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Molecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed “BioModME,” a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization.

All relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME.

 

Doping, or incremental substitution of one element for another, is an effective way to tailor a compound’s structure as well as its physical and chemical properties. Herein, we replaced up to 30% of Ni with Co in members of the family of layered LiNiB compounds, stabilizing the high-temperature polymorph of LiNiB while the room-temperature polymorph does not form. By studying this layered boride with in situ high-temperature powder diffraction, we obtained a distorted variant of LiNi0.7Co0.3B featuring a perfect interlayer placement of [Ni0.7Co0.3B] layers on top of each other─a structural motif not seen before in other borides. Because of the Co doping, LiNi0.7Co0.3B can undergo a nearly complete topochemical Li deintercalation under ambient conditions, resulting in a metastable boride with the formula Li0.04Ni0.7Co0.3B. Heating of Li0.04Ni0.7Co0.3B in anaerobic conditions led to yet another metastable boride, Li0.01Ni0.7Co0.3B, with a CoB-type crystal structure that cannot be obtained by simple annealing of Ni, Co, and B. No significant alterations of magnetic properties were detected upon Co-doping in the temperature-independent paramagnet LiNi0.7Co0.3B or its Li-deintercalated counterparts. Finally, Li0.01Ni0.7Co0.3B stands out as an exceptional catalyst for the selective hydrogenation of the vinyl C═C bond in 3-nitrostyrene, even in the presence of other competing functional groups. This research showcases an innovative approach to heterogeneous catalyst design by meticulously synthesizing metastable compounds. 

Integrated computational modeling provides a mechanistic and quantitative framework to characterize alterations in mitochondrial respiration and bioenergetics in response to different metabolic substrates in-silico. These alterations play critical roles in the pathogenesis of diseases affecting metabolically active organs such as heart and kidney. Therefore, the present study aimed to develop and validate thermodynamically constrained integrated computational models of mitochondrial respiration and bioenergetics in the heart and kidney cortex and outer medulla (OM). The models incorporated the kinetics of major biochemical reactions and transport processes as well as regulatory mechanisms in the mitochondria of these tissues. Intrinsic model parameters such as Michaelis–Menten constants were fixed at previously estimated values, while extrinsic model parameters such as maximal reaction and transport velocities were estimated separately for each tissue. This was achieved by fitting the model solutions to our recently published respirometry data measured in isolated rat heart and kidney cortex and OM mitochondria utilizing various NADH- and FADH2-linked metabolic substrates. The models were validated by predicting additional respirometry and bioenergetics data, which were not used for estimating the extrinsic model parameters. The models were able to predict tissue-specific and substrate-dependent mitochondrial emergent metabolic system properties such as redox states, enzyme and transporter fluxes, metabolite concentrations, membrane potential, and respiratory control index under diverse physiological and pathological conditions. The models were also able to quantitatively characterize differential regulations of NADH- and FADH2-linked metabolic pathways, which contribute differently toward regulations of oxidative phosphorylation and ATP synthesis in the heart and kidney cortex and OM mitochondria.

 

A map of central nervous system organization based on vascular networks or angiomes1 provides a layer of organization distinct from familiar neural networks or connectomes. As a well-established example, the capillary networks of the pituitary portal system enable a route for small amounts of neurochemical signals to reach local targets by traveling along specialized pathways, thereby avoiding dilution in the systemic circulation. Anatomical studies provided the first evidence of this vascular pathway in the brain. Specifically, Popa and Fielding identified a portal pathway linking the hypothalamus and the pituitary gland. Their anatomical work was based on hematoxylin and eosin-stained sections of the human brain. They also extensively discussed previous studies of this brain region. Based on the available literature and the appearance of India ink in the hypothalamus after it had been injected into the anterior pituitary, they vigorously argued that the direction of blood flow was from the pituitary gland to the hypothalamus 

There is an increase in usage of smaller cells or femtocells to improve performance and coverage of next-generation heterogeneous wireless networks (HetNets). However, the interference caused by femtocells to neighboring cells is a limiting performance factor in dense HetNets. This interference is being managed via distributed resource allocation methods. However, as the density of the network increases so does the complexity of such resource allocation methods. Yet, unplanned deployment of femtocells requires an adaptable and self-organizing algorithm to make HetNets viable. As such, we propose to use a machine learning approach based on Q-learning to solve the resource allocation problem in such complex networks. By defining each base station as an agent, a cellular network is modeled as a multi-agent network. Subsequently, cooperative Q-learning can be applied as an efficient approach to manage the resources of a multi-agent network. Furthermore, the proposed approach considers the quality of service (QoS) for each user and fairness in the network. In comparison with prior work, the proposed approach can bring more than a four-fold increase in the number of supported femtocells while using cooperative Q-learning to reduce resource allocation overhead.