Zhen Qi

Computational Systems Analysis of Dopamine Metabolism

A prominent feature of Parkinsons disease (PD) is the loss of dopamine in the striatum, and many therapeutic interventions for the disease are aimed at restoring dopamine signaling. Dopamine signaling includes the synthesis, storage, release, and recycling of dopamine in the presynaptic terminal and activation of pre- and post-synaptic receptors and various downstream signaling cascades. As an aid that might facilitate our understanding of dopamine dynamics in the pathogenesis and treatment in PD, we have begun to merge currently available information and expert knowledge regarding presynaptic dopamine homeostasis into a computational model, following the guidelines of biochemical systems theory. After subjecting our model to mathematical diagnosis and analysis, we made direct comparisons between model predictions and experimental observations and found that the model exhibited a high degree of predictive capacity with respect to genetic and pharmacological changes in gene expression or function. Our results suggest potential approaches to restoring the dopamine imbalance and the associated generation of oxidative stress. While the proposed model of dopamine metabolism is preliminary, future extensions and refinements may eventually serve as an in silico platform for prescreening potential therapeutics, identifying immediate side effects, screening for biomarkers, and assessing the impact of risk factors of the disease.

Steps of modeling complex biological systems.

A disease like schizophrenia results from the malfunctioning of a complex, multi-faceted biological system. As a consequence, the root causes of such a disease and the trajectories from health toward the disease are very difficult to comprehend with simple cause-and-effect reasoning. Similarly, reductionistic investigations are crucial for the discovery of specific disease mechanisms, but they are not sufficient for comprehensive assessments and explanations. A promising option for advancing the field is the utilization of mathematical models that can quantitatively account for hundreds of components and their interactions and thus have the potential of truly explaining complex diseases. While the potential of mathematical models is quite evident in principle, their practical implementation is a daunting task. On the one hand, many distinctly different approaches are possible. For instance, in the case of schizophrenia, models could focus on neurological aspects, physiological features, or the biochemical malfunctioning within some cell complexes in the brain, and each model would ultimately be very different. On the other hand, it seems that there are no rules or recommendations that guide the development of a new mathematical model from scratch. We discuss here that, even though mathematical models in biology and medicine may ultimately have a very different appearance, their development can be structured as a sequence of generic steps. Major drivers for many of the details of model development are the goals and objectives of the modeling task and the availability and quality of data that can be used for model design and validation.

Computational modeling of synaptic neurotransmission as a tool for assessing dopamine hypotheses of schizophrenia.

Schizophrenia is a severe and complex mental disorder that causes an enormous societal and financial burden. Following the identification of dopamine as a neurotransmitter and the invention of antipsychotic drugs, the dopamine hypothesis was formulated to suggest hyperdopaminergia as the cause of schizophrenia. Over time there have been modifications and improvements to the dopamine-based model of schizophrenia, as well as models that do not implicate dopamine dysregulation as a primary cause of the disease. It seems clear by now that disruption of dopamine homeostasis occurs in schizophrenia and likely plays a major contributory role to its symptoms. Three primary versions of the dopamine hypothesis of schizophrenia have been proposed. In this article, we review these hypotheses and subject their assumptions to a computational model of dopamine signaling. Based on this review and analysis, we propose slight revisions to the existing hypotheses. Although we are still at the beginning of a comprehensive modeling effort to capture relevant phenomena associated with schizophrenia, our preliminary models have already yielded intriguing results and identified the systems biological approach as a beneficial complement to clinical and experimental research and a powerful method for exploring human diseases like schizophrenia. It is hoped that the past, present and future models will support and guide refined experimentation and lead to a deeper understanding of schizophrenia.

The internal state of medium spiny neurons varies in response to different input signals

BackgroundParkinsons disease, schizophrenia, Huntingtons chorea and drug addiction are manifestations of malfunctioning neurons within the striatum region at the base of the human forebrain. A key component of these neurons is the protein DARPP-32, which receives and processes various types of dopamine and glutamate inputs and translates them into specific biochemical, cellular, physiological, and behavioral responses. DARPP-32s unique capacity of faithfully converting distinct neurotransmitter signals into appropriate responses is achieved through a complex phosphorylation-dephosphorylation system that evades intuition and predictability.ResultsTo gain deeper insights into the functioning of the DARPP-32 signal transduction system, we developed a dynamic model that is robust and consistent with available clinical, pharmacological, and biological observations. Upon validation, the model was first used to explore how different input signal scenarios are processed by DARPP-32 and translated into distinct static and dynamic responses. Secondly, a comprehensive perturbation analysis identified the specific role of each component on the systems signal transduction ability.ConclusionsOur study investigated the effects of various patterns of neurotransmission on signal integration and interpretation by DARPP-32 and showed that the DARPP-32 system has the capability of discerning surprisingly many neurotransmission scenarios. We also screened out potential mechanisms underlying this capability of the DARPP-32 system. This type of insight deepens our understanding of neuronal signal transduction in normal medium spiny neurons, sheds light on neurological disorders associated with the striatum, and might aid the search for intervention targets in neurological diseases and drug addiction.

A mathematical model of presynaptic dopamine homeostasis: implications for schizophrenia.

Several lines of evidence implicate altered dopamine neurotransmission in schizophrenia. Current drugs for schizophrenia focus on postsynaptic sites of the dopamine signaling pathways, but do not target presynaptic dopamine metabolism. We have begun to develop a mathematical model of dopamine homeostasis, which will aid our understanding of how genetic, environmental, and pharmacological factors alter the functioning of the presynaptic dopamine neuron. Formulated within the modeling framework of BIOCHEMICAL SYSTEMS THEORY, the mathematical model integrates relevant metabolites, enzymes, transporters, and regulators involved in the control of the biochemical environment within the dopamine neuron. In this report we use the model to assess several components and factors that affect the dopamine neuron and have been implicated in schizophrenia. These include the enzymes COMT, MAO, and TH, different dopamine transporters, as well as administration of amphetamine or cocaine. We also investigate scenarios that could increase (or decrease) dopamine neurotransmission and thus exacerbate (or alleviate) symptoms of schizophrenia. Our results indicate that the model predicts the effects of various factors related to schizophrenia on the homeostasis of the presynaptic dopamine neuron rather well. Upon further refinements and testing, the model has the potential of serving as a tool for screening novel therapeutics aimed at altering presynaptic dopamine function and thereby potentially ameliorating some of the symptomology of schizophrenia.

Computational analysis of determinants of dopamine (DA) dysfunction in DA nerve terminals.

Dopamine signaling is involved in a number of brain pathways, and its disruption has been suggested to be involved in the several disease states, including Parkinsons disease (PD), schizophrenia, and attention deficit hyperactivity disorder (ADHD). It has been hypothesized that altered storage, release, and reuptake of dopamine contributes to both the hypo‐ and hyperdopaminergic states that exist in various diseases. Here, we use our recently described mathematical model of dopamine metabolism, combined with a comprehensive Monte Carlo simulation analysis, to identify key determinants of dopamine metabolism associated with the dysregulation of dopamine homeostasis that may contribute to the pathogenesis of dopamine‐based disorders. Our model reveals that the dopamine transporter (DAT), the vesicular monoamine transporter (VMAT2), and the enzyme monoamine oxidase (MAO) are the most influential components controlling the synaptic level of dopamine and the formation of toxic intracellular metabolites. The results are consistent with experimental observations and point to metabolic processes and combinations of processes that may be biochemical drivers of dopamine neuron degeneration. Since many of the identified components can be targeted therapeutically, the model may aid in the design of combined therapeutic regimens aimed at restoring proper dopamine signaling with toxic intermediates under control. Synapse 63:1133–1142, 2009.

Rotenone and paraquat perturb dopamine metabolism: A computational analysis of pesticide toxicity

Pesticides, such as rotenone and paraquat, are suspected in the pathogenesis of Parkinsons disease (PD), whose hallmark is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta. Thus, compounds expected to play a role in the pathogenesis of PD will likely impact the function of dopaminergic neurons. To explore the relationship between pesticide exposure and dopaminergic toxicity, we developed a custom-tailored mathematical model of dopamine metabolism and utilized it to infer potential mechanisms underlying the toxicity of rotenone and paraquat, asking how these pesticides perturb specific processes. We performed two types of analyses, which are conceptually different and complement each other. The first analysis, a purely algebraic reverse engineering approach, analytically and deterministically computes the altered profile of enzyme activities that characterize the effects of a pesticide. The second method consists of large-scale Monte Carlo simulations that statistically reveal possible mechanisms of pesticides. The results from the reverse engineering approach show that rotenone and paraquat exposures lead to distinctly different flux perturbations. Rotenone seems to affect all fluxes associated with dopamine compartmentalization, whereas paraquat exposure perturbs fluxes associated with dopamine and its breakdown metabolites. The statistical results of the Monte-Carlo analysis suggest several specific mechanisms. The findings are interesting, because no a priori assumptions are made regarding specific pesticide actions, and all parameters characterizing the processes in the dopamine model are treated in an unbiased manner. Our results show how approaches from computational systems biology can help identify mechanisms underlying the toxicity of pesticide exposure.

Mesoscopic Models of Neurotransmission as Intermediates between Disease Simulators and Tools for Discovering Design Principles

Two grand challenges have been declared as premier goals of computational systems biology. The first is the discovery of network motifs and design principles that help us understand and rationalize why biological systems are organized in the manner we encounter them rather than in a different fashion. The second goal is the development of computational models supporting the investigation of complex systems, in particular, as simulation platforms in personalized medicine and predictive health. Interestingly, most published systems models in biology contain between a handful and a few dozen variables. They are usually too complicated for systemic analyses of organizing principles, but they are at the same time too coarse to allow reliable simulations of diseases. While it may thus appear that the modeling efforts of the past have missed the declared targets of systems biology, we argue in this article that midsized mesoscopic models are excellent starting points for pursuing both goals in computational systems biology.

Mathematical Models in Schizophrenia

Schizophrenia is a severe and complex mental disorder that causes an enormous societal and financial burden. Our current understanding of schizophrenia is very fragmented, and the disease is still regarded as an enigma even though its main features have been recognized for centuries. When the post-genomic era arrived, high-throughput instruments and methods ushered in an explosion in the generation of large datasets. This rich information began to facilitate the development of mathematical models, and these models are beginning to show the potential of propelling schizophrenia research onto a new, quantitative level. As schizophrenia is a complex disease that involves uncounted biological processes, there is no complete model which covers even the majority of aspects pertaining to schizophrenia. Instead, every currently available model focuses on a certain aspect of the disease. In this chapter, we review mathematical models of schizophrenia according to their mathematical foundation and structure, as well as the phenomenon they represent. Thus, an outline of mathematical modeling practices in schizophrenia is presented for biologists, psychiatrists, and clinicians. In the future, mathematical models may be expected to provide valuable guidance in the long-term investigation of complex diseases like schizophrenia.

A heuristic model of alcohol dependence.

Background Substance dependence poses a critical health problem. Sadly, its neurobiological mechanisms are still unclear, and this lack of real understanding is reflected in insufficient treatment options. It has been hypothesized that alcohol effects are due to an imbalance between neuroexcitatory and neuroinhibitory amino acids. However, glutamate and GABA interact with other neurotransmitters, which form a complicated network whose functioning evades intuition and should be investigated systemically with methods of biomedical systems analysis. Methods and Results We present a heuristic model of neurotransmitters that combines a neurochemical interaction matrix at the biochemical level with a mobile describing the balances between pairs of neurotransmitters at the physiological and behavioral level. We investigate the effects of alcohol on the integrated neurotransmitter systems at both levels. The model simulation results are consistent with clinical and experimental observations. The model demonstrates that the drug diazepam for symptoms of alcohol withdrawal effectively reduces the imbalances between neurotransmitters. Moreover, the acetylcholine signal is suggested as a novel target for treatment of symptoms associated with alcohol withdrawal. Conclusions Efficient means of integrating clinical symptoms across multiple levels are still scarce and difficult to establish. We present a heuristic model of systemic neurotransmitter functionality that permits the assessment of genetic, biochemical, and pharmacological perturbations. The model can serve as a tool to represent clinical and biological observations and explore various scenarios associated with alcohol dependence and its treatments. It also is very well suited for educational purposes.