Non equilibrium dynamics in Escherichia coli's gene regulatory network

Gene regulation is a key process in living organisms. It defines cells identity and behavior, and allows the cells to adapt to the external environment. From a theoretical point of view, a central question is how to mathematically characterize the many players and their complex interactions to understand the gene expression output as function of the regulatory inputs. A common approach is thermodynamic modelling, where the transcription is assumed to be in equilibrium with the concentration of transcription factors, and any fluctuation is averaged away. However, the advent of new experimental techniques providing precise measurements of gene expression at the single-cell level is challenging the general validity of the equilibrium assumption. In this thesis, we focus on the induction of the LexA regulon in the model organism Escherichia coli, which is involved in the repair of DNA damages. Tracking the single-cell expression dynamics of different genes under the exclusive control of the inhibitor LexA, we show that the induction is characterized by short bursts of production, which are incompatible with a thermodynamic model where gene transcription is in equilibrium with the concentration of LexA. On the other hand, we show that the network responds to transient fluctuations in the concentration of the regulator. Finally, we deal with the question of how to properly analyze flow cytometry data for bacterial populations. Flow cytometry is an attractive technology to quantify single-cell gene distribution in high-throughput. However, so far no systematic investigation has been carried out to estimate the accuracy of these measurements for small bacterial cells. Here, by comparing the fluorescence distribution of the same E. coli strain both in flow cytometry and in a microscopy setup, we show that the fluorescent signal contains a significant amount of electronic noise and background fluorescence. We then propose a robust method to correct for these spurious components, and we show that only after correcting for electronic noise and autofluorescence, measurements from the flow cytometry agree with the ones from the microscopy setup

Interconnecting GRRASP with additional platforms and tools: A feasibility study

As it has already been documents in several reports, the Geospatial Risk and Resilience Assessment Platform (GRRASP) under development ad the JRC is a World Wide Web oriented architecture bringing together geospatial technologies and computational tools towards the objective of supporting the analysis of critical infrastructures (CIs) A key aspect of this platform is its capability to serve as the vehicle to interlink the analysis modules and tools that over the years have been developed by the scientific community towards a one-stop-shop for critical infrastructure risk and resilience analysis. The present report illustrates how the software architecture of GRRASP has been designed and is being exploited to support the integration of GRRASP with different projects related to the analysis of CIs.JRC.E.2-Technology Innovation in Securit

Developing stress tests to improve the resilience of critical infrastructures: a feasibility analysis

In this report we aim to provide a feasibility study and describe the main elements for developing stress tests in the domain of critical infrastructures. Stress tests can be a very useful element for the assessment of the resilience of critical infrastructures against a number of hazards. Currently stress tests are developed in the domain of Nuclear and Financial sector in order to assess the safety, security and resilience of these systems. Taking stock of the activities in these sectors we aim to provide a feasibility analysis and sketch the main lines for the development of stress tests for other infrastructures, systems and sectors.JRC.G.5-Security technology assessmen

A fresh look at spinal alignment and deformities: Automated analysis of a large database of 9832 biplanar radiographs

We developed and used a deep learning tool to process biplanar radiographs of 9,832 non-surgical patients suffering from spinal deformities, with the aim of reporting the statistical distribution of radiological parameters describing the spinal shape and the correlations and interdependencies between them. An existing tool able to automatically perform a three-dimensional reconstruction of the thoracolumbar spine has been improved and used to analyze a large set of biplanar radiographs of the trunk. For all patients, the following parameters were calculated: spinopelvic parameters; lumbar lordosis; mismatch between pelvic incidence and lumbar lordosis; thoracic kyphosis; maximal coronal Cobb angle; sagittal vertical axis; T1-pelvic angle; maximal vertebral rotation in the transverse plane. The radiological parameters describing the sagittal alignment were found to be highly interrelated with each other, as well as dependent on age, while sex had relatively minor but statistically significant importance. Lumbar lordosis was associated with thoracic kyphosis, pelvic incidence and sagittal vertical axis. The pelvic incidence-lumbar lordosis mismatch was found to be dependent on the pelvic incidence and on age. Scoliosis had a distinct association with the sagittal alignment in adolescent and adult subjects. The deep learning-based tool allowed for the analysis of a large imaging database which would not be reasonably feasible if performed by human operators. The large set of results will be valuable to trigger new research questions in the field of spinal deformities, as well as to challenge the current knowledge

Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria

Fluorescence flow cytometry is increasingly being used to quantify single-cell expression distributions in bacteria in high-throughput. However, there has been no systematic investigation into the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We investigate these issues by measuring the same E. coli strains carrying fluorescent reporters using both flow cytometry and microscopic setups and systematically comparing the resulting single-cell expression distributions. Using these results, we develop methods for rigorous quantitative inference of single-cell expression distributions from fluorescence flow cytometry data. First, we present a Bayesian mixture model to separate debris from viable cells using all scattering signals. Second, we show that cytometry measurements of fluorescence are substantially affected by autofluorescence and shot noise, which can be mistaken for intrinsic noise in gene expression, and present methods to correct for these using calibration measurements. Finally, we show that because forward- and side-scatter signals scale non-linearly with cell size, and are also affected by a substantial shot noise component that cannot be easily calibrated unless independent measurements of cell size are available, it is not possible to accurately estimate the variability in the sizes of individual cells using flow cytometry measurements alone. To aid other researchers with quantitative analysis of flow cytometry expression data in bacteria, we distribute E-Flow, an open-source R package that implements our methods for filtering debris and for estimating true biological expression means and variances from the fluorescence signal. The package is available at https://github.com/vanNimwegenLab/E-Flow

Stability and optimal control for some classes of tritrophic systems

The objective of this paper is to study an optimal resource management problem for some classes of tritrophic systems composed by autotrophic resources (plants), bottom level consumers (herbivores) and top level consumers (humans). The first class of systems we discuss are linear chains, in which biomass flows from plants to herbivores, and from herbivores to humans. In the second class of systems humans are omnivorous and hence compete with herbivores for plant resources. Finally, in the third class of systems humans are omnivorous, but the plant resources are partitioned so that humans and herbivores do not complete for the same ones. The three trophic chains are expressed as Lotka-Volterra models, which seems to be a suitable choice in contexts where there is a shortage of food for the consumers. Our model parameters are taken from the literature on agro-pastoral systems in Sub-Saharan Africa

Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network

Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter's sequence, little is known about the extent to which noise levels of individual promoters vary across growth conditions. Using flow cytometry, we here quantify transcriptional noise in Escherichia coli genome-wide across 8 growth conditions and find that noise levels systematically decrease with growth rate, with a condition-dependent lower bound on noise. Whereas constitutive promoters consistently exhibit low noise in all conditions, regulated promoters are both more noisy on average and more variable in noise across conditions. Moreover, individual promoters show highly distinct variation in noise across conditions. We show that a simple model of noise propagation from regulators to their targets can explain a significant fraction of the variation in relative noise levels and identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. In addition, analysis of the genome-wide correlation structure of various gene properties shows that gene regulation, expression noise, and noise plasticity are all positively correlated genome-wide and vary independently of variations in absolute expression, codon bias, and evolutionary rate. Together, our results show that while absolute expression noise tends to decrease with growth rate, relative noise levels of genes are highly condition-dependent and determined by the propagation of noise through the gene regulatory network

Functional characterization of L-PBF produced FeSi2.9 Soft Magnetic Material

Additive manufacturing (AM) is a production technology attractive for various sectors such as aerospace, biomedical, and automotive. The advantages are various, including being able to create objects with complex geometry and through a careful study of topological optimization, reduce the weight while maintaining mechanical performance. The aim of the present work is to study the feasibility of producing ferromagnetic materials using AM technology for electrical application such as rotor for electrical machine or electromagnetic devices via Laser Powder Bed Fusion (L-PBF). L-PBF is shown to be effective to produce soft magnetic materials (SMMs) such as FeSi2.9. Dedicated test samples with various geometries have been manufactured for evaluating the electrical and magnetic performance under as-built conditions and after annealing

The strain distribution in the lumbar anterior longitudinal ligament is affected by the loading condition and bony features: An in vitro full-field analysis

The role of the ligaments is fundamental in determining the spine biomechanics in physiological and pathological conditions. The anterior longitudinal ligament (ALL) is fundamental in constraining motions especially in the sagittal plane. The ALL also confines the intervertebral discs, preventing herniation. The specific contribution of the ALL has indirectly been investigated in the past as a part of whole spine segments where the structural flexibility was measured. The mechanical properties of isolated ALL have been measured as well. The strain distribution in the ALL has never been measured under pseudo-physiological conditions, as part of multi-vertebra spine segments. This would help elucidate the biomechanical function of the ALL. The aim of this study was to investigate in depth the biomechanical function of the ALL in front of the lumbar vertebrae and of the intervertebral disc. Five lumbar cadaveric spine specimens were subjected to different loading scenarios (flexion-extension, lateral bending, axial torsion) using a state-of-the-art spine tester. The full-field strain distribution on the anterior surface was measured using digital image correlation (DIC) adapted and validated for application to spine segments. The measured strain maps were highly inhomogeneous: the ALL was generally more strained in front of the discs than in front of the vertebrae, with some locally higher strains both imputable to ligament fibers and related to local bony defects. The strain distributions were significantly different among the loading configurations, but also between opposite directions of loading (flexion vs. extension, right vs. left lateral bending, clockwise vs. counterclockwise torsion). This study allowed for the first time to assess the biomechanical behaviour of the anterior longitudinal ligament for the different loading of the spine. We were able to identify both the average trends, and the local effects related to osteophytes, a key feature indicative of spine degeneration

Neuroimaging Evidence of Major Morpho-Anatomical and Functional Abnormalities in the BTBR T+TF/J Mouse Model of Autism

BTBR T+tf/J (BTBR) mice display prominent behavioural deficits analogous to the defining symptoms of autism, a feature that has prompted a widespread use of the model in preclinical autism research. Because neuro-behavioural traits are described with respect to reference populations, multiple investigators have examined and described the behaviour of BTBR mice against that exhibited by C57BL/6J (B6), a mouse line characterised by high sociability and low self-grooming. In an attempt to probe the translational relevance of this comparison for autism research, we used Magnetic Resonance Imaging (MRI) to map in both strain multiple morpho-anatomical and functional neuroimaging readouts that have been extensively used in patient populations. Diffusion tensor tractography confirmed previous reports of callosal agenesis and lack of hippocampal commissure in BTBR mice, and revealed a concomitant rostro-caudal reorganisation of major cortical white matter bundles. Intact inter-hemispheric tracts were found in the anterior commissure, ventro-medial thalamus, and in a strain-specific white matter formation located above the third ventricle. BTBR also exhibited decreased fronto-cortical, occipital and thalamic gray matter volume and widespread reductions in cortical thickness with respect to control B6 mice. Foci of increased gray matter volume and thickness were observed in the medial prefrontal and insular cortex. Mapping of resting-state brain activity using cerebral blood volume weighted fMRI revealed reduced cortico-thalamic function together with foci of increased activity in the hypothalamus and dorsal hippocampus of BTBR mice. Collectively, our results show pronounced functional and structural abnormalities in the brain of BTBR mice with respect to control B6 mice. The large and widespread white and gray matter abnormalities observed do not appear to be representative of the neuroanatomical alterations typically observed in autistic patients. The presence of reduced fronto-cortical metabolism is of potential translational relevance, as this feature recapitulates previously-reported clinical observations