Testing Methods: The Evaluation of Discovery Operations in Evolutionary Biology

Scientific discovery requires both abstract, theoretically defined concepts and discovery operations formed by sets of rules that permit the empirical detection of instances of those concepts. In this paper, I examine the ontological status of discovery operations and the tests employed to evaluate them in evolutionary biology. Attention is drawn to the distinction between nomothetic (universal, predictive) and ideographic (historical, retrodictive) discovery operations, and between complementary and exclusive discovery operations. Three types of tests of discovery operations are commonly employed in evolutionary biology. Theoretical tests aim to show that a discovery operation is inconsistent with accepted, well-corroborated, empirical theories. Empirical tests evaluate the performance of competing discovery operations in terms of their results when applied to the same empirical data sets. Philosophical tests aim to show that an operation is inconsistent with logical and epistemological principles. Appropriately designed theoretical and philosophical tests of ideographic discovery operations may be scientifically valid. Empirical tests, however, are incapable of evaluating the scientific merits of competing discovery operations. Nonetheless, empirical comparisons (not tests ) of competing discovery operations may provide insight into the ways discovery operations may be misleading and therefore may play an important role in stimulating critical debate and eventually establishing a scientifically optimal operation. In practice, theoretical and philosophical tests are often combined to test competing discovery operations as rigorously as possible.     

Patterns of species discovery in the Western Ghats,a megadiversity hot spot in India

Even since Linnaeus, naturalists and taxonomists have been systematically describing species new to science. Besides indicating gaps in taxonomic effort, understanding the temporal patterns of species discovery could help in identifying drivers that determine discovery. In this study we report the patterns of discovery of eight taxa — birds, butterflies, frogs, tiger beetles, grasses, asters, ferns and orchids — in the Western Ghats, a megadiversity centre in India. Our results indicate that the discovery curves for birds and butterflies have been saturated while those for frogs and grasses continue to increase. Within each taxon, the major drivers of discovery were commonness of the species and their size. The average years taken for discovery across taxa were directly related to the per cent endemicity and species richness of the taxa. We discuss the trajectories of discovery with respect to rarity or endemicity of the species and life history features, and the implications these might have for strategizing the discovery process in India.     

The Discovery of Medicines from Plants: A Current Biological Perspective1

The Discovery of Medicines from Plants: A Current Biological Perspective. The last 50 years have seen tremendous innovations in the process of discovering novel bioactive compounds from plants. Every stage of the natural products discovery and development pipeline has seen major advances, so as a result, today it is possible to evaluate large numbers of plant extracts efficiently and in more effective ways against a wider array of disease targets. Despite all of these technological advances and numerous large-scale discovery efforts, the number of new drugs developed as natural products from plants to reach the market has been surprisingly low, very different from the continuing productive discovery work from microbes. The innovations in the discovery process are reviewed, possible explanations for low success of recent discovery efforts are explored, and an effort is made to estimate the future potential of plants as a discovery resource. It may be that the low rate of discovery during this developmental period of natural products discovery is a consequence of technological limitations of the discovery process rather than a lack of interesting compounds in plants, and that even when necessary assumptions are taken into account, conservative estimates would indicate great potential for the discovery of new drugs from plants.     

Fishing for new antimicrobials

The discovery of antibiotics and other antimicrobial agents in the 1930s is arguably the most significant therapeutic advance in medical history. Penicillin and the sulfa drugs touched off the search for and discovery of countless derivative compounds and several new antibiotic classes. However, the pace of discovery has slowed down, and there is growing appreciation that much of the low-lying fruit accessible to traditional methods of antimicrobial discovery has been harvested. Combating emerging drug-resistant strains of infectious agents may require the adoption of fresh approaches to drug target validation, small-molecule discovery and safety assessment. The recent development of several infectious disease models in zebrafish raises the possibility of a new paradigm in antimicrobial discovery.     

Industrializing AI/ML during the end-to-end drug discovery process

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.     

Mathematical algorithm for discovering states of expression from direct genetic comparison by microarrays

Highly specific direct genome-scale expression discovery from two biological samples facilitates functional discovery of molecular systems. Here, expression data from cDNA arrays are ranked and curve-fitted. The algorithm uses filters based on the derivatives (slopes) of the curve fits. The rules are set to (i) filter the largest number of artifactual ratios from same-to-same datasets and (ii) maximize discovery from direct comparisons of different samples. The unsupervised discovery is optimized without lowering specificity. The false discovery rates are significantly lower than other methods. The discovered states of genetic expression facilitate functional discovery and are validated by real-time RT–PCR. Better quality improves sensitivity.     

Fast food in ant communities: how competing species find resources

An understanding of foraging behavior is crucial to understanding higher level community dynamics; in particular, there is a lack of information about how different species discover food resources. We examined the effect of forager number and forager discovery capacity on food discovery in two disparate temperate ant communities, located in Texas and Arizona. We defined forager discovery capacity as the per capita rate of resource discovery, or how quickly individual ants arrived at resources. In general, resources were discovered more quickly when more foragers were present; this was true both within communities, where species identity was ignored, as well as within species. This pattern suggests that resource discovery is a matter of random processes, with ants essentially bumping into resources at a rate mediated by their abundance. In contrast, species that were better discoverers, as defined by the proportion of resources discovered first, did not have higher numbers of mean foragers. Instead, both mean forager number and mean forager discovery capacity determined discovery success. The Texas species used both forager number and capacity, whereas the Arizona species used only forager capacity. There was a negative correlation between a species’ prevalence in the environment and the discovery capacity of its foragers, suggesting that a given species cannot exploit both high numbers and high discovery capacity as a strategy. These results highlight that while forager number is crucial to determining time to discovery at the community level and within species, individual forager characteristics influence the outcome of exploitative competition in ant communities.     

Application of single nucleotide polymorphisms to non-model species: a technical review

Single nucleotide polymorphisms (SNPs) have gained wide use in humans and model species and are becoming the marker of choice for applications in other species. Technology that was developed for work in model species may provide useful tools for SNP discovery and genotyping in non-model organisms. However, SNP discovery can be expensive, labour intensive, and introduce ascertainment bias. In addition, the most efficient approaches to SNP discovery will depend on the research questions that the markers are to resolve as well as the focal species. We discuss advantages and disadvantages of several past and recent technologies for SNP discovery and genotyping and summarize a variety of SNP discovery and genotyping studies in ecology and evolution.     

High throughput sequencing approaches to mutation discovery in the mouse

Phenotype-driven approaches in mice are powerful strategies for the discovery of genes and gene functions and for unravelling complex biological mechanisms. Traditional methods for mutation discovery are reliable and robust, but they can also be laborious and time consuming. Recently, high-throughput sequencing (HTS) technologies have revolutionised the process of forward genetics in mice by paving the way to rapid mutation discovery. However, successful application of HTS for mutation discovery relies heavily on the sequencing approach employed and strategies for data analysis. Here we review current HTS applications and resources for mutation discovery and provide an overview of the practical considerations for HTS implementation and data analysis.     

On the Question of the Psychology of Scientific Creativity

In every scientific discovery, we can distinguish at least two aspects: first, the precise course of the discovery with all details and in all its individuality; second, its final result, abstracted from all circumstances and subjective considerations connected with the personal characteristics of the discoverer and the circumstances under which the discovery was made. In this "purified view" the substance of the discovery usually enters into science and establishes itself there in the quality of ascertained truth. Because of this, in the great majority of cases, the history of science does not present information about the specific path that a particular discovery followed; but even if it preserves this information, it is usually only in the form of anecdotes that have come down to us, or fragments about some one moment of the whole discovery, which possibly did not play a decisive role, but was preserved thanks to simple chance.     

Fragment-based ligand discovery meets phage display.

In a powerful complement to traditional ligand discovery methods such as high-throughput screening, fragment-based ligand discovery methods identify ligands piece by piece. A recent advance combines the concepts of fragment-based ligand discovery with phage-display technology to yield bivalent kinase inhibitors with high potency and specificity.     

The patterns of vascular plant discoveries in China

Aim(1) To understand geographic patterns of species discovery by examining the effect of growth form, range size, and geographic distribution on discovery probability of vascular plant species in China; (2) to find out which taxa harbor the largest number of undiscovered species and where those species locate; and (3) to find out the determinants of province‐level mean discovery time and inventory completeness.LocationChina.MethodsWe compiled the discovery time and province‐level geographic distributions of ~31,000 vascular plant species described between 1753 and 2013 from Flora of China. We used a Cox proportional hazard model to determine the biological and geographic correlates of discovery probability. Accumulation curves of species discoveries were fitted by a logistic discovery model to estimate inventory completeness of different growth forms and of different provinces. We then used linear regression to identify the determinants of mean discovery time and beta regression to identify the determinants of inventory completeness.ResultsWe found that species with larger range size and distributed in northeastern part of China have a higher discovery probability. Coastal species were discovered earlier than inland species. Trees and shrubs of seed plants have the highest discovery probability while ferns have the lowest discovery probability. Herbs have the largest number of undiscovered species in China. Most undiscovered species will be found in southwest China, where three global biodiversity hot spots locate. Spatial patterns of mean discovery time and inventory completeness are mainly driven by the total number of species, human population density in an area, and latitude and longitude of a province.Main ConclusionsSocioeconomic factors primarily determine the discovery patterns of vascular plants in China. Undiscovered species are most likely to be narrow‐ranged, inconspicuous endemic species such as herbs and ferns, which are prone to extinctions and locate in biodiversity hot spots in southwestern China.     

Quantitative trait Loci analysis using the false discovery rate

False discovery rate control has become an essential tool in any study that has a very large multiplicity problem. False discovery rate-controlling procedures have also been found to be very effective in QTL analysis, ensuring reproducible results with few falsely discovered linkages and offering increased power to discover QTL, although their acceptance has been slower than in microarray analysis, for example. The reason is partly because the methodological aspects of applying the false discovery rate to QTL mapping are not well developed. Our aim in this work is to lay a solid foundation for the use of the false discovery rate in QTL mapping. We review the false discovery rate criterion, the appropriate interpretation of the FDR, and alternative formulations of the FDR that appeared in the statistical and genetics literature. We discuss important features of the FDR approach, some stemming from new developments in FDR theory and methodology, which deem it especially useful in linkage analysis. We review false discovery rate-controlling procedures--the BH, the resampling procedure, and the adaptive two-stage procedure-and discuss the validity of these procedures in single- and multiple-trait QTL mapping. Finally we argue that the control of the false discovery rate has an important role in suggesting, indicating the significance of, and confirming QTL and present guidelines for its use.     

Applications of high-throughput ADME in drug discovery

Assessment of physicochemical and pharmacological properties is now conducted at very early stages of drug discovery for the purpose of accelerating the conversion of hits and leads into qualified development candidates. In particular, in vitro absorption, distribution, metabolism and elimination (ADME) assays and in vivo drug metabolism pharmacokinetic (DMPK) studies are being conducted throughout the discovery process, from hit generation through to lead optimization, with the goal of reducing the attrition rate of these potential drug candidates as they progress through development. Because the continuing trend in drug discovery has been to access ADME information earlier and earlier in the discovery process, the need has arisen within the analytical community to introduce faster and better analytical methods to enhance the 'developability' of drug leads. Strategies for streamlined ADME assessment of drug candidates in discovery and pre-clinical development are presented within.     

Drug discovery targeting cell division proteins,microtubules and FtsZ

Eukaryotic cell division or cytokinesis has been a major target for anticancer drug discovery. After the huge success of paclitaxel and docetaxel, microtubule-stabilizing agents (MSAs) appear to have gained a premier status in the discovery of next-generation anticancer agents. However, the drug resistance caused by MDR, point mutations, and overexpression of tubulin subtypes, etc., is a serious issue associated with these agents. Accordingly, the discovery and development of new-generation MSAs that can obviate various drug resistances has a significant meaning. In sharp contrast, prokaryotic cell division has been largely unexploited for the discovery and development of antibacterial drugs. However, recent studies on the mechanism of bacterial cytokinesis revealed that the most abundant and highly conserved cell division protein, FtsZ, would be an excellent new target for the drug discovery of next-generation antibacterial agents that can circumvent drug-resistances to the commonly used drugs for tuberculosis, MRSA and other infections. This review describes an account of our research on these two fronts in drug discovery, targeting eukaryotic as well as prokaryotic cell division.     

The effects of discovery learning in a lower-division biology course

This study investigated discovery learning pedagogy and its effects on students' achievement and attitudes toward instruction in a lower-division biology course, entitled Structure and Function of Organisms. Instruction was primarily lecture-based but included four discovery learning activities. Results indicate that students had greater achievement on content learned through discovery methods than lecture-based instruction. Findings regarding students' attitudes toward discovery-based instruction suggest that students enjoyed active, discovery-based problems, believed that discovery helped them gain an understanding of the material and helped them to develop skills that could be used in other courses. The study presented here shows that a moderate amount of discovery learning used in combination with traditional methods of instruction may be an effective means for promoting students' achievement.     

Drug discovery in academia

Drug discovery and development is generally done in the commercial rather than the academic realm. Drug discovery involves target discovery and validation, lead identification by high-throughput screening, and lead optimization by medicinal chemistry. Follow-up preclinical evaluation includes analysis in animal models of compound efficacy and pharmacology (ADME: administration, distribution, metabolism, elimination) and studies of toxicology, specificity, and drug interactions. Notwithstanding the high-cost, labor-intensive, and non-hypothesis-driven aspects of drug discovery, the academic setting has a unique and expanding niche in this important area of investigation. For example, academic drug discovery can focus on targets of limited commercial value, such as third-world and rare diseases, and on the development of research reagents such as high-affinity inhibitors for pharmacological "gene knockout" in animal models ("chemical genetics"). This review describes the practical aspects of the preclinical drug discovery process for academic investigators. The discovery of small molecule inhibitors and activators of the cystic fibrosis transmembrane conductance regulator is presented as an example of an academic drug discovery program that has yielded new compounds for physiology research and clinical development. high-throughput screening; drug development; pharmacology; fluorescence; cystic fibrosis transmembrane conductance regulator     

The oldest complete skeleton of an Australopithecus in Africa (StW 573)

Although 78 years have elapsed since the discovery at Taung of the Australopithecus africanus, and despite intensive fieldwork in East Africa which resulted in 32 years of non-stop excavation at Sterkfontein, there has not been a discovery to date of a reasonably intact skull and associated skeleton of an ape-man. The following report is an account of an extraordinary series of events that led to the discovery of a complete skeleton on an Australopithecus, and a preliminary assessment of the significance of the fossil, which is still 5 years after its discovery largely embedded in the Member 2 breccia of the Sterkfontein Caves near Krugersdorp, South Africa.