Analyzing the Behavior of Neuronal Pathways in Alzheimer's Disease Using Petri Net Modeling Approach

A new computational workflow to guide personalized drug therapy

OBJECTIVE

Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics.

METHODS

GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations.

RESULTS

To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months.

CONCLUSION

GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions.

Formal verification confirms the role of p53 protein in cell fate decision mechanism

The bio-cell cycle is controlled by a complex biochemical network of signaling pathways. Modeling such challenging networks accurately is imperative for the understanding of their detailed dynamical behavior. In this paper, we construct, analyze, and verify a hybrid Petri net (HPN) model of a complex biochemical network that captures the role of an important protein (namely p53) in deciding the fate of the cell. We model the behavior of the cell nucleus and cytoplasm as two stochastic and continuous Petri nets, respectively, combined together into a single HPN. We use simulative model checking to verify three different properties that capture the dynamical behavior of p53 protein with respect to the intensity of the ionizing radiation (IR) to which the cell is exposed. For each IR dose, 1000 simulation runs are carried out to verify each property. Our verification results showed that the fluctuations in p53, which relies on IR intensity, are compatible with the findings of the preceding simulation studies that have previously examined the role of p53 in cell fate decision.

Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on "Frontiers in Neuroinformatics"

In the field of neuroscience, the core of the cohort study project consists of collection, analysis, and sharing of multi-modal data. Recent years have witnessed a host of efficient and high-quality toolkits published and employed to improve the quality of multi-modal data in the cohort study. In turn, gleaning answers to relevant questions from such a conglomeration of studies is a time-consuming task for cohort researchers. As part of our efforts to tackle this problem, we propose a hierarchical neuroscience knowledge base that consists of projects/organizations, multi-modal databases, and toolkits, so as to facilitate researchers' answer searching process. We first classified studies conducted for the topic "Frontiers in Neuroinformatics" according to the multi-modal data life cycle, and from these studies, information objects as projects/organizations, multi-modal databases, and toolkits have been extracted. Then, we map these information objects into our proposed knowledge base framework. A Python-based query tool has also been developed in tandem for quicker access to the knowledge base, (accessible at https://github.com/Romantic-Pumpkin/PDT_fninf). Finally, based on the constructed knowledge base, we discussed some key research issues and underlying trends in different stages of the multi-modal data life cycle.

Potential innovation against Alzheimer's disorder: a tricomponent combination of natural antioxidants (vitamin E, quercetin, and basil oil) and the development of its intranasal delivery

Alzheimer's disorder (AD) is very difficult to manage and treat. The complexity of the brain, the blood-brain barrier influencing a multitude of parameters/biomarkers, as well as numerous other factors involved often contribute to the decline in the chances of treatment success. Development of the new drug moiety also takes time, being necessary to consider both its toxicity and related issues. As a strategic plan, a combined strategy is being developed and considered to address AD pathology using several approaches. A combination of vitamin E, quercetin, and basil oil in a nano-based formulation is designed to be administered nasally. The antioxidant present in these natural-based products helps to treat and alleviate AD if a synergistic approach is considered. The three active substances mentioned above are well known for the treatment of neurodegenerative disorders. The nanoformulation helps the co-delivery of the drug moiety to the brain through the intranasal route. In this review, a correlation and use of vitamin E, quercetin, and basil oil in a nano-based formulation is described as an effective way to treat AD. The intranasal administration of drugs is a promising approach for the treatment of neurodegenerative and mental disorders, as this route is non-invasive, enhances the bioavailability, allows a drug dose reduction, bypasses the blood-brain barrier, and reduces the systemic undesired effect. The use of natural products is generally considered to be just as safe; therefore, by using this combined approach, the level of toxicity can be minimized.

Collective behavior in a two-layer neuronal network with time-varying chemical connections that are controlled by a Petri net

In this paper, we propose and study a two-layer network composed of a Petri net in the first layer and a ring of coupled Hindmarsh-Rose neurons in the second layer. Petri nets are appropriate platforms not only for describing sequential processes but also for modeling information circulation in complex systems. Networks of neurons, on the other hand, are commonly used to study synchronization and other forms of collective behavior. Thus, merging both frameworks into a single model promises fascinating new insights into neuronal collective behavior that is subject to changes in network connectivity. In our case, the Petri net in the first layer manages the existence of excitatory and inhibitory links among the neurons in the second layer, thereby making the chemical connections time-varying. We focus on the emergence of different types of collective behavior in the model, such as synchronization, chimeras, and solitary states, by considering different inhibitory and excitatory tokens in the Petri net. We find that the existence of only inhibitory or excitatory tokens disturbs the synchronization of electrically coupled neurons and leads toward chimera and solitary states.

Petri Net modelling approach for analysing the behaviour of [inline-formula removed] and Ca2+ signalling pathways in arrhythmogenic right ventricular cardiomyopathy

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that may result in arrhythmia, heart failure and sudden death. The hallmark pathological findings are progressive myocyte loss and fibro fatty replacement, with a predilection for the right ventricle. This study focuses on the adipose tissue formation in cardiomyocyte by considering the signal transduction pathways including Wnt/[inline-formula removed]-catenin and Wnt/Ca²⁺ regulation system. These pathways are modelled and analysed using stochastic petri nets (SPN) in order to increase our comprehension of ARVC and in turn its treatment regimen. The Wnt/[inline-formula removed]-catenin model predicts that the dysregulation or absence of Wnt signalling, inhibition of dishevelled and elevation of glycogen synthase kinase 3 along with casein kinase I are key cytotoxic events resulting in apoptosis. Moreover, the Wnt/Ca²⁺ SPN model demonstrates that the Bcl2 gene inhibited by c-Jun N-terminal kinase protein in the event of endoplasmic reticulum stress due to action potential and increased amount of intracellular Ca²⁺ which recovers the Ca²⁺ homeostasis by phospholipase C, this event positively regulates the Bcl2 to suppress the mitochondrial apoptosis which causes ARVC.

Key Peptides and Proteins in Alzheimer's Disease

Alzheimer's Disease (AD) is a form of progressive dementia involving cognitive impairment, loss of learning and memory. Different proteins (such as amyloid precursor protein (APP), β- amyloid (Aβ) and tau protein) play a key role in the initiation and progression of AD. We review the role of the most important proteins and peptides in AD pathogenesis. The structure, biosynthesis and physiological role of APP are shortly summarized. The details of trafficking and processing of APP to Aβ, the cytosolic intracellular Aβ domain (AICD) and small soluble proteins are shown, together with other amyloid-forming proteins such as tau and α-synuclein (α-syn). Hypothetic physiological functions of Aβ are summarized. The mechanism of conformational change, the formation and the role of neurotoxic amyloid oligomeric (oAβ) are shown. The fibril formation process and the co-existence of different steric structures (U-shaped and S-shaped) of Aβ monomers in mature fibrils are demonstrated. We summarize the known pathogenic and non-pathogenic mutations and show the toxic interactions of Aβ species after binding to cellular receptors. Tau phosphorylation, fibrillation, the molecular structure of tau filaments and their toxic effect on microtubules are shown. Development of Aβ and tau imaging in AD brain and CSF as well as blood biomarkers is shortly summarized. The most probable pathomechanisms of AD including the toxic effects of oAβ and tau; the three (biochemical, cellular and clinical) phases of AD are shown. Finally, the last section summarizes the present state of Aβ- and tau-directed therapies and future directions of AD research and drug development.

Role of GTPases in the Regulation of Mitochondrial Dynamics in Alzheimer's Disease and CNS-Related Disorders

Data obtained from several studies have shown that mitochondria are involved and play a central role in the progression of several distinct pathological conditions. Morphological alterations and disruptions on the functionality of mitochondria may be related to metabolic and energy deficiency in neurons in a neurodegenerative disorder. Several recent studies demonstrate the linkage between neurodegeneration and mitochondrial dynamics in the spectrum of a promising era called precision mitochondrial medicine. In this review paper, an analysis of the correlation between mitochondria, Alzheimer's disease, and other central nervous system (CNS)-related disorders like the Parkinson's disease and the autism spectrum disorder is under discussion. The role of GTPases like the mfn1, mfn2, opa1, and dlp1 in mitochondrial fission and fusion is also under investigation, influencing mitochondrial population and leading to oxidative stress and neuronal damage.