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. 2012 Dec 24:6:187.
doi: 10.3389/fnins.2012.00187. eCollection 2012.

Systems biology, bioinformatics, and biomarkers in neuropsychiatry

Ali Alawieh  1 , Fadi A Zaraket, Jian-Liang Li, Stefania Mondello, Amaly Nokkari, Mahdi Razafsha, Bilal Fadlallah, Rose-Mary Boustany, Firas H Kobeissy
Affiliations

Systems biology, bioinformatics, and biomarkers in neuropsychiatry

Ali Alawieh et al. Front Neurosci. .

Abstract

Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a "top-down" method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders' complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software "omics"-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new _targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.

Keywords: autism; bioinformatics; biomarkers; data mining; omics; proteomics; psychiatry; systems biology.

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Figures

Figure 1
Figure 1
Components of neuro-systems biology. The different sources of information for the application of systems biology to NP diseases; the components that are a common _target of HT-discovery tools have been grouped together.
Figure 2
Figure 2
Computational tools in the context of systems biology. Process of systems biology starting from the HT-discovery tools till biomarker discovery and elucidation of biological network: results of HT-discover tools are collected and appropriate validation procedure is run like western blot. Data is then subjected to computational analysis, preprocessed to fit into the different algorithms for clustering and classification. These algorithms include supervised, semi-supervised, and unsupervised statistical and heuristic algorithms along others. Knowledge based and supervised/semi-supervised algorithms have information feeding from public databases that include ontologies, signaling, and interaction networks. Information from these databases are obtained through appropriate data mining tools and software and fed into the algorithms. These algorithms will give clustered data with an estimated accuracy. This clustered data is imported to network analysis, modeling, simulation, and visualization tools. These tools get also information from public databases fed through data mining techniques and also utilize the previous mentioned algorithms. The output of such tools is a model of the implicated network. This model is subject to computational analysis and in silico modeling and simulations for hypotheses screening using computer models. These simulations and models allow refining the proposed network and orient the design of appropriate in vitro and in vivo experiments on a fit animal model or concerned human cells. These in vivo and in vitro experiments are run to assess the hypotheses and predictions of the modeled network and allow for support or refinement of the network. After enough evidence is collected through these rounds of experimentation and hypotheses testing, a final representation of the system is devised that could account to a disease pathogenesis or normal physiology. This representation allows for biomarker and new therapeutic discovery, provides an insight into the pathophysiology or normal physiology and can help update the online databases.
Figure 3
Figure 3
Predictive model of dopamine stimulation. Inverted-U graph of prefrontal cortex function depending on the level of dopamine stimulation; Dopamine agonists have a therapeutic effect on Schizophrenia that is a state of reduced dopamine stimulation while these agonists cause adverse effects to controls who have optimal dopamine stimulation. (Adapted with modifications from Komek et al., 2012).
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