Target identification strategies in plant chemical biology

The current needs to understand gene function in plant biology increasingly require more dynamic and conditional approaches opposed to classic genetic strategies. Gene redundancy and lethality can substantially complicate research, which might be solved by applying a chemical genetics approach. Now understood as the study of small molecules and their effect on biological systems with subsequent target identification, chemical genetics is a fast developing field with a strong history in pharmaceutical research and drug discovery. In plant biology however, chemical genetics is still largely in the starting blocks, with most studies relying on forward genetics and phenotypic analysis for target identification, whereas studies including direct target identification are limited. Here, we provide an overview of recent advances in chemical genetics in plant biology with a focus on target identification. Furthermore, we discuss different strategies for direct target identification and the possibilities and challenges for plant biology

Target Identification

Identifying potential and druggable targets for developing new drugs is the first major step for curing a disease, which includes the understanding of the cellular, molecular, biochemical complexities associated with a specific disease. The classical hypothesis of ‘one gene, one drug, one disease’ in the drug discovery paradigm may have contributed to the low success rate in drug development. Using AI and related technologies in the target identification process allows scientists and pharmaceutical companies to really explore all the available evidence to better understand a disease and its underlying biology. Our team has focused on target identification, with growing amounts of data supporting early decision making

Target Identification Using Dictionary Matching of Generalized Polarization Tensors

The aim of this paper is to provide a fast and efficient procedure for (real-time) target identification in imaging based on matching on a dictionary of precomputed generalized polarization tensors (GPTs). The approach is based on some important properties of the GPTs and new invariants. A new shape representation is given and numerically tested in the presence of measurement noise. The stability and resolution of the proposed identification algorithm is numerically quantified.Comment: Keywords: generalized polarization tensors, target identification, shape representation, stability analysis. Submitted to Foundations of Computational Mathematic

Neuronal plasticity: cell-based strategy for target identification and validation

While growing neurites are relatively plastic during development, their plasticity levels drop rapidly as neurons mature and become integrated into neuronal networks. As a consequence, the central nervous system ability to reorganize itself in response to injury or disease is insufficient. One of the main limitations for the design of therapeutic strategies to enhance neurite sprouting following neurological diseases is our poor understanding of the mechanisms underlying neurite structural plasticity. 

To overcome this limitation, we have implemented a strategy to identify, characterize and validate the most therapeutically relevant drug targets to modulate neuronal plasticity. This strategy is based on the hypothesis that the molecular regulation of the neurite shaft (controlling its ability to sprout) shares similarities with other cell systems. In an initial selection step (1), candidate targets are identified from the literature based on predetermined criteria (e.g. involvement in cell migration and growth cone collapse). (2) Compounds acting on those targets are tested in a neuronal cell-based assay for their effects on neurite sprouting as well as for early detection of undesired effects. (3) The selected targets are further analyzed during a verification step where their subcellular expression and activity is assessed. The regulation of the activity of the potential targets by branching factors is also determined at this point. (4) Once the biological relevance has been established, the signalling pathway in which the potential targets operates within neurons is mapped using activity reporters to uncover additional potential targets. 

This candidate approach, combined with the use of primary neurons, is designed to increase the probability of identifying suitable targets at the same time that it minimizes costs and time to validation. By using this strategy, we have been able to identify a set of proteins controlling neuronal sprouting together with an unsuspected mechanism of regulation of structural plasticity in neurons

Therapeutic target discovery using Boolean network attractors: avoiding pathological phenotypes

Target identification, one of the steps of drug discovery, aims at identifying biomolecules whose function should be therapeutically altered in order to cure the considered pathology. This work proposes an algorithm for in silico target identification using Boolean network attractors. It assumes that attractors of dynamical systems, such as Boolean networks, correspond to phenotypes produced by the modeled biological system. Under this assumption, and given a Boolean network modeling a pathophysiology, the algorithm identifies target combinations able to remove attractors associated with pathological phenotypes. It is tested on a Boolean model of the mammalian cell cycle bearing a constitutive inactivation of the retinoblastoma protein, as seen in cancers, and its applications are illustrated on a Boolean model of Fanconi anemia. The results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice, thus requiring it to be used in combination with wet lab experiments. Nevertheless, it is expected that the algorithm is of interest for target identification, notably by exploiting the inexpensiveness and predictive power of computational approaches to optimize the efficiency of costly wet lab experiments.Comment: Since the publication of this article and among the possible improvements mentioned in the Conclusion, two improvements have been done: extending the algorithm for multivalued logic and considering the basins of attraction of the pathological attractors for selecting the therapeutic bullet

The effects of precipitation on radar target identification and imaging

The properties of precipitation which will influence radar system design are discussed. The spatial characteristics of rainfall and the sizes and shapes of raindrops are described. The dielectric behavior of water is combined with these characteristics to determine the effects of rain on electromagnetic waves. These effects include: absorption, scatter, noise emission, phase shift, and depolarization

Vibrotactile Guidance Cues For Target Identification

The purpose of this dissertation was to establish how vibrotactile guidance cues can be used to improve marksmanship. This work originated in an effort to provide covert communication, navigation, and weapon aiming cues for infantrymen. It is predominantly an application-driven investigation rather than driven a priori by specific theoretical predictions from models of human performance. Three experiments are presented. Experiment 1 established the affect on initial response to vibrotactile guidance cues of tactor placements on the palmer versus dorsal surface of the hand, and targets appearing left versus right of center. Results suggest that tactile cues provided on the left side of the medial line of the hand afford moving the hand to the left, while tactile cues provided on the right side of the medial line afford moving the hand to the right. Experiment 2 established the affect of continuous relative distance cues and on- versus off-target vibrotactile stimuli on reaction time and accuracy for target selection. Results indicated an interaction between the pulse rate of vibrotactile stimuli and the method used to highlight an on-target condition; the suppressed target condition was superior to the enhanced target condition when the pulse rate increased as the cursor moved closer to a target. Experiment 3 established if there are performance differences between discrete and continuous distance information for target selection, and investigated the interaction between the near-target pulse rate and on-target cues. Results indicate that maximizing the difference between near-target guidance cues and on-target cues reduces the target selection time, particularly when the near-target pulse rates are fast (ISI = 10 msec). The results also suggest that, as with vision, the vibrotactile off-target guidance cues are not necessary during the whole target selection task. Rather, the guidance cues can be provided only during the initial pop-up condition and during the sub-movements closing on the target