Mitochondrial chaotic dynamics: Redox-energetic behavior at the edge of stability

Mitochondria serve multiple key cellular functions, including energy generation, redox balance, and regulation of apoptotic cell death, thus making a major impact on healthy and diseased states. Increasingly recognized is that biological network stability/instability can play critical roles in determining health and disease. We report for the first-time mitochondrial chaotic dynamics, characterizing the conditions leading from stability to chaos in this organelle. Using an experimentally validated computational model of mitochondrial function, we show that complex oscillatory dynamics in key metabolic variables, arising at the “edge” between fully functional and pathological behavior, sets the stage for chaos. Under these conditions, a mild, regular sinusoidal redox forcing perturbation triggers chaotic dynamics with main signature traits such as sensitivity to initial conditions, positive Lyapunov exponents, and strange attractors. At the “edge” mitochondrial chaos is exquisitely sensitive to the antioxidant capacity of matrix Mn superoxide dismutase as well as to the amplitude and frequency of the redox perturbation. These results have potential implications both for mitochondrial signaling determining health maintenance, and pathological transformation, including abnormal cardiac rhythms.publishedVersionKembro, Jackelyn Melissa. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.Kembro, Jackelyn Melissa. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Biológicas y Tecnológicas; Argentina.Cortassa, Sonia. National Institutes of Health. NIH · NIA Intramural Research Program; Estados Unidos.Lloyd, David. Cardiff University. School of Biosciences 1; Inglaterra.Sollot, Steven. Johns Hopkins University. Laboratory of Cardiovascular Science; Estados Unidos.Sollot, Steven. Johns Hopkins University. Laboratory of Cardiovascular Science; Estados Unidos

Equifinality, sloppiness and emergent minimal structures of biogeochemical models

Process-based biogeochemical models consider increasingly the control of microorganisms on biogeochemical processes. These models are used for a number of important purposes, from small-scale (mm-cm) controls on pollutant turnover to impacts of global climate change. A major challenge is to validate mechanistic descriptions of microbial processes and predicted emergent system responses against experimental observations. The validity of model assumptions for microbial activity in soil is often difficult to assess due to the scarcity of experimental data. Therefore, most complex biogeochemical models suffer from equifinality, i.e. many different model realizations lead to the same system behavior. In order to minimize parameter equifinality and prediction uncertainty in biogeochemical modeling, a key question is to determine what can and cannot be inferred from available data. My thesis aimed at solving the problem of equifinality in biogeochemical modeling. Thereby, I opted to test a novel mathematical framework (the Manifold Boundary Approximation Method) that allows to systematically tailor the complexity of biogeochemical models to the information content of available data.Prozessbasierte Modelle des Kohlenstoffumsatzes im Boden berücksichtigen zunehmend direkt die Dynamik von mikrobiellen Gruppen und deren Auswirkung auf biogeochemische Prozesse. Der Einsatzbereich dieser Modelle reicht von kleinskaliger Modellierung (mm-cm) von Schadstoffumsätzen im Boden bis hin zu globalen Simulationen der Folgen des Klimawandels. Eine große Herausforderung ist es, mechanistische Beschreibungen mikrobieller Prozesse und das beobachtbare emergente Systemverhalten zu validieren. Besonders schwierig ist die Validierung von Modellannahmen zur Aktivität einzelner mikrobieller Gruppen im Boden, weil direkte Messungen fehlen. Die meisten komplexen biogeochemischen Modelle zeigen Äquifinalität, d.h. viele unterschiedliche Parameterkombinationen führen zu identischen Simulationen. Um die Parameter-Äquifinalität und die Vorhersageunsicherheit biogeochemischer Modelle zu minimieren, ist es wichtig, den Informationsgehalt verfügbarer Messdaten für die Modellparametrisierung zu quantifizieren. Ziel meiner Dissertation war es, das Problem der Äquifinalität zu lösen und einen allgemeingültigen mathematischen Formalismus zu finden, in dessen Rahmen die Komplexität biogeochemischer Modelle systematisch an den Informationsgehalt verfügbarer Daten angepasst werden kann