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Periodicals

EUKulele: Taxonomic annotation of the unsung eukaryotic microbes

The assessment of microbial species biodiversity is essential in ecology and evolutionary biology (Reaka-Kudla et al. 1996), but especially challenging for communities of microorganisms found in the environment (Das et al. 2006, Hillebrand et al. 2018). Beyond providing a census of organisms in the ocean, assessing marine microbial biodiversity can reveal how microbes respond to environmental change (Salazar et al. 2017), clarify the ecological roles of community members (Hehemann et al. 2016), and lead to biotechnology discoveries (Das et al. 2006). Computational approaches to characterize taxonomic diversity and phylogeny based on the quality of available data for environmental sequence datasets is fundamental for advancing our understanding of the role of these organisms in the environment. Even more pressing is the need for comprehensive and consistent methods to assign taxonomy to environmentally-relevant microbial eukaryotes. Here, we present EUKulele, an open-source software tool designed to assign taxonomy to microeukaryotes detected in meta-omic samples, and complement analysis approaches in other domains by accommodating assembly output and providing concrete metrics reporting the taxonomic completeness of each sample.

Biotechnology2020arXiv
Periodicals

Complex Reaction Kinetics in Chemistry: A unified picture suggested by Mechanics in Physics

Complex biochemical pathways or regulatory enzyme kinetics can be reduced to chains of elementary reactions, which can be described in terms of chemical kinetics. This discipline provides a set of tools for quantifying and understanding the dialogue between reactants, whose framing into a solid and consistent mathematical description is of pivotal importance in the growing field of biotechnology. Among the elementary reactions so far extensively investigated, we recall the socalled Michaelis-Menten scheme and the Hill positive-cooperative kinetics, which apply to molecular binding and are characterized by the absence and the presence, respectively, of cooperative interactions between binding sites, giving rise to qualitative different phenomenologies. However, there is evidence of reactions displaying a more complex, and by far less understood, pattern: these follow the positive-cooperative scenario at small substrate concentration, yet negative-cooperative effects emerge and get stronger as the substrate concentration is increased. In this paper we analyze the structural analogy between the mathematical backbone of (classical) reaction kinetics in Chemistry and that of (classical) mechanics in Physics: techniques and results from the latter shall be used to infer properties on the former.

Biotechnology2018arXiv
Periodicals

Site-specific recombinatorics: in situ cellular barcoding with the Cre Lox system

Cellular barcoding is a significant, recently developed, biotechnology tool that enables the familial identification of progeny of individual cells in vivo. Most existing approaches rely on ex vivo viral transduction of cells with barcodes, followed by adoptive transfer into an animal, which works well for some systems, but precludes barcoding cells in their native environment, such as those inside solid tissues. With a view to overcoming this limitation, we propose a new design for a genetic barcoding construct based on the Cre Lox system that induces randomly created stable barcodes in cells in situ by exploiting inherent sequence distance constraints during site-specific recombination. Leveraging this previously unused feature, we identify the cassette with maximal code diversity. This proves to be orders of magnitude higher than what is attainable with previously considered Cre Lox barcoding approaches and is well suited for its intended applications as it exceeds the number of lymphocytes or hematopoietic progenitor cells in mice. Moreover, it can be built using established technology.

Biotechnology2016arXiv
Periodicals

Push-forward method for piecewise deterministic biochemical simulations

A biochemical network can be simulated by a set of ordinary differential equations (ODE) under well stirred reactor conditions, for large numbers of molecules, and frequent reactions. This is no longer a robust representation when some molecular species are in small numbers and reactions changing them are infrequent. In this case, discrete stochastic events trigger changes of the smooth deterministic dynamics of the biochemical network. Piecewise-deterministic Markov processes (PDMP) are well adapted for describing such situations. Although PDMP models are now well established in biology, these models remain computationally challenging. Previously we have introduced the push-forward method to compute how the probability measure is spread by the deterministic ODE flow of PDMPs, through the use of analytic expressions of the corresponding semigroup. In this paper we provide a more general simulation algorithm that works also for non-integrable systems. The method can be used for biochemical simulations with applications in fundamental biology, biotechnology and biocomputing.This work is an extended version of the work presented at the conference CMSB2019.

Biotechnology2020arXiv
Periodicals

Direct Growth Graphene on Cu Nanoparticles by Chemical Vapor Deposition as Surface-Enhanced Raman Scattering Substrate for Label-Free Detection of Adenosine

We present a graphene/Cu nanoparticle hybrids (G/CuNPs) system as a surface-enhanced Raman scattering (SERS) substrate for adenosine detection. The Cu nanoparticles wrapped around a monolayer graphene shell were directly synthesized on flat quartz by chemical vapor deposition in a mixture of methane and hydrogen. The G/CuNPs showed an excellent SERS enhancement activity for adenosine. The minimum detected concentration of the adenosine in serum was demonstrated as low as 5 nM, and the calibration curve showed a good linear response from 5 to 500 nM. The capability of SERS detection of adenosine in real normal human urine samples based on G/CuNPs was also investigated and the characteristic peaks of adenosine were still recognizable. The reproducible and the ultrasensitive enhanced Raman signals could be due to the presence of an ultrathin graphene layer. The graphene shell was able to enrich and fix the adenosine molecules, which could also efficiently maintain chemical and optical stability of G/CuNPs. Based on G/CuNPs system, the ultrasensitive SERS detection of adenosine in varied matrices was expected for the practical applications of medicine and biotechnology

Biotechnology2015arXiv
Periodicals

Efficient Protein Optimization via Structure-aware Hamiltonian Dynamics

The ability to engineer optimized protein variants has transformative potential for biotechnology and medicine. Prior sequence-based optimization methods struggle with the high-dimensional complexities due to the epistasis effect and the disregard for structural constraints. To address this, we propose HADES, a Bayesian optimization method utilizing Hamiltonian dynamics to efficiently sample from a structure-aware approximated posterior. Leveraging momentum and uncertainty in the simulated physical movements, HADES enables rapid transition of proposals toward promising areas. A position discretization procedure is introduced to propose discrete protein sequences from such a continuous state system. The posterior surrogate is powered by a two-stage encoder-decoder framework to determine the structure and function relationships between mutant neighbors, consequently learning a smoothed landscape to sample from. Extensive experiments demonstrate that our method outperforms state-of-the-art baselines in in-silico evaluations across most metrics. Remarkably, our approach offers a unique advantage by leveraging the mutual constraints between protein structure and sequence, facilitating the design of protein sequences with similar structures and optimized properties. The code and data are publicly available at https://github.com/GENTEL-lab/HADES.

Biotechnology2026arXiv
Periodicals

Valuing Post-Revenue Biopharmaceutical Assets with Pfizer's Current Portfolio as a Case Study

This research paper addresses the critical challenge of accurately valuing post-revenue drug assets in the biotechnology and pharmaceutical sectors, a key factor influencing a wide range of strategic operations and investment decisions. Recognizing the importance of reliable valuations for stakeholders such as pharmaceutical companies, venture capitalists, and private equity firms, this study introduces a novel model for forecasting future sales of post-revenue biopharmaceutical assets. The proposed model leverages historical sales data, a resource known for its high quality and availability in company financial records, to produce distributional estimates of cumulative sales for individual assets. These estimates are instrumental in calculating the Net Present Value of each asset, thereby facilitating more informed and strategic investment decisions. A practical application of this model is demonstrated through its implementation in analyzing Pfizer's portfolio of post-revenue assets. This precision highlights the model's potential as a valuable tool in the financial assessment and decision-making processes within the biotech and pharmaceutical industries, offering a methodical approach to identifying investment opportunities and optimizing capital allocation.

Biotechnology2023arXiv
Periodicals

Topological Learning Prediction of Virus-like Particle Stoichiometry and Stability

Understanding the stoichiometry and associated stability of virus-like particles (VLPs) is crucial for optimizing their assembly efficiency and immunogenic properties, which are essential for advancing biotechnology, vaccine design, and drug delivery. However, current experimental methods for determining VLP stoichiometry are labor-intensive, and time consuming. Machine learning approaches have hardly been applied to the study of VLPs. To address this challenge, we introduce a novel persistent Laplacian-based machine learning (PLML) mode that leverages both harmonic and non-harmonic spectra to capture intricate topological and geometric features of VLP structures. This approach achieves superior performance on the VLP200 dataset compared to existing methods. To further assess robustness and generalizability, we collected a new dataset, VLP706, containing 706 VLP samples with expanded stoichiometry diversity. Our PLML model maintains strong predictive accuracy on VLP706. Additionally, through random sequence perturbative mutation analysis, we found that 60-mers and 180-mers exhibit greater stability than 240-mers and 420-mers.

Biotechnology2025arXiv
Periodicals

Demonstration Experiments

Adaptive experiments are used extensively in online platforms, healthcare and biotechnology, and the social sciences. Often, the primary goal is not to precisely estimate a treatment effect but to demonstrate that at least one candidate intervention yields a positive effect, for some subpopulation and on some measured outcome. We formalize this objective as testing the global null in a threshold bandit framework, and develop two inference procedures that are valid under general adaptive sampling: one that pools information across promising arms, and one based on time-uniform multiple testing of individual arm means. To support the latter, we establish a moderate-deviations principle for the sequential $t$-statistic, justifying asymptotic confidence sequences in settings where the number of arms is large relative to the sample size. To illustrate how adaptive designs can target the proposed statistics, we recast experimental design as bandit optimization with an arm's reward given by its signal-to-noise ratio, and analyze an allocation rule for which we establish a logarithmic regret bound. We apply the methods in a simulation study of targeting unconditional cash transfer programs.

Biotechnology2026arXiv
Periodicals

High Expression of CDK1 and NDC80 Predicts Poor Prognosis of Bladder Cancer

Background: Bladder cancer is the 10th most common cancer worldwide, and its prevalence is increasing, especially in developing countries. Objective: In the present study, we employed gene expression profiles from the GSE163209 data set in the GEO database to identify potential molecular and genetic markers in BC patients. Methods: The data set comprised 217 samples, with 113 stage Ta tumor tissue samples and 104 stage T1 tumor tissue samples. The top 766 genes were chosen. P.value<0.0001 and |logFC|=1 was used to change the cutoff criteria for defining DEGs. Moreover, the MCODE plugin and cytoHubba plugin were employed to produce a module and detect 20 hub genes in these DEGs. We used GO and KEGG pathway enrichment analyses to get a better understanding of these DEGs. Results: The KEGG pathway enrichment results indicated that the top genes were mainly involved: Systemic lupus erythematosus, Alcoholism, and Viral carcinogenesis. SLE activation in the renal glomeruli could explain the connection between this disease's route and bladder cancer, and according to our results and previous researches, heavy alcohol intake can increase the risk of BC in males and particular populations. Conclusion: According to our hub genes, we can consider CDK1 and NDC80 as bladder cancer biomarkers. Not much research has been done on the effect of this gene on bladder cancer.

Biotechnology2021arXiv
Periodicals

Improvement of attention in subjects diagnosed with hyperkinetic syndrome using BIOVIT Simulator

This study aimed to stimulate the brain's executive function through a series of tasks and rules based on dynamic perceptual stimuli using the Biotechnology Virtual Immersion Simulator (BIOVIT) and thus evaluate its usefulness to maintain and increase attention levels in subjects diagnosed with hyperkinetic syndrome. With a quantitative methodology framed in a longitudinal trend design, the cause of the exposure-outcome relationships was studied using the BIOVIT simulator. Exploratory analysis of oscillatory brain activity was measured using a graphical recording of brain electrical activity and attention levels. Data consisted of 77,566 observations from n = 18 separately studied participants. The findings established that the BIOVIT simulator maintained and increased the attention levels of the participants by 77.8%. Furthermore, the hypothesis was tested that virtual reality immersion technologies significantly affect attention levels in participants aged 8 to 12. The evidence shows that the BIOVIT simulator is an alternative to developing learning methodologies in vulnerable populations. The low implementation costs and the diversity of academic applications may allow schools in developing countries to solve this problem that afflicts thousands of children with attention deficit and hyperactivity disorder.

Biotechnology2023arXiv
Periodicals

Opsin vs opsin: new materials for biotechnological applications

The need of new diagnostic methods satisfying, as an early detection, a low invasive procedure and a cost-efficient value, is orienting the technological research toward the use of bio-integrated devices, in particular bio-sensors. The set of know-why necessary to achieve this goal is wide, from biochemistry to electronics and is summarized in an emerging branch of electronics, called \textit{proteotronics}. Proteotronics is here here applied to state a comparative analysis of the electrical responses coming from type-1 and type-2 opsins. In particular, the procedure is used as an early investigation of a recently discovered family of opsins, the proteorhodopsins activated by blue light, BPRs. The results reveal some interesting and unexpected similarities between proteins of the two families, suggesting the global electrical response are not strictly linked to the class identity.

Biotechnology2014arXiv
Periodicals

Shake It or Shrink It: Mass Transport and Kinetics in Surface Bioassays Using Agitation and Microfluidics

Surface assays, such as ELISA and immunofluorescence, are nothing short of ubiquitous in biotechnology and medical diagnostics today. The development and optimization of these assays generally focuses on three aspects: immobilization chemistry, ligand-receptor interaction and concentrations of ligands, buffers and sample. A fourth aspect, the transport of the analyte to the surface, is more rarely delved into during assay design and analysis. Improving transport is generally limited to the agitation of reagents, a mode of flow generation inherently difficult to control, often resulting in inconsistent reaction kinetics. However, with assay optimization reaching theoretical limits, the role of transport becomes decisive. This perspective develops an intuitive and practical understanding of transport in conventional agitation systems and in microfluidics, the latter underpinning many new life science technologies. We give rules of thumb to guide the user on system behavior, such as advection regimes and shear stress, and derive estimates for relevant quantities that delimit assay parameters. Illustrative cases with examples of experimental results are used to clarify the role of fundamental concepts such as boundary and depletion layers, mass diffusivity or surface tension.

Biotechnology2021arXiv
Periodicals

The poor altmetric performance of publications authored by researchers in mainland China

China's scientific output has risen precipitously over the past decade; it is now the world's second-largest producer of scientific papers, behind only the United States. The quality of China's research is also on the rise (Van Noorden, 2016). The online visibility and impact of China's research are also important issues worth exploring. In this study, we investigate the altmetric performance of publications in the field of Biotechnology and Applied Microbiology and published by authors from Chinese affiliations. We find that papers published by those authors from Chinese affiliations have much lower visibility on the social web than articles from other countries, when there is no significant difference for the citations. Fewer of China's publications get tweeted, and those tweeted publications attract less social attention. A geographical analysis of tweeters shows that scholarly articles get most of their social attention from the authors' home countries, a finding that is also confirmed by correlation and regression analysis. This situation, which is unfavorable for researchers from Chinese affiliations, is caused, in part, by the inaccessibility of mainstream social networking platforms in mainland China.

Biotechnology2016arXiv
Periodicals

Prediction of miRNA-disease associations with a vector space model

MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases.

Biotechnology2016arXiv
Periodicals

Human life is unlimited - but short

Does the human lifespan have an impenetrable biological upper limit which ultimately will stop further increase in life lengths? This question is important for understanding aging, and for society, and has led to intense controversies. Demographic data for humans has been interpreted as showing existence of a limit, or even as an indication of a decreasing limit, but also as evidence that a limit does not exist. This paper studies what can be inferred from data about human mortality at extreme age. We show that in western countries and Japan and after age 110 the probability of dying is about 47% per year. Hence there is no finite upper limit to the human lifespan. Still, given the present stage of biotechnology, it is unlikely that during the next 25 years anyone will live longer than 128 years in these countries. Data, remarkably, shows no difference in mortality after age 110 between sexes, between ages, or between different lifestyles or genetic backgrounds. These results, and the analysis methods developed in this paper, can help testing biological theories of ageing and aid confirmation of success of efforts to find a cure for ageing.

Biotechnology2017arXiv
Periodicals

Energy-based generative models for monoclonal antibodies

Since the approval of the first antibody drug in 1986, a total of 162 antibodies have been approved for a wide range of therapeutic areas, including cancer, autoimmune, infectious, or cardiovascular diseases. Despite advances in biotechnology that accelerated the development of antibody drugs, the drug discovery process for this modality remains lengthy and costly, requiring multiple rounds of optimizations before a drug candidate can progress to preclinical and clinical trials. This multi-optimization problem involves increasing the affinity of the antibody to the target antigen while refining additional biophysical properties that are essential to drug development such as solubility, thermostability or aggregation propensity. Additionally, antibodies that resemble natural human antibodies are particularly desirable, as they are likely to offer improved profiles in terms of safety, efficacy, and reduced immunogenicity, further supporting their therapeutic potential. In this article, we explore the use of energy-based generative models to optimize a candidate monoclonal antibody. We identify tradeoffs when optimizing for multiple properties, concentrating on solubility, humanness and affinity and use the generative model we develop to generate candidate antibodies that lie on an optimal Pareto front that satisfies these constraints.

Biotechnology2024arXiv
Periodicals

Lotka-Sharpe Neural Operators for Control of Population PDEs

Age-structured predator-prey integro-partial differential equations provide models of interacting populations in ecology, epidemiology, and biotechnology. A key challenge in feedback design for these systems is the scalar $ζ$, defined implicitly by the Lotka-Sharpe nonlinear integral condition, as a mapping from fertility and mortality rates to $ζ$. To solve this challenge with operator learning, we first prove that the Lotka-Sharpe operator is Lipschitz continuous, guaranteeing the existence of arbitrarily accurate neural operator approximations over a compact set of fertility and mortality functions. We then show that the resulting approximate feedback law preserves semi-global practical asymptotic stability under propagation of the operator approximation error through various other nonlinear operators, all the way through to the control input. In the numerical results, not only do we learn ``once-and-for-all'' the canonical Lotka-Sharpe (LS) operator, and thus make it available for future uses in control of other age-structured population interconnections, but we demonstrate the online usage of the neural LS operator under estimation of the fertility and mortality functions.

Biotechnology2026arXiv
Periodicals

Concept of photonic hook scalpel generated by shaped fiber tip with asymmetric radiation

Structured light have made deep impacts on modern biotechnology and clinical practice, with numerous optical systems and lasers currently being used in medicine to treat disease. We demonstrate a new concept of fiber-based optical hook scalpel. The subwavelength photonic hook is obtained in the vicinity of a shaped fiber tip with asymmetric radiation. A 1550 nm continuous-wave source, commonly used for medical imaging, has been required. Photonic hook with a lateral feature size less than the half-wavelength is achieved using a hemispherical shaped fiber tip with metallic mask. This breakthrough is carried out in ambient air by using a 4-μm-diameter fiber with a shaped tip. A good correlation is observed between the computed intensity distribution of photonic hook and the tip sizes. Photonic hook generated with a shaped fiber tip, easier to manipulate, shows far-reaching benefits for potential applications such as ophthalmic laser surgery, super-resolution microscopy, photolithography, and material processing.

Biotechnology2020arXiv
Periodicals

Label-free optical quantification of material composition of suspended virus-gold nanoparticle complexes

The interaction between metallic and biological nanoparticles (NPs) is widely used in various biotechnology and biomedical applications. However, detailed characterization of this type of interaction is challenging due to a lack of high-throughput techniques that can quantify both size and composition of suspended NP complexes. Here, we introduce a technique called ``twilight nanoparticle tracking analysis'' (tNTA) and demonstrate that it provides a quantitative relationship between the measured optical signal and the composition of suspended dielectric-metal NP complexes. We assess the performance of tNTA by analyzing the selective binding of tannic acid-modified gold nanoparticles (TaAuNPs) to herpes simplex viruses (HSV). Our results show that TaAuNPs bind specifically to HSV without causing substantial changes in the size or refractive index of the virus, suggesting that the binding does not cause virus disruption. Instead, the anti-viral properties of TaAuNPs appear to stem from direct particle binding to the virus.

Biotechnology2023arXiv
Periodicals

Control principles of metabolic networks

Deciphering the control principles of metabolism and its interaction with other cellular functions is central to biomedicine and biotechnology. Yet, understanding the efficient control of metabolic fluxes remains elusive for large-scale metabolic networks. Existing methods either require specifying a cellular objective or are limited to small networks due to computational complexity. Here we develop an efficient computational framework for flux control by introducing a complete set of flux coupling relations. We analyze 23 metabolic networks from all kingdoms of life, and identify the driver reactions facilitating their control on a large scale. We find that most unicellular organisms require less extensive control than multicellular organisms. The identified driver reactions are under strong transcriptional regulation in Escherichia coli. In human cancer cells driver reactions play pivotal roles in tumor development, representing potential therapeutic targets. The proposed framework helps us unravel the regulatory principles of complex diseases and design novel engineering strategies at the interface of gene regulation, signaling, and metabolism.

Biotechnology2015arXiv
Periodicals

Integrating Dictionary Feature into A Deep Learning Model for Disease Named Entity Recognition

In recent years, Deep Learning (DL) models are becoming important due to their demonstrated success at overcoming complex learning problems. DL models have been applied effectively for different Natural Language Processing (NLP) tasks such as part-of-Speech (PoS) tagging and Machine Translation (MT). Disease Named Entity Recognition (Disease-NER) is a crucial task which aims at extracting disease Named Entities (NEs) from text. In this paper, a DL model for Disease-NER using dictionary information is proposed and evaluated on National Center for Biotechnology Information (NCBI) disease corpus and BC5CDR dataset. Word embeddings trained over general domain texts as well as biomedical texts have been used to represent input to the proposed model. This study also compares two different Segment Representation (SR) schemes, namely IOB2 and IOBES for Disease-NER. The results illustrate that using dictionary information, pre-trained word embeddings, character embeddings and CRF with global score improves the performance of Disease-NER system.

Biotechnology2019arXiv
Periodicals

Modeling continuous levels of resistance to multidrug therapy in cancer

Multidrug resistance consists of a series of genetic and epigenetic alternations that involve multifactorial and complex processes, which are a challenge to successful cancer treatments. Accompanied by advances in biotechnology and high-dimensional data analysis techniques that are bringing in new opportunities in modeling biological systems with continuous phenotypic structured models, we study a cancer cell population model that considers a multi-dimensional continuous resistance trait to multiple drugs to investigate multidrug resistance. We compare our continuous resistance trait model with classical models that assume a discrete resistance state and classify the cases when the continuum and discrete models yield different dynamical patterns in the emerging heterogeneity in response to drugs. We also compute the maximal fitness resistance trait for various continuum models and study the effect of epimutations. Finally, we demonstrate how our approach can be used to study tumor growth regarding the turnover rate and the proliferating fraction, and show that a continuous resistance level may result in a different dynamics when compared with the predictions of other discrete models.

Biotechnology2018arXiv
Periodicals

Reaction-induced molecular dancing and boosted diffusion of enzymes

A novel mechanism of reaction-induced active molecular motion, not involving any kind of self-propulsion, is proposed and analyzed. Because of the momentum exchange with the surrounding solvent, conformational transitions in mechano-chemical enzymes are accompanied by motions of their centers of mass. As we show, in combination with rotational diffusion, such repeated reciprocal motions generate an additional random walk - or molecular dancing - and hence boost translational diffusion of an enzyme. A systematic theory of this phenomenon is developed, using as an example a simple enzyme model of a rigid two-state dumbbell. To support the analysis, numerical simulations are performed. Our conclusion is that the phenomenon of molecular dancing could underlie the observations of reaction-induced diffusion enhancement in enzymes. Major experimental findings, such as the occurrence of leaps, the anti-chemotaxis, the linear dependence on the reaction turnover rate and on the rate of energy supply, become thus explained. Moreover, the dancing behavior is possible in other systems, natural and synthetic, too. In the future, interesting biotechnology applications may be developed using such effects.

Biotechnology2022arXiv