Biology subjects

Amgalan, A.

Publications and source records attributed to Amgalan, A..

3 recordsLinked to original sources

bioRxiv2020

Development of an MRI Compatible Nasal Drug Delivery Method: Probing Nicotine Addiction Dynamics

BackgroundSubstance abuse is a fundamentally dynamic disease, characterized by repeated oscillation between craving, drug self-administration, reward, and satiety. To model nicotine addiction as a control system, an MR-compatible nicotine delivery system is needed to elicit cyclical cravings. MethodUsing a concentric nebulizer, inserted into one nostril, we delivered each dose--each equivalent to a single cigarette puff--using a syringe pump by nebulizing the nicotine solution using pressurized medical air. A control mechanism permits dual modes: one delivers puffs on a fixed interval programmed by researchers; with the other, subjects press a button to self-administer each nicotine dose. Subjects were therefore able to intuitively "smoke" the equivalent of a cigarette, one "puff" at a time. We dosed each "puff" such that one cigarette would be equal, in nicotine content, to 10 puffs. ResultsWe tested the viability of this delivery method for studying the brains response to nicotine addiction in three steps. First, we established the pharmacokinetics of nicotine delivery, using a dosing scheme designed to gradually achieve saturation, as with a cigarette. Second, we lengthened the time between micro-doses to elicit craving cycles, using both fixed-interval and subject-driven behavior. Finally, we confirmed that the fixed-interval protocol reliably activates brain circuits linked to addiction. ConclusionOur MR-compatible nasal delivery method enables the measurement of neural circuit responses to drug doses on a single-subject level, allowing the development of data-driven predictive models to quantify individual dysregulations of the reward control circuit causing addiction.

neuroscience↗

bioRxiv2020

Younger, but not older, brains are poised at a critical point of functional activity

Brain aging is associated with hypometabolism and associated global changes in functional connectivity. Using fMRI, we show that network synchrony, a collective property of brain activity, decreases with age. Applying quantitative methods from statistical physics, we provide a generative (Ising) model for these changes as a function of the average communication strength between brain regions. In particular, we find healthy brains to be poised at a critical point of this communication strength, enabling a balance between segregated (to functional domains) and integrated (between domains) patterns of synchrony. However, one characteristic of criticality is a high sensitivity to small changes. Thus, minute weakening of pairwise communication between regions, as seen in the aging brain, gives rise to qualitatively abrupt changes in synchrony. Finally, by experimentally modulating metabolic activity in younger adults, we show how metabolism alone-independent of other changes associated with aging-can provide a mechanism for global changes in synchrony.

neuroscience↗

bioRxiv2018

Maximum Caliber can build and infer models of oscillation in a three-gene feedback network

Single-cell protein expression time trajectories provide rich temporal data quantifying cellular variability and its role in dictating fitness. However, theoretical models to analyze and fully extract information from these measurements remain limited for three reasons: i) gene expression profiles are noisy, rendering models of averages inapplicable, ii) experiments typically measure only a few protein species while leaving other molecular actors - necessary to build traditional bottom-up models - unnoticed, and iii) measured data is in fluorescence, not particle number. We have recently addressed these challenges in an alternate top-down approach using the principle of Maximum Caliber (MaxCal) to model genetic switches with one and two protein species. In the present work we address scalability and broader applicability of MaxCal by extending to a three-gene (A, B, C) feedback network that exhibits oscillation, commonly known as the repressilator. We test MaxCals inferential power by using synthetic data of noisy protein number time traces - serving as a proxy for experimental data - generated from a known underlying model. We notice that the minimal MaxCal model - accounting for production, degradation, and only one type of symmetric coupling between all three species - reasonably infers several underlying features of the circuit such as the effective production rate, degradation rate, frequency of oscillation, and protein number distribution. Next, we build models of higher complexity including different levels of coupling between A, B, and C and rigorously assess their relative performance. While the minimal model (with four parameters) performs remarkably well, we note that the most complex model (with six parameters) allowing all possible forms of crosstalk between A, B, and C slightly improves prediction of rates, but avoids ad-hoc assumption of all the other models. It is also the model of choice based on Bayesian Information Criteria. We further analyzed time trajectories in arbitrary fluorescence (using synthetic trajectories) to mimic realistic data. We conclude that even with a three-protein system including both fluorescence noise and intrinsic gene expression fluctuations, MaxCal can faithfully infer underlying details of the network, opening up future directions to model other network motifs with many species.

synthetic biology↗