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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rezaee, Sayeh Nieto, Cesar Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE United States University of Delaware Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology Department of Electrical and Computer Engineering NewarkDE United States

ISBN: (纸本)9798350316339

Numerous biological timing mechanisms are associated with the duration required for key regulatory molecules to reach critical threshold levels. Recent studies have mainly explored how random fluctuations affect the timing of events as molecules accumulate to reach a high threshold. This study examines the complementary scenario: decrease in protein levels until they reach a low critical threshold. We aim to study the timing precision resulting from different degradation pathways and their inherent stochastic nature, especially under low-copy number conditions. More specifically, we explore how integer-valued protein molecular counts decrease stochastically following Michaelis-Menten kinetics. This approach encompasses both zero-order (where the net degradation rate is independent of protein levels) and first-order (where the rate is proportional to protein levels) decay processes. Our results show that, while zero-order decay can be sluggish in terms of mean timing, it provides the highest precision in timing with the lowest noise in threshold-crossing time. Conversely, upon considering randomness in the initial protein levels, first-order decay demonstrates better precision compared to zero-order. Interestingly, in the presence of multiple noise sources, timing stochasticity can be minimized when Michaelis-Menten decay kinetics operate under sub-saturation conditions. This characterization of timing driven by decreasing copy numbers has implications for improving the precision of biological clocks and understanding the timing of cell-cycle events. © 2024 IEEE.

关键词： Stochastic systems

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Feedforward Regulation of Interneuronal Communication 63

Feedforward Regulation of Interneuronal Communication

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Gambrell, Oliver Vahdat, Zahra Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States University of Delaware Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology Department of Electrical and Computer Engineering NewarkDE19716 United States

ISBN: (纸本)9798350316339

We formulate a mechanistic model capturing the dynamics of neurotransmitter release in a chemical synapse. The proposed modeling framework captures key aspects such as the random arrival of action potentials (AP) in the presynaptic (input) neuron, probabilistic docking and release of neurotransmitter-filled vesicles, and clearance of the released neurotransmitter from the synaptic cleft. Analysis of the model results in analytical expressions for the statistical moments of docked vesicle count and the number of neurotransmitters in the cleft as a function of model parameters, and the frequency of presynaptic APs. We next consider a postsynaptic (output) neuron that fires an AP based on integrating upstream neurotransmitter activity. Our results show the existence of an optimal presynaptic AP frequency that maximizes the probability of a postsynaptic AP firing. We extend these results to consider feedforward regulation where in addition to a direct excitatory synapse, the input neuron also impacts the output indirectly via an inhibitory interneuron, and identify parameter regimes where the feedforward neuronal networks result in band-pass filtering, i.e., the output neuron frequency is maximized at intermediate input neuron frequencies. © 2024 IEEE.

关键词： Feedforward neural networks

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Quantifying statistics of gene product copy-number fluctuations: A stochastic hybrid systems approach 63

Quantifying statistics of gene product copy-number fluctuati...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Vahdat, Zahra Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States University of Delaware Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology NewarkDE19716 United States

ISBN: (纸本)9798350316339

Across diverse cell types, genes are often expressed at low levels resulting in significant stochastic fluctuations (noise) in intracellular copy number of mRNAs and proteins over time. Motivated by single-cell experiments, we capture this stochasticity by considering a given gene that toggles between transcriptionally active and inactive states with the time spent in each state being an arbitrary random variable. mRNAs are synthesized from the active state as per a Poisson process and each molecule degrades after a random lifespan (i.e., random time from birth to death). Modeling this process using the Stochastic Hybrid System (SHS) formalism we derive exact analytical results for the statistical moments of molecular counts. Our results show that for fixed mean mRNA numbers, fluctuations in mRNA levels are amplified with decreasing noise in mRNA lifespan. Relaxing the Poisson process assumption, we next consider a scenario where transcription events occur such that the time between successive events is an arbitrarily distributed random variable. In this case, we show that decreasing noise in mRNA lifespan can both increase/decrease mRNA count fluctuations depending on the underlying transcriptional process. Finally, we extend these results to the protein level, where increasing noise in mRNA counts is sometimes associated with decreasing noise in protein copy numbers. © 2024 IEEE.

关键词： Stochastic systems

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Stochastic dynamics of the logistic growth model subjected to environmental perturbations

Stochastic dynamics of the logistic growth model subjected t...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Rezaee, Sayeh Nieto, Cesar Vahdat, Zahra Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE United States University of Delaware Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology Department of Electrical and Computer Engineering NewarkDE United States

ISBN: (纸本)9798350335446

The stochastic logistic model is widely used to capture random fluctuations arising from birth-death processes in ecological populations. We use this model to study the impact of environmental perturbations that may occur naturally or as a consequence of population harvesting. In our model formulation, environmental perturbations occur randomly as per a Poisson process, and the perturbations result in each individual dying with a certain probability of death. Moment closure schemes are employed to derive expressions for the mean and variability in population numbers. Moreover, to quantify the impact of population extinction in our model we compute the probability of extinction using the Finite State Projection (FSP) numerical method. Our analysis shows that rare environmental perturbations with a high probability of death lead to overall larger random fluctuations and extinction risk as compared to frequent perturbations with a low probability of death. Finally, we formulate the problem in the context of population harvesting to find the optimal harvesting rate that maximizes the cumulative yield. © 2023 IEEE.

关键词： Harvesting

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Optimizing Precision in Cellular Clocks Through Self-Regulated Accumulation Of Molecules 27

Optimizing Precision in Cellular Clocks Through Self-Regulat...

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27th International Conference on System Theory, Control and Computing, ICSTCC 2023

作者： Rezaee, Sayeh Nieto, César Singh, Abhyudai Department of Electrical and Computer Engineering University of Delaware NewarkDE United States Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology University of Delaware NewarkDE United States

ISBN: (纸本)9798350337983

The timing of cellular events is often determined by the instant when the level of a chemical species crosses a threshold from an initial value of zero. To attenuate the inherent random fluctuations in this first-passage time, the production rate can be a function of the current species level by implementing a feedback or self-regulation. In this study, we aim to identify the self-regulation strategy that minimizes timing noise around a specified mean first-passage time. Previous results showed that no-feedback is the optimal strategy in the absence of molecule degradation. Based on this result, we study the case where the species may undergo degradation. First, we set the feedback to be a linear function of the number of molecules. We derive analytical formulas concluding that the optimal strategy involves positive feedback, whereby the production rate increases with the number of molecules. When feedback constraints are removed, our numerical methods suggest that a mixed self-regulation scheme is optimal. This strategy involves an initial positive feedback phase, followed by a sharp transition to a negative feedback scheme in proximity to the threshold. In summary, this research quantifies the fundamental limits of timing noise reduction, considering different strategies of self-regulation in situations where time-keeping species may be subjected to degradation. © 2023 IEEE.

关键词： Molecules

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and biology, IEEECONF 2023

作者： Chang, Shun Cheng Liu, Chien Fu Misztal, Marta Hsieh, Yi Hsien Lin, Che Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Electrical Engineering Ntu Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350383386

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients. © 2023 IEEE.

关键词： computerized tomography

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Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations

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Research 2024年第2期2023卷 473-489页

作者： Zhe Liu Wei Qian Wenxiang Cai Weichen Song Weidi Wang Dhruba Tara Maharjan Wenhong Cheng Jue Chen Han Wang Dong Xu Guan Ning Lin Shanghai Mental Health Center Shanghai Jiao Tong University School of MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina Shanghai Key Laboratory of Psychotic Disorders ShanghaiChina School of Information Science and Technology Institute of Computational BiologyNortheast Normal UniversityChangchunChina Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65211USA Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA

Identifying pathogenetic variants and inferring their impact on protein-protein interactions sheds light on their functional consequences on *** by the availability of experimental data on the consequences of protein interaction,most existing methods focus on building models to predict changes in protein binding ***,we introduced MIPPI,an end-to-end,interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from *** was specifically trained to determine the types of variant impact(increasing,decreasing,disrupting,and no effect)on protein-protein *** demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights,which exhibit correlations with the amino acids interacting with the ***,we showed the practicality of MIPPI in prioritizing de novo mutations associated with complex neurodevelopmental disorders and the potential to determine the pathogenic and driving ***,we experimentally validated the functional impact of several variants identified in patients with such ***,MIPPI emerges as a versatile,robust,and interpretable model,capable of effectively predicting mutation impacts on protein-protein interactions and facilitating the discovery of clinically actionable variants.

关键词： consequences weights versatile

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Capturing non-Gaussian statistics in noise-injection driven dynamical systems

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IFAC-PapersOnLine 2024年 57卷 250-255页

作者： Zahra Vahdat Cesar Nieto Abhyudai Singh Department of Electrical and Computer Engineering University of Delaware Newark DE USA 19716 Department of Electrical and Computer Engineering Department of Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA 19716

The canonical approach to modeling stochasticity considers a dynamical system driven by Gaussian white noise. Here, we propose an alternative hybrid formulation, in which continuous dynamics is interspersed with noise injection events that occur at discrete times. The time interval between two successive events is drawn from an arbitrary, positively-valued continuous distribution. Although the hybrid approach converges to the classical stochastic differential equation when events occur sufficiently fast, the hybrid formulation captures a wider range of stochastic phenomena that deviate from Gaussian statistics when events occur at slower timescales. These deviations are mathematically captured by exact analytical expressions for higher-order moments, such as skewness and kurtosis of the state space. We illustrate this approach using the example of a nanosensor impacted by collisions from surrounding molecules. Our results provide an important generalization to capture the stochastic dynamics of physical, biological, and engineering systems and reveal a novel approach for exploiting higher-order moments to infer parameters that are not observable in lower-order moments.

关键词： Event Triggered Control Linear systems Theory Stochastic Modeling Stochastic Differential Equations Time Triggered Stochastic Hybrid systems

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Capturing statistical fluctuations in gene expression activity using discrete stochastic systems

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IFAC-PapersOnLine 2024年第1期58卷 276-281页

作者： Zahra Vahdat Sayeh Rezaee Abhyudai Singh Department of Electrical and Computer Engineering University of Delaware Newark DE USA 19716 Department of Electrical and Computer Engineering Department of Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA 19716

We present a discrete-event system formalism to capture experimentally observed activity of genes in single cells, where a gene can stochastically switch between an active state (synthesis of gene products, such as mRNA and proteins) and an inactive state (synthesis is turned OFF). Extending prior work that focused on memory-less switching kinetics, this contribution extends the models considering the time spent in each state to be an arbitrary positively-valued random variable. Here, memory-based switching is captured via the introduction of molecular timers that measure the time spent in a state, and switching rates are timer-dependent. Hence, a gene is more likely to turn OFF if it has been active for a long period. We provide exact analytical derivations of the mean and extent of stochasticity in the gene product level for these models and systematically study how different model parameters can be calibrated to attenuate or amplify noise in gene expression. These results can be combined with experimental data on measured levels inside individual cells to gain valuable insights into the gene expression process across different cell types, from bacterial to human cells.

关键词： Stochastic modeling Gene switching

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A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

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computers, Materials & Continua 2023年第4期75卷 697-714页

作者： Samra Rehman Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Usman Tariq Fayadh Alenezi Ye Jin Kim Byoungchol Chang Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Department of Management Information Systems CoBAPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Identifying fruit disease manually is time-consuming, expertrequired,and expensive;thus, a computer-based automated system is widelyrequired. Fruit diseases affect not only the quality but also the *** a result, it is possible to detect the disease early on and cure the fruitsusing computer-based techniques. However, computer-based methods faceseveral challenges, including low contrast, a lack of dataset for training amodel, and inappropriate feature extraction for final classification. In thispaper, we proposed an automated framework for detecting apple fruit leafdiseases usingCNNand a hybrid optimization algorithm. Data augmentationis performed initially to balance the selected apple dataset. After that, twopre-trained deep models are fine-tuning and trained using transfer ***, a fusion technique is proposed named Parallel Correlation Threshold(PCT). The fused feature vector is optimized in the next step using a hybridoptimization algorithm. The selected features are finally classified usingmachine learning algorithms. Four different experiments have been carriedout on the augmented Plant Village dataset and yielded the best accuracy of99.8%. The accuracy of the proposed framework is also compared to that ofseveral neural nets, and it outperforms them all.

关键词： Convolutional neural networks deep learning features fusion features optimization classification

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