Tumour growth control: analysis of alternative approaches.

In this work we address the problem of tumour growth control by properly exploiting a low-dimensional model that grounds on the Chemical Reaction Network (CRN) formalism. Originally conceived to work both in deterministic and stochastic frameworks, it is shown that, except for the case of very low number of tumour cells, the deterministic approach is appropriate to characterize the system behaviour, especially for control planning purposes. Two alternative control approaches are here investigated. One trivially assumes a constant infusion of external drug administration, the other is designed according to a state-feedback control scheme, with complete or partial knowledge of the state. Pros and cons of both control laws are investigated, showing that the tumour size at the beginning of the therapy plays a role of paramount importance for fixed infusion therapies, whilst only state-feedback laws can eradicate arbitrarily large tumours.

StaRTrEK:in silico estimation of RNA half-lives from genome-wide time-course experiments without transcriptional inhibition.

BACKGROUND: Gene expression is the result of the balance between transcription and degradation. Recent experimental findings have shown fine and specific regulation of RNA degradation and the presence of various molecular machinery purposely devoted to this task, such as RNA binding proteins, non-coding RNAs, etc. A biological process can be studied by measuring time-courses of RNA abundance in response of internal and/or external stimuli, using recent technologies, such as the microarrays or the Next Generation Sequencing devices. Unfortunately, the picture provided by looking only at the transcriptome abundance may not gain insight into its dynamic regulation. By contrast, independent simultaneous measurement of RNA expression and half-lives could provide such valuable additional insight. A computational approach to the estimation of RNAs half-lives from RNA expression time profiles data, can be a low-cost alternative to its experimental measurement which may be also affected by various artifacts. RESULTS: Here we present a computational methodology, called StaRTrEK (STAbility Rates ThRough Expression Kinetics), able to estimate half-life values basing only on genome-wide gene expression time series without transcriptional inhibition. The StaRTrEK algorithm makes use of a simple first order kinetic model and of a [Formula: see text]-norm regularized least square optimization approach to find its parameter values. Estimates provided by StaRTrEK are validated using simulated data and three independent experimental datasets of two short (6 samples) and one long (48 samples) time-courses. CONCLUSIONS: We believe that our algorithm can be used as a fast valuable computational complement to time-course experimental gene expression studies by adding a relevant kinetic property, i.e. the RNA half-life, with a strong biological interpretation, thus providing a dynamic picture of what is going in a cell during the biological process under study.

A data-driven model of the COVID-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in Italy.

An epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the inter-regional mobility restored at the end of the first strong lockdown in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March-October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of the production, trade, and education activities, determined a time advance of the contagion increase since September 2020.

Optimal design of lock-down and reopening policies for early-stage epidemics through SIR-D models.

The diffusion of COVID-19 represents a real threat for the health and economic system of a country. Therefore the governments have to adopt fast containment measures in order to stop its spread and to prevent the related devastating consequences. In this paper, a technique is proposed to optimally design the lock-down and reopening policies so as to minimize an aggregate cost function accounting for the number of individuals that decease due to the spread of COVID-19. A constraint on the maximal number of concomitant infected patients is also taken into account in order to prevent the collapse of the health system. The optimal procedure is built on the basis of a simple SIR model that describes the outbreak of a generic disease, without attempting to accurately reproduce all the COVID-19 epidemic features. This modeling choice is motivated by the fact that the containing measurements are actuated during the very first period of the outbreak, when the characteristics of the new emergent disease are not known but timely containment actions are required. In fact, as a consequence of dealing with poor preliminary data, the simplest modeling choice is able to reduce unidentifiability problems. Further, the relative simplicity of this model allows to compute explicitly its solutions and to derive closed-form expressions for the maximum number of infected and for the steady-state value of deceased individuals. These expressions can be then used to design static optimization problems so to determine the (open-loop) optimal lock-down and reopening policies for early-stage epidemics accounting for both the health and economic costs.

Dynamical Evolution of COVID-19 in Italy With an Evaluation of the Size of the Asymptomatic Infective Population.

The present work deals with an Ordinary Differential Equation (ODE) model specifically designed to describe the COVID-19 evolution in Italy. The model is particularised on the basis of National data about the infection status of the Italian population to obtain numerical solutions that effectively reproduce the real data. Our epidemic model is a classical SEIR model that incorporates two compartments of infected subpopulations, representing diagnosed and undiagnosed individuals respectively, and an additional quarantine compartment. Possible control actions representing social, political, and medical interventions are also included. The numerical results of the proposed model identification by least square fitting are analysed and commented with special emphasis on the estimation of the number of asymptomatic infective individuals. Our fitting results are in good agreement with the epidemiological data. Short and long-term predictions on the evolution of the disease are also given.

Disruption of redox homeostasis for combinatorial drug efficacy in K-Ras tumors as revealed by metabolic connectivity profiling.

BACKGROUND: Rewiring of metabolism induced by oncogenic K-Ras in cancer cells involves both glucose and glutamine utilization sustaining enhanced, unrestricted growth. The development of effective anti-cancer treatments targeting metabolism may be facilitated by the identification and rational combinatorial targeting of metabolic pathways. METHODS: We performed mass spectrometric metabolomics analysis in vitro and in vivo experiments to evaluate the efficacy of drugs and identify metabolic connectivity. RESULTS: We show that K-Ras-mutant lung and colon cancer cells exhibit a distinct metabolic rewiring, the latter being more dependent on respiration. Combined treatment with the glutaminase inhibitor CB-839 and the PI3K/aldolase inhibitor NVP-BKM120 more consistently reduces cell growth of tumor xenografts. Maximal growth inhibition correlates with the disruption of redox homeostasis, involving loss of reduced glutathione regeneration, redox cofactors, and a decreased connectivity among metabolites primarily involved in nucleic acid metabolism. CONCLUSIONS: Our findings open the way to develop metabolic connectivity profiling as a tool for a selective strategy of combined drug repositioning in precision oncology.

Sodium caseinate improves longevity and fertility of frozen bull semen.

This study aimed to evaluate the effect of sodium caseinate added into freezing extender on the sperm parameters of cryopreserved bull semen and in vitro and in vivo fertility. One ejaculate of 30 bulls was used and processed using Botu-Bov (Botupharma, Botucatu, Brazil) with the addition of 20% egg yolk (EY) or 15% egg yolk with 2% sodium caseinate (EY + SC), subsequently submitted to freezing. Semen from both groups were evaluated immediately after thawing (T0) and after thermic stress at 37 °C for 90 min (T90), for sperm kinetics, by CASA method, and plasma membrane integrity (PMI), superoxide (O2-) concentration and high mitochondrial potential (HMP) by flow cytometry. In vitro fertilization (IVF) was performed to assess embryo cleavage rate on day 3, and blastocyst rate on day 8. The in vivo fertility test was performed using fixed-time artificial insemination (FTAI). In sperm evaluation, trajectory velocity, linear velocity, curvilinear velocity, and lateral head movement were higher (P < 0.05) in EY + SC at T0. At T90, while rectilinearity and linearity did not differ between EY and EY + SC (P > 0.05), the other parameters evaluated were higher in EY + SC. Similarly, the integrity of the plasma and acrosomal membranes (iPAM) was higher (P < 0.05) at T90 in EY + SC, but did not differ (P > 0.05) between the groups at T0. For O2- and HMP, the values were lower (P < 0.05) in EY + SC group in both moments; furthermore, EY + SC showed higher cleavage and blastocyst rates in IVF. Likewise, pregnancy rates by FTAI were higher (P < 0.05) in the EY + SC group. In conclusion, the addition of sodium caseinate into freezing extender improves sperm parameters of frozen-thawed bull semen and fertility rates on during in vitro and in vivo tests.

Impact of ART-induced viral suppression on the HIV epidemic in Italy.

The present study aims to clarify the role of the fraction of patients under antiretroviral therapy (ART) achieving viral suppression (VS) (i.e. having plasma viral load below the detectability threshold) on the human immunodeficiency virus (HIV) epidemic in Italy. Based on the hypothesis that VS makes the virus untransmittable, we extend a previous model and we develop a time-varying ordinary differential equation model with immigration and treatment, where the naive and non-naive populations of infected are distinguished, and different compartments account for treated subjects virally suppressed and not suppressed. Moreover, naive and non-naive individuals with acquired immune deficiency syndrome (AIDS) are considered separately. Clinical data stored in the nationwide database Antiviral Response Cohort Analysis are used to reconstruct the history of the fraction of virally suppressed patients since highly active ART introduction, as well as to assess some model parameters. Other parameters are set according to the literature and the final model calibration is obtained by fitting epidemic data over the years 2003-2015. Predictions on the evolution of the HIV epidemic up to the end of 2035 are made assuming different future trends of the fraction of virally suppressed patients and different eligibility criteria for treatment. Increasing the VS fraction is found to reduce the incidence, the new cases of AIDS and the deaths from AIDS per year, especially in combination with early ART initiation. The asymptotic properties of a time-invariant formulation of the model are studied, and the existence and global asymptotic stability of a unique positive equilibrium are proved.

Closed-loop control of tumor growth by means of anti-angiogenic administration.

A tumor growth model accounting for angiogenic stimulation and inhibition is here considered, and a closed-loop control law is presented with the aim of tumor volume reduction by means of anti-angiogenic administration. To this end the output-feedback linearization theory is exploited, with the feedback designed on the basis of a state observer for nonlinear systems. Measurements are supposed to be acquired at discrete sampling times, and a novel theoretical development in the area of time-delay systems is applied in order to derive a continuous-time observer in spite of the presence of sampled measurements. The overall control scheme allows to set independently the control and the observer parameters thanks to the structural properties of the tumor growth model. Simulations are carried out in order to mimic a real experimental framework on mice. These results seem extremely promising: they provide very good performances according to the measurements sampling interval suggested by the experimental literature, and show a noticeable level of robustness against the observer initial estimate, as well as against the uncertainties affecting the model parameters.

A simple model of HIV epidemic in Italy: The role of the antiretroviral treatment.

In the present paper we propose a simple time-varying ODE model to describe the evolution of HIV epidemic in Italy. The model considers a single population of susceptibles, without distinction of high-risk groups within the general population, and accounts for the presence of immigration and emigration, modelling their effects on both the general demography and the dynamics of the infected subpopulations. To represent the intra-host disease progression, the untreated infected population is distributed over four compartments in cascade according to the CD4 counts. A further compartment is added to represent infected people under antiretroviral therapy. The per capita exit rate from treatment, due to voluntary interruption or failure of therapy, is assumed variable with time. The values of the model parameters not reported in the literature are assessed by fitting available epidemiological data over the decade 2003÷2012. Predictions until year 2025 are computed, enlightening the impact on the public health of the early initiation of the antiretroviral therapy. The benefits of this change in the treatment eligibility consist in reducing the HIV incidence rate, the rate of new AIDS cases, and the rate of death from AIDS. Analytical results about properties of the model in its time-invariant form are provided, in particular the global stability of the equilibrium points is established either in the absence and in the presence of infected among immigrants.

Insulin Signaling in Insulin Resistance States and Cancer: A Modeling Analysis.

Insulin resistance is the common denominator of several diseases including type 2 diabetes and cancer, and investigating the mechanisms responsible for insulin signaling impairment is of primary importance. A mathematical model of the insulin signaling network (ISN) is proposed and used to investigate the dose-response curves of components of this network. Experimental data of C2C12 myoblasts with phosphatase and tensin homologue (PTEN) suppressed and data of L6 myotubes with induced insulin resistance have been analyzed by the model. We focused particularly on single and double Akt phosphorylation and pointed out insulin signaling changes related to insulin resistance. Moreover, a new characterization of the upstream signaling of the mammalian target of rapamycin complex 2 (mTORC2) is presented. As it is widely recognized that ISN proteins have a crucial role also in cell proliferation and death, the ISN model was linked to a cell population model and applied to data of a cell line of acute myeloid leukemia treated with a mammalian target of rapamycin inhibitor with antitumor activity. The analysis revealed simple relationships among the concentrations of ISN proteins and the parameters of the cell population model that characterize cell cycle progression and cell death.