Computational Systems Biology Research Articles

Optimizing Promoters and Subcellular Localization for Constitutive Transgene Expression in Marchantia polymorpha.

Marchantia polymorpha has become an important model system for comparative studies and synthetic biology. The systematic characterization of genetic elements would make heterologous gene expression more predictable in this test bed for gene circuit assembly and bioproduction. Yet, the toolbox of genetic parts for Marchantia includes only a few constitutive promoters that need benchmarking to assess their utility. We compared the expression patterns of previously characterized and new constitutive promoters. We found that driving expression with the double enhancer version of the cauliflower mosaic virus 35S promoter (pro35S × 2) provided the highest yield of proteins, although it also inhibits the growth of transformants. In contrast, promoters derived from the Marchantia genes for ETHYLENE RESPONSE FACTOR 1 and the CLASS II HOMEODOMAIN-LEUCINE ZIPPER protein drove expression to higher levels across all tissues without a growth penalty and can provide intermediate levels of gene expression. In addition, we showed that the cytosol is the best subcellular compartment to target heterologous proteins for higher levels of expression without a significant growth burden. To demonstrate the potential of these promoters in Marchantia, we expressed RUBY, a polycistronic betalain synthesis cassette linked by P2A sequences, to demonstrate coordinated expression of metabolic enzymes. A heat-shock-inducible promoter was used to further mitigate growth burdens associated with high amounts of betalain accumulation. We have expanded the existing tool kit for gene expression in Marchantia and provided new resources for the Marchantia research community.

Simplifications and approximations in a single-gene circuit modeling

The absence of detailed knowledge about regulatory interactions makes the use of phenomenological assumptions mandatory in cell biology modeling. Furthermore, the challenges associated with the analysis of these models compel the implementation of mathematical approximations. However, the constraints these methods introduce to biological interpretation are sometimes neglected. Consequently, understanding these restrictions is a very important task for systems biology modeling. In this article, we examine the impact of such simplifications, taking the case of a single-gene autoinhibitory circuit; however, our conclusions are not limited solely to this instance. We demonstrate that models grounded in the same biological assumptions but described at varying levels of detail can lead to different outcomes, that is, different and contradictory phenotypes or behaviors. Indeed, incorporating specific molecular processes like translation and elongation into the model can introduce instabilities and oscillations not seen when these processes are assumed to be instantaneous. Furthermore, incorporating a detailed description of promoter dynamics, usually described by a phenomenological regulatory function, can lead to instability, depending on the cooperative binding mechanism that is acting. Consequently, although the use of a regulating function facilitates model analysis, it may mask relevant aspects of the system’s behavior. In particular, we observe that the two cooperative binding mechanisms, both compatible with the same sigmoidal function, can lead to different phenotypes, such as transcriptional oscillations with different oscillation frequencies.

Systems Biology in ELIXIR: modelling in the spotlight.

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Mechanism-based organization of neural networks to emulate systems biology and pharmacology models.

Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate mechanistic models, which simply learn a mapping between the inputs and outputs of mechanistic models, ignoring the underlying processes. Using a mechanistic model studying the pharmacological interaction between opioids and naloxone as a proof-of-concept example, we demonstrated that by reorganizing the neural networks' layers to mimic the structure of the mechanistic model, it is possible to achieve better training rates and prediction accuracy relative to the previously proposed black-box neural networks, while maintaining the interpretability of the mechanistic simulations. Our framework can be used to emulate mechanistic models in a large parameter space and offers an example on the utility of increasing the interpretability of deep learning networks.

Maboss for HPC environments: implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators

BackgroundComputational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial.ResultsWe present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations.ConclusionThese implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community.

Morphological Stability for in silico Models of Avascular Tumors

The landscape of computational modeling in cancer systems biology is diverse, offering a spectrum of models and frameworks, each with its own trade-offs and advantages. Ideally, models are meant to be useful in refining hypotheses, to sharpen experimental procedures and, in the longer run, even for applications in personalized medicine. One of the greatest challenges is to balance model realism and detail with experimental data to eventually produce useful data-driven models. We contribute to this quest by developing a transparent, highly parsimonious, first principle in silico model of a growing avascular tumor. We initially formulate the physiological considerations and the specific model within a stochastic cell-based framework. We next formulate a corresponding mean-field model using partial differential equations which is amenable to mathematical analysis. Despite a few notable differences between the two models, we are in this way able to successfully detail the impact of all parameters in the stability of the growth process and on the eventual tumor fate of the stochastic model. This facilitates the deduction of Bayesian priors for a given situation, but also provides important insights into the underlying mechanism of tumor growth and progression. Although the resulting model framework is relatively simple and transparent, it can still reproduce the full range of known emergent behavior. We identify a novel model instability arising from nutrient starvation and we also discuss additional insight concerning possible model additions and the effects of those. Thanks to the framework’s flexibility, such additions can be readily included whenever the relevant data become available.

Compact active analog device for novel applications useful for sensing and measurement

The presented research introduces a novel approach to modern modular active device and provides various practical application examples tailored for industrial sensing readouts. Straightforward implementation of single active device-based topologies in frequency bands from kHz up to MHz is presented. These applications include: an electronically linearly tunable special band-pass filter that can be easily modified into a voltage-controlled oscillator, a two-port quadratic transformer, an amplitude modulator, and an equalization circuit (which serves as a fractional-order differentiator and integrator). These circuits benefit from high-impedance voltage input and low-impedance output (easily matched to 50 Ω), high processed signal levels (often reaching hundreds of mV), and simplified topologies without redundant passive elements. These features are especially valuable in tunable and configurable solutions for modern communication, sensing, audio, and consumer electronic systems, as well as in modeling various physical and biological systems. The experiments have been provided by simulations and measurements using device VCA824.

Super-resolution radial fluctuations microscopy for optimal resolution and fidelity.

Fluorescence fluctuation super-resolution microscopy (FF-SRM) has emerged as a promising method for the fast, low-cost, and uncomplicated imaging of biological specimens beyond the diffraction limit. Among FF-SRM techniques, super-resolution radial fluctuation (SRRF) microscopy is a popular technique but is prone to artifacts, resulting in low fidelity, especially under conditions of high-density fluorophores. In this Letter, we developed a novel, to the best of our knowledge, combinatory computational super-resolution microscopy method, namely VeSRRF, that demonstrated superior performance in SRRF microscopy. VeSRRF combined intensity and gradient variance reweighted radial fluctuations (VRRF) and enhanced-SRRF (eSRRF) algorithms, leveraging the enhanced resolution achieved through intensity and gradient variance analysis in VRRF and the improved fidelity obtained from the radial gradient convergence transform in eSRRF. Our method was validated using microtubules in mammalian cells as a standard biological model system. Our results demonstrated that VeSRRF consistently achieved the highest resolution and exceptional fidelity compared to those obtained from other algorithms in both single-molecule localization microscopy (SMLM) and FF-SRM. Moreover, we developed the VeSRRF software package that is freely available on the open-source ImageJ/Fiji software platform to facilitate the use of VeSRRF in the broader community of biomedical researchers. VeSRRF is an exemplary method in which complementary microscopy techniques are integrated holistically, creating superior imaging performance and capabilities.

Development of Functional Foods with Enhanced Health Benefits

Purpose: This study aimed to explore the development of functional foods with enhanced health benefits. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the development of functional foods with enhanced health benefits. Preliminary empirical review revealed that these innovative food products offer promising avenues for improving health outcomes and preventing chronic diseases. By formulating functional foods with targeted bioactive compounds, researchers tailored interventions to address prevalent health concerns such as gut dysbiosis, hyperlipidemia, oxidative stress, and nutrient deficiencies. The study emphasized the need for continued research and innovation to optimize formulations, mechanisms of action, and delivery systems. Collaboration between researchers, food scientists, healthcare professionals, policymakers, and the food industry was highlighted as essential for driving innovation and translating scientific findings into tangible interventions that benefit public health. Overall, the study highlighted the potential of functional foods to revolutionize nutrition and disease prevention strategies. Unique Contribution to Theory, Practice and Policy: The Nutritional Epidemiology, Nutrigenomics and Systems Biology model may be used to anchor future studies on the development of functional foods with enhanced health benefits. The contributed significantly to theory, practice, and policy in the field of nutrition and public health. It provided valuable insights into the complex interactions between bioactive compounds and physiological processes, advancing theoretical frameworks in nutrition science. By emphasizing evidence-based formulation and product optimization, the study offered practical guidance for food scientists and manufacturers in creating functional foods with demonstrable health benefits. Moreover, the study advocated for transparent labeling and regulatory oversight to ensure consumer safety and quality control. Overall, its recommendations facilitated the integration of functional foods into dietary guidelines and public health initiatives, promoting improved health outcomes and reduced chronic disease burden.

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

BackgroundThe Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems.ResultsBiSDL’s effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers.ConclusionsBiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

Bioinspiration from bats and new paradigms for autonomy in natural environments

Achieving autonomous operation in complex natural environment remains an unsolved challenge. Conventional engineering approaches to this problem have focused on collecting large amounts of sensory data that are used to create detailed digital models of the environment. However, this only postpones solving the challenge of identifying the relevant sensory information and linking it to action control to the domain of the digital world model. Furthermore, it imposes high demands in terms of computing power and introduces large processing latencies that hamper autonomous real-time performance. Certain species of bats that are able to navigate and hunt their prey in dense vegetation could be a biological model system for an alternative approach to addressing the fundamental issues associated with autonomy in complex natural environments. Bats navigating in dense vegetation rely on clutter echoes, i.e. signals that consist of unresolved contributions from many scatters. Yet, the animals are able to extract the relevant information from these input signals with brains that are often less than 1 g in mass. Pilot results indicate that information relevant to location identification and passageway finding can be directly obtained from clutter echoes, opening up the possibility that the bats’ skill can be replicated in man-made autonomous systems.

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.

A taxonomy of multiple stable states in complex ecological communities.

Natural systems are built from multiple interconnected units, making their dynamics, functioning and fragility notoriously hard to predict. A fragility scenario of particular relevance concerns so-called regime shifts: abrupt transitions from healthy to degraded ecosystem states. An explanation for these shifts is that they arise as transitions between alternative stable states, a process that is well-understood in few-species models. However, how multistability upscales with system complexity remains a debated question. Here, we identify that four different multistability regimes generically emerge in models of species-rich communities and other archetypical complex biological systems assuming random interactions. Across the studied models, each regime consistently emerges under a specific interaction scheme and leaves a distinct set of fingerprints in terms of the number of observed states, their species richness and their response to perturbations. Our results help clarify the conditions and types of multistability that can be expected to occur in complex ecological communities.

Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses

Stress Knowledge Map (SKM; https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical, signaling, and regulatory molecular interactions in plants: a highly curated model of plant stress signaling (PSS; 543 reactions) and a large comprehensive knowledge network (488 390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entry point for investigations of plant stress response and related growth trade-offs, as well as interactive explorations of current knowledge. PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation and design of validation experiments, or to gain new insights into experimental observations in plant biology.

Solvation of Model Biomolecules in Choline-Aminoate Ionic Liquids: A Computational Simulation Using Polarizable Force Fields.

One can foresee a very near future where ionic liquids will be used in applications such as biomolecular chemistry or medicine. The molecular details of their interaction with biological matter, however, are difficult to investigate due to the vast number of combinations of both the biological systems and the variety of possible liquids. Here, we provide a computational study aimed at understanding the interaction of a special class of biocompatible ionic liquids (choline-aminoate) with two model biological systems: an oligopeptide and an oligonucleotide. We employed molecular dynamics with a polarizable force field. Our results are in line with previous experimental and computational evidence on analogous systems and show how these biocompatible ionic liquids, in their pure form, act as gentle solvents for protein structures while simultaneously destabilizing DNA structure.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Abstract 6236: Systems biology modeling identifies SMARCD3 as a master regulator facilitating everolimus resistance in ER+ breast cancer

Abstract Breast cancer is the most common malignancy in women and estrogen receptor positive (ER+) breast cancers represent nearly 75% of all breast tumors. Everolimus, an mTORC1 inhibitor, in combination with exemestane has been approved for patients with metastatic ER+ breast cancer. However, many patients eventually develop resistance to everolimus and leads to poor survival outcomes. Characterizing everolimus-resistant cells can reveal targetable molecular phenotypes that can eventually lead to new therapeutic targets. In this study, we elucidate systems-level phenotypic distinctions between everolimus sensitive and refractory cells. We generated several isogenic ER+ breast cancer cell lines resistant or sensitive to everolimus. We then leveraged transcriptomic profiles from sensitive and resistant cells before or after everolimus treatment to decipher the underlying mechanisms essential to the resistant state. Using linear mixed effect models, we identified several meta-phenotypes that were distinct between sensitive and resistant cell states. These included growth-factor signaling, cell cycle, metabolism, stemness, and apoptosis meta-phenotypes. Our analyses also identified strong activation of growth-factor receptors including IGF1R/INSR and ESR1 in the resistant cells, which were maintained despite everolimus treatment. Next, we adopted a transcriptional regulatory network reconstruction framework to identify the master regulators responsible for the activation of resistance meta-phenotypes. We identified SMARCD3 as the key master regulator controlling expression of genes across diverse resistance meta-phenotypes, including growth-factor signaling. Further, using transcriptomic profiles from a neoadjuvant trial of 23 post-menopausal ER+ breast cancers treated with everolimus, we found that SMARCD3 expression was elevated in resistant tumors. To confirm the role of SMARCD3 in everolimus resistance, we repressed transcription of the SMARCD3 gene using CRISPR interference to re-sensitize resistant cells to everolimus treatment. In conclusion, we used context-specific cell transcriptomic profiles and integrative systems-biology approaches to elucidate mechanisms of resistance in response to everolimus treatment in ER+ breast cancer. Our findings suggest SMARCD3 upregulation promotes resistance to everolimus treatment and may serve as a novel therapeutic target in ER+ breast cancer. Citation Format: Eric F. Medina, Patrick A. Cosgrove, Eleni Farmaki, Vince K. Grolmusz, Feng Chi, Jason I. Griffiths, Andrea H. Bild, Aritro Nath. Systems biology modeling identifies SMARCD3 as a master regulator facilitating everolimus resistance in ER+ breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6236.

INNOVATION IN BIOMATHEMATICS

The human anatomy is very complex structure created by God in which various organs and systems play their different roles. These processes are so complex that they cannot be easily studied wholly. Biomathematics is a field of study that helps indevelopment of predictive and analytical models of biological and medical systems. It has been used in many areas, including: Cellular neurobiology, Epidemic modelling and Population genetics. Innovation in Bio-mathematics can be a powerful tool for biological sciences. It can be useful for testing hypotheses, especially when a direct experiment cannot be conducted.

Mathematical modeling in autoimmune diseases: from theory to clinical application.

The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of "mechanistic granularity" chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others - as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML)models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.