Altered developmental programming of the mouse mammary gland in female offspring following perinatal dietary exposures : a systems-biology perspective.

Mishaps in prenatal development can influence mammary gland development and, ultimately, affect susceptibility to factors that cause breast cancer. This research was based on the underlying hypothesis that maternal dietary composition during pregnancy can alter developmental (fetal) programming of the mammary gland. We used a computational systems-biology approach and Bayesian-based stochastic search variable selection algorithm (SSVS) to identify differentially expressed genes and biological themes and pathways. Postnatal growth trajectories and gene expression in the mammary gland at 10-weeks of age in female mice were investigated following different maternal diet exposures during prenatal-lactational-early-juvenile development. This correlated a decrease in expression of energy pathways with a reciprocal increase in cytokine and inflammatory-signaling pathways. These findings suggest maternal dietary fat exposure significantly influences postnatal growth trajectories, metabolic programming, and signaling networks in the mammary gland of female offspring. In addition, the adipocytokine pathway may be a sensitive trigger to dietary changes and may influence or enhance activation of an immune response, a key event in cancer development

Projecting COVID-19 cases and hospital burden in Ohio

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”

Analysis of Multivariate High-Dimensional Complex Systems and Applications

Complex systems are those having interactions among system states, where states are each considered as input or output variables. These interactions can produce diverse phenomena, such as hysteresis, chaos, positive and negative feedback, higher-order behavior, and so on. In this dissertation, we consider complex systems having both inputs and outputs and examine these using high dimensional model representation and dynamical systems theory. We define high dimensional model representation for systems having multiple inputs and multiple outputs (V-HDMR). This representation can be interpreted as a communication system having direct and indirect communication channels. When sparse, data requirements for reconstruction are dramatically reduced using compressive sensing. These principles can be exploited in learning or control applications. Equipped with analysis tools, we examine the regulatory network of the epithelial mesenchymal transition (EMT), a key biological process related to cellular detachment events occurring during metastasis. In particular, we define and characterize mathematical models of EMT and generate biological hypotheses. We test and confirm these hypotheses with extensive in vitro and in vivo experimentation. These results reveal EMT to possess a sensitive bistable toggle switch and pronounced hysteresis

Hitting Times of Some Critical Events in RNA Origins of Life

Can a replicase be found in the vast sequence space by random drift? We partially answer this question through a proof-of-concept study of the times of occurrence (hitting times) of some critical events in the origins of life for low-dimensional RNA sequences using a mathematical model and stochastic simulation studies from Python software. We parameterize fitness and similarity landscapes for polymerases and study a replicating population of sequences (randomly) participating in template-directed polymerization. Under the ansatz of localization where sequence proximity correlates with spatial proximity of sequences, we find that, for a replicating population of sequences, the hitting and establishment of a high-fidelity replicator depends critically on the polymerase fitness and sequence (spatial) similarity landscapes and on sequence dimension. Probability of hitting is dominated by landscape curvature, whereas hitting time is dominated by sequence dimension. Surface chemistries, compartmentalization, and decay increase hitting times. Compartmentalization by vesicles reveals a trade-off between vesicle formation rate and replicative mass, suggesting that compartmentalization is necessary to ensure sufficient concentration of precursors. Metabolism is thought to be necessary to replication by supplying precursors of nucleobase synthesis. We suggest that the dynamics of the search for a high-fidelity replicase evolved mostly during the final period and, upon hitting, would have been followed by genomic adaptation of genes and to compartmentalization and metabolism, effecting degree-of-freedom gains of replication channel control over domain and state to ensure the fidelity and safe operations of the primordial genetic communication system of life