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Periodicals

Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are an important resource for the identification of new therapeutic targets and cardioprotective drugs. After differentiation iPSC-CMs show an immature, fetal-like phenotype. Cultivation of iPSC-CMs in lipid-supplemented maturation medium (MM) strongly enhances their structural, metabolic and functional phenotype. Nevertheless, assessing iPSC-CM maturation state remains challenging as most methods are time consuming and go in line with cell damage or loss of the sample. To address this issue, we developed a non-invasive approach for automated classification of iPSC-CM maturity through interpretable artificial intelligence (AI)-based analysis of beat characteristics derived from video-based motion analysis. In a prospective study, we evaluated 230 video recordings of early-state, immature iPSC-CMs on day 21 after differentiation (d21) and more mature iPSC-CMs cultured in MM (d42, MM). For each recording, 10 features were extracted using Maia motion analysis software and entered into a support vector machine (SVM). The hyperparameters of the SVM were optimized in a grid search on 80 % of the data using 5-fold cross-validation. The optimized model achieved an accuracy of 99.5 $\pm$ 1.1 % on a hold-out test set. Shapley Additive Explanations (SHAP) identified displacement, relaxation-rise time and beating duration as the most relevant features for assessing maturity level. Our results suggest the use of non-invasive, optical motion analysis combined with AI-based methods as a tool to assess iPSC-CMs maturity and could be applied before performing functional readouts or drug testing. This may potentially reduce the variability and improve the reproducibility of experimental studies.

Biotechnology• 2025• arXiv
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Periodicals

Calotropin from milk of Calotropis gigantean a potent inhibitor of COVID 19 corona virus infection by Molecular docking studies

SARS-CoV-2 (COVID-19), a positive single stranded RNA virus, member of corona virus family, is spreading its tentacles across the world due to lack of drugs at present. Being associated with cough, fever, and respiratory distress, this disease caused more than 15 % mortality worldwide. Due to its vital role in virus replication, Mpro/3CLpro has recently been regarded as a suitable target for drug design. The current study focused on the inhibitory activity of Calotropin, a component from milk of Calotropis gigantean, against Mpro protein from SARS-CoV-2. Till date there is no work is undertaken on in-silico analysis of this compound against Mpro of COVID-19 protein. In the present study, molecular docking studies were conducted by using Patchdock tool. Protein Interactions tool was used for protein interactions. The calculated parameters such as docking score indicated effective binding of Calotropin to Mpro protein. Interactions results indicated that, Mpro/ Calotropin complexes forms hydrophobic interactions. Therefore, Calotropin may represent potential herbal treatment to act as COVID-19 Mpro inhibitor. However, further research is necessary to investigate their potential medicinal use.

Biotechnology• 2020• arXiv
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Periodicals

The need for effective information security awareness practices in Oman higher educational institutions

The revolution of internet technology and its usage have led a significant increase in the number of online transactions and electronic data transfer, parallely increased the number of cybercrime incidents around the world. Steady economic growth in the Sultanate of Oman accelerated the volume of online utilization for e-commerce, banking, communication, education and so forth. Normally attackers target the users who ignore security practices due to the lack of information security awareness. Unawareness of information security practices, user negligence, lack of awareness programs and trainings are the root cause for information security threats. Earlier studies reveal there is a considerable and continuous cybercrime incident in Oman which compromises the security policy of the organizations, affecting the business continuity and the economic growth. In this study, a survey was performed among the educational institutions in Oman to investigate the level of information security awareness and based on the study, a security awareness model is proposed to enable information security practices in the educational institutions.

Biotechnology• 2016• arXiv
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Periodicals

Investigation of Genomic Effect of Zirconium Oxide Nanoparticles in Escherichia coli Bacteria

Due to the concerns of the society about the increase of antibiotic resistant infections, many studies and research have been done on nanoparticles and applications of nano-biotechnology. Zirconium Oxide ($\text{ZrO}_{2}$) in which called zirconia, is a white oxide of zirconium metal that its diameter is 20 nm. The colloidal size of these particles is often smaller than bacterial and eukaryotic cells. The main intention of this paper is to investigate the effect of different doses of $\text{ZrO}_{2}$ NPs on the sequences changes for the $\textit{Escherichia coli}$ ($\textit{E. coli}$) genome. At the first step, $\textit{E. coli}$ was cultured in eosin methylene blue agar and brain heart broth (BHB) mediums, respectively. Then, bacteria were treated with $\text{ZrO}_{2}$ NPs at concentrations of 100, 250, and 350 $μ$g/ml. After treatment, the growth of bacteria was evaluated by utilizing spectrophotometry at 600 nm after incubation times including 2, 4, 6, 8, and 24 hours at 37 $^{\circ}$C. At the second step, the extraction of DNA was performed by using control and treated samples. Then, the changes in the sequence of bacterial genome were investigated using RAPD markers. Finally, NTSYS-PC platform was employed in order to analyze of the results extracted by electrophoresis of products on agarose gel. In this paper, it was observed that $\text{ZrO}_{2}$ NPs can inhibit the growth of bacteria at concentrations of 250 and 350 $μ$g/ml after 8 hours of treatment. It was also found that the $\text{ZrO}_{2}$ NPs at different concentrations have not changed the genome sequence of $\textit{E. coli}$. Furthermore, it was concluded that the $\text{ZrO}_{2}$ NPs with the concentration of 350 $μ$g/ml had the highest inhibitory properties without significant changing in the genomic sequence of $\textit{E. coli}$.

Biotechnology• 2024• arXiv
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Periodicals

Abasy Atlas v2.2: The most comprehensive and up-to-date inventory of meta-curated, historical, bacterial regulatory networks, their completeness and system-level characterization

Some organism-specific databases about regulation in bacteria have become larger, accelerated by high-throughput methodologies, while others are no longer updated or accessible. Each database homogenize its datasets, giving rise to heterogeneity across databases. Such heterogeneity mainly encompasses different names for a gene and different network representations, generating duplicated interactions that could bias network analyses. Abasy (Across-bacteria systems) Atlas consolidates information from different sources into meta-curated regulatory networks in bacteria. The high-quality networks in Abasy Atlas enable cross-organisms analyses, such as benchmarking studies where gold standards are required. Nevertheless, network incompleteness still casts doubts on the conclusions of network analyses, and available sampling methods cannot reflect the curation process. To tackle this problem, the updated version of Abasy Atlas presented in this work provides historical snapshots of regulatory networks. Thus, network analyses can be performed at different completeness levels, making possible to identify potential bias and to predict future results. We leverage the recently found constraint in the complexity of regulatory networks to develop a novel model to quantify the total number of regulatory interactions as a function of the genome size. This completeness estimation is a valuable insight that may aid in the daunting task of network curation, prediction, and validation. The new version of Abasy Atlas provides 76 networks (204,282 regulatory interactions) covering 42 bacteria (64% Gram-positive and 36% Gram-negative) distributed in 9 species, containing 8,459 regulons and 4,335 modules.

Biotechnology• 2020• arXiv
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Periodicals

Poisson-Boltzmann model for protein-surface electrostatic interactions and grid-convergence study using the PyGBe code

Interactions between surfaces and proteins occur in many vital processes and are crucial in biotechnology: the ability to control specific interactions is essential in fields like biomaterials, biomedical implants and biosensors. In the latter case, biosensor sensitivity hinges on ligand proteins adsorbing on bioactive surfaces with a favorable orientation, exposing reaction sites to target molecules. Protein adsorption, being a free-energy-driven process, is difficult to study experimentally. This paper develops and evaluates a computational model to study electrostatic interactions of proteins and charged nanosurfaces, via the Poisson-Boltzmann equation. We extended the implicit-solvent model used in the open-source code PyGBe to include surfaces of imposed charge or potential. This code solves the boundary integral formulation of the Poisson-Boltzmann equation, discretized with surface elements. PyGBe has at its core a treecode-accelerated Krylov iterative solver, resulting in O(N log N) scaling, with further acceleration on hardware via multi-threaded execution on \gpu s. It computes solvation and surface free energies, providing a framework for studying the effect of electrostatics on adsorption. We then derived an analytical solution for a spherical charged surface interacting with a spherical molecule, then completed a grid-convergence study to build evidence on the correctness of our approach. The study showed the error decaying with the average area of the boundary elements, i.e., the method is O(1/N), which is consistent with our previous verification studies using PyGBe. We also studied grid-convergence using a real molecular geometry (protein GB1D4'), in this case using Richardson extrapolation (in the absence of an analytical solution) and confirmed the O(1/N) scaling in this case.

Biotechnology• 2015• arXiv
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Periodicals

SAGe: A Lightweight Algorithm-Architecture Co-Design for Mitigating the Data Preparation Bottleneck in Large-Scale Genome Sequence Analysis

Genome sequence analysis, which examines the DNA sequences of organisms, drives advances in many critical medical and biotechnological fields. Given its importance and the exponentially growing volumes of genomic sequence data, there are extensive efforts to accelerate genome sequence analysis. In this work, we demonstrate a major bottleneck that greatly limits and diminishes the benefits of state-of-the-art genome sequence analysis accelerators: the data preparation bottleneck, where genomic sequence data is stored in compressed form and needs to be first decompressed and formatted before an accelerator can operate on it. To mitigate this bottleneck, we propose SAGe, an algorithm-architecture co-design for highly-compressed storage and high-performance access of large-scale genomic sequence data. The key challenge is to improve data preparation performance while maintaining high compression ratios (comparable to genomic-specific compression algorithms) at low hardware cost. We address this challenge by leveraging key properties of genomic datasets to co-design (i) a lossless (de)compression algorithm, (ii) hardware that decompresses data with lightweight operations and efficient streaming accesses, (iii) storage data layout, and (iv) interface commands to access data. SAGe is highly versatile, as it supports datasets from different sequencing technologies and species. Due to its lightweight design, SAGe can be seamlessly integrated with a broad range of hardware accelerators for genome sequence analysis to mitigate their data preparation bottlenecks. Our results demonstrate that SAGe improves the average end-to-end performance and energy efficiency of two state-of-the-art genome sequence analysis accelerators by 3.0x-32.1x and 13.0x-34.0x, respectively, compared to when the accelerators rely on state-of-the-art software and hardware decompression tools.

Biotechnology• 2025• arXiv
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Periodicals

Predictive Modeling of Non-Viral Gene Transfer

In non-viral gene delivery, the variance of transgenic expression stems from the low number of plasmids successfully transferred. Here, we experimentally determine Lipofectamine- and PEI-mediated exogenous gene expression distributions from single cell time-lapse analysis. Broad Poisson-like distributions of steady state expression are observed for both transfection agents, when used with synchronized cell lines. At the same time, co-transfection analysis with YFP- and CFP-coding plasmids shows that multiple plasmids are simultaneously expressed, suggesting that plasmids are delivered in correlated units (complexes). We present a mathematical model of transfection, where a stochastic, two-step process is assumed, with the first being the low-probability entry step of complexes into the nucleus, followed by the subsequent release and activation of a small number of plasmids from a delivered complex. This conceptually simple model consistently predicts the observed fraction of transfected cells, the cotransfection ratio and the expression level distribution. It yields the number of efficient plasmids per complex and elucidates the origin of the associated noise, consequently providing a platform for evaluating and improving non-viral vectors.

Biotechnology• 2010• arXiv
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Periodicals

Testing of information condensation in a model reverberating spiking neural network

Information about external world is delivered to the brain in the form of structured in time spike trains. During further processing in higher areas, information is subjected to a certain condensation process, which results in formation of abstract conceptual images of external world, apparently, represented as certain uniform spiking activity partially independent on the input spike trains details. Possible physical mechanism of condensation at the level of individual neuron was discussed recently. In a reverberating spiking neural network, due to this mechanism the dynamics should settle down to the same uniform/periodic activity in response to a set of various inputs. Since the same periodic activity may correspond to different input spike trains, we interpret this as possible candidate for information condensation mechanism in a network. Our purpose is to test this possibility in a network model consisting of five fully connected neurons, particularly, the influence of geometric size of the network, on its ability to condense information. Dynamics of 20 spiking neural networks of different geometric sizes are modelled by means of computer simulation. Each network was propelled into reverberating dynamics by applying various initial input spike trains. We run the dynamics until it becomes periodic. The Shannon's formula is used to calculate the amount of information in any input spike train and in any periodic state found. As a result, we obtain explicit estimate of the degree of information condensation in the networks, and conclude that it depends strongly on the net's geometric size.

Biotechnology• 2010• arXiv
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Periodicals

Application of Traditional Vaccine Development Strategies to SARS-CoV-2

Over the past 150 years, vaccines have revolutionized the relationship between people and disease. During the COVID-19 pandemic, technologies such as mRNA vaccines have received attention due to their novelty and successes. However, more traditional vaccine development platforms have also yielded important tools in the worldwide fight against the SARS-CoV-2 virus. A variety of approaches have been used to develop COVID-19 vaccines that are now authorized for use in countries around the world. In this review, we highlight strategies that focus on the viral capsid and outwards, rather than on the nucleic acids inside. These approaches fall into two broad categories: whole-virus vaccines and subunit vaccines. Whole-virus vaccines use the virus itself, either in an inactivated or attenuated state. Subunit vaccines contain instead an isolated, immunogenic component of the virus. Here, we highlight vaccine candidates that apply these approaches against SARS-CoV-2 in different ways. In a companion manuscript, we review the more recent and novel development of nucleic-acid based vaccine technologies. We further consider the role that these COVID-19 vaccine development programs have played in prophylaxis at the global scale. Well-established vaccine technologies have proved especially important to making vaccines accessible in low- and middle-income countries. Vaccine development programs that use established platforms have been undertaken in a much wider range of countries than those using nucleic-acid-based technologies, which have been led by wealthy Western countries. Therefore, these vaccine platforms, though less novel from a biotechnological standpoint, have proven to be extremely important to the management of SARS-CoV-2.

Biotechnology• 2022• arXiv
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Periodicals

Diverse ATPase proteins in mobilomes constitute a large potential sink for prokaryotic host ATP

Prokaryote mobilome genomes rely on host machineries for survival and replication. Given that mobile genetic elements (MGEs) derive their energy from host cells, we investigated the diversity of ATP-utilizing proteins in MGE genomes to determine whether they might be associated with proteins that could suppress related host proteins that consume host energy. A comprehensive search of 353 huge phage genomes revealed that up to 9% of the proteins have ATPase domains. For example, ATPase proteins constitute ~3% of the genomes of Lak phages with ~550 kbp genomes that occur in the microbiomes of humans and other animals. Statistical analysis shows the number of ATPase proteins increases linearly with genome length, consistent with a large sink for host ATP during replication of megaphages. Using metagenomic data from diverse environments, we found 505 mobilome proteins with ATPase domains fused to diverse functional domains. Among these composite ATPase proteins, 61.6% have known functional domains that could contribute to host energy diversion during the mobilome life cycle. As many have domains that are known to interact with nucleic acids and proteins, we infer that numerous ATPase proteins are used during replication and for protection from host immune systems. We found a set of uncharacterized ATPase proteins with nuclease and protease activities, displaying unique domain architectures that are energy intensive based on the presence of multiple ATPase domains. In many cases, these composite ATPase proteins genomically co-localize with small proteins in genomic contexts that are reminiscent of toxin-antitoxin systems. Small proteins that function as inhibitors may be a common strategy for control of cellular processes, thus could inspire the development of new nucleic acid and protein manipulation tools, with diverse biotechnological applications.

Biotechnology• 2021• arXiv
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Periodicals

The hidden structure of innovation networks

Innovation emerges from complex collaboration patterns - among inventors, firms, or institutions. However, not much is known about the overall mesoscopic structure around which inventive activity self-organizes. Here, we tackle this problem by employing patent data to analyze both individual (\textit{co-inventorship}) and organization (\textit{co-ownership}) networks in three strategic domains (\textit{artificial intelligence}, \textit{biotechnology} and \textit{semiconductors}). We characterize the mesoscale structure (in terms of clusters) of each domain by comparing two alternative methods: a standard baseline - modularity maximization - and one based on the minimization of the Bayesian Information Criterion, within the Stochastic Block Model and its degree-corrected variant. We find that, across sectors, inventor networks are denser and more clustered than organization ones - consistently with the presence of small recurrent teams embedded into broader institutional hierarchies - whereas organization networks have a neater role-based structures, with few bridging firms coordinating the most peripheral ones; still, both are characterized by the presence of local core-periphery structures. We also find that the discovered meso-structures are connected to innovation output. In particular, Lorenz curves of forward citations show a pervasive inequality in technological influence: across sectors and methods, both inventor (especially) and organization networks consistently show high levels of concentration of citations in a few of the discovered clusters. Our results demonstrate that the baseline modularity-based method may not be capable of fully capturing the way collaborations drive the spreading of inventive impact across technological domains. This is due to the presence of local hierarchies that call for the more refined tools of Bayesian inference.

Biotechnology• 2026• arXiv
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Periodicals

Abasy Atlas: A comprehensive inventory of systems, global network properties and systems-level elements across bacteria

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy (Across-bacteria systems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties, and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes, and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78,649 regulatory interactions covering 42 bacteria in nine taxa, containing 3,708 regulons and 1,776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them.

Biotechnology• 2016• arXiv
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Periodicals

Estimation of the Accuracy of Method for Quantitative Determination of Volatile Compounds in Alcohol Products

Results of the estimation of the precision for determination volatile compounds in alcohol-containing products by gas chromatography: acetaldehyde, methyl acetate, ethyl acetate, methanol, isopropyl alcohol, propyl alcohol, isobutyl alcohol, butyl alcohol, isoamyl alcohol are presented. To determine the accuracy, measurements were planned in accordance with ISO 5725 and held at the gas chromatograph Chromatec-Crystal 5000. Standard deviation of repeatability, intermediate precision and their limits are derived from obtained experimental data. The uncertainty of the measurements was calculated on the base of an "empirical" method. The obtained values of accuracy indicate that the developed method allows measurement uncertainty extended from 2 to 20% depending on the analyzed compound and measured concentration.

Biotechnology• 2016• arXiv
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Periodicals

Characterizing RNA oligomers using Stochastic Titration Constant-pH Metadynamics simulations

RNA molecules exhibit various biological functions intrinsically dependent on their diverse ecosystem of highly flexible structures. This flexibility arises from complex hydrogen-bonding networks defined by canonical and non-canonical base pairs that require protonation events to stabilize or perturb these interactions. Constant pH molecular dynamics (CpHMD) methods provide a reliable framework to explore the conformational and protonation space of dynamic structures and for robust calculations of pH-dependent properties, such as the pK$_\mathrm{a}$ of titrable sites. Despite growing biological evidence concerning pH regulation of certain motifs and in biotechnological applications, pH-sensitive in silico methods have rarely been applied to nucleic acids. In this work, we extended the stochastic titration CpHMD method to include RNA parameters from the standard $χ$OL3 AMBER force field and highlighted its capability to depict titration events of nucleotides in single-stranded RNAs. We validated the method using trimers and pentamers with a single central titrable site while integrating a well-tempered metadynamics approach into the st-CpHMD methodology (CpH-MetaD) using PLUMED. This approach enhanced the convergence of the conformational landscape and enabled more efficient sampling of protonation-conformation coupling. Our pK$_\mathrm{a}$ estimates agree with experimental data, validating the method's ability to reproduce electrostatic changes around a titrable nucleobase in single-stranded RNA. These findings provided molecular insight into intramolecular phenomena, such as nucleobase stacking and phosphate interactions, that dictate the experimentally observed pK$_\mathrm{a}$ shifts between different strands. Overall, this work validates both the st-CpHMD and the metadynamics integration as reliable tools for studying biologically relevant RNA systems.

Biotechnology• 2024• arXiv
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Periodicals

Community Detection Analysis of Spatial Transcriptomics Data

The spatial transcriptomics (ST) data produced by recent biotechnologies, such as CosMx and Xenium, contain huge amount of information about cancer tissue samples, which has great potential for cancer research via detection of community: a collection of cells with distinct cell-type composition and similar neighboring patterns. But existing clustering methods do not work well for community detection of CosMx ST data, and the commonly used kNN compositional data method shows lack of informative neighboring cell patterns for huge CosMx data. In this article, we propose a novel and more informative disk compositional data (DCD) method, which identifies neighboring patterns of each cell via taking into account of ST data features from recent new technologies. After initial processing ST data into DCD matrix, a new innovative and interpretable DCD-TMHC community detection method is proposed here. Extensive simulation studies and CosMx breast cancer data analysis clearly show that our proposed DCD-TMHC method is superior to other methods. Based on the communities detected by DCD-TMHC method for CosMx breast cancer data, the logistic regression analysis results demonstrate that DCD-TMHC method is clearly interpretable and superior, especially in terms of assessment for different stages of cancer. These suggest that our proposed novel, innovative, informative and interpretable DCD-TMHC method here will be helpful and have impact to future cancer research based on ST data, which can improve cancer diagnosis and monitor cancer treatment progress.

Biotechnology• 2025• arXiv
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Structure-based RNA Design by Step-wise Optimization of Latent Diffusion Model

RNA inverse folding, designing sequences to form specific 3D structures, is critical for therapeutics, gene regulation, and synthetic biology. Current methods, focused on sequence recovery, struggle to address structural objectives like secondary structure consistency (SS), minimum free energy (MFE), and local distance difference test (LDDT), leading to suboptimal structural accuracy. To tackle this, we propose a reinforcement learning (RL) framework integrated with a latent diffusion model (LDM). Drawing inspiration from the success of diffusion models in RNA inverse folding, which adeptly model complex sequence-structure interactions, we develop an LDM incorporating pre-trained RNA-FM embeddings from a large-scale RNA model. These embeddings capture co-evolutionary patterns, markedly improving sequence recovery accuracy. However, existing approaches, including diffusion-based methods, cannot effectively handle non-differentiable structural objectives. By contrast, RL excels in this task by using policy-driven reward optimization to navigate complex, non-gradient-based objectives, offering a significant advantage over traditional methods. In summary, we propose the Step-wise Optimization of Latent Diffusion Model (SOLD), a novel RL framework that optimizes single-step noise without sampling the full diffusion trajectory, achieving efficient refinement of multiple structural objectives. Experimental results demonstrate SOLD surpasses its LDM baseline and state-of-the-art methods across all metrics, establishing a robust framework for RNA inverse folding with profound implications for biotechnological and therapeutic applications.

Biotechnology• 2026• arXiv
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Periodicals

Inhibiting amyloid-like aggregation through bio-conjugation of proteins with polymer surfactant

Prevention of protein aggregation and thus stabilization of proteins has large biological and biotechnological implications. Here, we show that inhibition of amyloid-like aggregates is possible in stoichiometric conjugates of polymer surfactant and bovine serum albumin (BSA) chosen as a model protein. We investigate using a combination of Thioflavin-T fluorescence spectroscopy, dynamic light scattering and FTIR spectroscopy the aggregation behavior in polymer surfactant modified and unmodified (native) BSA solutions. The BSA-polymer surfactant conjugates are stable up to 5 days under aggregation conditions, while native BSA forms amyloid fibrillar structures. Further, DLS-based micro-rheology studies performed with heat-treated 100 to 200 μM native BSA aggregates provided understanding of the equilibrium elastic and viscous moduli over a very large frequency range, reaching MHz, which are inaccessible using bulk rheology. Our results indicate that after 6 days of aggregation conditions, elastic moduli showed values between 1.2 to 3.6 Pa corresponding to an entanglement length (ξ) of 105 nm. Interestingly, heating 200 μM native BSA solution at 65 degree C for 2 days in a plastic Eppendorf resulted in self-standing films. These films exhibited strong ThT-fluorescence intensity and a predominant \b{eta}-sheet secondary structure from the FTIR studies, suggesting that self-standing microstructure resulted from hierarchical self-assembly of amyloid fibrils.

Biotechnology• 2020• arXiv
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Can Large Language Models Design Biological Weapons? Evaluating Moremi Bio

Advances in AI, particularly LLMs, have dramatically shortened drug discovery cycles by up to 40% and improved molecular target identification. However, these innovations also raise dual-use concerns by enabling the design of toxic compounds. Prompting Moremi Bio Agent without the safety guardrails to specifically design novel toxic substances, our study generated 1020 novel toxic proteins and 5,000 toxic small molecules. In-depth computational toxicity assessments revealed that all the proteins scored high in toxicity, with several closely matching known toxins such as ricin, diphtheria toxin, and disintegrin-based snake venom proteins. Some of these novel agents showed similarities with other several known toxic agents including disintegrin eristostatin, metalloproteinase, disintegrin triflavin, snake venom metalloproteinase, corynebacterium ulcerans toxin. Through quantitative risk assessments and scenario analyses, we identify dual-use capabilities in current LLM-enabled biodesign pipelines and propose multi-layered mitigation strategies. The findings from this toxicity assessment challenge claims that large language models (LLMs) are incapable of designing bioweapons. This reinforces concerns about the potential misuse of LLMs in biodesign, posing a significant threat to research and development (R&D). The accessibility of such technology to individuals with limited technical expertise raises serious biosecurity risks. Our findings underscore the critical need for robust governance and technical safeguards to balance rapid biotechnological innovation with biosecurity imperatives.

Biotechnology• 2025• arXiv
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Prokaryotic regulatory systems biology: Common principles governing the functional architectures of Bacillus subtilis and Escherichia coli unveiled by the natural decomposition approach

Escherichia coli and Bacillus subtilis are two of the best-studied prokaryotic model organisms. Previous analyses of their transcriptional regulatory networks have shown that they exhibit high plasticity during evolution and suggested that both converge to scale-free-like structures. Nevertheless, beyond this suggestion, no analyses have been carried out to identify the common systems-level components and principles governing these organisms. Here we show that these two phylogenetically distant organisms follow a set of common novel biologically consistent systems principles revealed by the mathematically and biologically founded natural decomposition approach. The discovered common functional architecture is a diamond-shaped, matryoshka-like, three-layer (coordination, processing, and integration) hierarchy exhibiting feedback, which is shaped by four systems-level components: global transcription factors (global TFs), locally autonomous modules, basal machinery and intermodular genes. The first mathematical criterion to identify global TFs, the $κ$-value, was reassessed on B. subtilis and confirmed its high predictive power by identifying all the previously reported, plus three potential, master regulators and eight sigma factors. The functional conserved cores of modules, basal cell machinery, and a set of non-orthologous common physiological global responses were identified via both orthologous genes and non-orthologous conserved functions. This study reveals novel common systems principles maintained between two phylogenetically distant organisms and provides a comparison of their lifestyle adaptations. Our results shed new light on the systems-level principles and the fundamental functions required by bacteria to sustain life.

Biotechnology• 2014• arXiv
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Osmotic Swelling Behavior of Surface-Charged Ionic Microgels

In recent years, ionic microgels have garnered much attention due to their unique properties, especially their stimulus-sensitive swelling behavior. The tunable response of these soft, permeable, compressible, charged colloidal particles is increasingly attractive for applications in medicine and biotechnologies, such as controlled drug delivery, tissue engineering, and biosensing. The ability to model and predict variation of the osmotic pressure of a single microgel with respect to changes in particle properties and environmental conditions proves vital to such applications. In this work, we apply both nonlinear Poisson-Boltzmann theory and molecular dynamics simulation to ionic microgels (macroions) in the cell model to compute density profiles of microions (counterions, coions), single-microgel osmotic pressure, and equilibrium swelling ratios of spherical microgels whose fixed charge is confined to the macroion surface. The basis of our approach is an exact theorem that relates the electrostatic component of the osmotic pressure to the microion density profiles. Close agreement between theory and simulation serves as a consistency check to validate our approach. We predict that surface-charged microgels progressively deswell with increasing microgel concentration, starting well below close packing, and with increasing salt concentration, in qualitative agreement with experiments. Comparison with previous results for microgels with fixed charge uniformly distributed over their volume demonstrates that surface-charged microgels deswell more rapidly than volume-charged microgels. We conclude that swelling behavior of ionic microgels in solution is sensitive to the distribution of fixed charge within the polymer-network gel and strongly depends on bulk concentrations of both microgels and salt ions.

Biotechnology• 2023• arXiv
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Morphogenesis Software based on Epigenetic Code Concept

The process of morphogenesis is an evolution of the shape of an organism together with the differentiation of its parts. This process encompasses numerous biological processes ranging from embryogenesis to regeneration following crisis such as amputation or transplantation. A fundamental theoretical question is where exactly do these instructions for (re-)construction reside and how are they implemented? We have recently proposed a set of concepts, aiming to respond to these questions and to provide an appropriate mathematical formalization of the geometry of morphogenesis [1]. First, we consider the possibility that evolution of shape is determined by epigenetic information, responsible for realization of different types of cell events. Second, we suggest a set of rules for converting this epigenetic information into instructive signals for cell events for each cell, as well as for transforming it after each cell event. Next we give notions of cell state, determined by its epigenetic array, and cell event, which is a change of cell state, and formalize development as a graph (tree) of cell states connected by 5 types of cell events, corresponding to the processes of cell division, cell growth, cell death, cell movement and cell differentiation. Here we present a Morphogenesis Software capable of simulating the evolution of a 3D embryo starting from zygote, following a set of rules based on our theoretical assumptions, and thus to provide a proof-of-concept for the hypothesis of epigenetic code regulation. The software creates a developing embryo and a corresponding graph of cell events according to the zygotic epigenetic spectrum and chosen parameters of the developmental rules. Variation of rules influencing the resulting shape of an embryo may help elucidating the principal laws underlying pattern formation.

Biotechnology• 2019• arXiv
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Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses

Efficient and robust bioprocess control is essential for maximizing performance and adaptability in advanced biotechnological systems. In this work, we present a reinforcement-learning framework for multi-setpoint and multi-trajectory tracking. Tracking multiple setpoints and time-varying trajectories in reinforcement learning is challenging due to the complexity of balancing multiple objectives, a difficulty further exacerbated by system uncertainties such as uncertain initial conditions and stochastic dynamics. This challenge is relevant, e.g., in bioprocesses involving microbial consortia, where precise control over population compositions is required. We introduce a novel return function based on multiplicative reciprocal saturation functions, which explicitly couples reward gains to the simultaneous satisfaction of multiple references. Through a case study involving light-mediated cybergenetic growth control in microbial consortia, we demonstrate via computational experiments that our approach achieves faster convergence, improved stability, and superior control compliance compared to conventional quadratic-cost-based return functions. Moreover, our method enables tuning of the saturation function's parameters, shaping the learning process and policy updates. By incorporating system uncertainties, our framework also demonstrates robustness, a key requirement in industrial bioprocessing. Overall, this work advances reinforcement-learning-based control strategies in bioprocess engineering, with implications in the broader field of process and systems engineering.

Biotechnology• 2025• arXiv
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Periodicals

Gaussian fluctuations of spatially inhomogeneous polymers

Inhomogeneous polymers play an important role in various cellular processes, both in nature and in biotechnological applications. At finite temperatures, inhomogeneous polymers exhibit non-trivial thermal fluctuations. In a broader context, these are relatively simple examples for fluctuations in spatially inhomogeneous systems, which are less understood compared to their homogeneous counterparts. We develop a statistical theory of torsional, extensional and bending Gaussian fluctuations of inhomogeneous polymers, where the inhomogeneity is an inclusion of variable size and mechanical properties, using both continuum and discrete approaches. First, we analytically calculate the complete eigenvalue and eigenmode spectrum of the inhomogeneous polymer within a continuum field theory. In particular, we show that the wavenumber inside and outside of the inclusion is nearly linear in the eigenvalue index, with a nontrivial coefficient. Second, we solve the corresponding discrete problem, and highlight fundamental differences between the continuum and discrete spectra. In particular, we demonstrate that above a certain wavenumber the discrete spectrum changes qualitatively and discrete evanescent eigenmodes, that do not have continuum counterparts, emerge. The statistical thermodynamic implications of these differences are then explored by calculating fluctuation-induced forces associated with free-energy variations with either the inclusion properties (e.g.~inhomogeneity formed by adsorbing molecules) or with an external geometric constraint. The former, which is the fluctuation-induced contribution to the adsorbing molecules binding force, is shown to be affected by short wavelengths and thus cannot be calculated using the continuum approach. The latter, on the other hand, is shown to be dominated by long wavelength shape fluctuations and hence is properly described by the continuum theory.

Biotechnology• 2016• arXiv
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