Agent-Based Modeling of Mitochondria Links Sub-Cellular Dynamics to Cellular Homeostasis and Heterogeneity.
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ABSTRACT: Mitochondria are semi-autonomous organelles that supply energy for cellular biochemistry through oxidative phosphorylation. Within a cell, hundreds of mobile mitochondria undergo fusion and fission events to form a dynamic network. These morphological and mobility dynamics are essential for maintaining mitochondrial functional homeostasis, and alterations both impact and reflect cellular stress states. Mitochondrial homeostasis is further dependent on production (biogenesis) and the removal of damaged mitochondria by selective autophagy (mitophagy). While mitochondrial function, dynamics, biogenesis and mitophagy are highly-integrated processes, it is not fully understood how systemic control in the cell is established to maintain homeostasis, or respond to bioenergetic demands. Here we used agent-based modeling (ABM) to integrate molecular and imaging knowledge sets, and simulate population dynamics of mitochondria and their response to environmental energy demand. Using high-dimensional parameter searches we integrated experimentally-measured rates of mitochondrial biogenesis and mitophagy, and using sensitivity analysis we identified parameter influences on population homeostasis. By studying the dynamics of cellular subpopulations with distinct mitochondrial masses, our approach uncovered system properties of mitochondrial populations: (1) mitochondrial fusion and fission activities rapidly establish mitochondrial sub-population homeostasis, and total cellular levels of mitochondria alter fusion and fission activities and subpopulation distributions; (2) restricting the directionality of mitochondrial mobility does not alter morphology subpopulation distributions, but increases network transmission dynamics; and (3) maintaining mitochondrial mass homeostasis and responding to bioenergetic stress requires the integration of mitochondrial dynamics with the cellular bioenergetic state. Finally, (4) our model suggests sources of, and stress conditions amplifying, cell-to-cell variability of mitochondrial morphology and energetic stress states. Overall, our modeling approach integrates biochemical and imaging knowledge, and presents a novel open-modeling approach to investigate how spatial and temporal mitochondrial dynamics contribute to functional homeostasis, and how subcellular organelle heterogeneity contributes to the emergence of cell heterogeneity.
Project description:Societies are complex. Properties of social systems can be explained by the interplay and weaving of individual actions. Rewards are key to understand people's choices and decisions. For instance, individual preferences of where to live may lead to the emergence of social segregation. In this paper, we combine Reinforcement Learning (RL) with Agent Based Modeling (ABM) in order to address the self-organizing dynamics of social segregation and explore the space of possibilities that emerge from considering different types of rewards. Our model promotes the creation of interdependencies and interactions among multiple agents of two different kinds that segregate from each other. For this purpose, agents use Deep Q-Networks to make decisions inspired on the rules of the Schelling Segregation model and rewards for interactions. Despite the segregation reward, our experiments show that spatial integration can be achieved by establishing interdependencies among agents of different kinds. They also reveal that segregated areas are more probable to host older people than diverse areas, which attract younger ones. Through this work, we show that the combination of RL and ABM can create an artificial environment for policy makers to observe potential and existing behaviors associated to rules of interactions and rewards.
Project description:As cellular variability and circadian rhythmicity play critical roles in immune and inflammatory responses, we present in this study an agent-based model of human endotoxemia to examine the interplay between circadian controls, cellular variability and stochastic dynamics of inflammatory cytokines. The model is qualitatively validated by its ability to reproduce circadian dynamics of inflammatory mediators and critical inflammatory responses after endotoxin administration in vivo. Novel computational concepts are proposed to characterize the cellular variability and synchronization of inflammatory cytokines in a population of heterogeneous leukocytes. Our results suggest that there is a decrease in cell-to-cell variability of inflammatory cytokines while their synchronization is increased after endotoxin challenge. Model parameters that are responsible for I?B production stimulated by NF?B activation and for the production of anti-inflammatory cytokines have large impacts on system behaviors. Additionally, examining time-dependent systemic responses revealed that the system is least vulnerable to endotoxin in the early morning and most vulnerable around midnight. Although much remains to be explored, proposed computational concepts and the model we have pioneered will provide important insights for future investigations and extensions, especially for single-cell studies to discover how cellular variability contributes to clinical implications.
Project description:Members of the KDM5 histone H3 lysine 4 demethylase family are associated with therapeutic resistance, including endocrine resistance in breast cancer, but the underlying mechanism is poorly defined. Here we show that genetic deletion of KDM5A/B or inhibition of KDM5 activity increases sensitivity to anti-estrogens by modulating estrogen receptor (ER) signaling and by decreasing cellular transcriptomic heterogeneity. Higher KDM5B expression levels are associated with higher transcriptomic heterogeneity and poor prognosis in ER+ breast tumors. Single-cell RNA sequencing, cellular barcoding, and mathematical modeling demonstrate that endocrine resistance is due to selection for pre-existing genetically distinct cells, while KDM5 inhibitor resistance is acquired. Our findings highlight the importance of cellular phenotypic heterogeneity in therapeutic resistance and identify KDM5A/B as key regulators of this process.
Project description:There can be significant intraspecific individual-level heterogeneity in the intracellular P of phytoplankton, which can affect the population-level growth rate. Several mechanisms can create this heterogeneity, including phenotypic variability in various physiological functions (e.g., nutrient uptake rate). Here, we use modeling to explore the contribution of various mechanisms to the heterogeneity in phytoplankton grown in a laboratory culture. An agent-based model simulates individual cells and their intracellular P. Heterogeneity is introduced by randomizing parameters (e.g., maximum uptake rate) of daughter cells at division. The model was calibrated to observations of the P quota of individual cells of the centric diatom Thalassiosira pseudonana, which were obtained using synchrotron X-ray fluorescence (SXRF). A number of simulations, with individual mechanisms of heterogeneity turned off, then were performed. Comparison of the coefficient of variation (CV) of these and the baseline simulation (i.e., all mechanisms turned on) provides an estimate of the relative contribution of these mechanisms. The results show that the mechanism with the largest contribution to variability is the parameter characterizing the maximum intracellular P, which, when removed, results in a CV of 0.21 compared to a CV of 0.37 with all mechanisms turned on. This suggests that nutrient/element storage capabilities/mechanisms are important determinants of intrapopulation heterogeneity.
Project description:Knowledge of the spatial and temporal dynamics of the gut microbiome is essential to understanding the state of human health, as over a hundred diseases have been correlated with changes in microbial populations. Unfortunately, due to the complexity of the microbiome and the limitations of in vivo and in vitro experiments, studying spatial and temporal dynamics of gut bacteria in a biological setting is extremely challenging. Thus, in silico experiments present an excellent alternative for studying such systems. In consideration of these issues, we have developed a user-friendly agent-based model, GutLogo, that captures the spatial and temporal development of four representative bacterial genera populations in the ileum. We demonstrate the utility of this model by simulating population responses to perturbations in flow rate, nutrition, and probiotics. While our model predicts distinct changes in population levels due to these perturbations, most of the simulations suggest that the gut populations will return to their original steady states once the disturbance is removed. We hope that, in the future, the GutLogo model is utilized and customized by interested parties, as GutLogo can serve as a basic modeling framework for simulating a variety of physiological scenarios and can be extended to capture additional complexities of interest.
Project description:Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent-based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM-specific strengths have yielded success in the area of preclinical mechanistic modeling.
Project description:Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of ?-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for 'synthetic cellular heterogeneity' that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a 'phenotypic sensitivity analysis' method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.
Project description:Mitochondria are a hallmark of eukaryal cells and play an important role in cellular metabolism. There is a vast amount of knowledge available on mitochondrial metabolism and essential mitochondrial functions, such as protein import and iron-sulfur cluster biosynthesis, including multiple studies on the mitochondrial proteome. Therefore, there is a need for in silico approaches to facilitate the analysis of these data. Here, we present a detailed model of mitochondrial metabolism Saccharomyces cerevisiae, including protein import, iron-sulfur cluster biosynthesis, and a description of the coupling between charge translocation processes and ATP synthesis. Model analysis implied a dual dependence of absolute levels of proteins in protein import, iron-sulfur cluster biogenesis and cluster abundance on growth rate and respiratory activity. The model is instrumental in studying dynamics and perturbations in these processes and given the high conservation of mitochondrial metabolism in humans, it can provide insight into their role in human disease.
Project description:Hydrogen peroxide (H2O2) promotes a range of phenotypes depending on its intracellular concentration and dosing kinetics, including cell death. While this qualitative relationship has been well established, the quantitative and mechanistic aspects of H2O2 signaling are still being elucidated. Mitochondria, a putative source of intracellular H2O2, have recently been demonstrated to be particularly vulnerable to localized H2O2 perturbations, eliciting a dramatic cell death response in comparison to similar cytosolic perturbations. We sought to improve our dynamic and mechanistic understanding of the mitochondrial H2O2 reaction network in HeLa cells by creating a kinetic model of this system and using it to explore basal and perturbed conditions. The model uses the most current quantitative proteomic and kinetic data available to predict reaction rates and steady-state concentrations of H2O2 and its reaction partners within individual mitochondria. Time scales ranging from milliseconds to one hour were simulated. We predict that basal, steady-state mitochondrial H2O2 will be in the low nM range (2-4 nM) and will be inversely dependent on the total pool of peroxiredoxin-3 (Prx3). Neglecting efflux of H2O2 to the cytosol, the mitochondrial reaction network is expected to control perturbations well up to H2O2 generation rates ~50 μM/s (0.25 nmol/mg-protein/s), above which point the Prx3 system would be expected to collapse. Comparison of these results with redox Western blots of Prx3 and Prx2 oxidation states demonstrated reasonable trend agreement at short times (≤ 15 min) for a range of experimentally perturbed H2O2 generation rates. At longer times, substantial efflux of H2O2 from the mitochondria to the cytosol was evidenced by peroxiredoxin-2 (Prx2) oxidation, and Prx3 collapse was not observed. A refined model using Monte Carlo parameter sampling was used to explore rates of H2O2 efflux that could reconcile model predictions of Prx3 oxidation states with the experimental observations.