- ASSOCIATE PROFESSOR Neuroscience
- ASSOCIATE PROFESSOR Structural and Chemical Biology
Columbia University College of Physicians and Surgeons
Ph.D., University of California
- Visit Vladimir Brezina's website Aplysia Research at Mount Sinai for more information.
Biological Control MechanismsOur experimental work focuses on the central pattern generating and neuromuscular circuits that participate in various feeding behavior of the mollusc Aplysia. To be able to produce integrated and efficient behavior under a variety of circumstances, these circuits have particular structures and incorporate a number of dynamic control mechanisms. For example, their output is controlled, both centrally and in the periphery, by complex local networks of interacting neuromodulators. The experimentally advantageous Aplysia system permits the cellular effects of the modulators to be dissected, using such techniques as voltage and patch clamp, optical recordings of contractions of single muscle fibers, and intracellular calcium measurements. The effects can then be functionally reconstructed in the behavioral context in semi-intact and intact preparations, and understood conceptually with the use of realistic as well as more abstract mathematical modeling techniques. The goal is to understand not just the Aplysia system but to derive from it more general principles governing the operation of such control mechanisms in biological systems. Our experiments and theoretical studies have raised and begun to address a number of interesting questions along these lines. Can one analyze the structure of the circuit or network in terms of functional modules on the level of which the logic of the system, and the actions of its control mechanisms, might be better understood? What is the operating logic in complex biological signaling networks such as those of the multiple neuromodulators? What functional significance do different dynamics of the signal transduction steps and different temporal patterns of the signals have? How is information encoded and decoded in such networks? And finally, how do all of these factors constrain or facilitate control of one such network by another, in particular, the ability of the central nervous system to control the behavioral performance of the periphery?
Brezina V, Orekhova IV, Weiss KR. Functional uncoupling of linked neurotransmitter effects by combinatorial convergence. Science 1996 Aug 9; 273(5276): 806-810.
Brezina V, Orekhova IV, Weiss KR. Control of time-dependent biological processes by temporally patterned input. Proc Natl Acad Sci U S A 1997 Sep 16; 94(19): 10444-10449.
Brezina V, Weiss KR. Analyzing the functional consequences of transmitter complexity. Trends Neurosci 1997 Nov; 20(11): 538-543.
Brezina V, Orekhova IV, Weiss K. The neuromuscular transform: the dynamic, nonlinear link between motor neuron firing patterns and muscle contraction in rhythmic behaviors. J Neurophysiol 2000 Jan; 83(1): 207-31.The nervous system issues motor commands to muscles to generate behavior. All such commands must, however, pass through a filter that we call here the neuromuscular transform (NMT). The NMT transforms patterns of motor neuron firing to muscle contractions. This work is motivated by the fact that the NMT is far from being a straightforward, transparent link between motor neuron and muscle. The NMT is a dynamic, nonlinear, and modifiable filter. Consequently motor neuron firing translates to muscle contraction in a complex way. This complexity must be taken into account by the nervous system when issuing its motor commands, as well as by us when assessing their significance. This is the first of three papers in which we consider the properties and the functional role of the NMT. Physiologically, the motor neuron-muscle link comprises multiple steps of presynaptic and postsynaptic Ca(2+) elevation, transmitter release, and activation of the contractile machinery. The NMT formalizes all these into an overall input-output relation between patterns of motor neuron firing and shapes of muscle contractions. We develop here an analytic framework, essentially an elementary dynamical systems approach, with which we can study the global properties of the transformation. We analyze the principles that determine how different firing patterns are transformed to contractions, and different parameters of the former to parameters of the latter. The key properties of the NMT are its nonlinearity and its time dependence, relative to the time scale of the firing pattern. We then discuss issues of neuromuscular prediction, control, and coding. Does the firing pattern contain a code by means of which particular parameters of motor neuron firing control particular parameters of muscle contraction? What information must the motor neuron, and the nervous system generally, have about the periphery to be able to control it effectively? We focus here particularly on cyclical, rhythmic contractions which reveal the principles particularly clearly. Where possible, we illustrate the principles in an experimentally advantageous model system, the accessory radula closer (ARC)-opener neuromuscular system of Aplysia. In the following papers, we use the framework developed here to examine how the properties of the NMT govern functional performance in different rhythmic behaviors that the nervous system may command.
Brezina V, Church P, Weiss K. Temporal pattern dependence of neuronal peptide transmitter release: models and experiments. J Neurosci 2000 Sep 15; 20(18): 6760-72.In this paper we construct, on the basis of existing experimental data, a mathematical model of firing-elicited release of peptide transmitters from motor neuron B15 in the accessory radula closer neuromuscular system of Aplysia. The model consists of a slow "mobilizing" reaction and the fast release reaction itself. Experimentally, however, it was possible to measure only the mean, heavily averaged release, lacking fast kinetic information. Considered in the conventional way, the data were insufficient to completely specify the details of the model, in particular the relative properties of the slow and the unobservable fast reaction. We illustrate here, with our model and with additional experiments, how to approach such a problem by considering another dimension of release, namely its pattern dependence. The mean release is sensitive to the temporal pattern of firing, even to pattern on time scales much faster than the time scale on which the release is averaged. The mean release varies with the time scale and magnitude of the pattern, relative to the time scale and nonlinearity of the release reactions with which the pattern interacts. The type and magnitude of pattern dependence, especially when correlated systematically over a range of patterns, can therefore yield information about the properties of the release reactions. Thus, temporal pattern can be used as a probe of the release process, even of its fast, directly unobservable components. More generally, the analysis provides insights into the possible ways in which such pattern dependence, widespread especially in neuropeptide- and hormone-releasing systems, might arise from the properties of the underlying cellular reactions.
Brezina V, Orekhova IV, Weiss KR. Neuromuscular modulation in Aplysia II Modulation of the neuromuscular transform in behavior. Journal of Neurophysiology 2003 Oct; 90(4): 2613-2628.
Brezina V, Orekhova IV, Weiss KR. Neuromuscular modulation in Aplysia I Dynamic model. Journal of Neurophysiology 2003 Oct; 90(4): 2592-2612.
Horn CC, Zhurov Y, Orekhova IV, Proekt A, Kupfermann I, Weiss KR, Brezina V. Cycle-to-cycle variability of neuromuscular activity in Aplysia feeding behavior. J Neurophysiol 2004 Jul; 92(1): 157-80.
Proekt A, Brezina V, Weiss KR. Dynamical basis of intentions and expectations in a simple neuronal network. Proc Natl Acad Sci U S A 2004 Jun 22; 101(25): 9447-52.
Brezina V, Horn CC, Weiss KR. Modeling neuromuscular modulation in Aplysia. III. Interaction of central motor commands and peripheral modulatory state for optimal behavior. J Neurophysiol 2005 Mar; 93(3): 1523-56.
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Dr. Brezina did not report having any of the following types of financial relationships with industry during 2015 and/or 2016: consulting, scientific advisory board, industry-sponsored lectures, service on Board of Directors, participation on industry-sponsored committees, equity ownership valued at greater than 5% of a publicly traded company or any value in a privately held company. Please note that this information may differ from information posted on corporate sites due to timing or classification differences.
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