Mathematical modelling plant signalling networks

During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This sub-cellular analysis paves the way for more comprehensive mathematical studies of hormonal transport and signalling in a multi-scale setting

Inference of the genetic network regulating lateral root initiation in Arabidopsis thaliana

Regulation of gene expression is crucial for organism growth, and it is one of the challenges in Systems Biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyse two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants to infer their regulatory network. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale-free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation

Pre-Clinical Tools for Predicting Drug Efficacy in Treatment of Tuberculosis

Combination therapy has, to some extent, been successful in limiting the emergence of drug-resistant tuberculosis. Drug combinations achieve this advantage by simultaneously acting on different targets and metabolic pathways. Additionally, drug combination therapies are shown to shorten the duration of therapy for tuberculosis. As new drugs are being developed, to overcome the challenge of finding new and effective drug combinations, systems biology commonly uses approaches that analyse mycobacterial cellular processes. These approaches identify the regulatory networks, metabolic pathways, and signaling programs associated with M. tuberculosis infection and survival. Different preclinical models that assess anti-tuberculosis drug activity are available, but the combination of models that is most predictive of clinical treatment efficacy remains unclear. In this structured literature review, we appraise the options to accelerate the TB drug development pipeline through the evaluation of preclinical testing assays of drug combinations

METEOROLOGICAL MODELLING INFLUENCE ON REGIONAL AND URBAN AIR POLLUTION PREDICTABILITY

ARPA Piemonte performs yearly air quality assessment running a modelling system based on a chemical transport model. The model is capable to simulate air pollutant emission, transport, diffusion and chemical transformation, to provide concentration fields of the main atmospheric pollutants (CO, NOX, SO2, PM10, PM2.5, O3, and benzene) on a hourly basis and to compute all the indicators required by EU legislation. Meteorological fields to drive air quality simulations are reconstructed assimilating ARPA Piemonte meteorological network observations within background fields obtained by ECMWF analyses. The reliability of mesoscale and urban scale meteorology is one of the key issues in determining an air quality modelling system effectiveness. Diagnostic meteorological analysis takes advantage of the wide local measurement network but cannot guarantee the dynamic and thermodynamic variables consistency provided e.g. by prognostic weather prediction models. Since July 2006 ARPA Piemonte operationally uses an air quality forecasting system driven by a numerical weather prediction model. The simultaneous availability of the two systems results provides the possibility to compare different meteorological modelling techniques effects on air pollution predictability. The two modelling systems results are compared by means of model evaluation statistical indexes showing very similar performances over a six months period. The comparison is completed by the analysis of short term critical episodes to highlight meteorological modelling effectiveness in reproducing severe air pollution episodes and short term concentrations variation. The prognostic meteorological fields showed a better capability to simulate peak episodes even if weather forecast errors can cause “false alarm” conditions due to concentration overestimation

METEOROLOGICAL MODELLING INFLUENCE ON REGIONAL AND URBAN AIR POLLUTION PREDICTABILITY

ARPA Piemonte performs yearly air quality assessment running a modelling system based on a chemical transport model. The model is capable to simulate air pollutant emission, transport, diffusion and chemical transformation, to provide concentration fields of the main atmospheric pollutants (CO, NOX, SO2, PM10, PM2.5, O3, and benzene) on a hourly basis and to compute all the indicators required by EU legislation. Meteorological fields to drive air quality simulations are reconstructed assimilating ARPA Piemonte meteorological network observations within background fields obtained by ECMWF analyses. The reliability of mesoscale and urban scale meteorology is one of the key issues in determining an air quality modelling system effectiveness. Diagnostic meteorological analysis takes advantage of the wide local measurement network but cannot guarantee the dynamic and thermodynamic variables consistency provided e.g. by prognostic weather prediction models. Since July 2006 ARPA Piemonte operationally uses an air quality forecasting system driven by a numerical weather prediction model. The simultaneous availability of the two systems results provides the possibility to compare different meteorological modelling techniques effects on air pollution predictability. The two modelling systems results are compared by means of model evaluation statistical indexes showing very similar performances over a six months period. The comparison is completed by the analysis of short term critical episodes to highlight meteorological modelling effectiveness in reproducing severe air pollution episodes and short term concentrations variation. The prognostic meteorological fields showed a better capability to simulate peak episodes even if weather forecast errors can cause “false alarm” conditions due to concentration overestimation

Major depressive disorder and oxidative stress: In silico investigation of fluoxetine activity against ROS

Major depressive disorder is a psychiatric disease having approximately a 20% lifetime prevalence in adults in the United States (U.S.), as reported by Hasin et al. in JAMA Psichiatry 2018 75, 336\u2013346. Symptoms include low mood, anhedonia, decreased energy, alteration in appetite and weight, irritability, sleep disturbances, and cognitive deficits. Comorbidity is frequent, and patients show decreased social functioning and a high mortality rate. Environmental and genetic factors favor the development of depression, but the mechanisms by which stress negatively impacts on the brain are still not fully understood. Several recent works, mainly published during the last five years, aim at investigating the correlation between treatment with fluoxetine, a non-tricyclic antidepressant drug, and the amelioration of oxidative stress. In this work, the antioxidant activity of fluoxetine was investigated using a computational protocol based on the density functional theory approach. Particularly, the scavenging of five radicals (HO\u2022, HOO\u2022, CH3OO\u2022, CH2=CHOO\u2022, and CH3O\u2022) was considered, focusing on hydrogen atom transfer (HAT) and radical adduct formation (RAF) mechanisms. Thermodynamic as well as kinetic aspects are discussed, and, for completeness, two metabolites of fluoxetine and serotonin, whose extracellular concentration is enhanced by fluoxetine, are included in our analysis. Indeed, fluoxetine may act as a radical scavenger, and exhibits selectivity for HO\u2022 and CH3O\u2022, but is inefficient toward peroxyl radicals. In contrast, the radical scavenging efficiency of serotonin, which has been demonstrated in vitro, is significant, and this supports the idea of an indirect antioxidant efficiency of fluoxetine

Analysis of time-profiles with in-beam PET monitoring in charged particle therapy

Background: Treatment verification with PET imaging in charged particle therapy is conventionally done by comparing measurements of spatial distributions with Monte Carlo (MC) predictions. However, decay curves can provide additional independent information about the treatment and the irradiated tissue. Most studies performed so far focus on long time intervals. Here we investigate the reliability of MC predictions of space and time (decay rate) profiles shortly after irradiation, and we show how the decay rates can give an indication about the elements of which the phantom is made up. Methods and Materials: Various phantoms were irradiated in clinical and near-clinical conditions at the Cyclotron Centre of the Bronowice proton therapy centre. PET data were acquired with a planar 16x16 cm$^2$ PET system. MC simulations of particle interactions and photon propagation in the phantoms were performed using the FLUKA code. The analysis included a comparison between experimental data and MC simulations of space and time profiles, as well as a fitting procedure to obtain the various isotope contributions in the phantoms. Results and conclusions: There was a good agreement between data and MC predictions in 1-dimensional space and decay rate distributions. The fractions of $^{11}$C, $^{15}$O and $^{10}$C that were obtained by fitting the decay rates with multiple simple exponentials generally agreed well with the MC expectations. We found a small excess of $^{10}$C in data compared to what was predicted in MC, which was clear especially in the PE phantom.Comment: 9 pages, 5 figures, 1 table. Proceedings of the 20th International Workshop on Radiation Imaging Detectors (iWorid2018), 24-28 June 2018, Sundsvall, Swede

Measurement of secondary particle production induced by particle therapy ion beams impinging on a PMMA target

Particle therapy is a technique that uses accelerated charged ions for cancer treatment and combines a high irradiation precision with a high biological effectiveness in killing tumor cells [1]. Informations about the secondary particles emitted in the interaction of an ion beam with the patient during a treatment can be of great interest in order to monitor the dose deposition. For this purpose an experiment at the HIT (Heidelberg Ion-Beam Therapy Center) beam facility has been performed in order to measure fluxes and emission profiles of secondary particles produced in the interaction of therapeutic beams with a PMMA target. In this contribution some preliminary results about the emission profiles and the energy spectra of the detected secondaries will be presente

Clinical relevance of the combined analysis of circulating tumor cells and anti-tumor T-cell immunity in metastatic breast cancer patients

Background: Metastatic breast cancer (mBC) is a heterogeneous disease with varying responses to treatments and clinical outcomes, still requiring the identification of reliable predictive biomarkers. In this context, liquid biopsy has emerged as a powerful tool to assess in real-time the evolving landscape of cancer, which is both orchestrated by the metastatic process and immune-surveillance mechanisms. Thus, we investigated circulating tumor cells (CTCs) coupled with peripheral T-cell immunity to uncover their potential clinical relevance in mBC. Methods: A cohort of 20 mBC patients was evaluated, before and one month after starting therapy, through the following liquid biopsy approaches: CTCs enumerated by a metabolism-based assay, T-cell responses against tumor-associated antigens (TAA) characterized by interferon-γ enzyme-linked immunosorbent spot (ELISpot), and the T-cell receptor (TCR) repertoire investigated by a targeted next-generation sequencing technique. TCR repertoire features were characterized by the Morisita’s overlap and the Productive Simpson Clonality indexes, and the TCR richness. Differences between groups were calculated by Fisher’s, Mann-Whitney or Kruskal-Wallis test, as appropriate. Prognostic data analysis was estimated by Kaplan-Meier method. Results: Stratifying patients for their prognostic level of 6 CTCs before therapy, TAA specific T-cell responses were detected only in patients with a low CTC level. By analyzing the TCR repertoire, the highest TCR clonality was observed in the case of CTCs under the cut-off and a positive ELISpot response (p=0.03). Whereas, at follow-up, patients showing a good clinical response coupled with a low number of CTCs were characterized by the most elevated TCR clonality (p<0.05). The detection of CTCs≥6 in at least one time-point was associated with a lower TCR clonality (p=0.02). Intriguingly, by combining overall survival analysis with TCR repertoire, we highlighted a potential prognostic role of the TCR clonality measured at follow-up (p=0.03). Conclusion: These data, whether validated in a larger cohort of patients, suggest that the combined analysis of CTCs and circulating anti-tumor T-cell immunity could represent a valuable immune-oncological biomarker for the liquid biopsy field. The clinical application of this promising tool could improve the management of mBC patients, especially in the setting of immunotherapy, a rising approach for BC treatment requiring reliable predictive biomarkers

In Vitro Modeling of Tumor-Immune System Interaction.

Immunotherapy has emerged during the past two decades as an innovative and successful form of cancer treatment. However, frequently, mechanisms of actions are still unclear, predictive markers are insufficiently characterized, and preclinical assays for innovative treatments are poorly reliable. In this context, the analysis of tumor/immune system interaction plays key roles, but may be unreliably mirrored by in vivo experimental models and standard bidimensional culture systems. Tridimensional cultures of tumor cells have been developed to bridge the gap between in vitro and in vivo systems. Interestingly, defined aspects of the interaction of cells from adaptive and innate immune systems and tumor cells may also be mirrored by 3D cultures. Here we review in vitro models of cancer/immune cell interaction and we propose that updated technologies might help develop innovative treatments, identify biologicals of potential clinical relevance, and select patients eligible for immunotherapy treatments