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Computational Modeling and Data Analysis in Biological and Health-Related Processes
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Computational Modeling and Data Analysis in Biological and Health-Related Processes

A Special Issue of Processes (ISSN 2227-9717).

Deadline for manuscript submissions: closed (28 August 2026) | Viewed by 4937

Editors

Dr. Zuyi (Jacky) Huang

E-Mail Website
Guest Editor
Department of Chemical Engineering, Villanova University, Villanova, PA 19085, USA
Interests: systems biology; drug discovery; bio-data analysis; foodborne pathogens; Alzheimer’s disease; Parkinson’s disease; cell therapy; gene therapy
Special Issues, Collections and Topics in MDPI journals
Dr. Yunyun Zhou

E-Mail Website
Guest Editor
Cancer Signaling & Microenvironment, Fox Chase Cancer Center, Philadelphia, PA 19111, USA
Interests: multi-omics analysis; machine learning; deep learning; gene discovery; natural language processing; integrative network methods

Special Issue Information

Dear Colleagues,

Advances in experimental biology, biotechnology, and data acquisition have led to a surge of high-resolution data across scales—from molecular signatures and clinical data to time-series measurements in microbial, cellular, and engineered systems. Alongside these developments, the integration of computational modeling, systems engineering, and artificial intelligence (AI) provides powerful new ways to simulate, analyze, and optimize complex biological processes.

This Special Issue, “Computational Modeling and Data Analysis in Biological and Health-Related Processes,” welcomes contributions that combine mechanistic or data-driven models with experimental or observational data to improve understanding, prediction, and control of biological systems. We are particularly interested in studies that connect data analysis with biological processes, from sub-cellular mechanisms and microbial interactions to bioreactor dynamics and health-related applications.

Topics may include (but are not limited to) the following:

  • Modeling metabolic, signaling, or genetic systems;
  • Model estimation and sensitivity analysis;
  • Machine learning for modeling biological systems;
  • Multi-modal data integration and biomarker discovery;
  • Data analysis in biological or health-related studies;
  • AI and decision-support in bio/health systems;
  • Optimization of bioprocesses or health-related workflows.

We hope this Special Issue can bring together researchers working across different fields to share new ideas and methods. We warmly invite your contributions and look forward to your support.

Dr. Zuyi (Jacky) Huang
Dr. Yunyun Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational modeling
  • systems biology
  • artificial intelligence (AI)
  • data analysis
  • bioprocess optimization
  • multi-modal data integration
  • biomarker discovery
  • sensitivity analysis
  • decision-support systems

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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35 pages, 1340 KB  
Article
Behind Macrophage Polarization in Wound Healing: A Mathematical Quest
by Prateek Gupta and Doraiswami Ramkrishna
Processes 2026, 14(5), 790; https://doi.org/10.3390/pr14050790 - 28 Feb 2026
Viewed by 815
Abstract
Cellular heterogeneity in immune responses helps us fight diverse pathogenic threats. Here, we present a resource-constrained, optimization-based flux-allocation paradigm by which phenotypic heterogeneity emerges in macrophage polarization. We build a six-dimensional mechanistic model of arginine metabolism and study how constrained resource allocation shapes [...] Read more.
Cellular heterogeneity in immune responses helps us fight diverse pathogenic threats. Here, we present a resource-constrained, optimization-based flux-allocation paradigm by which phenotypic heterogeneity emerges in macrophage polarization. We build a six-dimensional mechanistic model of arginine metabolism and study how constrained resource allocation shapes polarization and population heterogeneity. In a baseline formulation without explicit resource budgets, the system is effectively monostable under fixed cytokine inputs, and heterogeneous populations collapse onto a single polarization trajectory, consistent with well-resolving wounds. Introducing cybernetic variables that actively distribute finite resources between competing metabolic alternatives enables ultrasensitive resource partitioning that amplifies feedback and produces robust bistability under fixed cytokine cues. Embedding this intracellular model in a structured population balance framework, we show that in the advection-dominated regime, an initial macrophage cloud splits along two cybernetic equilibria, yielding approximately symmetric M1-like (iNOShigh/Arg1low) and M2-like (Arg1high/iNOSlow) peaks. Adding small isotropic diffusion in trait space, its coupling to NO-dependent apoptosis skews mass toward the Arg1high/iNOSlow branch, producing persistent asymmetric bimodality with a denser M2-like subpopulation. The model matches reported dynamics of iNOS expression, including gradual convergence of iNOSlow and iNOShigh subpopulations under ±IFNγ treatment. Such models can accelerate discovery of therapies for chronic wounds by predicting population-level responses to treatment. Full article
(This article belongs to the Special Issue Computational Modeling and Data Analysis in Biological and Health-Related Processes)
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21 pages, 1436 KB  
Article
Multimodal Biomarker Analysis of LRRK2-Linked Parkinson’s Disease Across SAA Subtypes
by Vivian Jiang, Cody K Huang, Grace Gao, Kaiqi Huang, Lucy Yu, Chloe Chan, Andrew Li and Zuyi Huang
Processes 2025, 13(11), 3448; https://doi.org/10.3390/pr13113448 - 27 Oct 2025
Cited by 1 | Viewed by 1647
Abstract
The LRRK2+ SAA− cohort of Parkinson’s disease (PD), characterized by the absence of hallmark α-synuclein pathology, remains under-explored. This limits opportunities for early detection and targeted intervention. This study analyzes data from this under-characterized subgroup and compares it with the LRRK2+ SAA+ cohort [...] Read more.
The LRRK2+ SAA− cohort of Parkinson’s disease (PD), characterized by the absence of hallmark α-synuclein pathology, remains under-explored. This limits opportunities for early detection and targeted intervention. This study analyzes data from this under-characterized subgroup and compares it with the LRRK2+ SAA+ cohort using longitudinal data from the Parkinson’s Progression Markers Initiative (PPMI). The PPMI dataset includes 115 LRRK2+ patients (70 SAA+, 45 SAA−) across 52 features encompassing clinical assessments, cognitive scores, DaTScan SPECT imaging, and motor severity. DaTScan binding ratios were selected as imaging-based indicators of early dopaminergic loss, while NP3TOT (MDS-UPDRS Part III total score) was used as a gold-standard clinical measure of motor symptom severity. Linear mixed-effects models were then applied to evaluate longitudinal predictors of DaTScan decline and NP3TOT progression, and statistical analyses of group comparisons revealed distinct drivers of symptoms differentiating SAA− from SAA+ patients. In SAA− patients, a decline in DaTScan was significantly associated with thermoregulatory impairment (p-value = 0.019), while NP3TOT progression was predicted by constipation (p-value = 0.030), sleep disturbances (p-value = 0.046), and longitudinal time effects (p-value = 0.043). In contrast, SAA+ patients showed significantly lower DaTScan values compared to SAA− (p-value = 0.0004) and stronger coupling with classical motor impairments, including freezing of gait (p-value = 0.016), rising from a chair (p-value = 0.007), and turning in bed (p-value = 0.016), along with cognitive decline (MoCA clock-hands test, p-value = 0.037). These findings support the hypothesis that LRRK2+ SAA− patients follow a distinct pathophysiological course, where progression is influenced more by autonomic and non-motor symptoms than by typical motor dysfunction. This study establishes a robust, multimodal modeling framework for examining heterogeneity in genetic PD and highlights the utility of combining DaTScan, NP3TOT, and symptom-specific features for early subtype differentiation. These findings have direct clinical implications, as stratifying LRRK2 carriers by SAA status may enhance patient monitoring, improve prognostic accuracy, and guide the design of targeted clinical trials for disease-modifying therapies. Full article
(This article belongs to the Special Issue Computational Modeling and Data Analysis in Biological and Health-Related Processes)
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Review

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45 pages, 2539 KB  
Review
Recent Advances and Challenges in AI-Integrated Lower-Limb Rehabilitation Exoskeletons: A Comprehensive Review
by Tianlian Pang, Wei Li, Dawen Sun, Zhenyang Qin, Qianjin Liu and Zhengwei Yue
Processes 2026, 14(10), 1614; https://doi.org/10.3390/pr14101614 - 16 May 2026
Viewed by 1700
Abstract
The aging population and the high incidence of neurological disorders have driven an increasing demand for lower-limb motor dysfunction rehabilitation. Traditional rehabilitation methods suffer from limitations such as low efficiency and a lack of personalization. Lower-limb rehabilitation exoskeleton robots have emerged as a [...] Read more.
The aging population and the high incidence of neurological disorders have driven an increasing demand for lower-limb motor dysfunction rehabilitation. Traditional rehabilitation methods suffer from limitations such as low efficiency and a lack of personalization. Lower-limb rehabilitation exoskeleton robots have emerged as a critical solution, with human–robot intelligent fusion serving as the core theoretical framework and technological pathway for performance enhancement. From the unique perspective of human–robot intelligent fusion, this paper systematically reviews the application and recent advances of artificial intelligence in three key aspects—intention perception, intelligent control, and human–robot integration—based on a layered architecture of “fusion perception, fusion decision-making, and fusion execution”. The definition, connotations, and realization mechanisms of human–robot intelligent fusion are clarified. Furthermore, this review analyzes the fusion mechanisms, applicable scenarios, and technical characteristics of different AI technologies and summarizes the human–robot intelligent fusion modes and clinical application status of representative products such as EksoNR, MyoSuit, and AiLegs. In addition, key challenges are identified from the perspectives of fusion generalization capabilities, the trade-off between real-time performance and robustness, algorithm interpretability, and multimodal deep fusion mechanisms. This paper provides a systematic theoretical reference and technical roadmap for establishing a unified human–robot intelligent fusion framework for lower-limb rehabilitation exoskeletons. Full article
(This article belongs to the Special Issue Computational Modeling and Data Analysis in Biological and Health-Related Processes)
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