NETISCE: A Network-Based Tool for Cell Fate Reprogramming

The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. NETISCE identifies reprogramming targets through the innovative use of control theory within a dynamical systems framework. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system that are relevant for the desired reprogramming task.

Quantifying the impacts of land cover change on hydrological responses in the Mahanadi river basin in India

The objective of this study is to assess the impacts of land cover change on the hydrological responses of the Mahanadi river basin, a large river basin in India. Commonly, such assessments are accomplished by using distributed hydrological models in conjunction with different land use scenarios. However, these models, through their complex interactions among the model parameters to generate hydrological processes, can introduce significant uncertainties to the hydrological projections. Therefore, we seek to further understand the uncertainties associated with model parameterization in those simulated hydrological responses due to different land cover scenarios. We performed a sensitivity-guided model calibration of a physically semi-distributed model, the Variable Infiltration Capacity (VIC) model, within a Monte Carlo framework to generate behavioural models that can yield equally good or acceptable model performances for subcatchments of the Mahanadi river basin. These behavioural models are then used in conjunction with historical and future land cover scenarios from the recently released Land-Use Harmonization version 2 (LUH2) dataset to generate hydrological predictions and related uncertainties from behavioural model parameterization. The LUH2 dataset indicates a noticeable increase in the cropland (23.3 % cover) at the expense of forest (22.65 % cover) by the end of year 2100 compared to the baseline year, 2005. As a response, simulation results indicate a median percent increase in the extreme flows (defined as the 95th percentile or higher river flow magnitude) and mean annual flows in the range of 1.8 % to 11.3 % across the subcatchments. The direct conversion of forested areas to agriculture (of the order of 30 000 km2) reduces the leaf area index, which subsequently reduces the evapotranspiration (ET) and increases surface runoff. Further, the range of behavioural hydrological predictions indicated variation in the magnitudes of extreme flows simulated for the different land cover scenarios; for instance, uncertainty in scenario labelled “Far Future” ranges from 17 to 210 m3 s−1 across subcatchments. This study indicates that the recurrent flood events occurring in the Mahanadi river basin might be influenced by the changes in land use/land cover (LULC) at the catchment scale and suggests that model parameterization represents an uncertainty which should be accounted for in the land use change impact assessment.

Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin

The development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the effects of the standard chemotherapeutic drug doxorubicin on the MCF-7 breast cancer cell line. We assume that the tumor is composed of two subpopulations: drug-resistant cells, which continue proliferating after treatment, and drug-sensitive cells, which gradually transition from proliferating to treatment-induced death. The model is fit to experimental data including variations in drug concentration, inter-treatment interval, and number of doses. Our model recapitulates tumor growth dynamics in all these scenarios (as quantified by the concordance correlation coefficient, CCC > 0.95). In particular, superior tumor control is observed with higher doxorubicin concentrations, shorter inter-treatment intervals, and a higher number of doses (p < 0.05). Longer inter-treatment intervals require adapting the model parameterization after each doxorubicin dose, suggesting the promotion of chemoresistance. Additionally, we propose promising empirical formulas to describe the variation of model parameters as functions of doxorubicin concentration (CCC > 0.78). Thus, we conclude that our mathematical model could deepen our understanding of the effects of doxorubicin and could be used to explore practical drug regimens achieving optimal tumor control.

Biological Crusts to Increase Soil Carbon Sequestration: New Challenges in a New Environment

The major priority of research in the present day is to conserve the environment by reducing GHG emissions. A proposed solution by an expert panel from 195 countries meeting at COP 21 was to increase global SOC stocks by 0.4% year−1 to compensate for GHG emissions, the ‘4 per 1000′ agreement. In this context, the application of biocrusts is a promising framework with which to increase SOC and other soil functions in the soil–plant continuum. Despite the importance of biocrusts, their application to agriculture is limited due to: (1) competition with native microbiota, (2) difficulties in applying them on a large scale, (3) a lack of studies based on carbon (C) balance and suitable for model parameterization, and (4) a lack of studies evaluating the contribution of biocrust weathering to increase C sequestration. Considering these four challenges, we propose three perspectives for biocrust application: (1) natural microbiome engineering by a host plant, using biocrusts; (2) quantifying the contribution of biocrusts to C sequestration in soils; and (3) enhanced biocrust weathering to improve C sequestration. Thus, we focus this opinion article on new challenges by using the specialized microbiome of biocrusts to be applied in a new environment to counteract the negative effects of climate change.

Specification of Modified Jarvis Model Parameterization for Pinus cembra

The high ambient ozone concentrations cause impairing effects on vegetation leading to plant injuries. The potential ozone uptake to vegetation through open stomata can be quantified using stomatal conductance measurements under the local environmental conditions. This study compares the ozone stomatal conductance to vegetation obtained with a modified Jarvis formula adopted from the Vegetation Manual of United Nations Economic Commission for Europe, and experimental field measurements’ data. The stomatal conductance was measured by a portable photosynthesis and gas exchange analyzer system LiCOR6400. The measurements were performed in the submontane environment of the High Tatra Mountains in Slovakia on Swiss pine (Pinus cembra), as a native species of the local flora. According to previous studies, Swiss pine is considered as an ozone-sensitive species. The modified Jarvis model for the ozone stomatal conductance is compared with the field measurements. The suitable parameterization of the modified Jarvis model for Swiss pine is obtained. The parameterization of stomatal conductance for Swiss pine in the local environment would help understand its specificity and similarity to other conifer species. In the case of using parameterization for a boreal coniferous from the Vegetation Manual of the International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops, validation of the model with the measurements without temperature adjustment of the conifer chamber achieved a coefficient of determination of R2=0.75. This result is not in contradiction with the previous researches. With the optimal set of parameters, obtained in this paper, the Jarvis model reaches R2=0.85. The data suggest that Jarvis-type models with appropriate parameterization are applicable for stomatal conductance estimation for Pinus cembra when the measurements do not modify the temperature regime.

Modeling Alleviative Effects of Ca, Mg, and K on Cu-Induced Oxidative Stress in Grapevine Roots Grown Hydroponically

The aim of this study was to determine the pattern of alleviation effects of calcium (Ca), magnesium (Mg), and potassium (K) on copper (Cu)-induced oxidative toxicity in grapevine roots. Root growth, Cu and cation accumulation, reactive oxygen species (ROS) production, and antioxidant activities were examined in grapevine roots grown in nutrient solutions. The experimental setting was divided into three sets; each set contained a check (Hoagland solution only) and four treatments of simultaneous exposure to 15 μM Cu with four cation levels (i.e., Ca set: 0.5, 2.5, 5, and 10 mM Ca; Mg set: 0.2, 2, 4, and 8 mM Mg; K set: 0.6, 2.4, 4.8, and 9.6 mM K). A damage assessment model (DAM)-based approach was then developed to construct the dose-effect relationship between cation levels and the alleviation effects on Cu-induced oxidative stress. Model parameterization was performed by fitting the model to the experimental data using a nonlinear regression estimation. All data were analyzed by a one-way analysis of variance (ANOVA), followed by multiple comparisons using the least significant difference (LSD) test. The results showed that significant inhibitory effects on the elongation of roots occurred in grapevine roots treated with 15 μM Cu. The addition of Ca and Mg significantly mitigated phytotoxicity in root growth, whereas no significant effect of K treatment on root growth was found. With respect to oxidative stress, ROS and malondialdehyde (MDA) contents, as well as antioxidant (superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX)) activities, were stimulated in the roots after exposure to 15 μM Cu for three days. Moreover, H2O2 levels decreased significantly as Ca, Mg, and K concentrations increased, indicating that the coexistence of these cations effectively alleviated Cu-induced oxidative stress; however, alleviative effects were not observed in the assessment of the MDA content and antioxidant enzyme activities. Based on the DAM, an exponential decay equation was developed and successfully applied to characterize the alleviative effects of Ca, Mg, and K on the H2O2 content induced by Cu in the roots. In addition, compared with Mg and K, Ca was the most effective cation in the alleviation of Cu-induced ROS. Based on the results, it could be concluded that Cu inhibited root growth and Ca and Mg absorption in grapevines, and stimulated the production of ROS, lipid peroxidation, and antioxidant enzymes. Furthermore, the alleviation effects of cations on Cu-induced ROS were well described by the DAM-based approach developed in the present study.