In vitro DNA recombination by random priming.

Snake venomics: From the inventory of toxins to biology

Optimality models used in behavioral ecology, and especially on insect parasitoids, have taken a variety of approaches, from classical analytical tools to individualbased simulations. The increasing awareness that much of the observable behavior in parasitoids depends on the state of the insect (be it the physiological or informational state) has led to the increasing use of stochastic dynamic programming models. However, optimal behaviors of one individual often depend upon the behavior of conspecifics, further complicating the issue. While classical game theory may be applied when behavior is not state dependent, genetic algorithms (GA) provide a powerful way in finding optimal behaviors for situations where such optimal behavior depends upon an animal’s state and on the frequency of alternative behaviors of conspecifics. More generally, GAs can be used when there is a need to find the optimal strategy among a number of different alternative behaviors whose number is far too great to be exhaustively checked. GAs are search algorithms that proceed in a fashion analogous to natural selection. They are individual-based simulations that identify optimal solutions by searching the enormous space of potential solutions mimicking the process of evolution in biological systems. In this chapter, we discuss research questions in parasitoid behavioral ecology that might benefit from applying GA as a research tool for optimality models. Examples will be developed and explained. Among others, using the foraging problem of estimating habitat quality, we show how fast GAs find optimal solutions for parameter spaces that are impossible to solve numerically otherwise.

Interactions among proteins mediate regulatory, mechanical, structural and transport functions and are fundamental in the modulation of all cellular activities. About 15 years ago, it was first noted that physical interactions link most if not all proteins to each other in complex networks. The analysis of maps representing current knowledge about these molecular networks can provide insights that range from specific hypotheses on the function of a given protein to system‐level properties on the organisation or evolution of biological systems. Here, we provide an updated introduction to the numerous ways and available tools by which protein–protein interaction maps can be analysed, and caveats that need to be kept in mind. Key Concepts Protein–protein interaction maps are important tools to develop functional hypothesis, contextualise ‘omic’ gene lists, analyse system properties and systems evolution and provide an important basis for analysing system dynamics. Networks can be assembled from small‐scale literature data, bioinformatic prediction and experimental high‐throughput technologies of which affinity‐purification followed by mass spectrometry or binary yeast‐based assays are most common. Global and local properties of protein–protein interaction maps can be influenced by sociological, technological and experimental biases that need to be taken into account. High‐throughput technologies can provide data that are in quality at least comparable to the high‐quality fraction from small‐scale studies in public databases. Carefully benchmarked and appropriate quality controls must be implemented for all approaches. Binary and co‐complex methodologies give rise to differential and complementary sets of protein interactions. Bioinformatic tools are available that enable nonspecialists to analyse and use protein–protein interaction networks.

(2006). Evolution of Biological Systems in Random Media: Limit Theorems and Stability. Journal of the American Statistical Association: Vol. 101, No. 473, pp. 397-397.

Recent studies in the green alga Chlamydomonas and other flagellated cells have revealed new insights into the relationships between the structure and function of the eukaryotic flagellum. These advances provide a basis from which a unified view can be constructed of how a flagellum operates. In addition, investigations of flagellar assembly offer new perspectives revealing the mechanisms used by cells to create these nanoscale structures. New developments in the molecular biology of Chlamydomonas provide powerful tools for the continued exploration of flagellar biology in this cell. These studies are of interest not only within the field of biology, but also in physics and materials science; the problems of fabrication, assembly, function and regulation of nanoscale machines have been elegantly solved during the evolution of biological systems, providing models from which much remains to be learned.

The laws of classical mechanics and quantum theory do not distinguish between the past and the future. But a lot of phenomena tell us that the "arrow of time" points only in one direction. For example, in biology we discover evolutionary processes leading to diversification and increasing complexity; it is a typical feature of such evolutionary processes that "forward" in time is possible but "backward" in time is impossible. At what level can phenomena like these be described? There is another problem in connection with time: within usual quantum theory, space-coordinates and time are not symmetrical to each other, which is in contrast to the basic results of special theory of relativity. In usual quantum theory coordinates are operators and play the role of statistical quantities; time however does not behave statistically and remains a simple parameter when we go from classical to quantum mechanics. For this reason there is no uncertainty relation for the time and energy within usual quantum theory which would agree in its physical content with that for the coordinates and momenta. The purpose of this paper is to show, on the basis of new aspects, that both one-sidedness of time and its uncertainty are charateristics at the fundamental level of quantum theory. We have extended usual quantum theory on the basis of space-time arguments. In this connection a time-operator appeared and there is in general temporal asymmetry at the microscopic level. The picture might be important in the description of phenomena with an "arrow of time", i.e., processes which belong to the physics of becoming. Furthermore, we have derived an uncertainty relation for the energy and time which agrees in its physical content with the uncertainty relation for the momentum and the coordinate; this relation is different from that of usual quantum theory. Within the frame of this physical picture all the concepts (diversity, natural selection, mutation), which are presently used in connection with the evolution of biological systems, are already established at the quantum mechanical level and, therefore, our theoretical picture might also be the foundation for the explanation of the phenomena at the biological (macro-molecular) level.

The presence of heavy metals in the environment can lead to ecological and health problems. The evolution of biological systems, such as activated sludge, exposed to heavy metals is still underexplored. Therefore, this study sought to develop a model of microorganism activity and growth in activated sludge and used it to investigate the toxicity of five metals: Cu, Cd, Ni, Zn, and Ag. Patterns in the evolution of the toxic effects caused by these metals were similar at the beginning of exposure. Differences in toxicity between metal ions were noted for longer exposure times. Changes in model parameters indicate the influence of metal ions on the mass and energy balance of living cells. Decreases in new enzyme units and biomass production yields in contaminated activated sludge indicate a shift from anabolic reactions to metal homeostasis and resistance.

The evolution of biological systems is strongly influenced by physical factors, such as applied forces, geometry or the stiffness of the micro-environment. Mechanical changes are particularly important in solid tumour development, as altered stromal-epithelial interactions can provoke a persistent increase in cytoskeletal tension, driving the gene expression of a malignant phenotype. In this work, we propose a novel multi-scale treatment of mechano-transduction in cancer growth. The avascular tumour is modelled as an expanding elastic spheroid, whilst growth may occur both as a volume increase and as a mass production within a cell rim. Considering the physical constraints of an outer healthy tissue, we derive the thermo-dynamical requirements for coupling growth rate, solid stress and diffusing biomolecules inside a heterogeneous tumour. The theoretical predictions successfully reproduce the stress-dependent growth curves observed by in vitro experiments on multicellular spheroids.

Inspired by the evolution of biological systems, genetic algorithms have been applied to generate solutions for optimization problems in a variety of scientific and engineering disciplines. For a given problem, a suitable genome representation must be defined along with a mutation operator to generate subsequent generations. Unlike natural systems, which display a variety of complex rearrangements (e.g., mobile genetic elements), mutation for genetic algorithms commonly utilizes only random pointwise changes. Furthermore, generalizing beyond pointwise mutations poses a key difficulty as useful genome rearrangements depend on the representation and problem domain. To move beyond the limitations of manually defined pointwise changes, here we propose the use of techniques from masked language models to automatically generate mutations. As a first step, common subsequences within a given population are used to generate a vocabulary. The vocabulary is then used to tokenize each genome. A masked language model is trained on the tokenized data in order to generate possible rearrangements (i.e., mutations). In order to illustrate the proposed strategy, we use string representations of molecules and use a genetic algorithm to optimize for drug-likeness and synthesizability. Our results show that moving beyond random pointwise mutations accelerates genetic algorithm optimization.

Most biologists agree that evolution is contingent on inherited information shaped by natural selection. This apparent consensus could be taken to indicate agreement on the forces shaping evolution, but vivid discussions reveal divergences on how evolution is perceived. The predominant Modern Synthesis (MS) paradigm holds the position that evolution occurs through random changes acting on genomic inheritance. However, studies from recent decades have revealed that evolutionary inheritance also includes DNA-methylation, RNA, symbionts, and culture, among other factors. This has fueled a demand of a broader evolutionary perspective, for example from the proponents of the Extended Evolutionary Synthesis (EES). Despite fundamental disagreements the different views agree that natural selection happens through dissimilar perpetuation of inheritable information. Yet, neither the MS, nor the ESS dwell extensively on the nature of hereditary information. We do - and conclude that information in and of itself is immaterial. We then argue that the quality upon which natural selection acts henceforth is also immaterial. Based on these notions, we arrive at the information-centric Information Continuum Model (ICM) of evolution. The ICM asserts that hereditary information is embedded in diverse physical forms (DNA, RNA, symbionts etc.) representing a continuum of evolutionary qualities, and that information may migrate between these physical forms. The ICM leaves theoretical exploration of evolution unrestricted by the limitations imposed by the individual physical forms wherein the hereditary information is embedded (e.g. genomes). ICM bestows us with a simple heuristic model that adds explanatory dimensions to be considered in the evolution of biological systems.

Nocturnal asthma (NA) is a high-prevalence disease that causes severe respiratory issues, leading to death from early midnight to early morning. In this research, nanoparticulate drug delivery system of methylprednisolone (MP) was developed using chitosan (CH) and pectin (PEC). MP is a synthetic corticosteroid medication widely used for its potent anti-inflammatory activity. Computational simulation study (AI-based blend analysis algorithm) was used to identify a better-mixing polymer with MP. MP nanoparticles were formulated by the ionic gelation method with the combination of CH and PEC. To modify the drug release properties, the formed beads were coated with chitosan succinate (CSSC). The morphological characteristics of the beads were determined by SEM analysis. The X-ray radiographic imaging study was used to observe the intactness of MP beads. Histopathological studies were also carried out to find out the toxicity of the beads in the organs of rats. Pectin and chitosan polymers were selected based on the computational simulation study. SEM analysis revealed that the beads had a spherical shape with a rough outer surface. CSSC-coated beads achieved sustained drug release for up to 24 h. X-ray imaging demonstrated the stability of the beads in acidic pH conditions. In vivo pharmacokinetic studies showed that CSSC-coated beads were more stable in the gastrointestinal tract (GIT) than PEC-CH beads and the pure drug. Histological evaluation confirmed that the beads are nontoxic and safe for use in rats. Based on the findings, it was concluded that CSSC-coated beads of MP exhibited superior release properties, making them suitable for a chronomodulated drug delivery system.

Introduction: Computational simulation study was carried out on bioactive compounds of Ganoderma lucidum (G. lucidum). Methods: Molecular docking and molecular dynamics (MD) simulations were performed. The input files for protein and ligands were retrieved from Protein Data Bank (PDB) and PubChem database. Human maltase-glucoamylase (PDB ID: 3L4Y) is the protein (α-glucosidase enzyme). The ligands are thirteen compounds derived from G. lucidum together with acarbose and miglitol as controls. Results: Docking result showed the lowest binding energy is from Ganomycin B (-7.8 kcal/mol) compared to acarbose and miglitol (-5.0 kcal/mol and -4.4 kcal/mol) respectively. MD simulation showed interaction of 3L4Y-Ganomycin B achieved stable interaction and conformation as follows: root mean square deviation (RMSD) is ± 2.7 Å, average distance of ±1.8 Å and constant hydrogen bonds around 1 - 3. Conclusion: Ganomycin B was found to have good binding affinity, embarking its potential as a potent α-glucosidase inhibitor.

High performance aptasensing platform development through in silico aptamer engineering for aflatoxin B1 monitoring

Tripping while walking has been identified as the most common cause of falls among the elderly as they tend to utilize a shuffling gait while walking, which increases the risk of falling. Since tripping occurs when toes make unexpected contact with objects on the floor, a number of studies have investigated the impact of foot clearance on the risk of tripping. However, only a few studies have examined the effects of foot–floor friction on the risk of tripping. Thus, in this current study, we investigated the effect of foot–floor friction on the probability of trip-induced falls during normal and shuffling gaits in a computational simulation study. We used a computational model with neural rhythm generators and neuromusculoskeletal systems to simulate gait in a self-organized manner. By changing the parameters of the neural rhythm generator, gait parameters such as step length, cadence, and foot clearance were automatically reduced, which simulated the shuffling gait. To alter the foot–floor friction, we changed the spring coefficient ratio of the floor in horizontal and vertical directions. As per our results, it was determined that slip-induced falls occurred under low foot–floor friction conditions in both normal and shuffling gaits, whereas trip-induced falls occurred under high foot–floor friction conditions only with a shuffling gait. These results suggest that optimal foot–floor friction may prevent trip- and slip-induced falls among the elderly.KeywordsShuffling gaitFrictionTrip-induced fallNeuromusculoskeletal model

Experimental as well as Computer Simulation studies have been systematically performed on the spontaneous generation of magnetic fields associated with laser-produced plasmas from massive solid targets in vacuum. The experimental studies have been performed by employing a pulsed ruby-laser beam, aluminum plane target and magnetic probe(s) located in the vicinity of target surface to measure generated magnetic fields. Numbers of new interesting facts have been found experimentally on the time evolution of spatial distribution of magnetic field generated, and possible physical interpretations for them are given. The comparisons of our experimental results are made with those from our computer simulations, as well as with the other experimental results which have been performed in the existence of background gases, rather than in vacuum.