References of "Mombaerts, Laurent 50009269"
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See detailGene regulatory network inference from sparsely sampled noisy data
Aalto, Atte UL; Viitasaari, Lauri; Ilmonen, Pauliina et al

in Nature Communications (2020), 11

The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing ... [more ▼]

The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO’s superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life. [less ▲]

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See detailAssessing suppression strategies against epidemicoutbreaks like COVID-19: the SPQEIR model
Proverbio, Daniele UL; Kemp, Francoise UL; Magni, Stefano UL et al

E-print/Working paper (2020)

The current COVID-19 outbreak represents a most serious challenge for societies worldwide. It isendangering the health of millions of people, and resulting in severe socioeconomic challenges dueto lock ... [more ▼]

The current COVID-19 outbreak represents a most serious challenge for societies worldwide. It isendangering the health of millions of people, and resulting in severe socioeconomic challenges dueto lock-down measures. Governments worldwide aim to devise exit strategies to revive the economywhile keeping the pandemic under control. The problem is that the effects of distinct measures arenot well quantified. This paper compares several suppression approaches and potential exit strategiesusing a new extended epidemic SEIR model. It concludes that while rapid and strong lock-down isan effective pandemic suppression measure, a combination of other strategies such as social distanc-ing, active protection and removal can achieve similar suppression synergistically. This quantitativeunderstanding will support the establishment of mid- and long-term interventions. Finally, the paperprovides an online tool that allows researchers and decision makers to interactively simulate diversescenarios with our model. [less ▲]

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See detailCausal dynamical modelling predicts novel regulatory genes of FOXP3 in human regulatory T cells
Sawlekar, Rucha UL; Magni, Stefano UL; Chapelle, Christophe et al

E-print/Working paper (2020)

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See detailDynamical Modeling Techniques for Biological Time Series Data
Mombaerts, Laurent UL

Doctoral thesis (2019)

The present thesis is articulated over two main topics which have in common the modeling of the dynamical properties of complex biological systems from large-scale time-series data. On one hand, this ... [more ▼]

The present thesis is articulated over two main topics which have in common the modeling of the dynamical properties of complex biological systems from large-scale time-series data. On one hand, this thesis analyzes the inverse problem of reconstructing Gene Regulatory Networks (GRN) from gene expression data. This first topic seeks to reverse-engineer the transcriptional regulatory mechanisms involved in few biological systems of interest, vital to understand the specificities of their different responses. In the light of recent mathematical developments, a novel, flexible and interpretable modeling strategy is proposed to reconstruct the dynamical dependencies between genes from short-time series data. In addition, experimental trade-offs and optimal modeling strategies are investigated for given data availability. Consistent literature on these topics was previously surprisingly lacking. The proposed methodology is applied to the study of circadian rhythms, which consists in complex GRN driving most of daily biological activity across many species. On the other hand, this manuscript covers the characterization of dynamically differentiable brain states in Zebrafish in the context of epilepsy and epileptogenesis. Zebrafish larvae represent a valuable animal model for the study of epilepsy due to both their genetic and dynamical resemblance with humans. The fundamental premise of this research is the early apparition of subtle functional changes preceding the clinical symptoms of seizures. More generally, this idea, based on bifurcation theory, can be described by a progressive loss of resilience of the brain and ultimately, its transition from a healthy state to another characterizing the disease. First, the morphological signatures of seizures generated by distinct pathological mechanisms are investigated. For this purpose, a range of mathematical biomarkers that characterizes relevant dynamical aspects of the neurophysiological signals are considered. Such mathematical markers are later used to address the subtle manifestations of early epileptogenic activity. Finally, the feasibility of a probabilistic prediction model that indicates the susceptibility of seizure emergence over time is investigated. The existence of alternative stable system states and their sudden and dramatic changes have notably been observed in a wide range of complex systems such as in ecosystems, climate or financial markets. [less ▲]

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See detailFrom Diagnosing Diseases to Predicting Diseases
Balling, Rudi UL; Goncalves, Jorge UL; Magni, Stefano UL et al

in Betz, Ulrich A.K. (Ed.) Curious2018 (2019)

Chronic diseases can be considered as perturbations of complex adaptive systems. Transitions from healthy states to chronic diseases are often characterized by sudden and unexpected onset of diseases ... [more ▼]

Chronic diseases can be considered as perturbations of complex adaptive systems. Transitions from healthy states to chronic diseases are often characterized by sudden and unexpected onset of diseases. These critical transitions or catastrophic shifts have been studied in theoretical and applied physics, ecology, social science, economics and recently also in biomedical applications. If we could understand the underlying mechanisms and the dynamics of critical transitions involved in the development of diseases, we would be better equipped to predict and eventually prevent them from arising. The current paper gives an overview of the potential application of the concept of critical transitions to biomedical applications. [less ▲]

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See detailDynamical differential expression (DyDE) reveals the period control mechanisms of the Arabidopsis circadian oscillator
Mombaerts, Laurent UL; Carignano, Alberto; Robertson, Fiona et al

in PLoS Computational Biology (2019)

The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The ... [more ▼]

The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The mechanistic basis underlying the adjustment of circadian rhythms to changing external conditions, however, has yet to be clearly elucidated. We explored the mechanism of action of nicotinamide in Arabidopsis thaliana, a metabolite that lengthens the period of circadian rhythms, to understand the regulation of circadian period. To identify the key mechanisms involved in the circadian response to nicotinamide, we developed a systematic and practical modeling framework based on the identification and comparison of gene regulatory dynamics. Our mathematical predictions, confirmed by experimentation, identified key transcriptional regulatory mechanisms of circadian period and uncovered the role of blue light in the response of the circadian oscillator to nicotinamide. We suggest that our methodology could be adapted to predict mechanisms of drug action in complex biological systems. [less ▲]

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See detailA multifactorial evaluation framework for gene regulatory network reconstruction
Mombaerts, Laurent UL; Aalto, Atte UL; Markdahl, Johan UL et al

in Foundations of Systems Biology in Engineering (2019)

In the past years, many computational methods have been developed to infer the structure of gene regulatory networks from time series data. However, the applicability and accuracy presumptions of such ... [more ▼]

In the past years, many computational methods have been developed to infer the structure of gene regulatory networks from time series data. However, the applicability and accuracy presumptions of such algorithms remain unclear due to experimental heterogeneity. This paper assesses the performance of recent and successful network inference strategies under a novel, multifactorial evaluation framework in order to highlight pragmatic tradeoffs in experimental design. The effects of data quantity and systems perturbations are addressed, thereby formulating guidelines for efficient resource management. Realistic data were generated from six widely used benchmark models of rhythmic and nonrhythmic gene regulatory systems with random perturbations mimicking the effect of gene knock-out or chemical treatments. Then, time series data of increasing lengths were provided to five state-of-the-art network inference algorithms representing distinctive mathematical paradigms. The performances of such network reconstruction methodologies are uncovered under various experimental conditions. We report that the algorithms do not benefit equally from data increments. Furthermore, at least for the studied rhythmic system, it is more profitable for network inference strategies to be run on long time series rather than short time series with multiple perturbations. By contrast, for the non-rhythmic systems, increasing the number of perturbation experiments yielded better results than increasing the sampling frequency. We expect that future benchmark and algorithm design would integrate such multifactorial considerations to promote their widespread and conscientious usage. [less ▲]

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See detailReconstruction of Gene Regulatory Networks using an Error Filtering Learning Scheme
Tzortzis, Ioannis; Hadjicostis, Christoforos; Mombaerts, Laurent UL

Scientific Conference (2017)

One of the fundamental and most challenging problems in system biology is the reconstruction of gene regulatory networks from input-output data based on non-linear differential equations. This paper ... [more ▼]

One of the fundamental and most challenging problems in system biology is the reconstruction of gene regulatory networks from input-output data based on non-linear differential equations. This paper presents an approach to estimate the unknown nonlinearities and to identify the true network that generated the data, based on an error filtering learning scheme and a Lyapunov synthesis method. Unknown nonlinearities are modelled by networks using radial basis functions and model validation is performed by taking advantage of the so-called persistency of excitation of input signals, a condition that is shown to play a significant role in the problem of uncovering the true network structure. The proposed methodology and the theoretical results are validated through an illustrative example. [less ▲]

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See detailOptimising time-series experimental design for modelling of circadian rhythms: the value of transient data
Mombaerts, Laurent UL; Mauroy, Alexandre UL; Goncalves, Jorge UL

in IFAC-PapersOnLine (2016, October)

Circadian clocks consist of complex networks that coordinate the daily cycle of most organisms. In light/dark cycles, the clock is synchronized (or entrained) by the environment, which corresponds to a ... [more ▼]

Circadian clocks consist of complex networks that coordinate the daily cycle of most organisms. In light/dark cycles, the clock is synchronized (or entrained) by the environment, which corresponds to a constant rephasing of the oscillations and leads to a steady state regime. Some circadian clocks are endogenous oscillators with rhythms of about 24-hours that persist in constant light or constant darkness. This operating mechanism with and without entrainment provides flexibility and robustness to the clock against perturbations. Most of the clock-oriented experiments are performed under constant photoperiodic regime, overlooking the transitory regime that takes place between light/dark cycles and constant light or darkness. This paper provides a comparative analysis of the informative potential of the transient time-series data with the other regimes for clock modelling. Realistic data were simulated from 2 experimentally validated plant circadian clock models and sliced into several time windows. These windows represent the different regimes that take place before, meanwhile and after the switch to constant light. Then, a network inference tool was used over each window and its capability of retrieving the ground-truth of the network was compared for each window. The results suggest that including the transient data to the network inference technique significally improves its performance. [less ▲]

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See detailContinuous Glucose Monitoring: Using CGM to Guide Insulin Therapy Virtual Trials Results
Mombaerts, Laurent UL; Thomas, Felicity; Signal, Matthew et al

in IFAC-PapersOnLine (2015)

Continuous glucose monitoring (CGM) devices can measure blood glucose levels through interstitial measurements almost continuously (1-5min sampling period). However, they are not as accurate as glucose ... [more ▼]

Continuous glucose monitoring (CGM) devices can measure blood glucose levels through interstitial measurements almost continuously (1-5min sampling period). However, they are not as accurate as glucose readings from blood measurements. The relation between tissue and blood glucose is dynamic and the sensor signal can degrade over time. In addition, CGM readings contains high frequency noise and can drift between measurements. However, maintaining continuous glucose monitoring has the potential to improve the level of glycemic control achieved and reduce nurse workload. For this purpose, a simple model was designed and tested to see the effect of inherent CGM error on the insulin therapy protocol, STAR (Stochastic TARgeted). An error model was generated from 9 patients that had one Guardian Real-Time CGM device (Medtronic Minimed, Northridge, CA, USA) inserted into their abdomen as part of an observation trial assesing the accuracy of CGM measurements compared to a blood gas analyser and glucometer readings. A resulting error model was then used to simulate the outcomes if the STAR protocol was guided by CGM values on 183 virtual patients. CGM alarms for hyper- and hypo-glycaemic region were included to improve patient safety acting as 'guardrails'. The STAR CGM protocol gave good performance and reduced workload by ~50%, reducing the number of measurements per day per patient from 13 to 7. The number of hypoglycaemic events increased compared to the current STAR from 0.03% <2.2mmol/L to 0.32%. However, in comparison to other published protocols it is still a very low level of hypoglycaemia and less than clinically acceptable value of 5% <4.0mmol/L. More importantly this study shows great promise for the future of CGM and their use in clinic. With the a newer generation of sensors, specifically designed for the ICU, promising less noise and drift suggesting that a reduced nurse workload without compromising safety or performance is with in reach. [less ▲]

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