Description:FIELD OF THE INVENTION
The present invention relates to a cellular network model for identifying biomarkers of Alzheimer's disease.
More particularly, the present invention relates to the field of systems biology, particularly to the identification of cellular biomarkers of Alzheimer's disease using mathematical modelling and biochemical systems theory.

BACKGROUND OF THE INVENTION
Neurological disorders are becoming one of the most common diseases, with a significant burden on patients, their families, and society. The most common neurodegenerative disorder is Alzheimer’s disease, an age-dependent disease that affects an individual’s memory and cognitive skills.

Alzheimer’s disease (AD) is attributed as an age-related progressive condition that causes neurodegeneration manifesting detrimental loss of memory and other cognitive functions. This has been a concern to the scientific and medical community about the increasing prevalence of the disease and its burden on patients, caregivers, and society. Due to the disease complexity and poor understanding of its cellular mechanisms, there are no medications found yet to completely stop the disease progression.

In the early stages, the detection and diagnosis of AD is not easy, since the symptoms are often misdiagnosed or related to normal cognitive changes of aging. This limits AD patients from several benefits such as anticipatory preparation for drug paradigms, support from caregivers, participation in clinical trials etc. Although there are existing FDA approved management medicine for AD, a cure for the disease is not available yet. Lack of usable models, ethical obstacles, inadequate systematic studies on biological pathways and species interactions, finding specific and reliable biomarkers at the onset are some of the reasons for it. Scientific studies focused on the identification of biomarkers in AD and other neurodegenerative diseases have increased over the last decade. Disease-specific biomarkers at early stages of AD are important for early diagnosis, predicting the rate of progression and patients' response to treatment. However, existing in vitro and in vivo models fail to exhibit a rate-based complete linking between clinical symptoms and biomarkers.

Some of the major challenges that could be focused on to develop solutions for early detection and treatment in the case of Alzheimer’s disease include understanding the emergent properties due to misfolded cellular proteins, finding adequate biomarkers, or developing early diagnosis tools. Misfolded cellular components cause neurotoxic effects in a concentration-dependent manner. It is not yet clear whether it is a single misfolded protein or other mechanisms involved in their signalling pathway is crucial in the disease progression.

Currently, there are no specific tests that exist to diagnose Alzheimer’s disease. Disease diagnosis is based on a person’s medical history and neurological examination. By the time the symptoms start showing in a patient, 60-80% of the neurons will be lost. As the disease begins to develop from cellular level changes, decades of research have been helped to identify genes/proteins that are responsible for Alzheimer’s. Although the prior state of art has high-quality experiments or biological data from technologies such as microarrays, mass spectrometry or nuclear magnetic resonance which describe cellular status at a metabolic, proteomic or genomic level. But unravelling how multiple factors interact to produce different phenotypes is challenging and demands a systems approach.

Developing computational models that simulate experimentally known pathways linking biochemical and molecular changes in the disease helps to analyze behavior of complex systems from the cellular level. These models can give better systems level understanding of common disease pathways and their progression that may lead to early diagnosis, or to novel therapeutic targets and discovery of new biomarkers.

Reference is made to Patent application no. WO2022104136A2 which discloses a method to diagnose and treat tauopathy, e.g., Alzheimer's disease, in a subject, the methods comprising, in part, identifying one or more post-translation modifications (PTMs) in the subject.y. The invention teaches diagnosing the disease based on identifying different post-translational modifications associated with a tauopathy. The method discloses in the invention depends on laboratory experiments.

Another reference is made to Patent application no. US20210405074A1 which discloses analysis and identification of global metabolic changes in Alzheimer's disease (AD). More particularly, the present disclosure provides materials and methods relating to the use of metabolomics as a biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. Defining metabolic changes during AD disease trajectory and their relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

Another reference is made to Patent application no. US8008025B2 which discloses methods for diagnosing neurodegenerative diseases, such as Alzheimer's Disease, Parkinson's Disease, and dementia with Lewy body disease by detecting a pattern of gene product expression in a cerebrospinal fluid sample and comparing the pattern of gene product expression from the sample to a library of gene product expression pattern known to be indicative of the presence or absence of a neurodegenerative disease. The methods also provide for monitoring neurodegenerative disease progression and assessing the effects of treatment. Also provided are kits, systems and devices for practicing the subject methods.

The computational models in the prior state of art to study the cellular reactions involved in Alzheimer’s are only targeted to study on protein accumulation and its significance in disease conditions, not the other mechanisms, critical feedback mechanisms or cross-talk. Without those details included in an entire network model, it is not possible to understand the exact disease mechanisms and key biomarkers.

There is an urgent need to develop a complete disease cellular network model to understand the disease mechanisms from a cellular level so that it may help to identify novel biomarkers and to develop a disease prediction tool for the early detection, diagnosis, analysis of the degree of progression, to find new therapeutic targets and to plan for patients’ medication, caregiving or end-of-life planning according to the disease stages.

ADVANTAGES OF THE PRESENT INVENTION OVER THE EXISTING STATE OF ART:
Currently there is no accurate method for detecting or diagnosing Alzheimer’s disease or any medicine to stop the disease. The present invention provides an evidence-based disease cellular network model of Alzheimer’s disease which helps to incorporate massive experimental data to study the complex behaviour and emergence of the disease. The present invention can map single or multiple protein changes to phenotypic changes and disease symptoms which helps to define disease stages and progression. The predictions from the model of the present system can be used to test different experimental hypotheses for disease biomarkers and drug targets and develop a clinical tool for early diagnosis and medication. The present invention helps researchers to take a multi-modal approach to identify promising drug targets, and design compounds or therapies that can selectively and effectively modulate these targets to treat diseases. It connects the model predictions to symptoms helps to understand cellular changes in Alzheimer’s disease across different disease stages, which is crucial in drug discovery as most of the current medications for Alzheimer’s disease fail to improve cognitive symptoms/ neurodegeneration in patients. It further connects the model predictions to potential conditions and developing a digital twin for Alzheimer’s disease help clinicians with early diagnosis, identify stages, and understand the progression in a personalized way for better treatment.

OBJECT OF THE INVENTION
In order to obviate the drawbacks in the existing state of the art, the principal object of the present invention is to provide a method for cellular network model and method to identify the cellular biomarkers associated with Alzheimer's disease by modeling complex cellular and molecular reaction networks and analyzing simulations to predict potential biomolecules leading to neurodegeneration.

Another object of the invention is to enable the reconstruction of intricate biochemical interactions in the model, utilizing tools like biochemical systems theory, time-dependent ordinary differential equations, and reaction rate equations, thereby enhancing the accuracy and depth of the model's predictions.

Yet another object of the invention is to ensure comprehensive data incorporation by integrating both laboratory experimental studies and patient-specific data into the model, ensuring a multifaceted approach to biomarker prediction.

A further object of the invention is to refine the model's predictions through the incorporation of feedback mechanisms and biomolecular cross-talk.

Another object of the invention is to analyze the key biomolecules, including but not limited to misfolded proteins like amyloid-beta and tau, as well as other entities such as inflammatory cytokines, reactive oxygen species, and insulin, providing a comprehensive perspective on potential biomarkers.

Yet another object of the invention is to map the identified biomolecules to clinical symptoms witnessed at various stages of Alzheimer's disease, facilitating more informed medical decisions.

Another object of the invention is to utilize biochemical systems theory based on ordinary differential equations and power-law formalism in the reconstruction of biochemical interactions, offering a mathematical approach that can adeptly handle the nonlinearities inherent in biological systems.

Yet another object of the invention is to provide a cellular network model that not only identifies potential biomarkers but also highlights their perturbations, allowing for early detection, diagnosis, and potential intervention in Alzheimer’s disease progression.

Yet another object of the invention is to provide a cellular network model for developing a digital twin for disease prediction capable of detecting neurodegeneration at an early stage.

SUMMARY OF THE INVENTION:
The present invention discloses a method for constructing a cellular network model for identifying cellular biomarkers of Alzheimer's disease. The cellular network model reconstructs intricate biochemical interactions through complex modeling of cellular and molecular reaction networks implicated in Alzheimer's pathogenesis.

The reconstruction utilizes biochemical systems theory, time-dependent differential equations, and reaction rate equations to model the dynamics of these networks. The cellular model integrates data from experimental laboratory studies and patient-specific clinical data, incorporating feedback mechanisms to highlight critical biomolecular entities like misfolded amyloid-beta and tau proteins.

Additionally, the cellular network model connects identified biomolecules to observed clinical symptoms at different stages of Alzheimer’s disease, facilitating improved detection, diagnosis and treatment. While focused on enabling early detection, the model also facilitates experimental validation of predicted biomarkers.

Overall, the present invention provides an evidence-based cellular network model of Alzheimer’s disease which integrates massive experimental data to study the complex behavior and emergence of the disease. The cellular network model maps single or multiple protein changes to phenotypic changes and disease symptoms, allowing definition of disease stages and progression. Model predictions can be used to test experimental hypotheses regarding disease biomarkers and drug targets.

The cellular network model has potential as a clinical tool for early diagnosis and optimized medication by leveraging computational modeling to recapitulate the intricate biochemical interactions underlying Alzheimer's pathogenesis. The data from the cellular network model can be used to develop a digital twin for disease prediction, to detect neurodegeneration at an early stage, and to make decisions on a patient's treatment and other related issues. A digital twin is a virtual representation of an object/ system that can be simulated using real-time data. A patient digital twin of a biochemical system helps to simulate the model with real-time data from a patient.

BRIEF DESCRIPTION OF DRAWINGS
Figure 1: Depicts Modelling Cellular Network of Alzheimer's Disease

DETAILED DESCRIPTION OF THE INVENTION ILLUSTRATIONS AND EXAMPLES
While the invention has been disclosed with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made, and equivalents may be substituted without departing from the scope of the invention. In addition, many modifications may be made to adapt to a particular situation or material to the teachings of the invention without departing from its scope.

Throughout the specification and claims, the following terms take the meanings explicitly associated herein unless the context clearly dictates otherwise. The meaning of “a”, “an”, and “the” include plural references. Additionally, a reference to the singular includes a reference to the plural unless otherwise stated or inconsistent with the disclosure herein.

Biochemical systems theory (BST) is a computational approach used to explicate complex biological systems and metabolic pathways. The modeling process starts by extracting biochemical data such as protein types, species interaction, cell signaling, molecular pathways etc. from previous literature and databases. These quantitative data can be used to model with the application of ordinary differential equations (ODE) and power law equations to represent each state transition in the biochemical pathways of a system. The interaction between species in the system can be analyzed from parameter values. After modeling the system, the predictions are made and validated by confirming with the experiments.

Developing computational models that simulate experimentally known pathways linking biochemical and molecular changes in the disease helps to analyze behavior of complex systems from the cellular level. These models can give a better systems- level understanding of common disease pathways and their progression; which may lead to early diagnosis, the identification of novel therapeutic targets and the discovery of new biomarkers. A combination of systems biology and mathematical modelling using biochemical systems theory helps to model the complex cellular and molecular reaction networks to identify possible biomarkers for neurodegeneration.

The present invention discloses a cellular network model dedicated to identifying cellular biomarkers of Alzheimer's disease. The cellular network utilizes a complex modeling of cellular and molecular reaction networks associated with Alzheimer's to reconstruct intricate biochemical interactions. This reconstruction employs biochemical systems theory, time-dependent ordinary differential equations, and reaction rate equations. The model integrates the data from laboratory experimental studies and patient-specific data and incorporates feedback mechanisms and highlights critical biomolecular entities, including misfolded proteins like amyloid-beta and tau.

The modelling includes collecting data from laboratory experimental studies and patient data from existing studies, mathematically reconstructing reactions and pathway networks for both control and diseased conditions, generating simulations, and analysing critical reactions and biomolecules where their perturbations can lead to neurodegeneration as observed in Alzheimer’s disease. The cellular network model includes key cellular interconnection, feedback mechanisms and cross-talk between biomolecules to study the emergent properties of the system.

Working of the Model:
Biochemical reactions occurring among a set of reactants define a kinetic reaction network. Mathematically, a set of reactions can be represented by nonlinear ordinary or partial differential equations and their solutions that provide a better understanding of the dynamics of the critical processes.

In the case of disease cellular networks, hundreds of thousands of reactions may be needed to describe the complex, coupled phenomena involved including certain feedback loops, bifurcations and cross-talk which can lead to the dynamical behaviour of the system.

As biological systems are well-organized structural hierarchies, a small change in a particular protein or gene concentration can give rise to significant relative fluctuations in the whole system. The traditional way of modelling the time evolution of the molecular populations in a reacting system is to use a set of coupled, first-order, differential equations and reaction rate equations. Canonical models from a non-linear approach can explain this better.

A disease cellular network model is a powerful framework for integrating heterogeneous experimental data which allows for interpreting the dynamic behaviour of complex biological systems and generating targeted experimentally testable hypotheses. To construct such models with predictive utility, it is critical to describe cellular mechanisms with the appropriate level of detail. The predictive kinetic cellular models compile information on the system process details including reaction rates and concentrations of molecular constituents.

Figure 1 depicts the Modelling Cellular Network of Alzheimer's Disease comprising of the following steps:
? Reconstruction of Alzheimer’s disease cellular networks:
Reconstruction of Alzheimer’s disease cellular networks is achieved through experimental data collection from previous literature and databases. Modelling cellular networks involves constructing mathematical or computational frameworks that reproduce the complex mechanisms of these networks. The first step is to identify biochemical processes within a system, such as enzyme-substrate interactions, metabolic pathways, or gene regulatory networks involved in Alzheimer’s disease from literature and databases and reconstruct them using biochemical modelling tools. Each variable needs to be defined, such that assigning of initial concentration to biochemical species (e.g., metabolites, enzymes, proteins) involved in the reactions. This helps to model the underlying cellular network implicated in Alzheimer’s disease.

? Conversion of biochemical reactions to mathematical equations using biochemical systems theory:
This step involves the conversion of data from biochemical reactions into mathematical representations, primarily through rate equations and mathematical modelling. First, biochemical reactions need to be quantitatively described using mathematical equations representing the rates at which these reactions occur. This is based on principles like the law of mass action or enzyme kinetics, rate equations are formulated to describe how the concentrations of reactants change over time. Once the rate equations are formulated, they are translated into mathematical models that represent the dynamic behaviour of biochemical systems. The rate relations are expressed as a system of ordinary differential equations, where each equation describes the rate of change of a species concentration over time. This process forms the basis for studying the dynamics, interactions, and emergent properties of biological networks at a cellular level.

? Generating predictions from model simulations:
By using computational tools, it becomes possible to simulate the behaviour of the modelled biochemical system under different conditions or perturbations. This helps to generate changes as observed in Alzheimer's disease and also to make new predictions from the model simulations. Simulations also help to identify new biomarkers as well as therapeutic targets for the disease.

? Validation of model predictions:
Validation can be done either by conducting real-world experiments to validate the accuracy of the model predictions or by comparing the model predictions with established real-world data to refine the models.

? Mapping model predictions to disease symptoms:
Mapping model predictions to disease symptoms involves bridging the outcomes or predictions generated by a model or simulation to the observable symptoms or manifestations across different stages of Alzheimer's disease. This process aims to establish a clear link between the predictions generated by the model and the observable clinical symptoms, enhancing the understanding and potential diagnostic or therapeutic implications of the disease model.

? Mapping symptoms to potential conditions:
Mapping symptoms to potential conditions to develop a clinical prediction tool and digital twin for early detection and to make decisions on a patient's treatment and other related issues. Creating a clinical prediction tool and digital twin involves mapping symptoms to potential conditions by integrating model data, extensive medical data, symptom profiles, and diagnostic patterns. By employing machine learning algorithms and data-driven approaches, this tool can correlate symptoms to a range of possible conditions, aiding in early detection. It would continuously update and refine its predictions based on real-time patient data, assisting clinicians in making informed treatment decisions, personalized care plans, and prognostic assessments for optimal patient outcomes.
The cellular network model of the present invention uses a combination of systems biology and mathematical modelling, which helps to model the complex cellular and molecular reaction networks to identify possible biomarkers for Alzheimer’s disease. It can be done by rebuilding some of the complex biochemical interactions with the applications of biochemical systems theory using power-law formalism, time-dependent ordinary differential equations and reaction rate equations.

The modelling includes mathematical reconstruction of cellular reactions and signalling pathway networks for both control and diseased conditions, incorporating cellular data into the model from both laboratory experimental studies and patient data, generating simulations, and analysing critical reactions to predict possible biomolecules, where their perturbations can lead to neurodegeneration as observed in Alzheimer’s disease.

The model includes key cellular interconnection and cross-talk between biomolecules to study the emergent properties of the system. Some of the important entities including misfolded proteins are amyloid-beta, tau, acetylcholine, calcium, glutamate, inflammatory cytokines, reactive oxygen and nitrogen species and insulin.
, Claims:1. A method for constructing a cellular network model for identifying biomarkers of Alzheimer's disease, the method comprising the steps of:
? modelling complex cellular biochemical reaction networks associated with Alzheimer’s disease from previous experimental studies;
? converting biochemical reactions into mathematical equations based on biochemical systems theory, time-dependent ordinary differential equations, reaction rate equations and power-law formalism;
? incorporating cellular data derived from laboratory experimental studies and patient data;
? generating simulations based on the incorporated data;
? analyzing the generated simulations to identify possible biomarkers wherein perturbations of said biomarkers lead to neurodegeneration observed in Alzheimer’s disease;
? validating and mapping model predictions to disease symptoms; and
? mapping symptoms to develop a clinical prediction tool and digital twin for early detection and to make decisions on a patient's treatment,
thereby providing an integrative and evidence-based predictive tool for early detection and understanding of biomolecular perturbations that lead to Alzheimer's disease progression.

2. The method as claimed in claim 1, further comprising incorporating feedback mechanisms and cross-talk between biomolecules into the model.

3. The method as claimed in claim 1, wherein the generated simulations identify new therapeutic targets.

4. The method as claimed in claim 1, wherein the validation of model predictions is achieved through experimental validation either through published studies or laboratory experiments.

5. The method as claimed in claim 1, wherein said biomarkers include but are not limited to misfolded proteins amyloid-beta, tau, acetylcholine, calcium, glutamate, inflammatory cytokines, reactive oxygen and nitrogen species, and insulin.

6. A cellular network model for identifying biomarkers of Alzheimer's disease as claimed in claim 1, the model being configured to:
? model complex cellular and molecular reaction networks associated with Alzheimer’s disease;
? reconstruct biochemical interactions based on biochemical systems theory, time-dependent ordinary differential equations, and reaction rate equations;
? incorporate cellular data derived from laboratory experimental studies and patient data to generate simulations; and
? analyse the generated simulations and predict possible biomarkers that lead to neurodegeneration in Alzheimer’s disease;
? develop a digital twin for disease prediction, to detect neurodegeneration at an early stage, and to make decisions on a patient's treatment and other related issues,
thereby providing an integrative and evidence-based predictive tool for early detection and understanding of biomolecular perturbations that lead to Alzheimer's disease progression.

7. The cellular network model as claimed in claim 6, further configured to incorporate feedback mechanisms and cross-talk between biomolecules.

8. The cellular network model as claimed in claim 6, wherein said model uses biochemical systems theory based on ordinary differential equations, reaction rate equations and power-law formalism for the reconstruction of biochemical interactions.

9. The cellular network model as claimed in claim 6, wherein said model is capable of mapping predicted biomarkers to clinical symptoms at various stages of Alzheimer’s disease.