Discovery
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Nanoinformatics is a novel, rapidly growing area of research that involves the application of computational techniques to several aspects of research in the field of nanotechnology, especially concerned its application to biotechnology. This reviews aims to explore, summarize and categorize recent publications on the subject.
Bioinformatics is a new discipline that addresses the need to manage and interpret the data that in the past decade was massively generated by genomic research. This discipline represents the convergence of genomics, biotechnology and information technology, and encompasses analysis and interpretation of data, modeling of biological phenomena, and development of algorithms and statistics. This article presents an introduction to bioinformatics
This study examines the roles of public and private sector actors in the development of mRNA vaccines, a breakthrough innovation in modern medicine. Using a dataset of 151 core patent families and 2,416 antecedent (cited) patents, we analyze the structure and dynamics of the mRNA vaccine knowledge network through network theory. Our findings highlight the central role of biotechnology firms, such as Moderna and BioNTech, alongside the crucial contributions of universities and public research organizations (PROs) in providing foundational knowledge.We develop a novel credit allocation framework, showing that universities, PROs, government and research centers account for at least 27% of the external technological knowledge base behind mRNA vaccine breakthroughs - representing a minimum threshold of their overall contribution. Our study offers new insights into pharmaceutical and biotechnology innovation dynamics, emphasizing how Moderna and BioNTech's mRNA technologies have benefited from academic institutions, with notable differences in their institutional knowledge sources.
We use the 2014 market history of two high-returning biotechnology exchange-traded funds to illustrate how ex post mean-variance analysis should not be done. Unfortunately, the way it should not be done is the way it generally is done -- to our knowledge.
The capacity to predict and control bioprocesses is perhaps one of the most important objectives of biotechnology. Computational simulation is an established methodology for the design and optimization of bioprocesses, where the finite elements method (FEM) is at the state-of-art engineering multi-physics simulation system, with tools such as Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD). Although FEA and CFD are currently applied to bioreactor design, most simulations are restricted to the multi-physics capabilities of the existing sofware packages. This manuscript is a contribution for the consolidation of FEM in computational biotechnology, by presenting a comprehensive review of finite element procedures of the most common enzymatic mechanisms found in biotechnological processes, such as, enzyme activation, Michaelis Menten, competitive inhibition, non-competitive inhibition, anti-competitive inhibition, competition by substrate, sequential random mechanism, ping-pong bi-bi and Theorel-Chance. Most importantly, the manuscript opens the possibility for the use of FEM in conjunction with «in-silico» models of metabolic networks, as well as, chemical networks in order to simulate complex bioprocesses in biotechnology, putting emphasis into flux balance analysis, pheno-metabolomics space exploration in time and space, overcoming the limitations of assuming chemostat conditions in systems biology computations.
Viruses are the most abundant biological entities on Earth and play central roles in shaping microbiomes and influencing ecosystem functions. Yet, most viral genes remain uncharacterized, comprising what is commonly referred to as "viral dark matter." Metagenomic studies across diverse environments consistently show that 40-90% of viral genes lack known homologs or annotated functions. This persistent knowledge gap limits our ability to interpret viral sequence data, understand virus-host interactions, and assess the ecological or applied significance of viral genes. Among the most intriguing components of viral dark matter are auxiliary viral genes (AVGs), including auxiliary metabolic genes (AMGs), regulatory genes (AReGs), and host physiology-modifying genes (APGs), which may alter host function during infection and contribute to microbial metabolism, stress tolerance, or resistance. In this review, we explore recent advances in the discovery and functional characterization of viral dark matter. We highlight representative examples of novel viral proteins across diverse ecosystems including human microbiomes, soil, oceans, and extreme environments, and discuss what is known, and still unknown, about their roles. We then examine the bioinformatic and experimental challenges that hinder functional characterization, and present emerging strategies to overcome these barriers. Finally, we highlight both the fundamental and applied benefits that multidisciplinary efforts to characterize viral proteins can bring. By integrating computational predictions with experimental validation, and fostering collaboration across disciplines, we emphasize that illuminating viral dark matter is both feasible and essential for advancing microbial ecology and unlocking new tools for biotechnology.
Corynebacterium glutamicum is a Gram-positive, anaerobic, rod-shaped soil bacterium able to grow on a diversity of carbon sources like sugars and organic acids. It is a biotechnological relevant organism because of its highly efficient ability to biosynthesize amino acids, such as L-glutamic acid and L-lysine. Here, we reconstructed the most complete C. glutamicum regulatory network to date and comprehensively analyzed its global organizational properties, systems-level features and functional architecture. Our analyses show the tremendous power of Abasy Atlas to study the functional organization of regulatory networks. We created two models of the C. glutamicum regulatory network: all-evidences (containing both weak and strong supported interactions, genomic coverage = 73%) and strongly-supported (only accounting for strongly supported evidences, genomic coverage = 71%). Using state-of-the-art methodologies, we prove that power-law behaviors truly govern the connectivity and clustering coefficient distributions. We found a non-previously reported circuit motif that we named complex feed-forward motif. We highlighted the importance of feedback loops for the functional architecture, beyond whether they are statistically over-represented or not in the network. We show that the previously reported top-down approach is inadequate to infer the hierarchy governing a regulatory network because feedback bridges different hierarchical layers, and the top-down approach disregards the presence of intermodular genes shaping the integration layer. Our findings all together further support a diamond-shaped, three-layered hierarchy exhibiting some feedback between processing and coordination layers, which is shaped by four classes of systems-level elements: global regulators, locally autonomous modules, basal machinery and intermodular genes.
Cuba is continuing attracting the attention of the international scientific community for some important and unexpected achievements in applied science such as health biotechnology. They represent outcomes of the 1959 decision of Cuba to develop an advanced scientific system in order to address the most urgent problems for the development of the country and to overcome the condition of subalternity. This ambitious objective was tackled in a very original way, making a broad and wide-ranging recourse to every effective support and collaboration, with Soviet but also Western scientists and institutions, in addition to a peculiar Cuban inventiveness. Indeed, immediately after the revolution, Cuba developed an advanced and articulated scientific system, and achieved a level of excellence in leading scientific fields, like biotechnology, quite independently from the Soviet Union, which was behind in this field. Even the collapse of the Soviet Union in the early 1990s, that could have put the achievements of the Revolution at risk, posing again the threat of subalternity, under an intentionally worsened American embargo, did not change this trend: once more Cuba addressed this challenge reconfirming the strategic choice of supporting its most advanced and profitable scientific sectors, especially the capital-intensive and typically American field of health biotechnologies. This strategy proved to be once again a well-chosen course of action.
Biotechnologies are being commercialized at historic rates. In 2020, 74 biotech startups went public through an Initial Public Offering (IPO), and 60 went through the IPO process in the first six months of 2021. However, the traits associated with biotech startups obtaining recent IPOs have not been reported. Here we build a database of biotechs that underwent an IPO in the first half of 2021. By analyzing leadership, technological focus, clinical trials, and financing, we found that advanced degrees among the leadership, clinical trials, and intellectual property are important factors for biotech startups. The data also suggest that large private rounds can decrease time-to-IPO and affect post-IPO stock performance. Notably, these traits were often exhibited by the 138 biotech IPOs in 2018-2019, suggesting 2021 data were not driven by COVID.
In the past few decades, the life sciences have experienced an unprecedented accumulation of data, ranging from genomic sequences and proteomic profiles to heavy-content imaging, clinical assays, and commercial biological products for research. Traditional static databases have been invaluable in providing standardized and structured information. However, they fall short when it comes to facilitating exploratory data interrogation, real-time query, multidimensional comparison and dynamic visualization. Integrated data-driven research environments aiming at supporting user-driven data queries and visualization offer promising new avenues for making the best use of the vast and heterogeneous data streams collected in biological research. This perspective article discusses the potential of interactive and integrated frameworks, highlighting the importance of implementing this model in biotechnology research, while going through the state-of-the-art in database design, technical choices behind modern data management systems, and emerging needs in multidisciplinary research. Special attention is given to data interrogation strategies, user interface design, and comparative analysis capabilities, along with challenges such as data standardization and scalability in data-heavy applications. Conceptual features for developing interactive data environments along diverse life science domains are then presented in the user case of cell line selection for in vitro research to bridge the gap between research data generation, actionable biological insight, experimental design, and clinical relevance.
Assistive ankle-foot orthoses (AAFOs) are powerful solutions to assist or rehabilitate gait on humans. Existing AAFO technologies include passive, quasi-passive, and active principles to provide assistance to the users, and their mechanical configuration and control depend on the eventual support they aim for within the gait pattern. In this research we analyze the state-of-the-art of AAFO and classify the different approaches into clusters, describing their basis and working principles. Additionally, we reviewed the purpose and experimental validation of the devices, providing the reader with a better view of the technology readiness level. Finally, the reviewed designs, limitations, and future steps in the field are summarized and discussed.
We here propose an automated pipeline for the microscopy image-based characterization of catalytically active inclusion bodies (CatIBs), which includes a fully automatic experimental high-throughput workflow combined with a hybrid approach for multi-object microbial cell segmentation. For automated microscopy, a CatIB producer strain was cultivated in a microbioreactor from which samples were injected into a flow chamber. The flow chamber was fixed under a microscope and an integrated camera took a series of images per sample. To explore heterogeneity of CatIB development during the cultivation and track the size and quantity of CatIBs over time, a hybrid image processing pipeline approach was developed, which combines an ML-based detection of in-focus cells with model-based segmentation. The experimental setup in combination with an automated image analysis unlocks high-throughput screening of CatIB production, saving time and resources. Biotechnological relevance - CatIBs have wide application in synthetic chemistry and biocatalysis, but also could have future biomedical applications such as therapeutics. The proposed hybrid automatic image processing pipeline can be adjusted to treat comparable biological microorganisms, where fully data-driven ML-based segmentation approaches are not feasible due to the lack of training data. Our work is the first step towards image-based bioprocess control.
A country's research success can be assessed from the power law function that links country and world rank numbers when publications are ordered by their number of citations; a similar function describes the distribution of country papers in world percentiles. These functions allow calculating the ep index and the probability of publishing highly cited papers, which measure the efficiency of the research system and the ability of achieving important discoveries or scientific breakthroughs, respectively. The aim of this paper was to use these metrics and other parameters derived from the percentile-based power law function to investigate research success in the USA, the EU, and other countries in hot medical, biochemical, and biotechnological topics. The results show that, in the investigated fields, the USA is scientifically ahead of all countries and that its research is likely to produce approximately 80% of the important global breakthroughs in the research topics investigated in this study. EU research has maintained a constant weak position with reference to USA research over the last 30 years.
In this study extremely halophilic and moderately thermophilic microorganisms from a hypersaline microbial mat were screened for their ability to produce antibacterial, antidiatom, antialgal and quorum sensing (QS) inhibitory compounds. Five bacterial strains belonging to the genera Marinobacter and Halomonas and one archaeal strain belonging to the genus Haloterrigena were isolated from a microbial mat. The strains were able to grow at a maximum salinity of 22-25% and a maximum temperature of 45-60°C. Hexanes, dichloromethane and butanol extracts from the strains inhibited the growth of at least one out of nine human pathogens. Only butanol extracts of supernatants of Halomonas sp. SK-1 inhibited growth of the microalga Dunaliella salina. Most extracts from isolates inhibited QS of the acyl homoserine lactone producer and reporter Chromobacterium violaceum CV017. Purification of QS inhibitory dichloromethane extracts of Marinobacter sp. SK-3 resulted in isolation of four related diketopiperazines (DKPs): cyclo(L-Pro-L-Phe), cyclo(L-Pro-L-Leu), cyclo(L-Pro-L-isoLeu) and cyclo(L-Pro-D-Phe). QS inhibitory properties of these DKPs were tested using C. violaceum CV017 and Escherichia coli-based QS reporters (pSB401 and pSB1075) deficient in AHL production. Cyclo(L-Pro-L-Phe) and cyclo(L-Pro-L-isoLeu) inhibited QS dependent production of violacein by C. violaceum CV017. Cyclo(L-Pro-L-Phe), cyclo(L-Pro-L-Leu), and cyclo(L-Pro-L-isoLeu) reduced QS dependent luminescence of the reporter E. coli pSB401 induced by 3-oxo-C6-HSL. Our study demonstrated the ability of halophilic and moderately thermophilic strains from a hypersaline microbial mat produce biotechnologically-relevant compounds that could be used as antifouling agents
The present paper extends the literature investigating key drivers leading certain patents to exert a stronger influence on the subsequent technological developments (inventions) than other ones. We investigated six key determinants, as (i) the use of scientific knowledge, (ii) the breadth of the technological base, (iii) the existence of collaboration in patent development, (iv) the number of claims, (v) the scope, and (vi) the novelty, and how the effect of these determinants varies when patent influence - as measured by the number of forward citations the patent received - is distinguished as within and across the industrial and organizational boundaries. We conducted an empirical analysis on a sample of 5671 patents granted to 293 US biotechnology firms from 1976 to 2003. Results reveal that the contribution of the determinants to patent influence differs across the domains that are identified by the industrial and organizational boundaries. Findings, for example, show that the use of scientific knowledge negatively affects patent influence outside the biotechnology industry, while it positively contributes to make a patent more relevant for the assignee's subsequent technological developments. In addition, the broader the scope of a patent the higher the number of citations the patent receives from subsequent non-biotechnology patents. This relationship is inverted-U shaped when considering the influence of a patent on inventions granted to other organizations than the patent's assignee. Finally, the novelty of a patent is inverted-U related with the influence the patent exerts on the subsequent inventions granted across the industrial and organizational boundaries.
It is worth mentioning that the high output of different physiological responses under the expression of vgb, may have a considerable effect on the enzyme productivity, dairy industry, heavy metal uptake, biodegradation of different organic pollutants and other applications. The expression of bacterial haemoglobin is useful in lessening the load of perceived toxic conditions such as high oxygen levels. This in turn probably has the same impact on some peripheral toxic materials. This, hemoglobin biotechnology can be extended to enhance production of pollutants degrading enzymes or production of some valuable manufacturing materials on the case by case bases. It is likely that the mechanism of bacterial hemoglobin (VHb) effects is intermediated via an oxygen trapping action. This may drive the enrichment of ATP production, which is mostly required for higher productivity of needed substances for that activity.
The escalating cost, extended timelines, and low success rates in pharmaceutical research demand a fundamental rethinking of biotechnology R&D infrastructure. This chapter introduces the concept of the AI-Integrated Biotechnology Hub, a purpose-built research ecosystem uniting residential, commercial, clinical, and research facilities under a central, AI-driven operating system. Designed as a multi-sided platform, the hub leverages continuous, multi-modal health data collection, advanced smart living environments, and federated learning models to enable secure, privacy-preserving biomedical research. By integrating real estate, biotechnology facilities, research hospitals, and community services, the model maximizes data utility, accelerates drug discovery, and enhances resident well-being. Transparency, accountability, and ethical stewardship are critical pillars of governance, enacted through dynamic consent, data trusts, and multi-stakeholder oversight. Scalable across urban and vertical architectures, this paradigm offers a viable, sustainable pathway toward improving healthspan, fostering innovation, and reshaping the economics of global drug development.
Developing accurate dynamical system models from physical insight or data can be impeded when only partial observations of the system state are available. Here, we combine conservation laws used in physics and engineering with artificial neural networks to construct "grey-box" system models that make accurate predictions even with limited information. These models use a time delay embedding (c.f., Takens embedding theorem) to reconstruct effect of the intrinsic states, and can be used for multiscale systems where macroscopic balance equations depend on unmeasured micro/meso scale phenomena. By incorporating physics knowledge into the neural network architecture, we regularize variables and may train the model more accurately on smaller data sets than black-box neural network models. We present numerical examples from biotechnology, including a continuous bioreactor actuated using light through optogenetics (an emerging technology in synthetic biology) where the effect of unmeasured intracellular information is recovered from the histories of the measured macroscopic variables.
This paper aims to discuss the use of reverse micelles in downstream processing of biotechnological products. The interest in this technology is piqued firstly by various advantages of a liquid-liquid extraction process, among which are cost effectiveness, and ease of scaling up and implementing a continuous process for whole broth processing. The use of reverse micelles is thought to be among the most promising due to the high efficiency and selectivity being achieved in some systems. However, there are various issues that have impeded the widespread use of reverse micelles such as the identification and development of suitable surfactants and ligands; as well as difficulties in the back extraction process. These issues, as well as latest developments and applications of reverse micelles in downstream processing of biotechnological products will be discussed in this paper.
In this paper, we propose an innovative investment framework incorporating asset allocation and class diversification oriented specifically for the biotechnology industry. With growing interests and capitalization in multiple biotech markets, investors require a more dynamic method of managing their assets within individual portfolios for optimal return efficiency. By selecting a single firm representative of identified industry trends, analyzing financial metrics relevant to the suggested approaches, and assessing financial health, we developed an adaptable investment methodology. We also performed analyses of industrial viability and investigated the implications of the selected strategies, with which we were able to optimize our framework for versatile application within specialized biotech markets.
Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a transformative tool in biotechnology.
Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) constitute a multi-functional, constantly evolving immune system in bacteria and archaea cells. A heritable, molecular memory is generated of phage, plasmids, or other mobile genetic elements that attempt to attack the cell. This memory is used to recognize and interfere with subsequent invasions from the same genetic elements. This versatile prokaryotic tool has also been used to advance applications in biotechnology. Here we review a large body of CRISPR-Cas research to explore themes of evolution and selection, population dynamics, horizontal gene transfer, specific and cross-reactive interactions, cost and regulation, non-immunological CRISPR functions that boost host cell robustness, as well as applicable mechanisms for efficient and specific genetic engineering. We offer future directions that can be addressed by the physics community. Physical understanding of the CRISPR-Cas system will advance uses in biotechnology, such as developing cell lines and animal models, cell labeling and information storage, combatting antibiotic resistance, and human therapeutics.
Microfluidic droplet screens serve as an innovative platform for high-throughput biotechnology, enabling significant advancements in discovery, product optimization, and analysis. This review sheds light on the emerging trend of interaction assays in microfluidic droplets, underscoring the unique suitability of droplets for these applications. Encompassing a diverse range of biological entities such as antibodies, enzymes, DNA, RNA, various microbial and mammalian cell types, drugs, and other molecules, these assays demonstrate their versatility and scope. Recent methodological breakthroughs have escalated these screens to novel scales of bioanalysis and biotechnological product design. Moreover, we highlight pioneering advancements that extend droplet-based screens into new domains: cargo delivery within human bodies, application of synthetic gene circuits in natural environments, 3D-printing, and the development of droplet structures responsive to environmental signals. The potential of this field is profound and only set to increase.
Quantum technologies leverage the laws of quantum physics to achieve performance advantages in applications ranging from computing to communications and sensing. They have been proposed to have a range of applications in biological science. This includes better microscopes and biosensors, improved simulations of molecular processes, and new capabilities to control the behaviour of biomolecules and chemical reactions. Quantum effects are also predicted, with much debate, to have functional benefits in biology, for instance, allowing more efficient energy transport and improving the rate of enzyme catalysis. Conversely, the robustness of biological systems to disorder from their environment has led to proposals to use them as components within quantum technologies, for instance as light sources for quantum communication systems. Together, this breadth of prospective applications at the interface of quantum and biological science suggests that quantum physics will play an important role in stimulating future biotechnological advances. This review aims to provide an overview of this emerging field of quantum biotechnology, introducing current capabilities, future prospects, and potential areas of impact. The review is written to be accessible to the non-expert and focuses on the four key areas of quantum-enabled sensing, quantum-enabled imaging, quantum biomolecular control, and quantum effects in biology.