Mission
The conceptualization of biological processes and function is the essence of physiological study. Conceptualization is a necessity in biological systems due to their inherent complexity including the emergence of properties, functions and processes, and the concomitant restriction of other properties, functions and processes. However, “conceptualization” is human endeavor and as such can over-, under- and simply mis-conceptualize. We contend, therefore, that bioscientific inquiry is as much about understanding and managing our own conceptualizations as it is understanding the biophysical processes at work. To this end, we have developed several novel approaches to modeling and analysis of biological systems that facilitate assessing our own uncertainty regarding both the biological system and our conceptualizations of it. The primary focus of this lab is the advancement of our understanding of neural-related physiological and pathological systems by application of these techniques.
What the heck does that mean?
While the basis of this work includes aspects of complexity theory, philosophy, systems biology, and computer science, in the end, we are simply practical neurophysiologists willing to utilize many approaches to speed up our research progress.
Motoneuron: Biophysics to Function
Motoneurons (the neurons that innervate muscles thereby controlling muscle activation) are amazing computational devices capable of both robust response to widely varied input and adaptability to changing conditions. The overarching goal of this project is to probe the inner workings of motoneurons to determine how whole-cell behavior arises from constituent sub-cellular structures. Motoneurons are ideal cells for this exercise due to the wide variety of behaviors they exhibit. For example, in the presence of neuromodulators such as serotonin and nor-epinephrine, motoneurons can greatly amplify their inputs resulting in greater output and much greater physical force. In addition, their firing rate is bistable. The same properties that make them of interest also pose challenges to study. In response, we utilize novel electrophysiological methods to deal with the large cell size and dynamic electrical properties. We also develop original computer models of neuronal behavior that help to break down the many factors that influence the input-output relationship of the cell. Not only do these models help to interpret results, they have also led to improved experimental protocols. This multi-faceted approach has already shown that some long held beliefs about the intrinsic behavior of motoneurons have never been properly examined. The combination of in vitro, in vivo, and computational modeling is the key to our current and future work.
Selected References
Shapiro NP, and Lee RH. Synaptic amplification versus bistability in motoneuron dendritic processing: a top-down modeling approach. J Neurophysiol 97: 3948-3960, 2007.
Jones SM, and Lee RH. Fast amplification of dynamic synaptic inputs in spinal motoneurons in vivo. J Neurophysiol 96: 2200-2206, 2006.
Kuo JJ, Lee RH, Zhang L, and Heckman CJ. Essential role of the persistent sodium current in spike initiation during slowly rising inputs in mouse spinal neurones. J Physiol 574: 819-834, 2006.
Heckman CJ, Lee RH, and Brownstone RM. Hyperexcitable dendrites in motoneurons and their neuromodulatory control during motor behavior. Trends Neurosci 26: 688-695, 2003.
Spinal Cord Injury: Insult to Recovery
The spinal cord serves as a conduit for over 13 million neurons, which directly conduct signals from the brain to the rest of the body. Spinal cord injury can result in a devastating loss of function below the level of insult, including the inability to breathe (in cervical injuries), the loss of sensation, and the loss of voluntary movement. To date, despite promising in vitro and in vivo experimental studies, there are no effective and reliable therapies to directly address the neural damage and subsequent functional losses associated with (SCI). Notably, much of the damage associated with SCI occurs post-insult as a result of a complex cellular cascade referred to as “secondary injury” in which the body’s own response to the mechanical insult, including the failure of cellular respiration, the accumulation of excitotoxic and free radical factors, the initiation of necrotic-apoptotic cascades, and the activation of the immune system, results in an increase in lesion size over the following weeks and months. The large number of interactions among these pathological factors across multiple physiological and time scales makes the experimental characterization and examination of secondary injury as a whole extremely difficult. Thus, our goal as modelers, is to take the numerous detailed pockets of experimental data, each representing an individuated “concept” into SCI, and to aggregate them into a holistic view which allows the overall dynamics of SCI to be revealed. Using our viewpoint aggregation techniques, we have developed a model of secondary injury that challenges pre-existing hypotheses surrounding pathology dynamics and the subsequent therapeutic direction of the SCI field. Our quantitative assessment of thousands of potential therapeutic strategies has resulted in new and exciting ideas for potential therapeutic alternatives.
Selected References
Mitchell, CS, and Lee, RH. A Comprehensive Examination of Secondary Spinal Cord Injury and Potential Multi-Faceted Neuroprotective Therapeutic Strategies. J Neurotrauma (in press) . doi:10.1089/neu.2008.0658.
Axonal Transport: Physiology to Pathologies
Motoneurons can have long axons, which can extend up to a meter in length. These axons contain microtubules and other cytoskeletal elements that serve as a railroad for the transport of necessary constituents (including neurotransmitters, organelles, and proteins) manufactured in the soma that must travel the entire axon length to provide cellular support to the axon itself and to the synapse. The transport carriers for this process are the molecular motors kinesin and dynein, which bind the cargos and take them to their destination. It is hypothesized that disruptions in axonal transport result in lesions that are characteristic of motoneuron diseases such as Amyotrophic Lateral Sclerosis (ALS). Like SCI, axonal transport is a highly interactive process. Because of its small physiological scale over a longer time scale, it too is a difficult pathology to fully characterize experimentally. Using our viewpoint aggregation techniques, we are able to develop physiological models that provide insight into several previously unanswered questions, including the identification of molecular motor performance properties and the interactions of molecular motors, cargos, and the microtubule tracks. Furthermore, we are utilizing our relational analysis methods to apply, evaluate and differentiate hypotheses within the model that describe the role of axonal transport in ALS and to reveal underlying intrinsic mechanisms, which can be used as possible therapeutic avenues.
Selected References
Mitchell, CS, and Lee, RH. A quantitative examination of the role of cargo-exerted forces in axonal transport. J Theor Biol (in press) doi:10.1016/j.jtbi.2008.12.011.
Synaptic Neurotransmitter Spillover: Biophysics to Function
A long-standing theorem in neuroscience is that synapses are “independent”. That is, synaptic communication is on a one-to-one basis such that only synapses that are intended to receive the signal are activated. However, conflicting experimental evidence within the last decade has left us to question the validity of this long-standing assumption. Synaptic spillover is a system in which the sub-cellular mechanisms of the individual components themselves (neurotransmitter release, receptor activation kinetics, etc) are very well known but the result of the interactions of these components on a larger scale (outside the synapse, between clusters of neighboring neurons, etc) are not. Thus, the goal of this project is to utilize our methodologies to relate these known biophysical aspects of the system to the unknown identification and function of neurotransmitter spillover in neural network and system function. Additionally, the intermediate level of complexity of this system makes it an excellent testbed for our analytical methods development. This project has already led to the characterization and identification of the substantial role of glutamate spillover in neural communication at the cerebellar glomerulus and has played an important role in our development and validation of our relational analysis techniques.
References
Mitchell CS, and Lee RH. Output-based comparison of alternative kinetic schemes for the NMDA receptor within a glutamate spillover model. J Neural Eng (in press): 2007.
Mitchell CS, Feng SS, and Lee RH. An analysis of glutamate spillover on the N-methyl-D-aspartate receptors at the cerebellar glomerulus. J Neural Eng 4: 276-282, 2007.
Methodologies
Viewpoint Aggregation
While the biological systems themselves are the ultimate “reference point” for any examination of their processes, it is our understanding of those systems that is the true subject of bioscientific inquiry. Consequently, our approach is to quantitatively compare and contrast multiple points of view as each provides its own unique insight into the system. While the viewpoints for a given system are unique to that system, they typically can be categorized as “top-down,” “bottom-up” or “middle-out.”
Top-down viewpoints tend to be teleological in nature as they hypothesize why a system does what it does. Thus, a top-down model of a system might be comprised of statements positing the purpose of various structures or processes within the model. “The purpose of the heart is to pump blood” would be an example of a top-down hypothesis/model.
Bottom-up viewpoints tend to be mechanistic/first principles-based descriptions from which higher-level behavior is expected to emerge. Thus, bottom-up models try to explain how a system does what it does. “The heart is a collection of contractile elements organized into chambers with one-way flow ports” would be an example of a bottom-up model.
Middle-out viewpoints represent experimental/clinical data. Thus, middle-out viewpoints are ideally devoid of assumptions, hypotheses and conclusions and instead encompass what the system does. “The heart receives neural signals, it contracts periodically and blood flows through it.”
Importantly, characterization of these viewpoints is context-dependent. That is, one researcher’s top-down conceptualizations are another researcher’s, bottom-up mechanisms. For example, the “contractile elements” mechanisms of the heart function researcher would be top-down conceptualizations of the cardiac cellular electrophysiologist.
Viewpoint aggregation is our method of quantitatively comparing these differing perspectives with the ultimate goal of reconciling them to one another. That is, the why, how and what should all represent the system equally well. Our primary tool for reconciling these disparate viewpoints is “relational analysis.” Relational analysis seeks to look past the model/experimental values and instead capture the relationships between those values. This approach is based on our theory on the fundamental nature of biological systems called Heuristic Emergence via Dimensional Restriction and has been shown to be quite effective in multiple systems.
Relational Analysis
A central feature of experiment-based biological research is the examination of the relationships exhibited by the system. Thus, statistical t-tests and linear regression are the hallmarks of experimental literature. Ironically, most model-based biological research focuses on values rather than relationships. A model is considered “validated” if it produces the desired output values. However, in systems with changing dimensionality, there is a very real potential for such a model to be non-unique. That is, the model may merely be one of many possible descriptions of the system. The reintroduction of relationships as part of the validation can ameliorate this problem to some degree in that a vastly richer set of validation criterion can be examined (N measures of output become potential N2 outputs and relationships).
We have developed a set of practical methods based on examining the presumed “intrinsic” relationships exhibited in a dataset (experiment or model derived) that we collectively call relational analysis. When used in conjunction with our viewpoint aggregation methodology, relational analysis is a powerful modeling tool that can dramatically speed model development, and generate predictive assessments of posed hypotheses as the “landscape” produced by relational analysis provides the means for the apples-to-apples comparisons between the various perspectives.
References
Mitchell CS, and Lee RH. Output-based comparison of alternative kinetic schemes for the NMDA receptor within a glutamate spillover model. J Neural Eng 4: 380-389, 2007.
Mechanistic and Conceptual Modeling
While high-minded discussions of complexity theory, analytical techniques, and biological principles are quite interesting, as “practical neurophysiologists” it is important to remember that the details are central in biological systems. This translates into a need for deep understanding of our chosen systems and detailed mechanistic and conceptual modeling. This in turn, translates into immersion into the literature and community of that system. Thus, our modeling techniques augment rather than replace traditional modeling approaches to speed model development and strengthen analysis and prediction.
We have developed cutting-edge models (or more accurately sets of models) of each of our chosen systems each based on both first-hand and published experimental data as well as proposed theories of system function. In accordance with our multiple viewpoint approach, we highly emphasize management and categorization of the embedded principles, assumptions, hypotheses and experimental data that form the bases of these models. Models are implemented in a variety of computer platforms based on the model needs as well as the need for customization and simulation automation.
References
Mitchell CS, Feng SS, and Lee RH. An analysis of glutamate spillover on the N-methyl-D-aspartate receptors at the cerebellar glomerulus. J Neural Eng 4: 276-282, 2007.
Shapiro NP, and Lee RH. Synaptic amplification versus bistability in motoneuron dendritic processing: a top-down modeling approach. J Neurophysiol 97: 3948-3960, 2007.
High-Performance Computing
In any modeling endeavor, there is the inevitable point of needing more computational power. To this end, we explore alternative computing platforms. For the past several years, the best of these alternatives has been reconfigurable computing devices called FPGA’s (field-programmable gate arrays). These devices are a mainstay of the high-end electronics industry and are ideally suited to simulating the systems of differential equations that underlie most biological models. We have repeatedly demonstrated performance gains of 20 or higher compared to general-purpose CPU-based simulation at a comparable cost. However, they are not easy to program. Consequently, our efforts have focused on facilitating FPGA-based simulation. Our latest endeavor is a “simulation compiler” that automatically generates the FPGA-compatible code directly from the differential equations (patent pending).
References
Weinstein RK, Reid MS, and Lee RH. Methodology and design flow for assisted neural-model implementations in FPGAs. IEEE Trans Neural Syst Rehabil Eng 15: 83-93, 2007.
Weinstein RK, and Lee RH. Architectures for high-performance FPGA implementations of neural models. J Neural Eng 3: 21-34, 2006.
Graas EL, Brown EA, and Lee RH. An FPGA-based approach to high-speed simulation of conductance-based neuron models. Neuroinformatics 2: 417-436, 2004.