Computational Systems Biology Research Articles

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.

Model biological systems demonstrate the inducibility of pathways that strongly reduce cryoprotectant toxicity

Cryoprotectant toxicity is a limiting factor for the cryopreservation of many living systems. We were moved to address this problem by the potential of organ vitrification to relieve the severe shortage of viable donor organs available for human transplantation. The M22 vitrification solution is presently the only solution that has enabled the vitrification and subsequent transplantation with survival of large mammalian organs, but its toxicity remains an obstacle to organ stockpiling for transplantation. We therefore undertook a series of exploratory studies to identify potential pretreatment interventions that might reduce the toxic effects of M22. Hormesis, in which a living system becomes more resistant to toxic stress after prior subtoxic exposure to a related stress, was investigated as a potential remedy for M22 toxicity in yeast, in the nematode worm C. elegans, and in mouse kidney slices. In yeast, heat shock pretreatment increased survival by 18-fold after exposure to formamide and by over 9-fold after exposure to M22 at 30 °C; at 0 °C and with two-step addition, treatment with 90% M22 resulted in 100% yeast survival. In nematodes, surveying a panel of pretreatment interventions revealed 3 that conferred nearly total protection from acute whole-worm M22-induced damage. One of these protective pretreatments (exposure to hydrogen peroxide) was applied to mouse kidney slices in vitro and was found to strongly protect nuclear and plasma membrane integrity in both cortical and medullary renal cells exposed to 75–100% M22 at room temperature for 40 min. These studies demonstrate for the first time that endogenous cellular defenses, conserved from yeast to mammals, can be marshalled to substantially ameliorate the toxic effects of one of the most toxic single cryoprotectants and the toxicity of the most concentrated vitrification solution so far described for whole organs.

Chaos in gene regulatory networks: Effects of time delays and interaction structure.

In biological system models, gene expression levels are typically described by regulatory feedback mechanisms. Many studies of gene network models focus on dynamical interactions between components, but often overlook time delays. Here we present an extended model for gene regulatory networks with time delayed negative feedback, which is described by delay differential equations. We analyze nonlinear properties of the model in terms of chaos and compare the conditions with the benchmark homogeneous gene regulatory network model. Chaotic dynamics depend strongly on the inclusion of time delays, but the minimum motifs that show chaos differ when both original and extended models are considered. Our results suggest that, for a particular higher order extension of the gene network, it is possible to observe chaotic dynamics in a two-gene system without adding any self-inhibition. This finding can be explained as a result of modification of the original benchmark model induced by previously unmodeled dynamics. We argue that the inclusion of additional parameters in regulatory gene circuit models substantially enhances the likelihood of observing non-periodic dynamics.

Engineering models of head and neck and oral cancers on-a-chip.

Head and neck cancers (HNCs) rank as the sixth most common cancer globally and result in over 450 000 deaths annually. Despite considerable advancements in diagnostics and treatment, the 5-year survival rate for most types of HNCs remains below 50%. Poor prognoses are often attributed to tumor heterogeneity, drug resistance, and immunosuppression. These characteristics are difficult to replicate using in vitro or in vivo models, culminating in few effective approaches for early detection and therapeutic drug development. Organs-on-a-chip offer a promising avenue for studying HNCs, serving as microphysiological models that closely recapitulate the complexities of biological tissues within highly controllable microfluidic platforms. Such systems have gained interest as advanced experimental tools to investigate human pathophysiology and assess therapeutic efficacy, providing a deeper understanding of cancer pathophysiology. This review outlines current challenges and opportunities in replicating HNCs within microphysiological systems, focusing on mimicking the soft, glandular, and hard tissues of the head and neck. We further delve into the major applications of organ-on-a-chip models for HNCs, including fundamental research, drug discovery, translational approaches, and personalized medicine. This review emphasizes the integration of organs-on-a-chip into the repertoire of biological model systems available to researchers. This integration enables the exploration of unique aspects of HNCs, thereby accelerating discoveries with the potential to improve outcomes for HNC patients.

Low-cost, versatile, and highly reproducible microfabrication pipeline to generate 3D-printed customised cell culture devices with complex designs.

Cell culture devices, such as microwells and microfluidic chips, are designed to increase the complexity of cell-based models while retaining control over culture conditions and have become indispensable platforms for biological systems modelling. From microtopography, microwells, plating devices, and microfluidic systems to larger constructs such as live imaging chamber slides, a wide variety of culture devices with different geometries have become indispensable in biology laboratories. However, while their application in biological projects is increasing exponentially, due to a combination of the techniques, equipment and tools required for their manufacture, and the expertise necessary, biological and biomedical labs tend more often to rely on already made devices. Indeed, commercially developed devices are available for a variety of applications but are often costly and, importantly, lack the potential for customisation by each individual lab. The last point is quite crucial, as often experiments in wet labs are adapted to whichever design is already available rather than designing and fabricating custom systems that perfectly fit the biological question. This combination of factors still restricts widespread application of microfabricated custom devices in most biological wet labs. Capitalising on recent advances in bioengineering and microfabrication aimed at solving these issues, and taking advantage of low-cost, high-resolution desktop resin 3D printers combined with PDMS soft lithography, we have developed an optimised a low-cost and highly reproducible microfabrication pipeline. This is thought specifically for biomedical and biological wet labs with not prior experience in the field, which will enable them to generate a wide variety of customisable devices for cell culture and tissue engineering in an easy, fast reproducible way for a fraction of the cost of conventional microfabrication or commercial alternatives. This protocol is designed specifically to be a resource for biological labs with limited expertise in those techniques and enables the manufacture of complex devices across the μm to cm scale. We provide a ready-to-go pipeline for the efficient treatment of resin-based 3D-printed constructs for PDMS curing, using a combination of polymerisation steps, washes, and surface treatments. Together with the extensive characterisation of the fabrication pipeline, we show the utilisation of this system to a variety of applications and use cases relevant to biological experiments, ranging from micro topographies for cell alignments to complex multipart hydrogel culturing systems. This methodology can be easily adopted by any wet lab, irrespective of prior expertise or resource availability and will enable the wide adoption of tailored microfabricated devices across many fields of biology.

Is Alzheimer's disease an individual-centered disease? Hypotheses from the atomic levels up to mathematical models for biological systems.

Is Alzheimer's disease an individual-centered disease? Hypotheses from the atomic levels up to mathematical models for biological systems.

The landscape of transcription factor promoter activity during vegetative development in Marchantia.

Transcription factors (TFs) are essential for the regulation of gene expression and cell fate determination. Characterizing the transcriptional activity of TF genes in space and time is a critical step toward understanding complex biological systems. The vegetative gametophyte meristems of bryophytes share some characteristics with the shoot apical meristems of flowering plants. However, the identity and expression profiles of TFs associated with gametophyte organization are largely unknown. With only ∼450 putative TF genes, Marchantia (Marchantia polymorpha) is an outstanding model system for plant systems biology. We have generated a near-complete collection of promoter elements derived from Marchantia TF genes. We experimentally tested reporter fusions for all the TF promoters in the collection and systematically analyzed expression patterns in Marchantia gemmae. This allowed us to build a map of expression domains in early vegetative development and identify a set of TF-derived promoters that are active in the stem-cell zone. The cell markers provide additional tools and insight into the dynamic regulation of the gametophytic meristem and its evolution. In addition, we provide an online database of expression patterns for all promoters in the collection. We expect that these promoter elements will be useful for cell-type-specific expression, synthetic biology applications, and functional genomics.

The Mediterranean mussel Mytilus galloprovincialis: a novel model for developmental studies in mollusks.

A model organism in developmental biology is defined by its experimental amenability and by resources created for the model system by the scientific community. For the most powerful invertebrate models, the combination of both has already yielded a thorough understanding of developmental processes. However, the number of developmental model systems is still limited, and their phylogenetic distribution heavily biased. Members of one of the largest animal lineages, the Spiralia, for example, have long been neglected. In order to remedy this shortcoming, we have produced a detailed developmental transcriptome for the bivalve mollusk Mytilus galloprovincialis, and have expanded the list of experimental protocols available for this species. Our high-quality transcriptome allowed us to identify transcriptomic signatures of developmental progression and to perform a first comparison with another bivalve mollusk: the Pacific oyster Crassostrea gigas. To allow co-labelling studies, we optimized and combined protocols for immunohistochemistry and hybridization chain reaction to create high-resolution co-expression maps of developmental genes. The resources and protocols described here represent an enormous boost for the establishment of Mytilus galloprovincialis as an alternative model system in developmental biology.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Application of the iPLUS non-coding sequence in improving biopharmaceuticals production.

The biotechnological landscape has witnessed significant growth in biological therapeutics particularly in the field of recombinant protein production. Here we investigate the function of 3'UTR cis-regulatory elements in increasing mRNA and protein levels in different biological therapeutics and model systems, spanning from monoclonal antibodies to mRNA vaccines. We explore the regulatory function of iPLUS - a universal sequence capable of consistently augmenting recombinant protein levels. By incorporating iPLUS in a vector to express a monoclonal antibody used in immunotherapy, in a mammalian cell line used by the industry (ExpiCHO), trastuzumab production increases by 2-fold. As yeast Pichia pastoris is widely used in the manufacture of industrial enzymes and pharmaceuticals, we then used iPLUS in tandem (3x) and iPLUSv2 (a variant of iPLUS) to provide proof-of-concept data that it increases the production of a reporter protein more than 100-fold. As iPLUS functions by also increasing mRNA levels, we hypothesize that these sequences could be used as an asset in the mRNA vaccine industry. In fact, by including iPLUSv2 downstream of Spike we were able to double its production. Moreover, the same effect was observed when we introduced iPLUSv2 downstream of MAGEC2, a tumor-specific antigen tested for cancer mRNA vaccines. Taken together, our study provides data (TLR4) showing that iPLUS may be used as a valuable asset in a variety of systems used by the biotech and biopharmaceutical industry. Our results underscore the critical role of non-coding sequences in controlling gene expression, offering a promising avenue to accelerate, enhance, and cost-effectively optimize biopharmaceutical production processes.

Review of Model Systems in Biology: History, Philosophy, and Practical Concerns by Georg Striedter

Review of <i>Model Systems in Biology: History, Philosophy, and Practical Concerns</i> by Georg Striedter

Breaking reflection symmetry: evolving long dynamical cycles in Boolean systems

In interacting dynamical systems, specific local interaction rules for system components give rise to diverse and complex global dynamics. Long dynamical cycles are a key feature of many natural interacting systems, especially in biology. Examples of dynamical cycles range from circadian rhythms regulating sleep to cell cycles regulating reproductive behavior. Despite the crucial role of cycles in nature, the properties of network structure that give rise to cycles still need to be better understood. Here, we use a Boolean interaction network model to study the relationships between network structure and cyclic dynamics. We identify particular structural motifs that support cycles, and other motifs that suppress them. More generally, we show that the presence of dynamical reflection symmetry in the interaction network enhances cyclic behavior. In simulating an artificial evolutionary process, we find that motifs that break reflection symmetry are discarded. We further show that dynamical reflection symmetries are over-represented in Boolean models of natural biological systems. Altogether, our results demonstrate a link between symmetry and functionality for interacting dynamical systems, and they provide evidence for symmetry’s causal role in evolving dynamical functionality.

Stability and bifurcation analysis of fractional‐order tumor–macrophages interaction model with multi‐delays

In the realm of modeling biological systems with memory, particularly those involving intricate interactions like tumor‐immune responses, the utilization of multiple time delays and Caputo‐type fractional‐order derivatives represents a cutting‐edge approach. In this research paper, we introduce a novel fractional‐order model to investigate the dynamic interplay between tumors and macrophages, a key component of the immune system, while incorporating multiple time delays into our framework. Our proposed model comprises a system of three Caputo‐type fractional‐order differential equations, each representing distinct cell populations: tumor cells, anti‐tumor cells (specifically ‐type macrophages with pro‐inflammatory properties), and pro‐tumor cells ( ‐type macrophages with immune‐suppressive characteristics). The stability of equilibria is discussed by analyzing the characteristic equations for each case, and the existence conditions for the Hopf bifurcation are obtained according to the critical values of delay parameters. Furthermore, numerical simulations are presented in order to verify the analytical results obtained for stability and Hopf‐bifurcation with respect to the two‐time delay parameters and . The analysis shows the rich dynamics of the model according to the fractional‐order parameter and the time delay parameters.

Signaling-biophysical modeling unravels mechanistic control of red blood cell phagocytosis by macrophages in sickle cell disease.

Red blood cell (RBC) aging manifests through progressive changes in cell morphology, rigidity, and expression of membrane proteins. To maintain the quality of circulating blood, splenic macrophages detect the biochemical signals and biophysical changes of RBCs and selectively clear them through erythrophagocytosis. In sickle cell disease (SCD), RBCs display alterations affecting their interaction with macrophages, leading to aberrant phagocytosis that may cause life-threatening spleen sequestration crises. To illuminate the mechanistic control of RBC engulfment by macrophages in SCD, we integrate a system biology model of RBC-macrophage signaling interactions with a biophysical model of macrophage engulfment, as well as in vitro phagocytosis experiments using the spleen-on-a-chip technology. Our modeling framework accurately predicts the phagocytosis dynamics of RBCs under different disease conditions, reveals patterns distinguishing normal and sickle RBCs, and identifies molecular targets including Src homology 2 domain-containing protein tyrosine phosphatase-1 (SHP1) and cluster of differentiation 47 (CD47)/signal regulatory protein α (SIRPα) as therapeutic targets to facilitate the controlled clearance of sickle RBCs in the spleen.

BioModME for building and simulating dynamic computational models of complex biological systems.

Molecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed "BioModME," a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization. All relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME.

TMELand: An end-to-end Pipeline for Quantification and Visualization of Waddington's Epigenetic Landscape based on Gene Regulatory Network.

Waddington's epigenetic landscape is a framework depicting the processes of cell differentiation and reprogramming under the control of a gene regulatory network (GRN). Traditional model-driven methods for landscape quantification focus on the Boolean network or differential equation-based models of GRN, which need sophisticated prior knowledge and hence hamper their practical applications. To resolve this problem, we combine data-driven methods for inferring GRNs from gene expression data with model-driven approach to the landscape mapping. Specifically, we build an end-to-end pipeline to link data-driven and model-driven methods and develop a software tool named TMELand for GRN inference, visualizing Waddington's epigenetic landscape, and calculating state transition paths between attractors to uncover the intrinsic mechanism of cellular transition dynamics. By integrating GRN inference from real transcriptomic data with landscape modeling, TMELand can facilitate studies of computational systems biology, such as predicting cellular states and visualizing the dynamical trends of cell fate determination and transition dynamics from single-cell transcriptomic data. The source code of TMELand, a user manual, and model files of case studies can be downloaded freely from https://github.com/JieZheng-ShanghaiTech/TMELand.