Structural Properties of an S-system Model of Mycobacterium Tuberculosis Gene Regulation

Magombedze and Mulder in 2013 studied the gene regulatory system of Mycobacterium Tuberculosis (Mtb) by partitioning this into three subsystems based on putative gene function and role in dormancy/latency development. Each subsystem, in the form of S-system, is represented by an embedded chemical reaction network (CRN), defined by a species subset and a reaction subset induced by the set of digraph vertices of the subsystem. For the embedded networks of S-system, we showed interesting structural properties and proved that all S-system CRNs (with at least two species) are discordant. Analyzing the subsystems as subnetworks, where arcs between vertices belonging to different subsystems are retained, we formed a digraph homomorphism from the corresponding subnetworks to the embedded networks. Lastly, we explored the modularity concept of CRN in the context of digraph.Comment: arXiv admin note: substantial text overlap with arXiv:1909.0294

Comparative Analysis of Kinetic Realizations of Insulin Signaling

Several studies have developed dynamical models to understand the underlying mechanisms of insulin signaling, a signaling cascade that leads to the production of glucose - the human body's main source of energy. Reaction network analysis allows us to extract formal properties of dynamical systems without depending on their parameter values. This study focuses on the comparison of reaction network analysis of insulin signaling in healthy cell (INSMS or INSulin Metabolic Signaling) and in type 2 diabetes (INRES or INsulin RESistance). INSMS and INRES are similar with respect to some network, structo-kinetic, and kinetic properties. However, they differ in the following network properties: the networks have different species sets and functional modules, INRES is more complex than INSMS, and INRES loses the concordance of INSMS. Based on structo-kinetic properties, INSMS is injective while INRES is not. And one of the most significant differences between INSMS and INRES in terms of kinetic properties is the loss of ACR species in INRES (INSMS has 8 ACR species). These results show the insights we gain from analyzing kinetic realization, beyond what we already know from analyzing the dynamical systems of insulin signaling in healthy and insulin-resistant cells.Comment: 30 pages, 1 figur

Mathematical modelling of insulin resistance linking type 2 diabetes and alzheimer\u27s disease

Insulin resistance (IR) is a physiological condition in which cells in the body become resistant to insulin. It is a known risk factor associated to type 2 diabetes (T2D). Recently, the idea that IR plays an important role in the progression of Alzheimer\u27s disease (AD) has been gaining a lot of attention. Comparing the components of the insulin signaling pathway in relation to T2D and AD, there seems to be a lot of commonality. However, on what role IR plays in linking T2D and AD remains unknown. Here, we extended an existing mathematical model (i.e. ODE based) to study and understand the role IR plays in linking T2D and AD. The simulations, together with the experimental data collected from the literature, show that the common components in T2D and AD express the same dynamical behaviors. This result provides the bases for further modelling of insulin signaling pathway in determining the link between T2D and AD

Stochastic process algebra modeling of APP processing influenced by SORLA in Alzheimer\u27s disease

Alzheimer\u27s disease (AD) is a fatal neurodegenerative disorder caused by the formation of neurotoxic beta-amyloid (Aβ) peptides, resulting from the proteolytic breakdown of amyloid precursor protein (APP) by secretases. In this study, I use stochastic process algebra (SPA), a more straightforward approach in modelling system with compartmental components, to model the processing of APP influenced by SORLA, a neuraI sorting receptor. I have shown that the expressivity of the SPA­-based model at the biochemical level is comparable to the ODE-based compartmental model that I developed earlier for APP processing. However, to determine if SPA can be used as an alternative to compartmental modeling, I have to evaluate the behaviour and predictive power of SPA using SPiM and MATLAB

Regulated trafficking of APP by SORLA in Alzheimer’s disease

Proteolytic breakdown of the amyloid precursor protein (APP) by secretases is a complex cellular process that results in formation of neurotoxic Aβ peptides, causative of neurodegeneration in Alzheimer’s disease (AD). Processing involves monomeric and dimeric forms of APP that traffic through distinct cellular compartments where the various secretases reside. Amyloidogenic processing is also influenced by modifiers such as sorting receptor-related protein (SORLA), an inhibitor of APP breakdown and major AD risk factor. This study aims to (i) model the neuronal factors central to the proteolytic processing of amyloid precursor protein (APP),(ii) trace the trafficking of APP in various compartments, and (iii) evaluate the influence of the SORLA on those factors. Using experimental data and literature-based, information we developed a multi-compartment model to simulate the complexity of APP processing in neurons, and to accurately describe the effects of SORLA on these processes. Our model enables regulation of trafficking of APP by SORLA through intracellular compartments. We have successfully confirmed our hypothesis that blockade of APP dimerization is an important aspect of SORLA action on AD. Using this model, we are able to uncover that SORLA not only affects amyloidogenic processing through interaction with APP but also specifically targets β-secretase-the enzyme responsible for initial amyloidogenic cleavage. Our model represents a major conceptual advancement by identifying APP dimers and β-secretase as the two distinct targets of the inhibitory action of SORLA in AD

A systems biology approach to understand amyloidogenic processing in Alzheimer\u27s disease: Making sense of data and providing meaning to models

Systems biology is an interdisciplinary approach that aims at understanding the dynamic interactions between components of living system. Using this approach, we have established mathematical models describing the interactome -of neuronal factors central to the proteolytic processing of amyloid precursor protein (APP) into Aβ, the main constituent of senile plaques in Alzheimer\u27s disease (AD). The models were built based a panel of cell lines in which the amount of APP and of accessory factors can be varied. The quantitative dose-response series have been used to estimate reaction constants of mathematical models describing APP processing. The simulations, together with our experimental data, support a model whereby SORLA prevents APP oligomerization, thereby causing secretases to switch from allosteric to non-allosteric mode of action. We also performed simulations for intermediate concentrations of SORLA and predicted a switch from cooperative to less efficient non-cooperative processing on a low amount of SORLA concentration. Using this model, we are able to uncover that SORLA not only affects amyloidogenic processing through interaction with APP but also specifically targets β-secretase - the enzyme responsible for initial amyloidogenic cleavage. Our model represents a major conceptual advancement by identifying APP dimers and β-secretase as the two distinct targets of the inhibitory action of SORLA in AD

Multi-compartmental modeling of APP processing influenced by SORLA in Alzheimer’s disease

The formation of Aβ plaques from the processing of amyloid precursor protein (APP) is central to the pathology of Alzheimer’s disease. Studies concerning APP processing are conventionally conducted in a single-compartment, where the reactants involved are either in a monomeric or a dimeric form of APP. In the single-compartment model we presented earlier, it showed that the sorting receptor-related protein, SORLA, affects APP processing in both its monomeric and dimeric forms. This study raised the interesting question of what the relative contribution of SORLA in each APP processing step is and how this affects the β-secretase. Results: To answer this question, we developed a multi-compartment model to simulate the complexity of APP processing in neurons, and to accurately describe the effects of SORLA on these processes. Based on dose-response data, our study concludes that SORLA specifically impairs processing of APP dimer, which is the preferred secretase substrate. Furthermore, our model shows how SORLA alters the dynamical behavior of β-secretase, the enzyme responsible for the initial step in the amyloidogenic processing cascade. Conclusions: Our multi-compartment model represents a major conceptual advance over single-compartment models previously used to simulate APP processing; and it identified APP dimers and β-secretase as the two distinct targets of the inhibitory action of SORLA in Alzheimer’s disease