Artificial Neural Network Prediction of COVID-19 Daily Infection Count
This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output...
Source: Bulletin of Mathematical Biology - April 1, 2024 Category: Bioinformatics Authors: Ning Jiang Charles Kolozsvary Yao Li Source Type: research
Artificial Neural Network Prediction of COVID-19 Daily Infection Count
This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output...
Source: Bulletin of Mathematical Biology - April 1, 2024 Category: Bioinformatics Authors: Ning Jiang Charles Kolozsvary Yao Li Source Type: research
Combinatorial Cooperativity in miR200-Zeb Feedback Network can Control Epithelial-Mesenchymal Transition
Bull Math Biol. 2024 Mar 30;86(5):48. doi: 10.1007/s11538-024-01277-1.ABSTRACTCarcinomas often utilize epithelial-mesenchymal transition (EMT) programs for cancer progression and metastasis. Numerous studies report SNAIL-induced miR200/Zeb feedback circuit as crucial in regulating EMT by placing cancer cells in at least three phenotypic states, viz. epithelial (E), hybrid (h-E/M), mesenchymal (M), along the E-M phenotypic spectrum. However, a coherent molecular-level understanding of how such a tiny circuit controls carcinoma cell entrance into and residence in various states is lacking. Here, we use molecular binding data...
Source: Bulletin of Mathematical Biology - March 30, 2024 Category: Bioinformatics Authors: Mubasher Rashid Brasanna M Devi Malay Banerjee Source Type: research
Combinatorial Cooperativity in miR200-Zeb Feedback Network can Control Epithelial-Mesenchymal Transition
Bull Math Biol. 2024 Mar 30;86(5):48. doi: 10.1007/s11538-024-01277-1.ABSTRACTCarcinomas often utilize epithelial-mesenchymal transition (EMT) programs for cancer progression and metastasis. Numerous studies report SNAIL-induced miR200/Zeb feedback circuit as crucial in regulating EMT by placing cancer cells in at least three phenotypic states, viz. epithelial (E), hybrid (h-E/M), mesenchymal (M), along the E-M phenotypic spectrum. However, a coherent molecular-level understanding of how such a tiny circuit controls carcinoma cell entrance into and residence in various states is lacking. Here, we use molecular binding data...
Source: Bulletin of Mathematical Biology - March 30, 2024 Category: Bioinformatics Authors: Mubasher Rashid Brasanna M Devi Malay Banerjee Source Type: research
Second-Order Effects of Chemotherapy Pharmacodynamics and Pharmacokinetics on Tumor Regression and Cachexia
Bull Math Biol. 2024 Mar 28;86(5):47. doi: 10.1007/s11538-024-01278-0.ABSTRACTDrug dose response curves are ubiquitous in cancer biology, but these curves are often used to measure differential response in first-order effects: the effectiveness of increasing the cumulative dose delivered. In contrast, second-order effects (the variance of drug dose) are often ignored. Knowledge of second-order effects may improve the design of chemotherapy scheduling protocols, leading to improvements in tumor response without changing the total dose delivered. By considering treatment schedules with identical cumulative dose delivered, we...
Source: Bulletin of Mathematical Biology - March 28, 2024 Category: Bioinformatics Authors: Luke Pierik Patricia McDonald Alexander R A Anderson Jeffrey West Source Type: research
Second-Order Effects of Chemotherapy Pharmacodynamics and Pharmacokinetics on Tumor Regression and Cachexia
Bull Math Biol. 2024 Mar 28;86(5):47. doi: 10.1007/s11538-024-01278-0.ABSTRACTDrug dose response curves are ubiquitous in cancer biology, but these curves are often used to measure differential response in first-order effects: the effectiveness of increasing the cumulative dose delivered. In contrast, second-order effects (the variance of drug dose) are often ignored. Knowledge of second-order effects may improve the design of chemotherapy scheduling protocols, leading to improvements in tumor response without changing the total dose delivered. By considering treatment schedules with identical cumulative dose delivered, we...
Source: Bulletin of Mathematical Biology - March 28, 2024 Category: Bioinformatics Authors: Luke Pierik Patricia McDonald Alexander R A Anderson Jeffrey West Source Type: research
Second-Order Effects of Chemotherapy Pharmacodynamics and Pharmacokinetics on Tumor Regression and Cachexia
Bull Math Biol. 2024 Mar 28;86(5):47. doi: 10.1007/s11538-024-01278-0.ABSTRACTDrug dose response curves are ubiquitous in cancer biology, but these curves are often used to measure differential response in first-order effects: the effectiveness of increasing the cumulative dose delivered. In contrast, second-order effects (the variance of drug dose) are often ignored. Knowledge of second-order effects may improve the design of chemotherapy scheduling protocols, leading to improvements in tumor response without changing the total dose delivered. By considering treatment schedules with identical cumulative dose delivered, we...
Source: Bulletin of Mathematical Biology - March 28, 2024 Category: Bioinformatics Authors: Luke Pierik Patricia McDonald Alexander R A Anderson Jeffrey West Source Type: research
Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer's Disease Using Biophysical Modeling and Deep Learning
Bull Math Biol. 2024 Mar 25;86(5):46. doi: 10.1007/s11538-024-01273-5.ABSTRACTAlzheimer's disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that combines deep learning with biophysical modeling, to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched w...
Source: Bulletin of Mathematical Biology - March 26, 2024 Category: Bioinformatics Authors: Soheil Saghafi Timothy Rumbell Viatcheslav Gurev James Kozloski Francesco Tamagnini Kyle C A Wedgwood Casey O Diekman Source Type: research
Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer's Disease Using Biophysical Modeling and Deep Learning
Bull Math Biol. 2024 Mar 25;86(5):46. doi: 10.1007/s11538-024-01273-5.ABSTRACTAlzheimer's disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that combines deep learning with biophysical modeling, to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched w...
Source: Bulletin of Mathematical Biology - March 26, 2024 Category: Bioinformatics Authors: Soheil Saghafi Timothy Rumbell Viatcheslav Gurev James Kozloski Francesco Tamagnini Kyle C A Wedgwood Casey O Diekman Source Type: research
Enumeration of Rooted Binary Unlabeled Galled Trees
Bull Math Biol. 2024 Mar 22;86(5):45. doi: 10.1007/s11538-024-01270-8.ABSTRACTRooted binary galled trees generalize rooted binary trees to allow a restricted class of cycles, known as galls. We build upon the Wedderburn-Etherington enumeration of rooted binary unlabeled trees with n leaves to enumerate rooted binary unlabeled galled trees with n leaves, also enumerating rooted binary unlabeled galled trees with n leaves and g galls, 0 ⩽ g ⩽ ⌊ n - 1 2 ⌋ . The enumerations rely on a recursive decomposition that considers subtrees descended from the nodes of a gall, adopting a restriction on galls that amounts to con...
Source: Bulletin of Mathematical Biology - March 23, 2024 Category: Bioinformatics Authors: Lily Agranat-Tamir Shaili Mathur Noah A Rosenberg Source Type: research
Enumeration of Rooted Binary Unlabeled Galled Trees
Bull Math Biol. 2024 Mar 22;86(5):45. doi: 10.1007/s11538-024-01270-8.ABSTRACTRooted binary galled trees generalize rooted binary trees to allow a restricted class of cycles, known as galls. We build upon the Wedderburn-Etherington enumeration of rooted binary unlabeled trees with n leaves to enumerate rooted binary unlabeled galled trees with n leaves, also enumerating rooted binary unlabeled galled trees with n leaves and g galls, 0 ⩽ g ⩽ ⌊ n - 1 2 ⌋ . The enumerations rely on a recursive decomposition that considers subtrees descended from the nodes of a gall, adopting a restriction on galls that amounts to con...
Source: Bulletin of Mathematical Biology - March 23, 2024 Category: Bioinformatics Authors: Lily Agranat-Tamir Shaili Mathur Noah A Rosenberg Source Type: research
Enumeration of Rooted Binary Unlabeled Galled Trees
Bull Math Biol. 2024 Mar 22;86(5):45. doi: 10.1007/s11538-024-01270-8.ABSTRACTRooted binary galled trees generalize rooted binary trees to allow a restricted class of cycles, known as galls. We build upon the Wedderburn-Etherington enumeration of rooted binary unlabeled trees with n leaves to enumerate rooted binary unlabeled galled trees with n leaves, also enumerating rooted binary unlabeled galled trees with n leaves and g galls, 0 ⩽ g ⩽ ⌊ n - 1 2 ⌋ . The enumerations rely on a recursive decomposition that considers subtrees descended from the nodes of a gall, adopting a restriction on galls that amounts to con...
Source: Bulletin of Mathematical Biology - March 23, 2024 Category: Bioinformatics Authors: Lily Agranat-Tamir Shaili Mathur Noah A Rosenberg Source Type: research
NIAID/SMB Workshop on Multiscale Modeling of Infectious and Immune-Mediated Diseases
Bull Math Biol. 2024 Mar 21;86(5):44. doi: 10.1007/s11538-024-01276-2.ABSTRACTOn July 19th, 2023, the National Institute of Allergy and Infectious Diseases co-organized a workshop with the Society of Mathematical Biology, with the authors of this paper as the organizing committee. The workshop, "Bridging multiscale modeling and practical clinical applications in infectious diseases" sought to create an environment for mathematical modelers, statisticians, and infectious disease researchers and clinicians to exchange ideas and perspectives.PMID:38512541 | PMC:PMC10957590 | DOI:10.1007/s11538-024-01276-2 (Source: Bulletin of...
Source: Bulletin of Mathematical Biology - March 21, 2024 Category: Bioinformatics Authors: Reed S Shabman Morgan Craig Reinhard Laubenbacher Daniel Reeves Liliana L Brown Source Type: research
NIAID/SMB Workshop on Multiscale Modeling of Infectious and Immune-Mediated Diseases
Bull Math Biol. 2024 Mar 21;86(5):44. doi: 10.1007/s11538-024-01276-2.ABSTRACTOn July 19th, 2023, the National Institute of Allergy and Infectious Diseases co-organized a workshop with the Society of Mathematical Biology, with the authors of this paper as the organizing committee. The workshop, "Bridging multiscale modeling and practical clinical applications in infectious diseases" sought to create an environment for mathematical modelers, statisticians, and infectious disease researchers and clinicians to exchange ideas and perspectives.PMID:38512541 | DOI:10.1007/s11538-024-01276-2 (Source: Bulletin of Mathematical Biology)
Source: Bulletin of Mathematical Biology - March 21, 2024 Category: Bioinformatics Authors: Reed S Shabman Morgan Craig Reinhard Laubenbacher Daniel Reeves Liliana L Brown Source Type: research
Impact of Resistance on Therapeutic Design: A Moran Model of Cancer Growth
In this study, a Moran process is used to capture stochastic mutations arising in cancer cells, inferring treatment resistance. The model is used to predict the probability of cancer recurrence given varying treatment modalities. The simulations predict that sustained-low dose therapies would be virtually ineffective for a cancer with a non-negligible probability of developing a sub-clone with resistance tendencies. Furthermore, calibrating the model to in vivo measurements for breast cancer treatment with Herceptin, the model suggests that standard treatment regimens are ineffective in this mouse model. Using a simple Mor...
Source: Bulletin of Mathematical Biology - March 19, 2024 Category: Bioinformatics Authors: Mason S Lacy Adrianne L Jenner Source Type: research
