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Metabolic dysfunction-associated steatotic liver disease (MASLD) is the commonest cause of chronic liver disease worldwide and represents an unmet precision medicine challenge. We established a retrospective national cohort of 940 histologically defined patients (55.4% men, 44.6% women; median body mass index 31.3; 32% with type 2 diabetes) covering the complete MASLD severity spectrum, and created a secure, searchable, open resource (SteatoSITE). In 668 cases and 39 controls, we generated hepatic bulk RNA sequencing data and performed differential gene expression and pathway analysis, including exploration of gender-specific differences. A web-based gene browser was also developed. We integrated histopathological assessments, transcriptomic data and 5.67 million days of time-stamped longitudinal electronic health record data to define disease-stage-specific gene expression signatures, pathogenic hepatic cell subpopulations and master regulator networks associated with adverse outcomes in MASLD. We constructed a 15-gene transcriptional risk score to predict future hepatic decompensation events (area under the receiver operating characteristic curve 0.86, 0.81 and 0.83 for 1-, 3- and 5-year risk, respectively). Additionally, thyroid hormone receptor beta regulon activity was identified as a critical suppressor of disease progression. SteatoSITE supports rational biomarker and drug development and facilitates precision medicine approaches for patients with MASLD., (© 2023. The Author(s).)

Cybergenetics is a new area of research aimed at developing digital and biological controllers for living systems. Synthetic biologists have begun exploiting cybergenetic tools and platforms to both accelerate the development of mathematical models and develop control strategies for complex biological phenomena. Here, we review the state of the art in cybergenetic identification and control. Our aim is to lower the entry barrier to this field and foster the adoption of methods and technologies that will accelerate the pace at which Synthetic Biology progresses toward applications., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

To swim up gradients of nutrients, E. coli senses nutrient concentrations within its periplasm. For small nutrient molecules, periplasmic concentrations typically match extracellular concentrations. However, this is not necessarily the case for saccharides, such as maltose, which are transported into the periplasm via a specific porin. Previous observations have shown that, under various conditions, E. coli limits maltoporin abundance so that, for extracellular micromolar concentrations of maltose, there are predicted to be only nanomolar concentrations of free maltose in the periplasm. Thus, in the micromolar regime, the total uptake of maltose from the external environment into the cytoplasm is limited not by the abundance of cytoplasmic transport proteins but by the abundance of maltoporins. Here, we present results from experiments and modeling suggesting that this porin-limited transport enables E. coli to sense micromolar gradients of maltose despite having a high-affinity ABC transport system that is saturated at these micromolar levels. We used microfluidic assays to study chemotaxis of E. coli in various gradients of maltose and methyl-aspartate and leveraged our experimental observations to develop a mechanistic transport-and-sensing chemotaxis model. Incorporating this model into agent-based simulations, we discover a trade-off between uptake and sensing: although high-affinity transport enables higher uptake rates at low nutrient concentrations, it severely limits the range of dynamic sensing. We thus propose that E. coli may limit periplasmic uptake to increase its chemotactic sensitivity, enabling it to use maltose as an environmental cue., Competing Interests: Declaration of interests The authors declare no competing interests., (Published by Elsevier Inc.)

Non-alcoholic fatty liver disease (NAFLD) represents a global healthcare challenge, affecting 1 in 4 adults, and death rates are predicted to rise inexorably. The progressive form of NAFLD, non-alcoholic steatohepatitis (NASH), can lead to fibrosis, cirrhosis, and hepatocellular carcinoma. However, no medical treatments are licensed for NAFLD-NASH. Identifying efficacious therapies has been hindered by the complexity of disease pathogenesis, a paucity of predictive preclinical models and inadequate validation of pharmacological targets in humans. The development of clinically relevant in vitro models of the disease will pave the way to overcome these challenges. Currently, the combined application of emerging technologies (e.g., organ-on-a-chip/microphysiological systems) and control engineering approaches promises to unravel NAFLD biology and deliver tractable treatment candidates. In this review, we will describe advances in preclinical models for NAFLD-NASH, the recent introduction of novel technologies in this space, and their importance for drug discovery endeavors in the future., Competing Interests: JA Fallowfield has served as a consultant or advisory board member for Redx Pharma, Novartis, Ferring Pharmaceuticals, Macrophage Pharma, Galecto Biotech, Caldan Therapeutics, Cypralis Ltd, Rallybio, Tectonic Therapeutic, River 2 Renal Corp.,Gilde Healthcare, Guidepoint, Techspert.io and has received research grant funding from Novartis and Intercept Pharmaceuticals. M Jimenez-Ramos MRC iCASE PhD studentship is partially funded by Galecto Biotech., (© 2021 The Author(s).)

Split inteins are powerful tools for seamless ligation of synthetic split proteins. Yet, their use remains limited because the already intricate split site identification problem is often complicated by the requirement of extein junction sequences. To address this, we augment a mini-Mu transposon-based screening approach and devise the intein-assisted bisection mapping (IBM) method. IBM robustly reveals clusters of split sites on five proteins, converting them into AND or NAND logic gates. We further show that the use of inteins expands functional sequence space for splitting a protein. We also demonstrate the utility of our approach over rational inference of split sites from secondary structure alignment of homologous proteins, and that basal activities of highly active proteins can be mitigated by splitting them. Our work offers a generalizable and systematic route towards creating split protein-intein fusions for synthetic biology.

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.

Dynamic modeling in systems and synthetic biology is still quite a challenge-the complex nature of the interactions results in nonlinear models, which include unknown parameters (or functions). Ideally, time-series data support the estimation of model unknowns through data fitting. Goodness-of-fit measures would lead to the best model among a set of candidates. However, even when state-of-the-art measuring techniques allow for an unprecedented amount of data, not all data suit dynamic modeling.Model-based optimal experimental design (OED) is intended to improve model predictive capabilities. OED can be used to define the set of experiments that would (a) identify the best model or (b) improve the identifiability of unknown parameters. In this chapter, we present a detailed practical procedure to compute optimal experiments using the AMIGO2 toolbox.

Synthetic biology has so far made limited use of mathematical models, mostly because their inference has been traditionally perceived as expensive and/or difficult. We have recently demonstrated how in silico simulations and in vitro/vivo experiments can be integrated to develop a cyber-physical platform that automates model calibration and leads to saving 60-80% of the effort. In this book chapter, we illustrate the protocol used to attain such results. By providing a comprehensive list of steps and pointing the reader to the code we use to operate our platform, we aim at providing synthetic biologists with an additional tool to accelerate the pace at which the field progresses toward applications.

Hybrid promoter engineering takes advantage of the modular nature of eukaryotic promoters by combining discrete promoter motifs to confer novel regulatory function. By combinatorially screening sequence libraries for trans-acting transcriptional operators, activators, repressors and core promoter sequences, it is possible to derive constitutive or inducible promoter collections covering a broad range of expression strengths. However, combinatorial approaches to promoter design can result in highly complex, multidimensional design spaces, which can be experimentally costly to thoroughly explore in vivo. Here, we describe an in silico pipeline for the design of hybrid promoter libraries that employs a Design of Experiments (DoE) approach to reduce experimental burden and efficiently explore the promoter fitness landscape. We also describe a software pipeline to ensure that the designed promoter sequences are compatible with the YTK assembly standard.

Respiratory droplets are the primary transmission route for SARS-CoV-2, a principle which drives social distancing guidelines. Evidence suggests that virus transmission can be reduced by face coverings, but robust evidence for how mask usage might affect safe distancing parameters is lacking. Accordingly, we set out to quantify the effects of face coverings on respiratory tract droplet deposition. We tested an anatomically realistic manikin head which ejected fluorescent droplets of water and human volunteers, in speaking and coughing conditions without a face covering, or with a surgical mask or a single-layer cotton face covering. We quantified the number of droplets in flight using laser sheet illumination and UV-light for those that had landed at table height at up to 2 m. For human volunteers, expiratory droplets were caught on a microscope slide 5 cm from the mouth. Whether manikin or human, wearing a face covering decreased the number of projected droplets by less than 1000-fold. We estimated that a person standing 2 m from someone coughing without a mask is exposed to over 10 000 times more respiratory droplets than from someone standing 0.5 m away wearing a basic single-layer mask. Our results indicate that face coverings show consistent efficacy at blocking respiratory droplets and thus provide an opportunity to moderate social distancing policies. However, the methodologies we employed mostly detect larger (non-aerosol) sized droplets. If the aerosol transmission is later determined to be a significant driver of infection, then our findings may overestimate the effectiveness of face coverings., Competing Interests: We declare we have no competing interests., (© 2020 The Authors.)

Modeling parts and circuits represents a significant roadblock to automating the Design-Build-Test-Learn cycle in synthetic biology. Once models are developed, discriminating among them requires informative data, computational resources, and skills that might not be readily available. The high cost entailed in model discrimination frequently leads to subjective choices on the selected structures and, in turn, to suboptimal models. Here, we outline frequentist and Bayesian approaches to model discrimination. We ranked three candidate models of a genetic toggle switch, which was adopted as a test case, according to the support from in vivo data. We show that, in each framework, efficient model discrimination can be achieved via optimally designed experiments. We offer a dynamical-systems interpretation of our optimization results and investigate their sensitivity to key parameters in the characterization of synthetic circuits. Our approach suggests that optimal experimental design is an effective strategy to discriminate between competing models of a gene regulatory network. Independent of the adopted framework, optimally designed perturbations exploit regions in the input space that maximally distinguish predictions from the competing models.

Optimal foraging theory provides a framework to understand how organisms balance the benefits of harvesting resources within a patch with the sum of the metabolic, predation, and missed opportunity costs of foraging. Here, we show that, after accounting for the limited environmental information available to microorganisms, optimal foraging theory and, in particular, patch use theory also applies to the behavior of marine bacteria in particle seascapes. Combining modeling and experiments, we find that the marine bacterium Vibrio ordalii optimizes nutrient uptake by rapidly switching between attached and planktonic lifestyles, departing particles when their nutrient concentration is more than hundredfold higher than background. In accordance with predictions from patch use theory, single-cell tracking reveals that bacteria spend less time on nutrient-poor particles and on particles within environments that are rich or in which the travel time between particles is smaller, indicating that bacteria tune the nutrient concentration at detachment to increase their fitness. A mathematical model shows that the observed behavioral switching between exploitation and dispersal is consistent with foraging optimality under limited information, namely, the ability to assess the harvest rate of nutrients leaking from particles by molecular diffusion. This work demonstrates how fundamental principles in behavioral ecology traditionally applied to animals can hold right down to the scale of microorganisms and highlights the exquisite adaptations of marine bacterial foraging. The present study thus provides a blueprint for a mechanistic understanding of bacterial uptake of dissolved organic matter and bacterial production in the ocean-processes that are fundamental to the global carbon cycle., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Swimming spermatozoa from diverse organisms often have very similar morphologies, yet different motilities as a result of differences in the flagellar waveforms used for propulsion. The origin of these differences has remained largely unknown. Using high-speed video microscopy and mathematical analysis of flagellar shape dynamics, we quantitatively compare sperm flagellar waveforms from marine invertebrates to humans by means of a novel phylokinematic tree. This new approach revealed that genetically dissimilar sperm can exhibit strikingly similar flagellar waveforms and identifies two dominant flagellar waveforms among the deuterostomes studied here, corresponding to internal and external fertilizers. The phylokinematic tree shows marked discordance from the phylogenetic tree, indicating that physical properties of the fluid environment, more than genetic relatedness, act as an important selective pressure in shaping the evolution of sperm motility. More broadly, this work provides a physical axis to complement morphological and genetic studies to understand evolutionary relationships.

Stable-isotope probing is widely used to study the function of microbial taxa in their natural environment, but sorting of isotopically labelled microbial cells from complex samples for subsequent genomic analysis or cultivation is still in its early infancy. Here, we introduce an optofluidic platform for automated sorting of stable-isotope-probing-labelled microbial cells, combining microfluidics, optical tweezing and Raman microspectroscopy, which yields live cells suitable for subsequent single-cell genomics, mini-metagenomics or cultivation. We describe the design and optimization of this Raman-activated cell-sorting approach, illustrate its operation with four model bacteria (two intestinal, one soil and one marine) and demonstrate its high sorting accuracy (98.3 ± 1.7%), throughput (200-500 cells h -1 ; 3.3-8.3 cells min -1 ) and compatibility with cultivation. Application of this sorting approach for the metagenomic characterization of bacteria involved in mucin degradation in the mouse colon revealed a diverse consortium of bacteria, including several members of the underexplored family Muribaculaceae, highlighting both the complexity of this niche and the potential of Raman-activated cell sorting for identifying key players in targeted processes.

Aims: Hypertension is common. Recent data suggest that macrophages (Mφ) contribute to, and protect from, hypertension. Endothelin-1 (ET-1) is the most potent endogenous vasoconstrictor with additional pro-inflammatory properties. We investigated the role of the ET system in experimental and clinical hypertension by modifying Mφ number and phenotype., Methods and Results: In vitro, Mφ ET receptor function was explored using pharmacological, gene silencing, and knockout approaches. Using the CD11b-DTR mouse and novel mice with myeloid cell-specific endothelin-B (ETB) receptor deficiency (LysMETB-/-), we explored the effects of modifying Mφ number and phenotype on the hypertensive effects of ET-1, angiotensin II (ANG II), a model that is ET-1 dependent, and salt. In patients with small vessel vasculitis, the impacts of Mφ depleting and non-depleting therapies on blood pressure (BP) and endothelial function were examined. Mouse and human Mφ expressed both endothelin-A and ETB receptors and displayed chemokinesis to ET-1. However, stimulation of Mφ with exogenous ET-1 did not polarize Mφ phenotype. Interestingly, both mouse and human Mφ cleared ET-1 through ETB receptor mediated, and dynamin-dependent, endocytosis. Mφ depletion resulted in an augmented chronic hypertensive response to both ET-1 and salt. LysMETB-/- mice displayed an exaggerated hypertensive response to both ET-1 and ANG II. Finally, in patients who received Mφ depleting immunotherapy BP was higher and endothelial function worse than in those receiving non-depleting therapies., Conclusion: Mφ and ET-1 may play an important role in BP control and potentially have a critical role as a therapeutic target in hypertension., (© The Author(s) 2019. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Aerotaxis, the directed migration along oxygen gradients, allows many microorganisms to locate favorable oxygen concentrations. Despite oxygen's fundamental role for life, even key aspects of aerotaxis remain poorly understood. In Bacillus subtilis, for example, there is conflicting evidence of whether migration occurs to the maximal oxygen concentration available or to an optimal intermediate one, and how aerotaxis can be maintained over a broad range of conditions. Using precisely controlled oxygen gradients in a microfluidic device, spanning the full spectrum of conditions from quasi-anoxic to oxic (60 n mol/l-1 m mol/l), we resolved B. subtilis' 'oxygen preference conundrum' by demonstrating consistent migration towards maximum oxygen concentrations ('monotonic aerotaxis'). Surprisingly, the strength of aerotaxis was largely unchanged over three decades in oxygen concentration (131 n mol/l-196 μ mol/l). We discovered that in this range B. subtilis responds to the logarithm of the oxygen concentration gradient, a rescaling strategy called 'log-sensing' that affords organisms high sensitivity over a wide range of conditions. In these experiments, high-throughput single-cell imaging yielded the best signal-to-noise ratio of any microbial taxis study to date, enabling the robust identification of the first mathematical model for aerotaxis among a broad class of alternative models. The model passed the stringent test of predicting the transient aerotactic response despite being developed on steady-state data, and quantitatively captures both monotonic aerotaxis and log-sensing. Taken together, these results shed new light on the oxygen-seeking capabilities of B. subtilis and provide a blueprint for the quantitative investigation of the many other forms of microbial taxis., Competing Interests: The authors declare no conflict of interest.

Chemotaxis underpins important ecological processes in marine bacteria, from the association with primary producers to the colonization of particles and hosts. Marine bacteria often swim with a single flagellum at high speeds, alternating "runs" with either 180° reversals or ∼90° "flicks," the latter resulting from a buckling instability of the flagellum. These adaptations diverge from Escherichia coli's classic run-and-tumble motility, yet how they relate to the strong and rapid chemotaxis characteristic of marine bacteria has remained unknown. We investigated the relationship between swimming speed, run-reverse-flick motility, and high-performance chemotaxis by tracking thousands of Vibrio alginolyticus cells in microfluidic gradients. At odds with current chemotaxis models, we found that chemotactic precision-the strength of accumulation of cells at the peak of a gradient-is swimming-speed dependent in V. alginolyticus Faster cells accumulate twofold more tightly by chemotaxis compared with slower cells, attaining an advantage in the exploitation of a resource additional to that of faster gradient climbing. Trajectory analysis and an agent-based mathematical model revealed that this unexpected advantage originates from a speed dependence of reorientation frequency and flicking, which were higher for faster cells, and was compounded by chemokinesis, an increase in speed with resource concentration. The absence of any one of these adaptations led to a 65-70% reduction in the population-level resource exposure. These findings indicate that, contrary to what occurs in E. coli, swimming speed can be a fundamental determinant of the gradient-seeking capabilities of marine bacteria, and suggest a new model of bacterial chemotaxis.

The RecA protein is the main bacterial recombinase and the activator of the SOS system. In Escherichia coli and Salmonella enterica sv. Typhimurium, RecA is also essential for swarming, a flagellar-driven surface translocation mechanism widespread among bacteria. In this work, the direct interaction between RecA and the CheW coupling protein was confirmed, and the motility and chemotactic phenotype of a S. Typhimurium ΔrecA mutant was characterized through microfluidics, optical trapping, and quantitative capillary assays. The results demonstrate the tight association of RecA with the chemotaxis pathway and also its involvement in polar chemoreceptor cluster formation. RecA is therefore necessary for standard flagellar rotation switching, implying its essential role not only in swarming motility but also in the normal chemotactic response of S. Typhimurium.

We describe an innovative experimental and computational approach to control the expression of a protein in a population of yeast cells. We designed a simple control algorithm to automatically regulate the administration of inducer molecules to the cells by comparing the actual protein expression level in the cell population with the desired expression level. We then built an automated platform based on a microfluidic device, a time-lapse microscopy apparatus, and a set of motorized syringes, all controlled by a computer. We tested the platform to force yeast cells to express a desired fixed, or time-varying, amount of a reporter protein over thousands of minutes. The computer automatically switched the type of sugar administered to the cells, its concentration and its duration, according to the control algorithm. Our approach can be used to control expression of any protein, fused to a fluorescent reporter, provided that an external molecule known to (indirectly) affect its promoter activity is available.

Diseases are an emerging threat to ocean ecosystems. Coral reefs, in particular, are experiencing a worldwide decline because of disease and bleaching, which have been exacerbated by rising seawater temperatures. Yet, the ecological mechanisms behind most coral diseases remain unidentified. Here, we demonstrate that a coral pathogen, Vibrio coralliilyticus, uses chemotaxis and chemokinesis to target the mucus of its coral host, Pocillopora damicornis. A primary driver of this response is the host metabolite dimethylsulfoniopropionate (DMSP), a key element in the global sulfur cycle and a potent foraging cue throughout the marine food web. Coral mucus is rich in DMSP, and we found that DMSP alone elicits chemotactic responses of comparable intensity to whole mucus. Furthermore, in heat-stressed coral fragments, DMSP concentrations increased fivefold and the pathogen's chemotactic response was correspondingly enhanced. Intriguingly, despite being a rich source of carbon and sulfur, DMSP is not metabolized by the pathogen, suggesting that it is used purely as an infochemical for host location. These results reveal a new role for DMSP in coral disease, demonstrate the importance of chemical signaling and swimming behavior in the recruitment of pathogens to corals and highlight the impact of increased seawater temperatures on disease pathways.

Although competition-dispersal tradeoffs are commonly invoked to explain species coexistence for animals and plants in spatially structured environments, such mechanisms for coexistence remain unknown for microorganisms. Here we show that two recently speciated marine bacterioplankton populations pursue different behavioral strategies to exploit nutrient particles in adaptation to the landscape of ephemeral nutrient patches characteristic of ocean water. These differences are mediated primarily by differential colonization of and dispersal among particles. Whereas one population is specialized to colonize particles by attaching and growing biofilms, the other is specialized to disperse among particles by rapidly detecting and swimming toward new particles, implying that it can better exploit short-lived patches. Because the two populations are very similar in their genomic composition, metabolic abilities, chemotactic sensitivity, and swimming speed, this fine-scale behavioral adaptation may have been responsible for the onset of the ecological differentiation between them. These results demonstrate that the principles of spatial ecology, traditionally applied at macroscales, can be extended to the ocean's microscale to understand how the rich spatiotemporal structure of the resource landscape contributes to the fine-scale ecological differentiation and species coexistence among marine bacteria.

We describe an innovative experimental approach, and a proof of principle investigation, for the application of System Identification techniques to derive quantitative dynamical models of transcriptional regulation in living cells. Specifically, we constructed an experimental platform for System Identification based on a microfluidic device, a time-lapse microscope, and a set of automated syringes all controlled by a computer. The platform allows delivering a time-varying concentration of any molecule of interest to the cells trapped in the microfluidics device (input) and real-time monitoring of a fluorescent reporter protein (output) at a high sampling rate. We tested this platform on the GAL1 promoter in the yeast Saccharomyces cerevisiae driving expression of a green fluorescent protein (Gfp) fused to the GAL1 gene. We demonstrated that the System Identification platform enables accurate measurements of the input (sugars concentrations in the medium) and output (Gfp fluorescence intensity) signals, thus making it possible to apply System Identification techniques to obtain a quantitative dynamical model of the promoter. We explored and compared linear and nonlinear model structures in order to select the most appropriate to derive a quantitative model of the promoter dynamics. Our platform can be used to quickly obtain quantitative models of eukaryotic promoters, currently a complex and time-consuming process.

The ongoing merge between engineering and biology has contributed to the emerging field of synthetic biology. The defining features of this new discipline are abstraction and standardisation of biological parts, decoupling between parts to prevent undesired cross-talking, and the application of quantitative modelling of synthetic genetic circuits in order to guide their design. Most of the efforts in the field of synthetic biology in the last decade have been devoted to the design and development of functional gene circuits in prokaryotes and unicellular eukaryotes. Researchers have used synthetic biology not only to engineer new functions in the cell, but also to build simpler models of endogenous gene regulatory networks to gain knowledge of the "rules" governing their wiring diagram. However, the need for innovative approaches to study and modify complex signalling and regulatory networks in mammalian cells and multicellular organisms has prompted advances of synthetic biology also in these species, thus contributing to develop innovative ways to tackle human diseases. In this work, we will review the latest progress in synthetic biology and the most significant developments achieved so far, both in unicellular and multicellular organisms, with emphasis on human health., (Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.)

Synthetic biology aims at designing and building new biological functions in living organisms. The complexity of cellular regulation (regulatory, metabolic, and signaling interactions, and their coordinated action) can be tackled via the development of quantitative mathematical models. These models are useful to test biological hypotheses and observations, and to predict the possible behaviors of a synthetic network. Indeed, synthetic biology uses such models to design synthetic networks, prior to their construction in the cell, to perform specific tasks, or to change a biological process in a desired way. The synthetic network is built by assembling biological "parts" taken from different systems; therefore it is fundamental to identify, isolate, and test regulatory motifs which occur frequently in biological pathways. In this chapter, we describe how to model and predict the behavior of synthetic networks in two difference cases: (1) a synthetic network composed of five genes regulating each other through a variety of regulatory interactions in the yeast Saccharomyces cerevisiae (2) a synthetic transcriptional positive feedback loop stably integrated in Human Embryonic Kidney 293 cells (HEK293).

Understanding the relationship between topology and dynamics of transcriptional regulatory networks in mammalian cells is essential to elucidate the biology of complex regulatory and signaling pathways. Here, we characterised, via a synthetic biology approach, a transcriptional positive feedback loop (PFL) by generating a clonal population of mammalian cells (CHO) carrying a stable integration of the construct. The PFL network consists of the Tetracycline-controlled transactivator (tTA), whose expression is regulated by a tTA responsive promoter (CMV-TET), thus giving rise to a positive feedback. The same CMV-TET promoter drives also the expression of a destabilised yellow fluorescent protein (d2EYFP), thus the dynamic behaviour can be followed by time-lapse microscopy. The PFL network was compared to an engineered version of the network lacking the positive feedback loop (NOPFL), by expressing the tTA mRNA from a constitutive promoter. Doxycycline was used to repress tTA activation (switch off), and the resulting changes in fluorescence intensity for both the PFL and NOPFL networks were followed for up to 43 h. We observed a striking difference in the dynamics of the PFL and NOPFL networks. Using non-linear dynamical models, able to recapitulate experimental observations, we demonstrated a link between network topology and network dynamics. Namely, transcriptional positive autoregulation can significantly slow down the "switch off" times, as compared to the non-autoregulated system. Doxycycline concentration can modulate the response times of the PFL, whereas the NOPFL always switches off with the same dynamics. Moreover, the PFL can exhibit bistability for a range of Doxycycline concentrations. Since the PFL motif is often found in naturally occurring transcriptional and signaling pathways, we believe our work can be instrumental to characterise their behaviour.

DNA copy number alterations have been discovered to be key genetic events in development and progression of cancer. No clear data of familial and sporadic breast cancer are available. We focused on looking for an independent platform as a tool to identify the chromosomal profile in familial versus sporadic breast cancer patients. A total of 124 breast cancer patients were studied utilizing aCGH. The dataset was analyzed using Gaussian Mixture Models to determine the thresholds in order to assess gene copy number changes and to minimize the impact of noise on further data analyses. The identification of regions of consistent aberration across samples was carried out with statistical approaches and machine learning tools to draw profiles for familial and sporadic groups. Familial and sporadic cases resulted with a chromosome imbalance of 15% [false discovery rate (FDR): q=718E-5] and 18% (FDR: q=632E-13), respectively. The differential map evidenced two cytogenetic bands (8p23 and 11q13-11q14) significantly altered in familial versus sporadic cases (FDR: q=7E-4). The application of a new bioinformatics tool that discovers fuzzy classification rules (IFRAIS) let to individualize association of genes alterations that identify familial or sporadic cases. These results are comparable to those of the other systems used and are consistent from the biological point of view.

Background: Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments., Results: We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification., Conclusion: We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich--data poor' paradox in Systems Biology.

The aim of the study was to evaluate the performance of immunohistochemical MS110 expression in a series of familial and sporadic breast cancer patients. An immunohistochemical study was performed on TMA samples from 93 sporadic and 94 familial breast cancer patients with (7/94) and without BRCA1 germline mutations. BRCA1 protein expression level was evaluated using the monoclonal MS110 antibody. Immunohistochemistry, performed on TMA samples, showed positive nuclear staining for BRCA1 in 34 sporadic and 37 familial breast tumours, respectively. All the tumours from patients carrying BRCA1 mutations showed complete loss of both BRCA1 and ERalpha expression, regardless of the type of mutation. The percentage of MS110 positive cases was significantly lower in mutated versus wild type BRCA1 familial cases (p=0.02) while the percentage of patients with higher ERalpha expression was significantly lower in BRCA1-mutated versus BRCA1-wild type familial patients (p=0.05). Interestingly, the presence of the E1038G polymorphism in BRCA1 exon 11 was significantly associated with protein expression (p=0.029). The frequency of MS110 negative cases also detected in BRCA1-wild type tumours, points to the inability of the BRCA1 IHC expression in discriminating between familial and sporadic breast cancer.

The need for fast, efficient, and less costly means to screen genetic variants associated with disease predisposition led us to develop an oligo-nucleotide array-based process for gene-specific single nucleotide polymorphism (SNP) genotyping. This cost-effective, high-throughput strategy has high sensitivity and the same degree of accuracy as direct sequencing, the current gold standard for genetic screening. We used the BRCA1 breast and ovarian cancer predisposing gene model for the validation of the accuracy and efficiency of our strategy. This process could detect point mutations, insertions or deletions of any length, of known and unknown variants even in heterozygous conditions without affecting sensitivity and specificity. The system could be applied to other disorders and can also be custom-designed to include a number of genes related to specific clinical conditions. This system is particularly useful for the screening of long genomic regions with relatively infrequent but clinically relevant variants, while drastically cutting time and costs in comparison to high-throughput sequencing.

In considering key events of genomic disorders in the development and progression of cancer, the correlation between genomic instability and carcinogenesis is currently under investigation. In this work, we propose an inductive logic programming approach to the problem of modeling evolution patterns for breast cancer. Using this approach, it is possible to extract fingerprints of stages of the disease that can be used in order to develop and deliver the most adequate therapies to patients. Furthermore, such a model can help physicians and biologists in the elucidation of molecular dynamics underlying the aberrations-waterfall model behind carcinogenesis. By showing results obtained on a real-world dataset, we try to give some hints about further approach to the knowledge-driven validations of such hypotheses.