Decoding Metabolic Pathway: Leveraging Computational Tools for Insight

Sabuj Chakraborty; Rojina Khatun; Sudeshna Sengupta; Malavika Bhattacharya

doi:doi:10.11648/j.cbb.20251301.11

Review Article |

| Peer-Reviewed

Decoding Metabolic Pathway: Leveraging Computational Tools for Insight

Sabuj Chakraborty

, Rojina Khatun

, Sudeshna Sengupta

, Malavika Bhattacharya^*

Published in Computational Biology and Bioinformatics (Volume 13, Issue 1)

Received: 23 April 2025 Accepted: 7 May 2025 Published: 25 June 2025

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

His chapter introduces us to the role of cellular signaling pathways and their significance in understanding the intricate working of an organism’s functioning, life processes and enable us in deepening of our understanding of many diseases. Through time many relevant pathways has been discovered, we are yet to discover more and even identify missing pieces of existing pathways. Use of novel computational tools, that integrates principles from computer science, mathematics, and biology help us to enhance our understanding of signaling pathways. Its significance lies in its ability to predict pathway behavior under different conditions, analyze large signaling networks and model biological processes using tools like BioNetGen, Copasi and Virtual Cell. The biological data is sourced from pathway databases (e.g., KEGG, Reactome, BioGRID). The application of machine learning for pattern recognition and pathway inference and use of AI to predict novel interactions or missing components in pathways aid in decoding signaling networks. Computational tools help us to identify drug targets by modeling pathways. Analysis of pathways further assist in drug discovery and drug re-purposing. Predictive modeling systems gives us new insights into cancer and neuro-degenerative diseases (e.g., Alzheimer's), and autoimmune disorders while engineering novel pathways for biotechnological applications thus enhancing development of synthetic biology.

Published in	Computational Biology and Bioinformatics (Volume 13, Issue 1)
DOI	10.11648/j.cbb.20251301.11
Page(s)	1-16
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Cellular Signaling Pathways, Artificial Intelligence, Cancer, Autoimmune Disorders, Computational Biology Copasi, BioNetGen

1. Introduction

1.1. Why Do We Need It

The need for computational analysis of cellular signaling pathways is dictated by the large complexity of datasets that must be analyzed quickly and accurately. These tools assist in discovering protein interfaces, predicting the behavior of pathways, and modeling biological processes which would be feasibly difficult and time-consuming to do manually. Pathway modeling and visualization enhances the comprehension of cellular architecture, mechanisms of diseases as well as the development of focused treatments.

1.2. Overview

Cell signaling is a crucial biological process that allows cells to detect and react to environmental stimuli. It involves the interaction of signaling molecules with specific receptors, influencing cellular behavior like growth, differentiation, and survival

[1]

. These complex networks, including protein kinases, adaptor proteins, and nuclear effectors, interact through functional-domain interactions

[1, 2]

. Dysregulation of signaling pathways is common in diseases, particularly cancer effectors

[3]

1.3. Types of Tyrosine Kinases (RTKs)

Ligand binding activates RTKs, which results in transphosphorylation and dimerization.

Among the important pathways are-

1. The MAPK pathway, which is required in both cell differentiation and proliferation.

2. The mTOR pathway controls the growth and metabolism of cells.

3. The Her2/Neu Pathway is linked to the development of breast cancer.

[4, 5]

1.4. G Protein-Coupled Receptors (GPCRs)

1. GPCRs affect many physiological processes by mediating responses to different ligands.

2. They trigger downstream signaling cascades that are essential for cancer and immune responses, such as the NF-κB and JAK/STAT pathways.

[6, 7]

1.5. Other Notable Pathways

1. Wnt Signaling: Controls cell division and fate, especially during development.

2. TGF-β pathway: Involved in apoptosis, differentiation, and cell growth.

[7, 8]

1.6. Key Components of Cellular Signaling Pathways

The intricate networks of interconnected proteins that control different cellular functions are known as cellular signaling pathways. Effectors, transducers, sensors, and receptor molecules are important parts

[9]

. These pathways are arranged spatially and frequently use scaffolding proteins to create macromolecular complexes in particular parts of the cell

[10]

. Important cell surface receptors called receptor tyrosine kinases (RTKs) trigger a variety of downstream pathways, such as mTOR, Her2/Neu, MAPK, and Pak kinases.

[4]

1.7. Importance in Physiological Processes and Disease States

1.7.1. Role in Physiological Processes

(i). Cell Communication

Signaling pathways allow cells to coordinate responses during immunological function, tissue repair, and development.

[12]

(ii). Dynamic Regulation

Signal processing and integration pathways such as receptor tyrosine kinases (RTKs) and G protein-coupled receptors (GPCRs) impact vital cellular decisions including proliferation and death.

[5]

(iii). Contextual Adaptation

Pathways demonstrate memory-like characteristics, adjusting responses according to prior stimuli, which is essential for physiological adaptability.

[13]

1.7.2. Implications in Disease States

(i). Dysregulation and Disease

Targeted therapies are necessary because aberrant signaling is linked to a number of illnesses, such as autoimmune disorders and cancer.

[12, 14]

(ii). Therapeutic Potential

Novel treatment development can be aided by an understanding of signaling pathways, especially for chronic illnesses where traditional medicines may not be effective.

[15]

Although the emphasis on signaling pathways is important, a more comprehensive picture of health and disease may be suggested by taking into account the larger context of cellular interactions and environmental factors that can affect signaling results.

2. Modelling and Simulation

2.1. Modeling Techniques: Deterministic and Stochastic

Decoding signaling pathways requires various modeling methodologies, broadly divided into two categories: stochastic and deterministic. Developing computer modeling of cellular pathways requires an understanding of them in biological systems, especially in relation to signal transduction.

2.1.1. Deterministic Modeling Techniques

(i). Petri Nets

These are frequently used to model signaling pathways because they eliminate the need for kinetic parameters and enable the investigation of system dynamics. They are able to accurately depict the interactions and signal flow between proteins.

[16]

(ii). Boolean Networks

A deterministic framework for modeling signaling pathways is offered by extended Boolean network models, which concentrate on protein binary states and how they interact.

[17]

2.1.2. Stochastic Modeling Techniques

(i). Stochastic Processes

The inherent randomness in biological systems, such as variations in protein concentrations, is taken into account by these models. They are essential for faithfully modeling pathways where noise can have a substantial impact on results, such as the MAPK signaling pathway.

[17, 18]

(ii). Hybrid Approaches

In order to handle both randomness and uncertainty in kinetic parameters, stochastic and fuzzy approaches can be used to create more accurate models of signaling pathways.

[19]

Deterministic models may ignore the complexity brought up by stochastic events, despite their clarity and simplicity. On the other hand, although they can be computationally demanding, stochastic models offer a more detailed insight. The modeling of biological signaling networks must be advanced by striking a balance between various methods.

2.2. How Simulation Tools Predict Pathway Behavior Under Different Conditions

By using sophisticated computational frameworks, simulation tools such as BioNetGen, Copasi, and Virtual Cell are essential for forecasting route behavior under various circumstances. These tools efficiently analyze complicated biochemical networks by using a variety of modeling techniques, such as deterministic and stochastic simulations.

2.2.1. BioNetGen

With its emphasis on rule-based modeling, BioNetGen enables users to specify biochemical interactions using a collection of rules as opposed to direct reactions.

[20]

This method captures the dynamic behavior of molecular interactions and makes it possible to efficiently simulate vast and complicated networks.

Download: Download full-size image

Figure 1. Workflow diagram illustrating the integration of rule-based and network-based modeling tools in decoding metabolic pathways. Starting from SBML models, Atomizer and RuleBender facilitate model creation and visualization using BioNetGen Language (BNGL). The network is expanded, simulated using population-based methods (SSA, ODE, PLA) or particle-based methods (NFsim), and exported for trajectory analysis. Hybrid models and lumping techniques further optimize simulation efficiency. Output can be visualized via SBML-compatible platforms like MATLAB and MCell.

2.2.2. Copasi

1. Copasi provides an intuitive biochemical network modeling interface that supports both deterministic and stochastic approaches.

[21] [11]

2. It makes a number of investigations easier, including as sensitivity analysis and parameter scans, which aid in comprehending how conditions alter pathway behavior.

[22]

Download: Download full-size image

Figure 2. Schematic representation of COPASI (Complex Pathway Simulator) workflow for enzymatic and metabolic network modeling. The system integrates metabolites, reactions, and parameters to build models, which can be created de novo or imported via formats like SBML or CPS. KEGG pathway data is incorporated using KeggTranslator, with networks read and modified for analysis. Tasks such as perturbation studies, fold-change visualization, and attribute mapping support dynamic simulation and hypothesis testing.

2.2.3. Virtual Cell

1. By combining temporal and spatial modeling, Virtual Cell makes it possible to simulate cellular functions in three-dimensional settings.

[23]

2. By integrating transport mechanisms and reaction kinetics, it sheds light on how spatial organization affects biochemical pathways.

2.3. Network Analysis Tools for Analyzing Large Signaling Networks (e.g., Cytoscape)

By making it easier to visualize and interpret intricate biological relationships, network analysis tools—especially Cytoscape and related applications—play a critical role in helping researchers extract valuable insights from large datasets. Important examples of network analysis tools pertinent to signaling networks are described in the sections that follow.

2.3.1. Cytoscape Overview

1. A flexible tool for visualizing biological pathways and networks of molecular interactions is Cytoscape.

2. Users can generate lists of edges and nodes, which can then be saved as CSV files for additional examination

[24]

3. Several plugging are supported by the platform, which improves its usefulness when performing specific analysis.

2.3.2. Other Specialized Applications

(i). MAPPINGS

In order to find important phosphorylation events and pathways, especially in the setting of infections, this tool analyses phosphorylation datasets using random pathways.

[25, 26]

(ii). OCSANA+

An application for signaling network simulation and optimal control that assesses the impact of perturbations based on network topology and assists in identifying important nodes for intervention.

[27]

3. Data Integration and Analysis

3.1. Experimental Data

1. In order to investigate biological macromolecules and capture their dynamic nature and interactions, experimental data is crucial.

[28]

2. Reaction rates from experimental data are frequently analyzed using methods like least-square regression and finite difference equations.

[29]

3. Extensive data repositories facilitate accessibility and reproducibility by gathering simulation scripts, empirically measured data, and derived numerical data.

[30]

3.2. High-throughput Techniques for Single-cell Sequencing and Omics Data

Understanding cellular heterogeneity and molecular profiling has greatly improved because to high-throughput methods in single-cell sequencing as well as omics data. These developments give scientists the ability to examine individual cells, providing them with new information on intricate biological processes. Important developments in this area are outlined in the sections that follow.

3.2.1. High-Throughput Single-Cell Sequencing Techniques

(i). Single-Cell Whole-Genome Sequencing (scWGS)

Better detection of genetic changes associated with ageing and cancer is now possible thanks to recent advancements in genome coverage and throughput.

[31]

(ii). Single-Cell Omics

The identification of rare cell populations and dynamic states is made possible by these technologies, which offer high-resolution insights into cellular variety that are not possible with conventional bulk approaches.

[32]

3.2.2. Multi-Omics Integration

(i). Comprehensive Analysis

Ursa, a tool that integrates genomes, transcriptomics, and proteomics at the level of a single-cell, improves our comprehension of cellular connections and regulatory networks.

[33]

(ii). Dimensionality Reduction

Algorithms like SnapATAC2 enhance the study of high-dimensional single-cell data by ensuring efficient processing while retaining cellular interactions.

[34]

3.2.3. Proteomic Data Integration

SCPRO-HI Algorithm: This method improves the incorporation of single-cell proteomic datasets by tackling batch effects and enhancing the correctness of cell clustering based on distinctive proteins.

[35]

3.3. Integration of Multi-omics Data and Pathway Databases into Computational Models

3.3.1. Multi-Omics Integration Techniques

(i). DPM Methodology

This method combines diverse omics datasets by evaluating the directionality and importance of gene interactions, allowing the detection of consistent gene and pathway regulation spanning datasets.

[36]

(ii). PathIntegrate Framework

By using predictive models to rank pathways according to their contribution to outcomes, this tool converts multi-omics data into pathway-level insights, improving interpretability.

[37, 38]

3.3.2. Pathway Databases Utilization

1. Consensus pathway evaluation utilizing databases like KEGG and Reactome is made easier by tools like CCPA, which give researchers easily navigable resources for comparing various trials.

[39]

2. The creation of cloud-based modules facilitates the incorporation of pathway analysis methodologies, enabling researchers to effectively evaluate and understand multi-omics data.

[39]

By concentrating on the connections between molecular data and biological pathways, DPM and Path Integrate make integration easier and provide a more thorough comprehension of intricate biological systems.

3.4. Application of Machine Learning for Pattern Recognition and Pathway Inference

The following sections outline key aspects of this application.

3.4.1. Machine Learning in Pattern Recognition

(i). Types of Learning

Supervised learning for pattern recognition tasks is the main emphasis of the four types of machine learning techniques: supervised, semi-supervised, weakly supervised, and unsupervised learning.

[40]

(ii). Feature Extraction

Feature extraction and representation are essential for classifier performance and are necessary for effective pattern recognition.

[41]

3.4.2. Pathway Inference Techniques

(i). Pathway Activity Inference

Innovative approaches combine biochemical pathways and gene expression profiles to improve the precision of disease classification. Using this method, gene patterns are condensed into a composite property known as pathway activity.

[42]

(ii). Network Inference

By identifying the key genes influencing gene expression variability, machine learning algorithms such as CoVar provide light on regulatory networks and their functions in the cause of disease

[43]

. The PARE Approach shows how machine learning (ML) can be used to anticipate complicated genetic relationships by using gene expression data to figure out time-lagged genetic interactions.

[44]

The comprehension of intricate biological systems has been improved with the utilization of machine learning (ML) in pattern recognition and pathway inference. Researchers can find patterns in data and deduce biological pathways—which are essential for classifying diseases and comprehending gene interactions—by utilizing a various machine learning methods.

3.5. Use of AI to Predict Novel Interactions or Missing Components in Pathways

Using a variety of machine learning approaches, the use of artificial intelligence (AI) to forecast new connections and absent elements in biological pathways has grown significantly. The BN+1 algorithm used to E. coli microarray data demonstrates how Bayesian networks (BNs) have become a reliable technique for pathway extension, allowing the discovery of novel elements and interactions from gene expression data

[45]

. Furthermore, a network-based method that focuses on particular signaling pathways has been created to anticipate missing contacts in protein networks; this method has been successful in identifying new interactions in yeast

[46]

. Additionally, statistical relational learning techniques are put forth to find a solution to the difficulties of data interoperability by combining data from several biological databases to anticipate protein interactions and functions

[47]

. To further comprehend signaling networks, unsupervised machine learning algorithms like ACSNI (which uses gene expression patterns) have also been established to discover tissue-specific pathway components

[48]

. All things considered, these AI-driven techniques greatly improve predictability of biological interactions and pathway elements, enabling a better understanding of cellular mechanisms

[49]

3.6. Case Studies Using Machine Learning to Decode Signaling Networks

3.6.1. Neural Signaling and Brain-Computer Interfaces

1. The creation of intelligent signals for neural communication is made easier by artificial neural networks (ANNs), especially in brain-computer interfaces (BCIs).

2. To decode brain signals and operate devices, EEG-based control systems use a variety of classification methods, including adaptive classifiers and deep learning.

[50]

3.6.2. CODEX: A Data-Driven Approach

1. Without the use of predetermined features, CODEX analyses live cell fluorescence biosensor data using convolution neural networks (CNNs) to find patterns in signaling dynamics.

2. This approach has improved knowledge of cellular responses to growth factors by shedding light on the ERK and Akt signaling pathways.

[51]

3.6.3. Bayesian Active Learning for Network Inference

1. By optimizing experimental interventions to infer signaling networks, a Bayesian active learning technique considerably lowers detection errors in network systems.

2. This method increases the effectiveness of signaling network reconstruction by integrating historical data and pathway databases to inform experimental design.

[52]

Download: Download full-size image

Figure 3. Workflow of Bayesian Active Learning for developing accurate Bayesian force fields. Molecular dynamics (MD) simulations generate atomic structures, which are assessed for uncertainty. High-uncertainty configurations trigger DFT quantum calculations, while low-uncertainty data refine the GP regression model. The mapped Gaussian process (GP) informs the Bayesian force field, iteratively improving model accuracy through size-scaled simulations.

3.6.4. Reconstructing Signaling Pathways

1. Molecular signaling pathways and gene regulatory networks have been reconstructed using machine learning approaches, with hierarchical Bayesian models showing increased accuracy.

2. These models' ability to effectively infer dynamic structure of networks has been demonstrated through their application to datasets from a variety of organisms.

[53]

3.6.5. Enhancing Simulation with Big Data

1. Cell signaling networks, especially the PI3K/AKT/mTOR pathway, may now be better simulated by combining big data and machine learning with conventional modeling.

2. By using data mining tools to examine interactions within the signaling system, this method improves prediction power.

[54]

4. AI/ML Applications

4.1. Drug Discovery

The use of computational tools has become essential to contemporary drug discovery, greatly increasing productivity and cutting expenses at different phases of the procedure. Researchers can find interesting candidates more quickly by using methods like molecular docking and virtual screening, which enable them to predict drug-target interactions and assess large compound libraries, respectively.

[55, 56]

Furthermore, the drug development pipeline is being streamlined by the growing use of artificial intelligence (AI) and machine learning (ML) to forecast toxicity and optimize lead compounds

[57, 58]

. By evaluating pharmacokinetics and toxicological characteristics, in silico technologies also aid in early decision-making by reducing the need for conventional, resource-intensive experimental techniques

[59]

. This convergence of computational approaches promotes the 3Rs (reduction, refinement, and replacement) in research by expediting drug discovery while simultaneously adhering to ethical issues by minimizing the necessity for animal experimentation.

[59]

4.2. How Computational Tools Identify Drug Targets by Modeling Pathways

Large compound libraries can be screened using in silico techniques, which also predict drug toxicity and metabolism. This speeds up early decision-making and lessens the need for conventional experimental techniques

[59]

. Molecular dynamics simulations and high-throughput virtual screening are other methods that have been effectively used to find potential medications for a number of illnesses, including breast cancer

[60]

. By combining structural representations with transcriptome data, novel techniques like AI-DTI significantly enhance the prediction of drug-target interactions and uncover previously unidentified mechanisms of drug action

[61]

. Additionally, hybrid models such as GTIE-RT successfully associate medications with metabolic pathways, attaining a high degree of precision in forecasting drug effects—a critical component in comprehending drug-drug interactions

[62]

. All things considered, these computational methods are crucial for accelerating medication discovery and improving treatment effectiveness.

4.3. Examples of Drug Re-purposing Through Pathway Analysis

Particularly in the context of COVID-19, drug repurposing via route analysis has become an essential tactic in tackling pressing health emergencies. To find current medications that can target particular biological pathways linked to diseases, a variety of computational methods and methodologies have been created. This method improves the effectiveness of clinical trials while also speeding up the drug discovery process.

4.3.1. DPNetinfer Method

1. In pan-cancer networks, DPNetinfer predicts drug-pathway connections with good accuracy by using network-based techniques

[63]

2. It demonstrated the promise of non-oncology medications in cancer therapy by identifying their unexpected anticancer effects on the PI3K-Akt pathway.

4.3.2. Signaling Network Analysis

1. Drugs that potentially block these signaling pathways were discovered as a result of research on how SARS-CoV-2 infection alters them, which aided in the design of clinical trials

[64]

2. Important pathways with therapeutic potential were discovered, including MYD88/CXCR6 and JAK-STAT.

4.3.3. iDPath Framework

1. Outperforming conventional techniques, the iDPath architecture combines multilayer biological networks to find medications based on their mechanisms of activity

[65]

2. Its success in discovering novel, FDA-approved medications for prostate cancer exemplifies its usefulness in drug repurposing.

Download: Download full-size image

Figure 4. Illustration of the iDPath Framework, which models the interplay between information diffusion, disease spread, and behavioral adoption within a contact network. The framework categorizes individuals by informational status (Informed/Uninformed), disease state (Susceptible/Infectious/Recovered), and behavior (Adopted/Not Adopted). External factors such as mass media campaigns, disease control strategies, and behavioral interventions influence transitions across these layers, facilitating integrated public health responses.

4.4. Disease Mechanisms

Key elements of the disease mechanisms of several significant illnesses, including cancer, neurological diseases (like Alzheimer's), and autoimmune disorders, are described in the sections that follow.

4.4.1. Common Molecular Pathways

(i). Signaling Pathways

Dysregulated pathways, including those involving inflammation and apoptosis, are shared by cancer and neurodegenerative illnesses and can result in different consequences for cell survival and death

[66]

(ii). Ferroptosis

Both neurodegenerative and neuroimmune diseases are linked to this type of controlled cell death, suggesting a possible target for treatment

[67]

4.4.2. Immune Dysregulation

(i). Neuroinflammation

Immune responses can become maladaptive in neurodegenerative illnesses like Alzheimer's, causing neuronal damage through processes like microglia activation and cytokine release

[68, 69]

(ii). Autoimmunity

Autoimmune responses can cause neurodegeneration, as seen by conditions like multiple sclerosis, where auto reactive T and B cells contribute to neuronal death

[70]

4.5. Personalized Medicine and Patient-specific Pathway Analysis

Patient-specific route analysis is used in personalized medicine to improve clinical results and treatment efficacy. This method combines clinical insights with multi-omics data, including proteomics, genomics, and metabolomics, to customize therapies to the molecular profiles of specific patients

[71, 72]

. Cutting-edge computational technologies, like as network analysis and machine learning, make it easier to extract and visualize pertinent biological relationships, allowing for the creation of patient-specific pathways that guide therapy choices

[73, 72]

. As an illustration of the predictive power of incorporating new biomarkers into pre-existing models, mechanism-based machine learning models have been used to find important genes and proteins linked to patient survival in colorectal cancer

[74]

. Personalized therapeutic decision-making is further supported by the differential Personal Pathway index (dPPi), which provides a comprehensive picture of a person's proteome response to illness

[75]

. Collectively, these techniques demonstrate personalized medicine's revolutionary potential for improving patient care.

5. Case Studies

5.1. Interactive Kinetic Modeling with COPASI & Cytoscape

Kaya & Naidoo (2023) developed CytoCopasi, a Cytoscape app that embeds COPASI’s simulation engine within the Cytoscape network-visualization environment.

5.1.1. Tools

COPASI; Cytoscape (via CytoCopasi)

5.1.2. Biological Focus

Drug-perturbation analysis of the RAF/MEK/ERK signaling pathway in cancer cells

[76]

5.1.3. Workflow

(i). Model Construction in COPASI

Build or import an SBML pathway model in COPASI, specifying metabolites, reactions, and kinetic parameters (e.g., Michaelis–Menten rate laws).

(ii). Database-assisted Parameterization

Use COPASI’s access to KEGG and BRENDA databases to retrieve reaction stoichiometries and enzyme kinetics, then auto-populate model parameters.

(iii). Network Import into Cytoscape

Launch CytoCopasi within Cytoscape to import the COPASI model as a network of nodes (species) and edges (reactions).

(iv). Simulation and Visual Analytics

From the Cytoscape interface, run time-course or steady-state simulations using COPASI’s solvers; simulation results (concentration profiles) are overlaid directly on network nodes.

(v). Perturbation Analysis

Apply parameter scans (e.g. drug inhibition of RAF kinase) in CytoCopasi; visualize shifts in metabolite concentrations across the network in real time.

[76]

5.2. Elementary Flux Mode Analysis via COBRA Toolbox & Cytoscape

Sarathy et al. (2020) introduced EFMviz, an extension to the COBRA Toolbox that selects and exports elementary flux modes (EFMs) for visualization in Cytoscape.

5.2.1. Tools

COBRA Toolbox; EFMviz; Cytoscape

5.2.2. Biological Focus

Comparison of glycolytic EFMs in E. coli and human metabolic models

[77]

5.2.3. Workflow

(i). GSMM Loading and EFM Enumeration

Load a genome-scale metabolic model (e.g. E. coli iAF1260 or human Recon 2.2) into the COBRA Toolbox and enumerate EFMs using TreeEFM algorithms.

(ii). Data-driven EFM Selection

Filter EFMs by yield metrics or overlay of transcriptomic/flux-omics data to identify EFMs of interest (e.g. high acetate yield).

(iii). Submodel Extraction

Use EFMviz functions to extract an SBML submodel containing only reactions in the selected EFMs.

(iv). Network Visualization in Cytoscape

Import the submodel into Cytoscape; map omics data (gene expression fold-changes, flux values) onto nodes and edges for comparative visualization.

[77]

5.3. Pathway-Level Multi-Omics Integration with PathIntegrate & Cytoscape

Wieder et al. (2024) developed PathIntegrate, which transforms multi-omics data into pathway-activity features and visualizes results in Cytoscape.

5.3.1. Tools

PathIntegrate; Cytoscape

5.3.2. Biological Focus

Predicting COPD vs. control status from transcriptomic, proteomic, and metabolomic profiles

[37]

5.3.3. Workflow

(i). Single-sample Pathway Scoring

Apply single-sample pathway analysis (ssPA) to each omics layer to compute per-sample pathway activity scores.

(ii). Multi-view Predictive Modeling

Train multi-view machine-learning models on pathway-level features to predict phenotype, with shared latent spaces across omics modalities.

(iii). Feature Importance Ranking

Derive pathway importance scores for each omics layer and overall, enabling identification of key dysregulated pathways.

(iv). Network Visualization

Load ranked pathways into Cytoscape; color-code nodes by importance and overlay layer-specific contributions for intuitive exploration.

[37]

6. Challenges and Limitations

6.1. Incomplete or Noisy Biological Data

6.1.1. Impact of Data Incompleteness

1. Missing data can cause crucial inaccuracies in assessing biological interactions, as observed in species like E. coli and C. elegans, where a significant number of genes do not have functional evidence

[78]

2. The human interactome research has identified only a small percentage of the protein interactions, revealing a widespread issue of incompleteness in biological databases

[78]

6.1.2. Effects of Noisy Data

1. Noise from biological variability and technical factors can mask underlying biological signals, making reliable inference difficult.

[79, 80]

2. Network filtering approaches have demonstrated potential in decreasing noise, improving prediction precision by up to 58% in protein expression tasks.

[80]

Incomplete or noisy biological data severely reduces the quality of systems biology models, resulting in incorrect conclusions and faulty predictions. Data incompleteness and noise might skew our knowledge of biological interactions as well as mechanisms, hindering model performance.

6.2. Computational Limitations (e.g., Model Complexity, Scalability)

The inherent complexity of biological systems, as well as the constraints imposed by present modeling methodologies, cause computational difficulties. These limitations impede the capacity to develop precise, scalable models capable of accurately predicting biological behavior. The following sections discuss significant features of these constraints.

6.2.1. Model Complexity

Traditional modeling approaches, such as differential equations, frequently struggle with accuracy and efficiency, restricting their usefulness to complex biological pathways.

[81].

6.2.2. Scalability Issues

The computational requirements of simulating vast biological systems may exceed existing capabilities. High-throughput technologies generate enormous volumes of data, demanding complex computational models that can consolidate and analyse this information effectively.

[82]

7. Future Directions in Computational Pathway Analysis

7.1. Integration of AI and Big Data Analytics

The incorporation of artificial intelligence (AI) and big data analytics into pathway analysis considerably improves its applications by increasing predicting accuracy, streamlining design processes, and enabling personalized treatment options. AI approaches, such as machine learning, allow the detection of complicated patterns within vast datasets, which is critical for engineering dynamic pathway and metabolic production systems

[83]

. For example, graph-based learning algorithms have been proven to successfully incorporate multidimensional genomic data, resulting in more accurate estimation of clinical phenotypes in cancer research

[84]

. Furthermore, AI-driven systems can simplify the construction of synthetic pathways by using reaction knowledge graphs and retro synthetic predictions, overcoming the constraints provided by huge search spaces in chemical processes

[85]

. Furthermore, the integration of futuristic data analytics with AI supports improved decision-making in clinical pathways, as seen by individualized treatment strategies for lung cancer patients

[86, 87]

. Overall, this integration not only improves the efficiency of route analysis, but it also encourages the creation of novel solutions in metabolic engineering and healthcare.

7.2. Potential for Improving Personalized Medicine and Precision Healthcare

7.2.1. Integration of Omics Data

Systems biology collects extensive biological data using a variety of omics technologies (genomics, proteomics, and metabolomics), which makes it possible to identify the molecular pathways underlying diseases.

[88, 89]

Understanding interindividual differences in illness development and medication response is improved by integrating these datasets using machine learning and network analysis.

[88, 90]

7.2.2. Personalized Treatment Strategies

Cancer treatments can be customized depending on each patient's unique tumour profile thanks to tools like the PHENOSTAMP program, which generates phenotypic maps that illustrate tumour heterogeneity.

[90]

Given that systems biology combines high-throughput omics data with sophisticated computational methods to customize medical interventions for specific patients, it has enormous promise to improve precision healthcare and personalized medicine. Deeper comprehension of intricate biological systems and disease pathways is made possible by this method, which eventually results in more potent treatments.

8. Summary & Outlook

The increasing dependence on computational tools in biological research is changing how scientists approach problems in biology and medicine. Here are some thoughts on their significance:

More Accurate Research: The use of computational tools promotes the use of more precise and accurate research methods, with the possibility of obtaining more reliable results. This is especially important in the field of interactomics, where even slight differences can have profound effects.

The Future of Health Diagnostics: With the growth of computational tools, the realization of personalized medicine concepts seems to be a matter of time. Through an understanding of each person’s genetic information and how diseases develop, it will be possible to predict which treatment is best suited for which patient.

Decreased Time In Drug Development: The application of computational models can normalize the timelines associated with drug discovery by predicting interactions of candidate drugs with biological systems. This cans then fast track the development of new therapies.

Trajectory Analytic: As AI and machine learning develop, predictive analysis will continue to improve and help researchers and healthcare providers to prepare for an epidemic or predict patients’ reactions to specific therapies.

To summarize, the application of computational tools in biological studies is not simply a movement but a transformation that will define healthcare in the future. It enhances the prospects of bettering outcomes for complex biological systems through novel approaches.

Abbreviations

RTKs	Receptor Tyrosine Kinases
MAPK	Mitogen-Activated Protein Kinase
mTOR	Mechanistic Target of Rapamycin
Her2/Neu	Human Epidermal Growth Factor Receptor 2 / Neural
GPCRs	G Protein-Coupled Receptors
NF-κB	Nuclear Factor Kappa-light-chain-enhancer of Activated B Cells
JAK/STAT	Janus Kinase / Signal Transducer and Activator of Transcription
TGF-β	Transforming Growth Factor Beta
Pak	p21-activated Kinase
BioNetGen	Biological Network Generator (Software/Tool)
Copasi	COmplex PAthway SImulator
SBML	Systems Biology Markup Language
BNGL	BioNetGen Language
SSA	Stochastic Simulation Algorithm
ODE	Ordinary Differential Equation
PLA	Piecewise-Linear Approximation
NFsim	Network-Free Simulator
MATLAB	MATrix LABoratory (Math Software)
MCell	Monte Carlo Cell (Spatial Modeling Tool)
CSV	Comma-Separated Values
MAPPINGS	Multi-Analysis of Patterns and Pathways Involving Network-Guided Systems
OCSANA+	Optimal Control and Simulation of Signaling Networks Analysis
scWGS	Single-Cell Whole-Genome Sequencing
SnapATAC2	Single-nucleus ATAC-seq Analysis Tool, Version 2
SCPRO-HI	Single-Cell Proteomic Robustness with High Integration
DPM	Directionality and Pathway Modulation
CCPA	Cloud-based Consensus Pathway Analysis
AI	Artificial Intelligence
ML	Machine Learning
CoVar	Coordinated Variability Algorithm
PARE	Pattern-based Approach to Regulatory Estimation
BN+1	Bayesian Network +1 Algorithm
ACSNI	Adaptive Clustering for Signaling Network Inference
ANNs	Artificial Neural Networks
BCIs	Brain-Computer Interfaces
EEG	Electroencephalogram
CNNs	Convolutional Neural Networks
CODEX	Co-Detection by Indexing (Named CNN Approach for Signaling Dynamics)
GP	Gaussian Process
PI3K/AKT/mTOR	Phosphoinositide 3-Kinase / AKT / Mechanistic Target of Rapamycin
AI-DTI	Artificial Intelligence-based Drug-Target Interaction
GTIE-RT	Graph-based Target Interaction Estimator for Response Type
DPNetinfer	Drug-Pathway Network Inference Tool
MYD88	Myeloid Differentiation Primary Response 88
CXCR6	C-X-C Motif Chemokine Receptor 6
iDPath	Integrated Drug Pathway Inference Framework
dPPi	Differential Personal Pathway Index
CPS	COPASI File Format
DFT	Density Functional Theory
FBA	Flux Balance Analysis
S	Stoichiometric Matrix
v	Flux Vector
COBRA	Constraint-based Reconstruction and Analysis
EFM	Elementary Flux Mode
GML	Graph Modeling Language

Acknowledgments

I would like to express my sincere appreciation to Techno India University for providing me with knowledge and guidance required.

I would also like to thank my teacher’s, senior research scholars and colleagues for their valuable feedback, suggestions, and discussions that have helped me refine my ideas and strengthen my arguments.

Author Contributions

Sabuj Chakraborty: Manuscript Writing & editing

Rojina Khatun: Writing – review & editing

Sudeshna Sengupta: Writing – review & editing

Malavika Bhattacharya: Conceptualization, Supervision

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Esbenshade & Duzic, 1998. Esbenshade, T. A., & Duzic, E. (1998). Overview of Signal Transduction. Current Protocols in Pharmacology, 31. https://doi.org/10.1002/0471141755.ph0201s31
[2]	Iyengar, 2005. Iyengar, R. (2005). Introduction: Overview of Pathways and Networks and GPCR Signaling. Science's STKE, 2005, tr4 - tr4.) https://doi.org/10.1126/stke.2702005tr4
[3]	Harvey, 2018. Harvey, A. J. (2018). Overview of Cell Signaling Pathways in Cancer. Predictive Biomarkers in Oncology. https://doi.org/10.1007/978-3-319-95228-4_12
[4]	Cordover & Minden, 2020. Emma, Cordover., Audrey, Minden. (2020). 1. Signaling pathways downstream to receptor tyrosine kinases: targets for cancer treatment. Journal of Cancer Metastasis and Treatment, https://doi.org/10.20517/2394-4722.2020.101
[5]	Ahmad, 2022. Fridoon, Jawad, Ahmad. (2022). Receptor Tyrosine Kinases Mediated Cell Signalling Pathways. Pakistan biomedical journal, https://doi.org/10.54393/pbmj.v5i7.572
[6]	"Cell Signaling Pathways in Cancer", (2023). 2. Cell Signaling Pathways in Cancer. https://doi.org/10.2174/9789815080384123010009
[7]	"Cellular signaling", (2023). 5. Cellular signaling. https://doi.org/10.1016/b978-0-12-824459-3.00004-4
[8]	Trindade et al., 2020. Fábio, Trindade., Inês, Falcão-Pires., Andreas, N., Kavazis., Adelino, F., Leite-Moreira., Daniel, Moreira-Gonçalves., Rita, Nogueira-Ferreira. (2020). 4. Key Signaling Pathways in the Cardiovascular System. https://doi.org/10.1007/978-3-030-44436-5_12
[9]	Ullo & Case, 2023. Maria, F, Ullo., Lindsay, B., Case. (2023). 1. How cells sense and integrate information from different sources.. Wires Mechanisms Of Disease, https://doi.org/10.1002/wsbm.1604
[10]	Berridge Michael John, Cobbold P. H., Cuthbertson K. S. R. (1988) Spatial and temporal aspects of cell signalling. Phil. Trans. R. Soc. Lond. B320325–343 https://doi.org/10.1098/rstb.1988.0080
[11]	Stefan, Hoops., Sven, Sahle., Ralph, Gauges., Christine, Lee., Jürgen, Pahle., Natalia, Simus., Mudita, Singhal., Liang, Xu., Pedro, Mendes., Ursula, Kummer. (2006). 3. COPASI---a COmplex PAthway SImulator. Bioinformatics, https://doi.org/10.1093/BIOINFORMATICS/BTL485
[12]	Shahi, 2023. Mehdi, Hayat, Shahi. (2023). 2. Introduction to Cell Signaling Pathways and Brain Tumors. https://doi.org/10.1007/978-981-15-8473-2_1
[13]	"Cellular Signaling Pathways as Plastic, Proto-cognitive Systems: Implications for Biomedicine", (2022). 1. Cellular Signaling Pathways as Plastic, Proto-cognitive Systems: Implications for Biomedicine. https://doi.org/10.31219/osf.io/c6n9r
[14]	Mehta et al., 2020. Meenu, Mehta., Daljeet, Singh, Dhanjal., Keshav, Raj, Paudel., Bhupender, Singh., Gaurav, Gupta., Shanmugam, Rajeshkumar., Lakshmi, Thangavelu., Murtaza, M., Tambuwala., Hamid, A., Bakshi., Dinesh, Kumar, Chellappan., Parijat, Pandey., Harish, Dureja., Nitin, Charbe., Sachin, Kumar, Singh., Shakti, D., Shukla., Srinivas, Nammi., Alaa, A., A., Aljabali., Peter, Wich., Philip, M., Hansbro., Philip, M., Hansbro., Philip, M., Hansbro., Saurabh, Satija., Saurabh, Satija., Kamal, Dua. (2020). 5. Cellular signalling pathways mediating the pathogenesis of chronic inflammatory respiratory diseases: an update.. Inflammopharmacology, https://doi.org/10.1007/S10787-020-00698-3
[15]	Zhang, 2023. Yiming, Zhang. (2023). 4. Exploring the mechanisms of cellular signal transduction pathways and their implications for diseases. https://doi.org/10.54254/2753-8818/22/20230998
[16]	Koch & Büttner, 2023. Ina, Koch., Bianca, Büttner. (2023). 1. Computational modeling of signal transduction networks kinetic parameters - Petri net approaches.. American Journal of Physiology-cell Physiology, https://doi.org/10.1152/ajpcell.00487.2022
[17]	Kim & Kim, 2022. Minsoo, Kim., Eunjung, Kim. (2022). 2. Mathematical model of the cell signaling pathway based on the extended Boolean network model with a stochastic process. BMC Bioinformatics, https://doi.org/10.1186/s12859-022-05077-z
[18]	Hu et al., 2020. Jun, Hu., Hui-Jia, Li., Jie, Gao., Zebo, Zhou., Chengyi, Xia. (2020). 5. Identifying Desirable Function Perturbations in Signaling Pathways Through Stochastic Analysis. IEEE Access, https://doi.org/10.1109/ACCESS.2020.2966229
[19]	Akçay & Bashirov, 2021. Nimet, İlke, Akçay., Rza, Bashirov. (2021). 3. Comparison of modelling approaches demonstrated for p16-mediated signalling pathway in higher eukaryotes.. BioSystems, https://doi.org/10.1016/J.BIOSYSTEMS.2021.104562
[20]	Michael, L., Blinov., James, R., Faeder., Byron, Goldstein., William, S., Hlavacek. (2004). 9. BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics, https://doi.org/10.1093/BIOINFORMATICS/BTH378
[21]	Sven, Sahle., Ralph, Gauges., Jürgen, Pahle., Natalia, Simus., Ursula, Kummer., Stefan, Hoops., Christine, Lee., Mudita, Singhal., Liang, Xu., Pedro, Mendes. (2006). 2. Simulation of biochemical networks using COPASI: a complex pathway simulator. https://doi.org/10.5555/1218112.1218421.
[22]	[Pedro, Mendes., Stefan, Hoops., Sven, Sahle., Ralph, Gauges., Joseph, O., Dada., Ursula, Kummer. (2009). 4. Computational modeling of biochemical networks using COPASI.. Methods of Molecular Biology, https://doi.org/10.1007/978-1-59745-525-1_2
[23]	[Diana, C., Resasco., Fei, Gao., Frank, Morgan., Igor, L., Novak., James, C., Schaff., Boris, M., Slepchenko. (2012). 5. Virtual Cell: computational tools for modeling in cell biology.. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, https://doi.org/10.1002/WSBM.165
[24]	"Network analysis using cytoscape", (2023). 1. Network analysis using cytoscape. https://doi.org/10.21203/rs.3.rs-2487773/v1
[25]	"MAPPINGS, a tool for network analysis of large phospho-signalling datasets: application to host erythrocyte response to Plasmodium infection", (2022). 3. MAPPINGS, a tool for network analysis of large phospho-signalling datasets: application to host erythrocyte response to Plasmodium infection. Current research in microbial sciences, https://doi.org/10.1016/j.crmicr.2022.100149
[26]	Adderley et al., 2021. Jack, D., Adderley., Finn, O'Donoghue., Christian, Doerig., Stephen, Davis. (2021). 5. Network analysis of large phospho-signalling datasets: application to Plasmodium-erythrocyte interactions. bioRxiv, https://doi.org/10.1101/2021.05.07.443051
[27]	Marazzi et al., 2020. Lauren, Marazzi., Andrew, Gainer-Dewar., Paola, Vera-Licona. (2020). 4. OCSANA+: optimal control and simulation of signaling networks from network analysis. Bioinformatics, https://doi.org/10.1093/BIOINFORMATICS/BTAA625
[28]	Cárdenas et al., 2020. Reinier, Cárdenas., Javier, Martínez-Seoane., Carlos, Amero. (2020). 1. Combining Experimental Data and Computational Methods for the Non-Computer Specialist.. Molecules, https://doi.org/10.3390/MOLECULES25204783
[29]	Klasson, 1997. K., Thomas, Klasson. (1997). 4. Experimental Data Analysis. Applied Biochemistry and Biotechnology, https://doi.org/10.1007/978-1-4612-2312-2_29
[30]	Kochmann et al., 2019. Dennis, M., Kochmann., Katia, Bertoldi., Ahmad, Rafsanjani., Romik, Khajehtourian., J., Howard, Mueller., Lishuai, Jin. (2019). 2. Data Repository. https://doi.org/10.6084/m9.figshare.10048724
[31]	Qiao et al., 2024. Yi, Qiao., Tianguang, Cheng., Z., Miao., Yue, Cui., Jing, Tu. (2024). 1. Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods. Small methods, https://doi.org/10.1002/smtd.202400789
[32]	Lim et al., 2024. Jongsu, Lim., Minjae, Kim., Hyukhee, Kim., Junil, Kim., Dong-Sung, Lee. (2024). 2. Advances in single-cell omics and multiomics for high-resolution molecular profiling.. Experimental and Molecular Medicine, https://doi.org/10.1038/s12276-024-01186-2
[33]	Pan et al., 2023. Lu, Pan., Tian, Mou., Yue, Huang., Weifeng, Hong., Min, Yu., Xue-xiao, Li. (2023). 3. Ursa: A Comprehensive Multi-Omics Toolbox for High-Throughput Single-Cell Analysis.. Molecular Biology and Evolution, https://doi.org/10.1093/molbev/msad267
[34]	Zhang et al., 2024. Kai, Zhang., Nathan, R, Zemke., Ethan, J., Armand., Bing, Ren. (2024). 4. A fast, scalable and versatile tool for analysis of single-cell omics data.. Nature Methods, https://doi.org/10.1038/s41592-023-02139-9
[35]	Koca & Sevilgen, 2023. Mehmet, Burak, Koca., Fatih, Erdogan, Sevilgen. (2023). 5. Integration of single-cell proteomic datasets through distinctive proteins in cell clusters. Proteomics, https://doi.org/10.1002/pmic.202300282
[36]	Slobodyanyuk et al., 2024. Mykhaylo, Slobodyanyuk., Alexander, T., Bahcheli., Zoe, P., Klein., Masroor, Bayati., Lisa, J., Strug., Jüri, Reimand. (2024). 1. Directional integration and pathway enrichment analysis for multi-omics data. Nature Communications, https://doi.org/10.1038/s41467-024-49986-4
[37]	Wieder et al., 2024. Cecilia, Wieder., Juliette, Cooke., Clement, Frainay., Nathalie, Poupin., Russell, P., Bowler., Fabien, Jourdan., Katerina, J., Kechris., Rachel, Pj, Lai., Timothy, Ebbels. (2024). 2. PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1011814
[38]	Wieder et al., 2024. Cecilia, Wieder., Juliette, Cooke., Clement, Frainay., Nathalie, Poupin., Russell, Bowler., Fabien, Jourdan., Katerina, J., Kechris., Rachel, PJ, Lai., Timothy, Ebbels. (2024). 3. PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. bioRxiv, https://doi.org/10.1101/2024.01.09.574780
[39]	Nguyen et al., 2024. Ha, Nam, Nguyen., Khoi, Pham., Hung, Nguyen., Bang, Tran., Juli, Petereit., Tin, Nguyen. (2024). 5. CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome. Briefings in Bioinformatics, https://doi.org/10.1093/bib/bbae222
[40]	"Multivariate pattern recognition by machine learning methods", (2023). 1. Multivariate pattern recognition by machine learning methods. https://doi.org/10.1016/b978-0-323-90408-7.00002-2
[41]	Adugna et al., 2024. Teshome, Adugna., Arulmurugan, Ramu., Anandakumar, Haldorai. (2024). 5. A Review of Pattern Recognition and Machine Learning. Journal of machine and computing, https://doi.org/10.53759/7669/jmc202404020
[42]	Yang et al., 2014. Lingjian, Yang., Chrysanthi, Ainali., Sophia, Tsoka., Lazaros, G., Papageorgiou. (2014). 2. Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework. BMC Bioinformatics, https://doi.org/10.1186/S12859-014-0390-2
[43]	Roy et al., 2023. Satyaki, Roy., Shehzad, Z., Sheikh., Terrence, S., Furey. (2023). 3. CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression. bioRxiv, https://doi.org/10.1101/2023.01.12.523808
[44]	Chuang et al., 2008. Cheng-Long, Chuang., Chih-Hung, Jen., Chung-Ming, Chen., Grace, S., Shieh. (2008). 4. A pattern recognition approach to infer time-lagged genetic interactions. Bioinformatics, https://doi.org/10.1093/BIOINFORMATICS/BTN098
[45]	Hodges et al., 2011. Andrew, Hodges., Peter, J., Woolf., Yongqun, He, ξ. (2011). 1. Prediction of Novel Pathway Elements and Interactions Using Bayesian Networks. https://doi.org/10.5772/18402
[46]	Navlakha et al., 2012. Saket, Navlakha., Anthony, Gitter., Ziv, Bar-Joseph. (2012). 2. A Network-based Approach for Predicting Missing Pathway Interactions. PLOS Computational Biology, https://doi.org/10.1371/JOURNAL.PCBI.1002640
[47]	Investigator & Passerini, 2013. Principal, Investigator., Andrea, Passerini. (2013). 3. Integrated Prediction of Protein Function, Interactions and Pathways with Statistical Relational Learning.
[48]	Anene et al., 2021. Chinedu, Anthony, Anene., Faraz, Khan., Findlay, Bewicke-Copley., Eleni, Maniati., Jun, Wang. (2021). 4. ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles.. https://doi.org/10.1016/J.PATTER.2021.100270
[49]	Sarkar & Saha, 2019. Debasree, Sarkar., Sudipto, Saha. (2019). 5. Machine-learning techniques for the prediction of protein–protein interactions. Journal of Biosciences, https://doi.org/10.1007/S12038-019-9909-Z
[50]	"Neural signaling and communication using machine learning", (2023). 1. Neural signaling and communication using machine learning. https://doi.org/10.1016/b978-0-323-90277-9.00010-9
[51]	Jacques et al., 2021. Marc-Antoine, Jacques., Maciej, Dobrzyński., Paolo, Armando, Gagliardi., Raphael, Sznitman., Olivier, Pertz. (2021). 2. CODEX, a neural network approach to explore signaling dynamics landscapes. Molecular Systems Biology, https://doi.org/10.15252/MSB.202010026
[52]	Ness et al., 2018. Robert, Osazuwa, Ness., Karen, Sachs., Parag, Mallick., Olga, Vitek. (2018). 3. A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.. Journal of Computational Biology, https://doi.org/10.1089/CMB.2017.0247
[53]	Dondelinger, 2013. Frank, Dondelinger. (2013). 4. Machine learning approach to reconstructing signalling pathways and interaction networks in biology.
[54]	Sánchez-Gutiérrez & González-Pérez, 2022. Máximo, Sánchez-Gutiérrez., Pedro, Pablo, González-Pérez. (2022). 5. Modeling and Simulation of Cell Signaling Networks for Subsequent Analytics Processes Using Big Data and Machine Learning. Bioinformatics and Biology Insights, https://doi.org/10.1177/11779322221091739
[55]	Oli, 2024. Bharti, Oli. (2024). 2. Revolutionizing Drug Discovery: A Comprehensive Review of Computer-Aided Drug Design Approaches. International Journal For Science Technology And Engineering, https://doi.org/10.22214/ijraset.2024.63563
[56]	Gangadharan et al., 2024. Arun, Kumar, Gangadharan., Varun, Thachan, Kundil., Abhithaj, Jayanandan. (2024). 4. Computational Tools in Drug-Lead Identification and Development. https://doi.org/10.1007/978-981-99-9183-9_4
[57]	Rao et al., 2024. Gali, Nageswara, Rao., C., Gunasundari., S., B, G, Tilak, Babu., G, Pavithra., Vijay, Kumar, Dwivedi., Bazani, Shaik. (2024). 1. AI-Driven Drug Discovery: Computational Methods and Applications. https://doi.org/10.1109/icrtcst61793.2024.10578434
[58]	Ncube et al., 2024. Nomagugu, B., Ncube., Matshawandile, Tukulula., Krishna, G., Govender. (2024). 3. Leveraging computational tools to combat malaria: assessment and development of new therapeutics. Journal of Cheminformatics, https://doi.org/10.1186/s13321-024-00842-z
[59]	Kommalapati et al., 2023. Hema, Sree, Kommalapati., Pushpa, Pilli., Vijaya, Madhyanapu, Golla., Nehal, Bhatt., Gananadhamu, Samanthula. (2023). 5. In Silico Tools to Thaw the Complexity of the Data: Revolutionizing Drug Research in Drug Metabolism, Pharmacokinetics and Toxicity Prediction.. Current Drug Metabolism, https://doi.org/10.2174/0113892002270798231201111422
[60]	Odunitan et al., 2023. T., T., Odunitan., Oluwatosin, A, Saibu., Boluwatife, T., Apanisile., DA, Omoboyowa., Toheeb, A., Balogun., Adeyoola, V., Awe., Temitope, M., Ajayi., Grace, V., Olagunju., Fatimah, M., Muhammad., M., Akinboade., CA, Adeniji., Waliu, O., Abdulazeez. (2023). 3. Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets.. Computers in Biology and Medicine, https://doi.org/10.1016/j.compbiomed.2023.107737
[61]	Lee et al., 2023. Won-Yung, Lee., Choong-Yeol, Lee., Chang-Eop, Kim. (2023). 4. Predicting activatory and inhibitory drug–target interactions based on structural compound representations and genetically perturbed transcriptomes. PLOS ONE, https://doi.org/10.1371/journal.pone.0282042
[62]	Shah et al., 2024. Hayat, Ali, Shah., Juan, Liu., Zhihui, Yang. (2024). 5. GTIE-RT: A Comprehensive Graph Learning Model for Predicting Drugs Targeting Metabolic Pathways in Human. Journal of Bioinformatics and Computational Biology, https://doi.org/10.1142/s0219720024500100
[63]	Wang et al., 2021. Jiye, Wang., Zengrui, Wu., Yayuan, Peng., Weihua, Li., Guixia, Liu., Yun, Tang. (2021). 2. Pathway-Based Drug Repurposing with DPNetinfer: A Method to Predict Drug-Pathway Associations via Network-Based Approaches.. Journal of Chemical Information and Modeling, https://doi.org/10.1021/ACS.JCIM.1C00009
[64]	Li et al., 2020. Fuhai, Li., Andrew, P., Michelson., Randi, E., Foraker., Philip, R., O., Payne. (2020). 4. Signaling network analysis and drug repurposing for COVID-19 treatment based on transcriptional response of lung host cells. arXiv: Molecular Networks.
[65]	Yang et al., 2022. Jiannan, Yang., Zhen, Li., W., Wu., Shi, Yu., Qian, Chu., Qingpeng, Zhang. (2022). 5. Deep Learning Can Identify Explainable Reasoning Paths of Mechanism of Drug Action for Drug Repurposing from Multilayer Biological Network. https://doi.org/10.21203/rs.3.rs-1269212/v1
[66]	Varela & Garcia-Rendueles, 2022. Luis, Varela., Maria, E. R., Garcia-Rendueles. (2022). 2. Oncogenic Pathways in Neurodegenerative Diseases. International Journal of Molecular Sciences, https://doi.org/10.3390/ijms23063223
[67]	Li et al., 2024. Zihao, Li., Meiling, Ji., Chenglong, Wu., Senlin, Ji. (2024). 3. Targeting ferroptosis in neuroimmune and neurodegenerative disorders for the development of novel therapeutics. https://doi.org/10.1016/j.biopha.2024.116777
[68]	Marchi et al., 2023. Fabiola, De, Marchi., Ivana, Munitic., Lea, Vidatic., Eliša, Papić., Valentino, Rački., Jerneja, Nimac., Igor, Jurak., Gabriela, Novotni., Boris, Rogelj., Vladimira, Vuletic., Rajka, M., Liscic., Jason, R., Cannon., Emanuele, Buratti., Letizia, Mazzini., Silva, Hecimovic. (2023). 1. Overlapping Neuroimmune Mechanisms and Therapeutic Targets in Neurodegenerative Disorders. Biomedicines, https://doi.org/10.3390/biomedicines11102793
[69]	Meek et al., 2023. Autumn, Meek., Yanfei, Wang., Fan, Wu., M., Reed., Christopher, J., Barden., Donald, Weaver. (2023). 4. Alzheimer’s Disease as an Innate Autoimmune Disease Regulated by Tryptophan and Arginine Metabolism. https://doi.org/10.1002/alz.075644
[70]	Sonar & Lal, 2019. Sandip, Ashok, Sonar., Girdhari, Lal. (2019). 5. Overview of Mechanisms Underlying Neuroimmune Diseases. https://doi.org/10.1007/978-3-030-19515-1_1
[71]	Vitorino, 2024. Rui, Vitorino. (2024). 1. Navigating the Omics Landscape in Precision Medicine: A Bidirectional Approach to Patient Care. https://doi.org/10.2139/ssrn.4807692
[72]	Ahmed, 2022. Zeeshan, Ahmed. (2022). 4. Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis.. Progress in Molecular Biology and Translational Science, https://doi.org/10.1016/bs.pmbts.2022.02.002
[73]	Sakdapipanich, 2022. Jitladda, Sakdapipanich. (2022). 2. A knowledge base for generating patient-specific pathways for individualized treatment decisions in clinical applications. https://doi.org/10.53846/goediss-9457
[74]	Nwaokorie & Fey, 2021. Annabelle, Nwaokorie., Dirk, Fey. (2021). 3. Personalised Medicine for Colorectal Cancer Using Mechanism-Based Machine Learning Models.. International Journal of Molecular Sciences, https://doi.org/10.3390/IJMS22189970
[75]	Carvalho et al., 2021. Lara, Carvalho., José, Luis, Capelo., Carlos, Lodeiro., Rajiv, Dhir., Pinheiro, Lc., Medeiros, M., Hugo, M., Santos., Hugo, M., Santos. (2021). 5. Pathway centered analysis to guide clinical decision-making in precision medicine. bioRxiv, https://doi.org/10.1101/2021.04.30.442131
[76]	Kaya, H. E., & Naidoo, K. J. (2023). CytoCopasi: a chemical systems biology target and drug discovery visual data analytics platform. Bioinformatics, 39(12), https://doi.org/10.1093/bioinformatics/btad745
[77]	Sarathy, C., Kutmon, M., Lenz, M., Adriaens, M. E., Evelo, C. T., & Arts, I. C. W. (2020). EFMviz: A COBRA Toolbox extension to visualize Elementary Flux Modes in Genome-Scale Metabolic Models. Metabolites, 10(2), 66. https://doi.org/10.3390/metabo10020066
[78]	Kondratyeva et al., 2022. L., G., Kondratyeva., I., V., Alekseenko., Igor, P., Chernov., E. D., Sverdlov. (2022). 1. Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism. Biology, https://doi.org/10.3390/biology11081208
[79]	Kavran et al., 2021. Andrew, J, Kavran., Aaron, Clauset., Aaron, Clauset. (2021). 2. Denoising large-scale biological data using network filters. BMC Bioinformatics, https://doi.org/10.1186/S12859-021-04075-X
[80]	Kavran & Clauset, 2020. Andrew, J, Kavran., Aaron, Clauset. (2020). 4. Denoising large-scale biological data using network filters. bioRxiv, https://doi.org/10.1101/2020.03.12.989244
[81]	Ganesan et al., 2016. Narayan, Ganesan., Jie, Li., Vishakha, Sharma., Hanyu, Jiang., Adriana, Compagnoni. (2016). 5. Process Simulation of Complex Biological Pathways in Physical Reactive Space and Reformulated for Massively Parallel Computing Platforms. IEEE/ACM Transactions on Computational Biology and Bioinformatics, https://doi.org/10.1109/TCBB.2015.2443784
[82]	Ji et al., 2017. Zhiwei, Ji., Ke, Yan., Wenyang, Li., Haigen, Hu., Xiaoliang, Zhu. (2017). 4. Mathematical and Computational Modeling in Complex Biological Systems. BioMed Research International, https://doi.org/10.1155/2017/5958321
[83]	Merzbacher & Oyarzún, 2023. Charlotte, Merzbacher., Diego, A., Oyarzún. (2023). 1. Applications of artificial intelligence and machine learning in dynamic pathway engineering. Biochemical Society Transactions, https://doi.org/10.1042/bst20221542
[84]	Dellinger et al., 2014. Andrew, Dellinger., Andrew, B., Nixon., Herbert, Pang. (2014). 2. Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data. Cancer Informatics, https://doi.org/10.4137/CIN.S13634
[85]	Lee et al., 2021. Nagyeong, Lee., Joonsoo, Jeong., Dongil, Shin. (2021). 4. AI System for Supporting Generation of Optimal Synthetic Pathways Based on Chemical Reaction Big Data. https://doi.org/10.1016/B978-0-323-88506-5.50162-5
[86]	Kang et al., 2020. Yan, Kang., Wenbo, Xu., Yan, Zhu., Fang, Xie., Shuangshuang, Dai., Weihui, Dai. (2020). 3. Intelligent Analysis for Improving The Clinical Pathway of Lung Cancer. https://doi.org/10.1109/INISTA49547.2020.9194643
[87]	Challa et al., 2022. Narayana, Challa., Satish, Kathiriya., Mahesh, Babu, Munjala. (2022). 5. Integrating Advanced Data Analytics with Artificial Intelligence: Unveiling Synergies for Enhanced Decision-Making. https://doi.org/10.47363/jaicc/2022(1)185
[88]	Hegde et al., 2024. Subrahmanya, Hegde., S., M., Andrian., Harshith, Sajal., Anil, Kumar, Kotermane, Mallikarjunappa., Angel, Benny., Sumitha, Elayaperumal. (2024). 1. Systems Biology Approaches for Precision Medicine. https://doi.org/10.1007/978-981-99-9462-5_11
[89]	Tebani & Bekri, 2022. A., Tebani., Sadia, Bekri. (2022). 5. Les promesses des sciences omiques à l’ère de la médecine de précision. Revue de Médecine Interne, https://doi.org/10.1016/j.revmed.2022.07.009
[90]	Bhattacharyya et al., 2023. Sayantan, Bhattacharyya., Shafqat, F., Ehsan., Loukia, G., Karacosta. (2023). 2. Phenotypic maps for precision medicine: a promising systems biology tool for assessing therapy response and resistance at a personalized level. Frontiers in Network Physiology, https://doi.org/10.3389/fnetp.2023.1256104

Cite This Article

Plain Text BibTeX RIS

APA Style

Chakraborty, S., Khatun, R., Sengupta, S., Bhattacharya, M. (2025). Decoding Metabolic Pathway: Leveraging Computational Tools for Insight. Computational Biology and Bioinformatics, 13(1), 1-16. https://doi.org/10.11648/j.cbb.20251301.11

Copy | Download

ACS Style

Chakraborty, S.; Khatun, R.; Sengupta, S.; Bhattacharya, M. Decoding Metabolic Pathway: Leveraging Computational Tools for Insight. Comput. Biol. Bioinform. 2025, 13(1), 1-16. doi: 10.11648/j.cbb.20251301.11

Copy | Download

AMA Style

Chakraborty S, Khatun R, Sengupta S, Bhattacharya M. Decoding Metabolic Pathway: Leveraging Computational Tools for Insight. Comput Biol Bioinform. 2025;13(1):1-16. doi: 10.11648/j.cbb.20251301.11

Copy | Download

@article{10.11648/j.cbb.20251301.11,
  author = {Sabuj Chakraborty and Rojina Khatun and Sudeshna Sengupta and Malavika Bhattacharya},
  title = {Decoding Metabolic Pathway: Leveraging Computational Tools for Insight},
  journal = {Computational Biology and Bioinformatics},
  volume = {13},
  number = {1},
  pages = {1-16},
  doi = {10.11648/j.cbb.20251301.11},
  url = {https://doi.org/10.11648/j.cbb.20251301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20251301.11},
  abstract = {His chapter introduces us to the role of cellular signaling pathways and their significance in understanding the intricate working of an organism’s functioning, life processes and enable us in deepening of our understanding of many diseases. Through time many relevant pathways has been discovered, we are yet to discover more and even identify missing pieces of existing pathways. Use of novel computational tools, that integrates principles from computer science, mathematics, and biology help us to enhance our understanding of signaling pathways. Its significance lies in its ability to predict pathway behavior under different conditions, analyze large signaling networks and model biological processes using tools like BioNetGen, Copasi and Virtual Cell. The biological data is sourced from pathway databases (e.g., KEGG, Reactome, BioGRID). The application of machine learning for pattern recognition and pathway inference and use of AI to predict novel interactions or missing components in pathways aid in decoding signaling networks. Computational tools help us to identify drug targets by modeling pathways. Analysis of pathways further assist in drug discovery and drug re-purposing. Predictive modeling systems gives us new insights into cancer and neuro-degenerative diseases (e.g., Alzheimer's), and autoimmune disorders while engineering novel pathways for biotechnological applications thus enhancing development of synthetic biology.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Decoding Metabolic Pathway: Leveraging Computational Tools for Insight
AU  - Sabuj Chakraborty
AU  - Rojina Khatun
AU  - Sudeshna Sengupta
AU  - Malavika Bhattacharya
Y1  - 2025/06/25
PY  - 2025
N1  - https://doi.org/10.11648/j.cbb.20251301.11
DO  - 10.11648/j.cbb.20251301.11
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 1
EP  - 16
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20251301.11
AB  - His chapter introduces us to the role of cellular signaling pathways and their significance in understanding the intricate working of an organism’s functioning, life processes and enable us in deepening of our understanding of many diseases. Through time many relevant pathways has been discovered, we are yet to discover more and even identify missing pieces of existing pathways. Use of novel computational tools, that integrates principles from computer science, mathematics, and biology help us to enhance our understanding of signaling pathways. Its significance lies in its ability to predict pathway behavior under different conditions, analyze large signaling networks and model biological processes using tools like BioNetGen, Copasi and Virtual Cell. The biological data is sourced from pathway databases (e.g., KEGG, Reactome, BioGRID). The application of machine learning for pattern recognition and pathway inference and use of AI to predict novel interactions or missing components in pathways aid in decoding signaling networks. Computational tools help us to identify drug targets by modeling pathways. Analysis of pathways further assist in drug discovery and drug re-purposing. Predictive modeling systems gives us new insights into cancer and neuro-degenerative diseases (e.g., Alzheimer's), and autoimmune disorders while engineering novel pathways for biotechnological applications thus enhancing development of synthetic biology.
VL  - 13
IS  - 1
ER  -

Copy | Download

Author Information

Sabuj Chakraborty

Department of Biotechnology, Techno India University, Kolkata, India

http://orcid.org/0009-0006-1014-6152
Rojina Khatun

Department of Biotechnology, Techno India University, Kolkata, India

http://orcid.org/0009-0000-8680-9334
Sudeshna Sengupta

Department of Biotechnology, Techno India University, Kolkata, India

http://orcid.org/0009-0000-5027-4534
Malavika Bhattacharya

Department of Biotechnology, Techno India University, Kolkata, India

Contact Email

http://orcid.org/0000-0002-7225-3029

Download PDF

Plain Text BibTeX RIS

APA Style

Chakraborty, S., Khatun, R., Sengupta, S., Bhattacharya, M. (2025). Decoding Metabolic Pathway: Leveraging Computational Tools for Insight. Computational Biology and Bioinformatics, 13(1), 1-16. https://doi.org/10.11648/j.cbb.20251301.11

Copy | Download

ACS Style

Chakraborty, S.; Khatun, R.; Sengupta, S.; Bhattacharya, M. Decoding Metabolic Pathway: Leveraging Computational Tools for Insight. Comput. Biol. Bioinform. 2025, 13(1), 1-16. doi: 10.11648/j.cbb.20251301.11

Copy | Download

AMA Style

Chakraborty S, Khatun R, Sengupta S, Bhattacharya M. Decoding Metabolic Pathway: Leveraging Computational Tools for Insight. Comput Biol Bioinform. 2025;13(1):1-16. doi: 10.11648/j.cbb.20251301.11

Copy | Download

@article{10.11648/j.cbb.20251301.11,
  author = {Sabuj Chakraborty and Rojina Khatun and Sudeshna Sengupta and Malavika Bhattacharya},
  title = {Decoding Metabolic Pathway: Leveraging Computational Tools for Insight},
  journal = {Computational Biology and Bioinformatics},
  volume = {13},
  number = {1},
  pages = {1-16},
  doi = {10.11648/j.cbb.20251301.11},
  url = {https://doi.org/10.11648/j.cbb.20251301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20251301.11},
  abstract = {His chapter introduces us to the role of cellular signaling pathways and their significance in understanding the intricate working of an organism’s functioning, life processes and enable us in deepening of our understanding of many diseases. Through time many relevant pathways has been discovered, we are yet to discover more and even identify missing pieces of existing pathways. Use of novel computational tools, that integrates principles from computer science, mathematics, and biology help us to enhance our understanding of signaling pathways. Its significance lies in its ability to predict pathway behavior under different conditions, analyze large signaling networks and model biological processes using tools like BioNetGen, Copasi and Virtual Cell. The biological data is sourced from pathway databases (e.g., KEGG, Reactome, BioGRID). The application of machine learning for pattern recognition and pathway inference and use of AI to predict novel interactions or missing components in pathways aid in decoding signaling networks. Computational tools help us to identify drug targets by modeling pathways. Analysis of pathways further assist in drug discovery and drug re-purposing. Predictive modeling systems gives us new insights into cancer and neuro-degenerative diseases (e.g., Alzheimer's), and autoimmune disorders while engineering novel pathways for biotechnological applications thus enhancing development of synthetic biology.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Decoding Metabolic Pathway: Leveraging Computational Tools for Insight
AU  - Sabuj Chakraborty
AU  - Rojina Khatun
AU  - Sudeshna Sengupta
AU  - Malavika Bhattacharya
Y1  - 2025/06/25
PY  - 2025
N1  - https://doi.org/10.11648/j.cbb.20251301.11
DO  - 10.11648/j.cbb.20251301.11
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 1
EP  - 16
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20251301.11
AB  - His chapter introduces us to the role of cellular signaling pathways and their significance in understanding the intricate working of an organism’s functioning, life processes and enable us in deepening of our understanding of many diseases. Through time many relevant pathways has been discovered, we are yet to discover more and even identify missing pieces of existing pathways. Use of novel computational tools, that integrates principles from computer science, mathematics, and biology help us to enhance our understanding of signaling pathways. Its significance lies in its ability to predict pathway behavior under different conditions, analyze large signaling networks and model biological processes using tools like BioNetGen, Copasi and Virtual Cell. The biological data is sourced from pathway databases (e.g., KEGG, Reactome, BioGRID). The application of machine learning for pattern recognition and pathway inference and use of AI to predict novel interactions or missing components in pathways aid in decoding signaling networks. Computational tools help us to identify drug targets by modeling pathways. Analysis of pathways further assist in drug discovery and drug re-purposing. Predictive modeling systems gives us new insights into cancer and neuro-degenerative diseases (e.g., Alzheimer's), and autoimmune disorders while engineering novel pathways for biotechnological applications thus enhancing development of synthetic biology.
VL  - 13
IS  - 1
ER  -

Copy | Download