Bettering Humanity through Biology

Biology is arguably referred to as the “science of the XXIth century”. This prestigious title intrinsically contains a huge responsibility. For many centuries, Biology has contributed directly or indirectly to bettering humankind, although its obvious and objective effects have only been made evident since the XIXth century. There are three main domains to which Biology has made significant contributions: Agriculture, Environment and Medicine. Several scientific disciplines connected to Biology have been involved such as Genetics (mendelian and molecular), Cell Biology, Ecology, Microbiology, and what was known for a long time as “Natural History” (today we would include these roughly within Botany and Zoology). Agronomy, a relatively recent science, has made a tremendous impact by providing knowledge on growing plants and animals, and developing new and better crops. One specific moment in time, following WW II, known as the “Green Revolution” benefitted humanity immensely, by combating hunger in countries such as India and Mexico. The “father” of the Green Revolution, Norman Borlaug, was awarded the 1970 Peace Nobel Prize for such achievements. In the XXIth century, biologists and agronomists are working hard to develop new and better crops to feed almost 8 billion people. In the medical field, the contributions are inumerable, from the discovery and development of vaccines (Jenner and Pasteur), to antibiotics (Fleming) and combatting diseases. This has increased the average life expectancy of humans from around 30-40 in the beginning of the XXth century, to a present value of around 75 (depending on the country). These achievements have been recognized by society, through dozens of Nobel Prizes in Medicine. All these successes have been made possible through Biology. In the past 30-40 years, numerous voices have been raised alerting for the environmental degradation of our planet, its land and oceans, its biomes and ecosystems. We have been depleting our planet at an incredible rate. But today, biologists and environmental scientists have the knowledge and tools to better the planet. We know how the ecosystems function and what causes harm them. There is still time, together with a strong public opinion, to halt the damage. Once again, Biology is a principal actor

Record breaking achievements by spiders and the scientists who study them

Organismal biology has been steadily losing fashion in both formal education and scientific research. Simultaneous with this is an observable decrease in the connection between humans, their environment, and the organisms with which they share the planet. Nonetheless, we propose that organismal biology can facilitate scientific observation, discovery, research, and engagement, especially when the organisms of focus are ubiquitous and charismatic animals such as spiders. Despite being often feared, spiders are mysterious and intriguing, offering a useful foundation for the effective teaching and learning of scientific concepts and processes. In order to provide an entryway for teachers and students—as well as scientists themselves—into the biology of spiders, we compiled a list of 99 record breaking achievements by spiders (the “Spider World Records”). We chose a worldrecord style format, as this is known to be an effective way to intrigue readers of all ages. We highlighted, for example, the largest and smallest spiders, the largest prey eaten, the fastest runners, the highest fliers, the species with the longest sperm, the most venomous species, and many more. We hope that our compilation will inspire science educators to embrace the biology of spiders as a resource that engages students in science learning. By making these achievements accessible to nonarachnologists and arachnologists alike, we suggest that they could be used: (i) by educators to draw in students for science education, (ii) to highlight gaps in current organismal knowledge, and (iii) to suggest novel avenues for future research efforts. Our contribution is not meant to be comprehensive, but aims to raise public awareness on spiders, while also providing an initial database of their record breaking achievements

The computer revolution in science: steps towards the realization of computer-supported discovery environments

The tools that scientists use in their search processes together form so-called discovery environments. The promise of artificial intelligence and other branches of computer science is to radically transform conventional discovery environments by equipping scientists with a range of powerful computer tools including large-scale, shared knowledge bases and discovery programs. We will describe the future computer-supported discovery environments that may result, and illustrate by means of a realistic scenario how scientists come to new discoveries in these environments. In order to make the step from the current generation of discovery tools to computer-supported discovery environments like the one presented in the scenario, developers should realize that such environments are large-scale sociotechnical systems. They should not just focus on isolated computer programs, but also pay attention to the question how these programs will be used and maintained by scientists in research practices. In order to help developers of discovery programs in achieving the integration of their tools in discovery environments, we will formulate a set of guidelines that developers could follow

Big data analytics in computational biology and bioinformatics

Big data analytics in computational biology and bioinformatics refers to an array of operations including biological pattern discovery, classification, prediction, inference, clustering as well as data mining in the cloud, among others. This dissertation addresses big data analytics by investigating two important operations, namely pattern discovery and network inference. The dissertation starts by focusing on biological pattern discovery at a genomic scale. Research reveals that the secondary structure in non-coding RNA (ncRNA) is more conserved during evolution than its primary nucleotide sequence. Using a covariance model approach, the stems and loops of an ncRNA secondary structure are represented as a statistical image against which an entire genome can be efficiently scanned for matching patterns. The covariance model approach is then further extended, in combination with a structural clustering algorithm and a random forests classifier, to perform genome-wide search for similarities in ncRNA tertiary structures. The dissertation then presents methods for gene network inference. Vast bodies of genomic data containing gene and protein expression patterns are now available for analysis. One challenge is to apply efficient methodologies to uncover more knowledge about the cellular functions. Very little is known concerning how genes regulate cellular activities. A gene regulatory network (GRN) can be represented by a directed graph in which each node is a gene and each edge or link is a regulatory effect that one gene has on another gene. By evaluating gene expression patterns, researchers perform in silico data analyses in systems biology, in particular GRN inference, where the “reverse engineering” is involved in predicting how a system works by looking at the system output alone. Many algorithmic and statistical approaches have been developed to computationally reverse engineer biological systems. However, there are no known bioin-formatics tools capable of performing perfect GRN inference. Here, extensive experiments are conducted to evaluate and compare recent bioinformatics tools for inferring GRNs from time-series gene expression data. Standard performance metrics for these tools based on both simulated and real data sets are generally low, suggesting that further efforts are needed to develop more reliable GRN inference tools. It is also observed that using multiple tools together can help identify true regulatory interactions between genes, a finding consistent with those reported in the literature. Finally, the dissertation discusses and presents a framework for parallelizing GRN inference methods using Apache Hadoop in a cloud environment

Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

Recently, a number of organisations have called for open access to scientific information and especially to the data obtained from publicly funded research, among which the Royal Society report and the European Commission press release are particularly notable. It has long been accepted that building research on the foundations laid by other scientists is both effective and efficient. Regrettably, some disciplines, chemistry being one, have been slow to recognise the value of sharing and have thus been reluctant to curate their data and information in preparation for exchanging it. The very significant increases in both the volume and the complexity of the datasets produced has encouraged the expansion of e-Research, and stimulated the development of methodologies for managing, organising, and analysing "big data". We review the evolution of cheminformatics, the amalgam of chemistry, computer science, and information technology, and assess the wider e-Science and e-Research perspective. Chemical information does matter, as do matters of communicating data and collaborating with data. For chemistry, unique identifiers, structure representations, and property descriptors are essential to the activities of sharing and exchange. Open science entails the sharing of more than mere facts: for example, the publication of negative outcomes can facilitate better understanding of which synthetic routes to choose, an aspiration of the Dial-a-Molecule Grand Challenge. The protagonists of open notebook science go even further and exchange their thoughts and plans. We consider the concepts of preservation, curation, provenance, discovery, and access in the context of the research lifecycle, and then focus on the role of metadata, particularly the ontologies on which the emerging chemical Semantic Web will depend. Among our conclusions, we present our choice of the "grand challenges" for the preservation and sharing of chemical information

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline