensive study establishing a label-free quantitative X-ray dose-response profiling using a novel FC modality based on 3D Quantitative Phase Imaging, termed Holo-Tomographic Flow Cytometry (HTFC). This approach enables fully label-free 3D refractive index (RI) measurements, allowing detailed and quantitative characterization of the biophysical properties and morphology of living cells exposed to X-rays. By analyzing datasets of 3D RI tomograms from cells irradiated at graded doses, we identify intracellular biophysical markers that define a robust X-ray dose-response curve. Validation against standard clonogenic survival assays on three model cancer cell lines reveal a high correlation (>90%). HTFC not only eliminates labeling and operator bias but also markedly reduces experimental time from 1-2 weeks to 24 hours, offering a fully automated and objective readout. While clonogenic survival remains the benchmark for radiosensitivity assessment, our findings establish HTFC as a powerful label-free platform for fast assessment of radiation damage. This technology paves the way for predictive biosensors that can capture patient-specific responses, thereby supporting the transition from conventional, uniform radiotherapy protocols to personalized treatment strategies.

biophysics↗

bioRxiv2025

Temporal dose inversion properties of adaptive biomolecular circuits

Cells have the capacity to encode and decode information in the temporal features of molecular signals. Many pathways, for example, generate either sustained or pulsatile responses depending on the context, and such diverse temporal behaviors have a profound impact on cell fate. Here we focus on how molecular pathways can convert the temporal features of dynamic signals, in particular how they can convert transient signals into persistent downstream events and vice versa. We refer to this type of behavior as temporal dose inversion, and we demonstrate that it can be achieved through adaptive molecular circuits. Using a cell-free synthetic gene circuit implementing an incoherent feedforward loop (IFFL), we experimentally demonstrate the temporal dose inversion. To understand the design principles of the temporal dose inversion, we analyze adaptive circuit motifs including incoherent feedforward loops (IFFLs) and negative feedback loops (NFLs). Through numerical simulations with expensive parameter exploration, we identify parametric regimes in which these circuits exhibit temporal dose inversion. We further examine more detailed biological models of the IFFL and NFL circuits, including enzymatic signaling models and gene regulatory network models, showing that the IFFL circuits is more likely to exhibit temporal dose inversion compared with the NFL circuits. Finally, we analyze a generalized IFFL topology, and we find that both the time delay in the inhibition pathway and the relative signal intensities of the activation and inhibition signals are key determinants for temporal dose inversion. Together, our results establish a design principle of temporal dose inversion on the adaptive biomolecular circuits and provide mechanistic insight into how molecular networks process temporal information in dynamic signals.

systems biology↗

bioRxiv2024

Engineering cell fate with adaptive feedback control

Engineering cell fate is fundamental for optimizing stem cell-based therapies aimed at replacing cells in patients suffering from trauma or disease. By timely administering molecular regulators--such as transcription factors, RNAs, or small molecules--in a process that mimics in vivo embryonic development, stem cell differentiation can be guided toward a specific cell fate. A significant challenge in scaling up these therapies is that such differentiation strategies often result in mixed cellular populations. While synthetic biology approaches have been proposed to increase the yield of desired cell types, designing gene circuits that effectively redirect cell fate decisions requires mechanistic insight into the dynamics of endogenous regulatory networks that govern decision-making. In this work, we present a biomolecular adaptive controller based on an Incoherent Feedforward Loop (IFFL)-like topology designed to favor a specific cell fate. This controller requires minimal knowledge of the endogenous network as it exhibits adaptive, non-reference-based behavior. The synthetic circuit operates through a sequestration mechanism and a delay introduced by an intermediate species, producing an output that asymptotically approximates a discrete temporal derivative of its input, provided there is a sufficiently fast sequestration rate. By allowing the controller to actuate over a target species involved in the decision-making process, a tunable, synthetic bias is created that favors the production of the desired species with minimal alteration to the overall equilibrium landscape of the endogenous network. Through theoretical and computational analysis, we provide design guidelines for the controllers optimal operation, evaluate its performance under parametric perturbations, and extend its applicability to various examples of common multistable systems in biology.

synthetic biology↗

bioRxiv2024

Leveraging HILIC/ERLIC Separations for Online Nanoscale LC- MS/MS Analysis of Phosphopeptide Isoforms from RNA Polymerase II C-terminal Domain

The eukaryotic RNA polymerase II (Pol II) multi-protein complex transcribes mRNA and coordinates several steps of co-transcriptional mRNA processing and chromatin modification. The largest Pol II subunit, Rpb1, has a C-terminal domain (CTD) comprising dozens of repeated heptad sequences (Tyr1-Ser2-Pro3-Thr4-Ser5-Pro6-Ser7), each containing five phospho-accepting amino acids. The CTD heptads are dynamically phosphorylated, creating specific patterns correlated with steps of transcription initiation, elongation, and termination. This CTD phosphorylation code choreographs dynamic recruitment of important co-regulatory proteins during gene transcription. Genetic tools were used to engineer protease cleavage sites across the CTD (msCTD), creating tryptic peptides with unique sequences amenable to mass spectrometry analysis. However, phosphorylation isoforms within each msCTD sequence are difficult to resolve by standard reversed phase chromatography typically used for LC-MS/MS applications. Here, we use a panel of synthetic CTD phosphopeptides to explore the potential of hydrophilic interaction and electrostatic repulsion hydrophilic interaction (HILIC and ERLIC) chromatography as alternatives to reversed phase separation for CTD phosphopeptide analysis. Our results demonstrate that ERLIC provides improved performance for separation of singly- and doubly-phosphorylated CTD peptides for sequence analysis by LC-MS/MS. Analysis of native yeast msCTD confirms that phosphorylation on Ser5 and Ser2 represents the major endogenous phosphoisoforms. We expect this methodology will be especially useful in the investigation of pathways where multiple protein phosphorylation events converge in close proximity.

biochemistry↗

bioRxiv2024

Design of a sequestration-based network with tunable pulsing dynamics

Incoherent feedforward networks exhibit the ability to generate temporal pulse behavior. However, exerting control over specific dynamic properties, such as amplitude and rise time, poses a challenge and is intricately tied to the networks implementation. In this study, we focus on analyzing sequestration-based networks capable of exhibiting pulse behavior. By employing time-scale separation in the fast sequestration regime, we approximate the temporal dynamics of these networks. This approach allows us to establish a mapping that elucidates the impact of varying the kinetic rates and pulse specifications, including amplitude and rise time. Furthermore, we introduce a positive feedback mechanism to regulate the amplitude of the pulsing response.

synthetic biology↗

bioRxiv2024

Neural networks built from enzymatic reactions can operate as linear and nonlinear classifiers

The engineering of molecular programs capable of processing patterns of multi-input biomarkers holds great potential in applications ranging from in vitro diagnostics (e.g., viral detection, including COVID-19) to therapeutic interventions (e.g., discriminating cancer cells from normal cells). For this reason, mechanisms to design molecular networks for pattern recognition are highly sought after. In this work, we explore how enzymatic networks can be used for both linear and nonlinear classification tasks. By leveraging steady-state analysis and showing global stability, we demonstrate that these networks can function as molecular perceptrons, fundamental units of artificial neural networks--capable of processing multiple inputs associated with positive and negative weights to achieve linear classification. Furthermore, by composing orthogonal enzymatic reactions, we show that multi-layer networks can be constructed to achieve nonlinear classification.

synthetic biology↗

bioRxiv2023

Uncoupling the TFIIH Core and Kinase Modules Leads To Unregulated RNA Polymerase II CTD Serine 5 Phosphorylation

TFIIH is an essential transcription initiation factor for RNA polymerase II (RNApII). This multi-subunit complex comprises two modules that are physically linked in S. cerevisiae by the subunit Tfb3 (MAT1 in metazoans). The Core Module, with two DNA-dependent ATPases and several additional subunits, promotes DNA unwinding. The Kinase Module phosphorylates the C-terminal domain (CTD) of RNApII subunit Rpb1, initiating a cycle of CTD modifications that coordinate exchange of initiation and elongation factors. Why these two disparate activities are bundled into one factor is not obvious, but the connection may provide temporal coordination during early initiation. When Tfb3 is split into two parts to uncouple the TFIIH modules, the resulting cells are viable but grow very slowly. Chromatin immunoprecipitation of the split TFIIH shows that the Core Module, but not the Kinase, is properly recruited to promoters. Instead of the normal promoter-proximal peak, high CTD Serine 5 phosphorylation is seen throughout transcribed regions. Therefore, coupling the TFIIH modules is necessary to localize and limit CTD kinase activity to early stages of transcription. These results are consistent with the idea that the two TFIIH modules began as independent functional entities that later became connected by Tfb3 during early eukaryotic evolution. Impact StatementThe TFIIH subunit Tfb3/MAT1 can be split into two parts to uncouple the TFIIH kinase and DNA translocase modules, resulting in unfocused CTD phosphorylation.

molecular biology↗

bioRxiv2023

Exploring alternative quorum sensing model structures and quorum quenching strategies

Bacterial quorum sensing (QS) is a cell-to-cell communication mechanism through which bacteria share information about cell density, and tune gene expression accordingly. Pathogens exploit QS to orchestrate virulence and regulate the expression of genes related to antimicrobial resistance. Despite the vast literature on QS, the properties of the underlying molecular network are not entirely clear. We compare two synthetic QS circuit architectures: in the first, a single positive feedback loop autoinduces the synthesis of the signal molecule; the second includes an additional positive feedback loop enhancing the synthesis of the signal molecule receptor. Our comprehensive analysis of the two systems and their equilibria highlights the differences in the bistable and hysteretic behaviors of the alternative QS structures. Finally, we investigate three different QS inhibition approaches; numerical analysis predicts their effect on the steady-state behavior of the two different QS models, revealing critical parameter thresholds that guarantee an effective QS suppression.

synthetic biology↗

bioRxiv2022

Building molecular band-pass filters via molecular sequestration

Engineered genetic circuits with tailored functions that mimic how cells process information in changing environments (e.g. cell fate decision, chemotaxis, immune response) have great applications in biomedicine and synthetic biology. Although there is a lot of progress toward the design of gene circuits yielding desired steady states (e.g. logic-based networks), building synthetic circuits for dynamic signal processing (e.g. filters, frequency modulation, and controllers) is still challenging. Here, we provide a model-based approach to build gene networks that can operate as band-pass filters by taking advantage of molecular sequestration. By suitably approximating the dynamics of molecular sequestration, we analyze an Incoherent Feed-Forward Loop (IFFL) and a Negative Feedback (NF) circuit and illustrate how they can achieve band-pass filter behavior. Computational analysis shows that a circuit that incorporates both IFFL and NF motifs improves the filter performance. Our approach facilitates the design of sequestration-based filters, and may support the synthesis of molecular controllers with desired specifications.

synthetic biology↗

bioRxiv2021

Computational characterization of recombinase circuits for periodic behaviors

In nature, recombinases are site-specific proteins capable of rearranging DNA, and they are expanding the repertoire of gene editing tools used in synthetic biology. The on/off response of recombinases, achieved by inverting the direction of a promoter, makes them suitable for Boolean logic computation; however, recombinase-based logic gate circuits are single-use due to the irreversibility of the DNA rearrangement, and it is still unclear how a dynamical circuit, such as an oscillator, could be engineered using recombinases. Preliminary work has demonstrated that recombinase-based circuits can yield periodic behaviors in a deterministic setting. However, since a few molecules of recombinase are enough to perform the inverting function, it is crucial to assess how the inherent stochasticity at low copy number affects the periodic behavior. Here, we propose six different circuit designs for recombinase-based oscillators. We model them in a stochastic setting, leveraging the Gillespie algorithm for extensive simulations, and we show that they can yield periodic behaviors. To evaluate the incoherence of oscillations, we use a metric based on the statistical properties of auto-correlation functions. The main core of our design consists of two self-inhibitory, recombinase-based modules coupled by a common promoter. Since each recombinase inverts its own promoter, the overall circuit can give rise to switching behavior characterized by a regular period. We introduce different molecular mechanisms (transcriptional regulation, degradation, sequestration) to tighten the control of recombinase levels, which slows down the response timescale of the system and thus improves the coherence of oscillations. Our results support the experimental realization of recombinase-based oscillators and, more generally, the use of recombinases to generate dynamic behaviors in synthetic biology.

synthetic biology↗

bioRxiv2020

Signaling-based neural networks for cellular computation

Cellular signaling pathways are responsible for decision making that sustains life. Most signaling pathways include post-translational modification cycles, that process multiple inputs and are tightly interconnected. Here we consider a model for phosphorylation/dephosphorylation cycles, and we show that under some assumptions they can operate as molecular neurons or perceptrons, that generate sigmoidal-like activation functions by processing sums of inputs with positive and negative weights. We carry out a steady-state and structural stability analysis for single molecular perceptrons as well as for feedforward interconnections, concluding that interconnected phosphorylation/dephosphorylation cycles may work as multi-layer biomolecular neural networks (BNNs) with the capacity to perform a variety of computations. As an application, we design signaling networks that behave as linear and non-linear classifiers.

synthetic biology↗