Collaborating Authors

Pharmaceuticals & Biotechnology

Robots may soon be able to reproduce - will this change how we think about evolution? Emma Hart

The Guardian

From the bottom of the oceans to the skies above us, natural evolution has filled our planet with a vast and diverse array of lifeforms, with approximately 8 million species adapted to their surroundings in a myriad of ways. Yet 100 years after Karel Čapek coined the term robot, the functional abilities of many species still surpass the capabilities of current human engineering, which has yet to convincingly develop methods of producing robots that demonstrate human-level intelligence, move and operate seamlessly in challenging environments, and are capable of robust self-reproduction. But could robots ever reproduce? This, undoubtedly, forms a pillar of "life" as shared by all natural organisms. A team of researchers from the UK and the Netherlands have recently demonstrated a fully automated technology to allow physical robots to repeatedly breed, evolving their artificial genetic code over time to better adapt to their environment.

Network Learning -- from Network Propagation to Graph Convolution


You've probably heard about graph convolution as it is such a hot topic at the time. Although less well known, network propagation is a dominating method in computational biology for learning on networks. In this post, we'll dive deep into the theory and intuition behind network propagation, and we'll also see that network propagation is a special case of graph convolution. Networks arise naturally from many real-world data, such as social networks, transportation networks, biological networks, just to name a few. In computational biology, it has been shown that biological networks such as Protein-Protein Interactions (PPI), where the nodes are proteins and the edges represent how likely two proteins interact with each other, are very useful in reconstructing biological processes, even unveil disease genes [1,2].

Machine Learning in Enzyme Engineering


Enzyme engineering is the process of customizing new biocatalysts with improved properties by altering their constituting sequences of amino acids. Despite the immensity of possible alterations, this procedure has already yielded remarkable results in new designs and optimization of enzymes for chemical and pharmaceutical biosynthesis, regenerative medicine, food production, waste biodegradation and biosensing.(1 The two established and widely used enzyme engineering strategies are rational design(5,6) and directed evolution.(7,8) The former approach is based on the structural analysis and in-depth computational modeling of enzymes by accounting for the physicochemical properties of amino acids and simulating their interactions with the environment. The latter approach takes after the natural evolution in using mutagenesis for iterative production of mutant libraries, which are then screened for enzyme variants with the desired properties. These two strategies may naturally complement each other: e.g., site-directed or saturation mutagenesis may be applied on the rationally chosen hotspots.(9)

A Conversation with Teckro's Newest Leadership Hires


World-class talent makes a company not only successful on paper but also an exciting and energetic place to work. In the nearly six years I've been with Teckro, we continue to expand our leadership team and bring on many talented new hires across all disciplines – engineering and product, our services team, and many new faces on the commercial side. Today we introduce two of our newest "Teckronauts" – Malia Lewin and Silvina Baudino, both joining to scale our global market strategy. Malia: Right now, the life sciences industry is leveraging technology to change the game of scientific discovery. There are beautiful, sophisticated, amazing things happening in the lab, but these are often hampered from getting to market and saving the lives of patients because of old, slow, paper-based models.

Who do we invent for? Patents by women focus more on womens health, but few women get to invent


Members of social groups may be more likely to patent inventions targeted toward their own group's needs and interests. Koning et al. examined US biomedical patents and found that although fewer women engage in commercial patenting compared with men, their patents are more likely to focus on women's health (see the Perspective by Murray). In an evaluation of biomedical articles, the researchers found that women were also more likely to make scientific discoveries that might lead to women's health patents. These findings demonstrate that a lack of representation among inventors translates into a lack of breadth in inventions. Science , aba6990, this issue p. [1345][1]; see also abh3178, p. [1260][2] Women engage in less commercial patenting and invention than do men, which may affect what is invented. Using text analysis of all U.S. biomedical patents filed from 1976 through 2010, we found that patents with all-female inventor teams are 35% more likely than all-male teams to focus on women’s health. This effect holds over decades and across research areas. We also found that female researchers are more likely to discover female-focused ideas. These findings suggest that the inventor gender gap is partially responsible for thousands of missing female-focused inventions since 1976. More generally, our findings suggest that who benefits from innovation depends on who gets to invent. [1]: /lookup/doi/10.1126/science.aba6990 [2]: /lookup/doi/10.1126/science.abh3178

Common genetic variation influencing human white matter microstructure


The white matter of the brain, which is composed of axonal tracts connecting different brain regions, plays key roles in both normal brain function and a variety of neurological disorders. Zhao et al. combined detailed magnetic resonance imaging–based assessment of brain structures with genetic data on nearly 44,000 individuals (see the Perspective by Filley). On the basis of this comprehensive analysis, the authors identified structural and genetic abnormalities associated with neurological and psychiatric disorders, as well as some nondisease traits, thus creating a valuable resource and providing some insights into the underlying neurobiology. Science , abf3736, this issue p. [eabf3736][1]; see also abj1881, p. [1265][2] ### INTRODUCTION White matter in the human brain serves a critical role in organizing distributed neural networks. Diffusion magnetic resonance imaging (dMRI) has enabled the study of white matter in vivo, showing that interindividual variations in white matter microstructure are associated with a wide variety of clinical outcomes. Although white matter differences in general population cohorts are known to be heritable, few common genetic variants influencing white matter microstructure have been identified. ### RATIONALE To identify genetic variants influencing white matter microstructure, we conducted a genome-wide association study (GWAS) of dMRI data from 43,802 individuals across five data resources. We analyzed five major diffusion tensor imaging (DTI) model–derived parameters along 21 cerebral white matter tracts. ### RESULTS In the discovery GWAS with 34,024 individuals of British ancestry, we replicated 42 of the 44 genomic regions discovered in the largest previous GWAS and identified 109 additional regions associated with white matter microstructure ( P < 2.3 × 10−10, adjusted for the number of phenotypes studied). These results indicate strong polygenic influences on white matter microstructure. Of the 151 regions, 52 passed the Bonferroni significance level ( P < 5 × 10−5) in our analysis of nine independent validation datasets, including four with subjects of non-European ancestry. On average, common genetic variants explained 41% (standard error = 2%) of the variation in white matter microstructure. The 151 identified genomic regions can explain 32.3% of heritability for white matter microstructure, whereas the 44 previously identified genomic regions can only explain 11.7% of heritability. As a biological validation of our GWAS findings, we observed heritability enrichment within regulatory elements active in oligodendrocytes and other glia, whereas no enrichment was observed in neurons. These results are expected and suggest that genetic variation leads to changes in white matter microstructure by affecting gene regulation in glia. We observed genetic correlations and colocalizations of white matter microstructure with a wide range of brain-related complex traits and diseases, such as cognitive functions, cardiovascular risk factors, as well as various neurological and psychiatric diseases. For example, of the 25 reported genetic risk regions of glioma, 11 were also associated with white matter microstructure, which illustrates the close genetic relationship between glioma and white matter integrity. Additionally, we found that 14 white matter microstructure–associated genes ( P < 1.2 × 10−8) were targets for 79 commonly used nervous system drugs, such as antipsychotics, antidepressants, anticonvulsants, and drugs for Parkinson’s disease and dementia. ### CONCLUSION This large-scale study of dMRI scans from 43,802 subjects improved our understanding of the highly polygenic genetic architecture of human brain white matter tracts. We identified 151 genomic regions associated with white matter microstructure. The GWAS findings were supported by enrichments within cell types that make up white matter microstructure. Moreover, we uncovered genetic relationships between white matter and various clinical endpoints, such as stroke, major depressive disorder, schizophrenia, and attention deficit hyperactivity disorder. The targets of many drugs commonly used for disabling cognitive disorders have genetic associations with white matter, which suggests that the neuropharmacology of many disorders can potentially be improved by studying how these medications work in the brain white matter. ![Figure][3] Identifying genetic variants influencing human brain white matter microstructure. (Top left) Quantifying the microstructure in white matter tracts using DTI models. (Bottom left) Genomic locations of common genetic variants associated with white matter microstructure. (Top right) Selected genetic correlations between white matter microstructure and brain disorders (stroke and major depressive disorder). (Bottom right) Partitioned heritability enrichment analysis in brain cell types. FDR, false discovery rate. Brain regions communicate with each other through tracts of myelinated axons, commonly referred to as white matter. We identified common genetic variants influencing white matter microstructure using diffusion magnetic resonance imaging of 43,802 individuals. Genome-wide association analysis identified 109 associated loci, 30 of which were detected by tract-specific functional principal components analysis. A number of loci colocalized with brain diseases, such as glioma and stroke. Genetic correlations were observed between white matter microstructure and 57 complex traits and diseases. Common variants associated with white matter microstructure altered the function of regulatory elements in glial cells, particularly oligodendrocytes. This large-scale tract-specific study advances the understanding of the genetic architecture of white matter and its genetic links to a wide spectrum of clinical outcomes. [1]: /lookup/doi/10.1126/science.abf3736 [2]: /lookup/doi/10.1126/science.abj1881 [3]: pending:yes

Echolocation in soft-furred tree mice


Echolocation is a well demonstrated convergent sensory mode in bats and toothed whales. These lineages are not closely related, and this sense might be more broadly distributed than we recognize. Using a suite of approaches, He et al. show that the lineage of soft-furred tree mice (genus Typhlomys ) includes multiple echolocators. Clear evidence of the behavioral use of echolocation under fully dark conditions was supported by the convergence of ear bone morphology and hearing-related genes with other echolocating mammals. Science , aay1513, this issue p. [eaay1513][1] ### INTRODUCTION Echolocation is a form of orientation behavior in which some animals can assess environments in which vision is ineffective. Echolocating mammals have been recognized for decades, including microbats and toothed whales. Recently, the Chapa soft-furred tree mouse ( Typhlomys chapensis ) of the rodent family Platacanthomyidae was suggested to echolocate but this was not practically evidenced. There are three other recognized species in the soft-furred tree mice genus ( Typhlomys ) that share similar ecological and morphological traits, suggesting that echolocation may be a general trait within this genus. In this study, we performed behavioral, morphological, genomic, and functional analyses to test whether echolocation is present across the four species of soft-furred tree mice. ### RATIONALE Echolocation occurs when emitted sonic signals are compared with received signals to facilitate orientation and object identification. This adaptive trait is expected to be reflected at the behavioral, morphological, and molecular levels of the organism. To test whether soft-furred tree mice generally have evolved echolocation, we conducted multiple behavioral experiments to assess the performance of different species in detecting and avoiding obstacles dependent on hearing and examined the anatomical structures of their vocal and hearing apparatus. We further investigated genome-wide convergence in hearing-related genes and the functional convergence of a well-documented echolocation-related gene, prestin , between the soft-furred tree mouse and other known echolocating mammals. ### RESULTS All four recognized soft-furred tree mouse species were capable of emitting regular ultrasonic vocalizations (USVs) with a peak frequency of ~98 kHz. When these mice explored in a cluttered environment and approached an obstacle, they produced USVs with a significantly larger pulse rate. In tests for echolocation, the soft-furred tree mice spent a longer time exploring and emitted more sonic pulses in the sector of the central disk over the escaping platform, consequently dropping to the platform. When their ears were plugged, they lost the preference for the over-platform sector, as shown by exploration time and emitting pluses, and could not land on the platform. The above preference was regained when the earplugs were removed or a plastic tube was inserted into the ear canals. The stylohyal bone fuses with the tympanic bone in soft-furred tree mice, which is an anatomic characteristic previously exclusively observed in laryngeally echolocating bats. By sequencing a high-quality genome of the soft-furred tree mouse, we found a significant genome-wide convergence in hearing-related genes with other echolocating mammals, including the well-documented echolocation-related gene prestin . In vitro experimental analyses also showed a functional convergence of prestin between the soft-furred tree mouse and other echolocating mammals, which was largely contributed by the identified convergent amino acids. ### CONCLUSION Our findings from behavioral experiments, anatomical structures, evolutionary genomics, and gene functional analyses provide strong evidence that soft-furred tree mice are a new echolocating lineage within mammals. The discovery of this echolocating rodent genus suggests that echolocation may be an underappreciated trait in mammals. The genome-wide convergent evolution in hearing-related genes implicates a similar molecular mechanism underlying the origination or elaboration of this complex adaptive phenotype. ![Figure][2] Multiple lines of evidence for echolocation in soft-furred tree mice ( Typhlomys ). When the soft-furred tree mice explored on a central disk, they spent more time exploring and emitted ultrasonic pulses at a higher rate in the sector over the escaping platform. The stylohyal bone fuses with the tympanic bone in soft-furred tree mice, which was previously observed exclusively in laryngeally echolocating bats. Significant molecular convergence in hearing-related genes and functional convergence of the well-documented echolocation-related gene prestin were found between the soft-furred tree mouse and known echolocating mammals. Echolocation is the use of reflected sound to sense features of the environment. Here, we show that soft-furred tree mice ( Typhlomys ) echolocate based on multiple independent lines of evidence. Behavioral experiments show that these mice can locate and avoid obstacles in darkness using hearing and ultrasonic pulses. The proximal portion of their stylohyal bone fuses with the tympanic bone, a form previously only seen in laryngeally echolocating bats. Further, we found convergence of hearing-related genes across the genome and of the echolocation-related gene prestin between soft-furred tree mice and echolocating mammals. Together, our findings suggest that soft-furred tree mice are capable of echolocation, and thus are a new lineage of echolocating mammals. [1]: /lookup/doi/10.1126/science.aay1513 [2]: pending:yes

Artificial intelligence yields new antibiotic


Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world's most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models. The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering.

Robotic Automation Boosts Efficiency and Quality in Drug Compounding


"Minimizing risks associated with aseptic processing is crucial when working with compounding systems," agrees Randy Fraatz, vice-president of North American Operations at Steriline, which produces aseptic manufacturing equipment. "The primary benefit of robotics and automation in general is safety, coming from the reduction of human-related mistakes in the entire process." Other benefits, says Fraatz, include the accuracy, efficiency, and reliability that result from automation. "If errors are reduced, process reliability and quality improve," he explains. Despite these advantages, uptake of robotics is slow, and many 503B outsourcing facilities continue to have technicians working in laminar flow hoods or biological safety cabinets to handle beakers and flasks for solution compounding and fill vials manually using syringes, notes Smalley.

Artificial Intelligence in the Pharmaceutical market worth US$27,156.1 Million in 2031. Visiongain Research Inc.


Visiongain has published a new report on "AI in Pharmaceuticals Market 2021-2031". Embracing Technology to Revolutionize Pharmaceutical Industry There are other fields where the R&D process can be influenced by AI and machine learning. Better approaches to predict chemicals' properties in order to reduce the number of substances that need to be synthesized is obviously an opportunity. This would allow for the consideration of a larger chemical universe and enrich the'chemical palette' open to medicinal chemists. Another field where researchers are starting to use AI and machine learning is mining genomic, proteomic, and metabolic data for improved disease biomarkers and medication efficacy surrogate markers.