Читать книгу Modern Trends in Structural and Solid Mechanics 3 - Группа авторов - Страница 14

Neuronal networks and their countless pathways, as well as their companion cells, the glia, are the foundation of our functioning brain. These neurons and glia send signals to each other and process information in tremendously complex ways, which we are only beginning to have some understanding of. How pathway choices in neurons lead to physiological or pathological responses are of critical interest. What causes one progression path to be followed rather than another? Are there underlying principles, for example, an optimization of energy or time, a minimization of materials or some other underlying rules and characteristics?

Each neuron cell is composed of numerous organelles and other components, each with their own function, all of which communicate intracellularly and respond as a team to the needs of the cell, as well as to extracellular signals. One of those organelles is the mitochondria. It is well established that the connections between energetics and mitochondria – the organelle responsible for almost all of the energy production in the cell – determine whether physiological or pathological pathways are taken at all levels: subcellular, cellular, tissue and organism. Dysfunction in the mechanisms of energy production appears to be at the center of neurological and neuropsychiatric pathologies. Thus, the profound interest is in understanding how these organelles function and govern their operations. One example of dysfunction is how secondary pathologies in traumatic brain injuries result from energetic dysfunction.

Neurons and glia are the components of brain function, but energy homeostasis must be maintained in order to assure proper functioning, and begins within the subcellular organelle, the mitochondria. This homeostasis is the product of metabolic reactions that are coupled to energy demands in space and time throughout the brain, and are regulated by feedforward and feedback mechanisms. Any mismatch between supply and demand over significant time intervals invariably initiates cascades of dysfunction leading to well-known neurodegenerative and neuropsychiatric pathologies.

These mechanisms, at all scales, “choose” from numerous progression paths, some of which lead to dysfunction due, in large part, to ineffective energy production. Understanding how these “choices” are made requires us to formulate models of the mechanisms alluded to above. If there are governing optimal “choices” or mechanisms, then dysfunction and defects may also sometimes be optimal choices for the organism, and perhaps the optimizations are energy-dependent given the criticality of energy production and usage. Perhaps, optimal choices can lead to negative outcomes. Given the multiple constraints for a successful living organism, there may only be local sub-optimizations. Thus, when we refer to optimization, we are having the above discussion, about how we frame the multitude of progression paths within the cell and external to it. Evolution governed which organisms survived, and which did not, based on their fitness for the environment. At the cellular level, this may entail a minimization of energy use, or perhaps the quickest transfer of information between two neurons.

Understanding these optimality decisions can provide clues for clinical interventions and eventually, cures for some of humanity’s most serious neurodegenerative diseases. Optimal pathways may be identified via the multitude of techniques that have been developed for the physical sciences and engineering, taking the morphological (and mechanical), biochemical and metabolic constraints into account. Constraints such as signaling mechanisms at all scales, cell and organelle morphology, feedback mechanisms, and imbalances of energetics and other intermediate products of mitochondrial functioning are part of a possible formulation. The community is at the beginning of formulating such models. This overview aims to pull together a very brief summary of current thoughts and evidence that, at least for the mitochondrial organelle at the subcellular level, the responses are evolutionarily conserved local and global optimizations.

In the following sections, we discuss some of the key functions of the mitochondria and identify, or suggest how these appear to be evolutionarily conserved properties that are based on an optimization. While optimization in biology has been discussed for decades, the application of optimization methodologies to such systems has been slow to develop, in part due to an incomplete understanding of significant aspects of functioning and an incomplete dataset.

We also refer to the work of Elishakoff (e.g. 1994, 2003, and with Qiu 2001). Elishakoff developed the concept of anti-optimization, where system uncertainties are studied by combining conventional optimization methods with interval analysis. In this approach, the optimal solution is a domain, rather than a point and is a two-level process. At one level, the optimal values of system parameters are obtained, and at the other level, uncertainties are anti-optimized. The anti-optimization yields the least favorable and most favorable system response and relies on knowledge of the bounds of uncertainty, rather than probability density functions. Such an approach can be potentially useful in biological systems where data can be sparse, with uncertainties only known via the upper and lower bounds.