Numerical simulation of a stochastic model for cancerous cells submitted to chemotherapy

A stochastic model is proposed to study the problem of inherent resistance by cell populations when chemotherapeutic agents are used to control tumor growth. Stochastic differential equations are introduced and numerically integrated to simulate expected response to the chemotherapeutic strategies as a function of different parameters. Satisfactory demonstration runs of the model indicate that it could represent a useful tool in verifying the results of experimental and clinical chemotherapy courses and planning treatment strategies. Some types of behaviour are illustrated graphically.Work supported by the C.N.R. Grants: 85.02652.01; 86.02116.01     

An experiment to re-establish the red-winged grasshopper, Oedipoda germanica (Latr.) (Caelifera: Acrididae), threatened with extinction in Germany

In the first attempt to re-establish a population of the red-winged grasshopper, Oedipoda germanica (Latr.), threatened with extinction in Germany, 32 adults (19 females, 13 males) were taken from a population in a limestone quarry, individually marked, and translocated to the “Leutratal” nature reserve near Jena, Thuringia, formerly inhabited by the species. A simulation model with two scenarios was run to determine the probability of re-establishment of this small translocated population. One of the two scenarios indicated that populations of 20 females could survive with optimal life history parameters. In 1994, the year of translocation, during seven nights of observation between July and September, 25 adults were re-observed at least once. About 50% of the females and 70% of the males were found in a 20 m radius of the release point, whereas a maximum distance of about 70 m was recorded for a female. Using minimum female life-span and oviposition data from greenhouse experiments, 545 eggs were estimated to have been laid by the initial inoculum in 1994. In the subsequent years 1995 and 1996, only seven and three grasshoppers were observed within 30 m of the release point in 1994. However, the translocated population obviously went extinct in 1997 because no O. germanica could be found in that or following years. By comparing source and release habitat, the possible causes of the failed re-establishment were elucidated. In the year of release a short mean adult life span of 26 days was observed, assumed to be the consequence of high predation, and resulting in a lower reproductive rate. In the following years, low soil temperature of the release habitat probably delayed egg hatching for about 1 week, delaying phenology and thereby reducing the period over which eggs could be laid. A rapid population decline resulted in only three females found in the year before extinction.     

Molecular dynamics and simulation analysis against superoxide dismutase (SOD) target of Micrococcus luteus with secondary metabolites from Bacillus licheniformis recognized by genome mining approach

Micrococcus luteus, also known as M. luteus, is a bacterium that inhabits mucous membranes, human skin, and various environmental sources. It is commonly linked to infections, especially among individuals who have compromised immune systems. M. luteus is capable of synthesizing the enzyme superoxide dismutase (SOD) as a component of its protective response to reactive oxygen species (ROS). This enzyme serves as a promising target for drug development in various diseases. The current study utilized a subtractive genomics approach to identify potential therapeutic targets from M. luteus. Additionally, genome mining was employed to identify and characterize the biosynthetic gene clusters (BGCs) responsible for the production of secondary metabolites in Bacillus licheniformis (B. licheniformis), a bacterium known for its production of therapeutically relevant secondary metabolites. Subtractive genomics resulted in identification of important extracellular protein SOD as a drug target that plays a crucial role in shielding cells from damage caused by ROS. Genome mining resulted in identification of five potential ligands (secondary metabolites) from B. licheniformis such as, Bacillibactin (BAC), Paenibactin (PAE), Fengycin (FEN), Surfactin (SUR) and Lichenysin (LIC). Molecular docking was used to predict and analyze the binding interactions between these five ligands and target protein SOD. The resulting protein–ligand complexes were further analyzed for their motions and interactions of atoms and molecules over 250 ns using molecular dynamics (MD) simulation analysis. The analysis of MD simulations suggests, Bacillibactin as the probable candidate to arrest the activities of SOD. All the five compounds reported in this study were found to act by directly/indirectly interacting with ROS molecules, such as superoxide radicals (O₂–) and hydrogen peroxide (H₂O₂), and transforming them into less reactive species. This antioxidant activity contributes to its protective effects against oxidative stress-induced damage in cells making them likely candidate for various applications, including in the development of antioxidant-based therapies, nutraceuticals, and functional foods.     

Interactions between landcover pattern and geospatial processing methods: Effects on landscape metrics and classification accuracy

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric “number of patches” than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty.     

Modeling Plasmalemma Ion Transport of the Aquatic Plant Egeria densa

Fresh-water plants generate extraordinarily high electric potential differences at the plasma membrane. For a deeper understanding of the underlying transport processes a mathematical model of the electrogenic plasmalemma ion transport was developed based on experimental data mainly obtained from Egeria densa. The model uses a general nonlinear network approach and assumes coupling of the transporters via membrane potential. A proton pump, an outward-rectifying K⁺ channel, an inward-rectifying K⁺ channel, a Cl⁻ channel and a (2H-Cl)⁺ symporter are considered to be elements of the system. The model takes into consideration the effects of light, external pH and ionic content of the bath medium on ion transport. As a result it does not only satisfactorily describe the membrane potential as a function of these external physiological factors but also succeeds in simulating the effects of specific inhibitors as well as I-V-curves obtained with the patch-clamp technique in the whole cell mode. The quality of the model was checked by stability and sensitivity analyses. Received: 18 March 1996/Revised: 17 July 1996     

Computational experiments reveal plausible mechanisms for changing patterns of hepatic zonation of xenobiotic clearance and hepatotoxicity

No concrete, causal, mechanistic theory is available to explain how different hepatic zonation patterns of P450 isozyme levels and hepatotoxicity emerge following dosing with different compounds. We used the synthetic method of modeling and simulation to discover, explore, and experimentally challenge concrete mechanisms that show how and why biomimetic zonation patterns can emerge and change within agent-based analogues, expecting that those mechanisms may have counterparts in rats. Mobile objects map to compounds. One analogue represents a cross-section through a lobule. It is comprised of 460 identical, quasi-autonomous functional units called sinusoidal segments (SSs). SSs detect and respond to compound-generated response signals and the local level of an endogenous gradient. Each SS adapts by using those signals to adjust (or not) the probability that it will clear a detected compound during the next simulation cycle. The adjustment decision is based on the value of a biomimetic algorithm that is based on an assumed, evolution imposed, genetic mandate that normal hepatocytes resist increasing the cost of their actions. The algorithm estimates the long-term, discounted cost to a given SS of continuing to use its current clearance effort. Upon compound exposure, lobular analogues developed a variety of clearance and hepatotoxicity patterns that were strikingly similar to those reported in the literature. A degree of quantitative validation was achieved against data on hepatic zonation of CYP1A2 mRNA expression caused by three different doses of TCDD (2,3,7,8-tetracholorodibenzo-p-dioxone).