Model Of Signaling Network Research Articles

Applications of single-cell RNA sequencing in rheumatoid arthritis.

Single cell RNA sequencing (scRNA-seq) is a relatively new technology that provides an unprecedented, detailed view of cellular heterogeneity and function by delineating the transcriptomic difference among individual cells. This will allow for mapping of cell-type-specific signaling during physiological and pathological processes, to build highly specific models of cellular signaling networks between the many discrete clusters that are present. This technology therefore provides a powerful approach to dissecting the cellular and molecular mechanisms that contribute to autoimmune diseases, including rheumatoid arthritis (RA). scRNA-seq can offer valuable insights into RA unique cellular states and transitions, potentially enabling development of novel drug targets. However, some challenges that still limit its mainstream utilization and include higher costs, a lower sensitivity for low-abundance transcripts, and a relatively complex data analysis workflow relative to bulk or traditional RNA-seq. This minireview explores the emerging application of scRNA-seq in RA research, highlighting its role in producing important insights that can help pave the way for innovative and more effective therapeutic strategies.

Integrative Modeling of Signaling Network Dynamics Identifies Cell Type-Selective Therapeutic Strategies for FGFR4-Driven Cancers.

Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple-negative breast cancer and hepatocellular carcinoma. However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and to provide network-level insights into resistance mechanisms driven by signaling dynamics. An integrated approach, combining computational network modeling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in triple-negative breast cancer cells. Analyzing the effects of cotargeting specific network nodes by systematically simulating the model predicted synergy of cotargeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, cotargeting FGFR4 and PI3K was not synergistic. Protein expression data from hundreds of cancer cell lines was incorporated to adapt the model to diverse cellular contexts. This revealed that although AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven hepatocellular carcinoma cell line, in which there is a synergistic effect of cotargeting FGFR4 and MEK but not AKT. In summary, this study offers key insights into drug-induced network remodeling and the role of protein expression heterogeneity in targeted therapy responses. These findings underscore the utility of computational network modeling for designing cell type-selective combination therapies and enhancing precision cancer treatment. Significance: Computational predictive modeling of signaling networks can decipher mechanisms of cancer cell resistance to targeted therapies and enable identification of more effective cancer type-specific combination treatment strategies.

The role of uncertainty and negative feedback loops in the evolution of induced immune defenses.

Organisms use constitutive or induced defenses against pathogens and other external threats. Constitutive defenses are constantly on, whereas induced defenses are activated when needed. Each of these strategies has costs and benefits, which can affect the type of defense that evolves in response to pathogens. In addition, induced defenses are usually regulated by multiple negative feedback mechanisms that prevent overactivation of the immune response. However, it is unclear how negative feedback affects the costs, benefits, and evolution of induced responses. To address this gap, we developed a mechanistic model of the well-characterized Drosophila melanogaster immune signaling network that includes 3 separate mechanisms of negative feedback as a representative of the widespread phenomenon of multilevel regulation of induced responses. We show that, under stochastic fly-bacteria encounters, an induced defense is favored when bacterial encounters are rare or uncertain, but in ways that depend on the bacterial proliferation rate. Our model also predicts that the specific negative regulators that optimize the induced response depend on the bacterial proliferation rate, linking negative feedback mechanisms to the factors that favor induction.

Abstract We133: In Silico Model of Cardiomyopathy Caused by a MYBPC3-induced HCM Mutation

Introduction: Hypertrophic cardiomyopathy (HCM) is the most frequent hereditary cardiomyopathy and a leading cause of sudden cardiac death, particularly in adolescent. Despite recent improved prognosis and FDA approved drugs, current cardiac therapeutics cannot efficiently prevent remodeling across all HCM patient populations due to genetic variability. Therefore, we require a systems biology approach to understand how the complex cardiac signaling networks regulate cardiomyocyte shape and size due to an HCM mutation. This study leverages a computational systems approach to explore MYBPC3-induced HCM mutation regulation of cardiomyocyte hypertrophy. Method: For the signaling network model, we employed a logic-based differential equations model (NETFLUX) to construct a signaling network validated using in vitro data from published studies. This model captured signaling dynamics and cross talks between pathways governing cardiomyocyte size and shape. Additionally, a MYBPC3 HCM mutation is integrated in the model as an input providing a comparison between a control and simulated HCM responses. Sensitivity analyses are performed to investigate the most influential signaling pathways that mediate changes in mass. Results: We created a MYBPC3-induced HCM model by integrating an additional cardiac myosin binding protein-C node to a previously validated inherited cardiomyopathy signaling network. This updated model predicted an increase in mass when the HCM mutation is simulated. The simulated inhibition of each node in the model revealed that inhibition of calcium pathway, growth factor receptor, and titin stiffness could mitigate the HCM phenotype by reducing mass. The sensitivity analysis results also suggested PI3K, Ras, and ERK1/2 pathways as potential mechanisms mediating the decrease in mass and served as potential therapeutic targets. Discussion and Future Work: Our systems approach gave insights on the mechanisms governing the cardiomyocyte response to a MYBPC3-induced HCM mutation. This will be further validated via a MYBPC3-HCM model using human iPSC-CMs with MYBPC3-induced mutations. This will create a cell line that will provide genetically precise comparisons for identifying new molecular mechanisms of disease, thereby offering a strategy for advancing precision medicine.

Reviewing mathematical models of sperm signaling networks.

Dave Garbers' work significantly contributed to our understanding of sperm's regulated motility, capacitation, and the acrosome reaction. These key sperm functions involve complex multistep signaling pathways engaging numerous finely orchestrated elements. Despite significant progress, many parameters and interactions among these elements remain elusive. Mathematical modeling emerges as a potent tool to study sperm physiology, providing a framework to integrate experimental results and capture functional dynamics considering biochemical, biophysical, and cellular elements. Depending on research objectives, different modeling strategies, broadly categorized into continuous and discrete approaches, reveal valuable insights into cell function. These models allow the exploration of hypotheses regarding molecules, conditions, and pathways, whenever they become challenging to evaluate experimentally. This review presents an overview of current theoretical and experimental efforts to understand sperm motility regulation, capacitation, and the acrosome reaction. We discuss the strengths and weaknesses of different modeling strategies and highlight key findings and unresolved questions. Notable discoveries include the importance of specific ion channels, the role of intracellular molecular heterogeneity in capacitation and the acrosome reaction, and the impact of pH changes on acrosomal exocytosis. Ultimately, this review underscores the crucial importance of mathematical frameworks in advancing our understanding of sperm physiology and guiding future experimental investigations.

Phosphatidic acid produced by phospholipase Dα1 and Dδ is incorporated into the internal membranes but not involved in the gene expression of RD29A in the abscisic acid signaling network in Arabidopsis thaliana.

Core protein components of the abscisic acid (ABA) signaling network, pyrabactin resistance (PYR), protein phosphatases 2C (PP2C), and SNF1-related protein kinase 2 (SnRK2) are involved in the regulation of stomatal closure and gene expression downstream responses in Arabidopsis thaliana. Phosphatidic acid (PA) produced by the phospholipases Dα1 and Dδ (PLDs) in the plasma membrane has been identified as a necessary molecule in ABA-inducible stomatal closure. On the other hand, the involvement of PA in ABA-inducible gene expression has been suggested but remains a question. In this study, the involvement of PA in the ABA-inducible gene expression was examined in the model plant Arabidopsis thaliana and the canonical RD29A ABA-inducible gene that possesses a single ABA-responsive element (ABRE) in the promoter. The promoter activity and accumulation of the RD29A mRNA during ABA exposure to the plants were analyzed under conditions in which the production of PA by PLDs is abrogated through chemical and genetic modification. Changes in the subcellular localization of PA during the signal transduction were analyzed with confocal microscopy. The results obtained in this study suggest that inhibition of PA production by the PLDs does not affect the promoter activity of RD29A. PA produced by the PLDs and exogenously added PA in the plasma membrane are effectively incorporated into internal membranes to transduce the signal. However, exogenously added PA induces stomatal closure but not RD29A expression. This is because PA produced by the PLDs most likely inhibits the activity of not all but only the selected PP2C family members, the negative regulators of the RD29A promoter. This finding underscores the necessity for experimental verifications to adapt previous knowledge into a signaling network model before its construction.

Extending BioMASS to construct mathematical models from external knowledge.

Mechanistic modeling based on ordinary differential equations has led to numerous findings in systems biology by integrating prior knowledge and experimental data. However, the manual curation of knowledge necessary when constructing models poses a bottleneck. As the speed of knowledge accumulation continues to grow, there is a demand for a scalable means of constructing executable models. We previously introduced BioMASS-an open-source, Python-based framework-to construct, simulate, and analyze mechanistic models of signaling networks. With one of its features, Text2Model, BioMASS allows users to define models in a natural language-like format, thereby facilitating the construction of large-scale models. We demonstrate that Text2Model can serve as a tool for integrating external knowledge for mathematical modeling by generating Text2Model files from a pathway database or through the use of a large language model, and simulating its dynamics through BioMASS. Our findings reveal the tool's capabilities to encourage exploration from prior knowledge and pave the way for a fully data-driven approach to constructing mathematical models. The code and documentation for BioMASS are available at https://github.com/biomass-dev/biomass and https://biomass-core.readthedocs.io, respectively. The code used in this article are available at https://github.com/okadalabipr/text2model-from-knowledge.

Computational screen for sex-specific drug effects in a cardiac fibroblast signaling network model

Heart disease is the leading cause of death in both men and women. Cardiac fibrosis is the uncontrolled accumulation of extracellular matrix proteins, which can exacerbate the progression of heart failure, and there are currently no drugs approved specifically to target matrix accumulation in the heart. Computational signaling network models (SNMs) can be used to facilitate discovery of novel drug targets. However, the vast majority of SNMs are not sex-specific and/or are developed and validated using data skewed towards male in vitro and in vivo samples. Biological sex is an important consideration in cardiovascular health and drug development. In this study, we integrate a cardiac fibroblast SNM with estrogen signaling pathways to create sex-specific SNMs. The sex-specific SNMs demonstrated high validation accuracy compared to in vitro experimental studies in the literature while also elucidating how estrogen signaling can modulate the effect of fibrotic cytokines via multi-pathway interactions. Further, perturbation analysis and drug screening uncovered several drug compounds predicted to generate divergent fibrotic responses in male vs. female conditions, which warrant further study in the pursuit of sex-specific treatment recommendations for cardiac fibrosis. Future model development and validation will require more generation of sex-specific data to further enhance modeling capabilities for clinically relevant sex-specific predictions of cardiac fibrosis and treatment.

Kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models

BackgroundComputational models of cell signaling networks are extremely useful tools for the exploration of underlying system behavior and prediction of response to various perturbations. By representing signaling cascades as executable Boolean networks, the previously developed rxncon (“reaction-contingency”) formalism and associated Python package enable accurate and scalable modeling of signal transduction even in large (thousands of components) biological systems. The models are split into reactions, which generate states, and contingencies, that impinge on reactions; this avoids the so-called “combinatorial explosion” of system size. Boolean description of the biological system compensates for the poor availability of kinetic parameters which are necessary for quantitative models. Unfortunately, few tools are available to support rxncon model development, especially for large, intricate systems.ResultsWe present the kboolnet toolkit (https://github.com/Kufalab-UCSD/kboolnet, complete documentation at https://github.com/Kufalab-UCSD/kboolnet/wiki), an R package and a set of scripts that seamlessly integrate with the python-based rxncon software and collectively provide a complete workflow for the verification, validation, and visualization of rxncon models. The verification script VerifyModel.R checks for responsiveness to repeated stimulations as well as consistency of steady state behavior. The validation scripts TruthTable.R, SensitivityAnalysis.R, and ScoreNet.R provide various readouts for the comparison of model predictions to experimental data. In particular, ScoreNet.R compares model predictions to a cloud-stored MIDAS-format experimental database to provide a numerical score for tracking model accuracy. Finally, the visualization scripts allow for graphical representations of model topology and behavior. The entire kboolnet toolkit is cloud-enabled, allowing for easy collaborative development; most scripts also allow for the extraction and analysis of individual user-defined “modules”.ConclusionThe kboolnet toolkit provides a modular, cloud-enabled workflow for the development of rxncon models, as well as their verification, validation, and visualization. This will enable the creation of larger, more comprehensive, and more rigorous models of cell signaling using the rxncon formalism in the future.

McSNAC: A software to approximate first-order signaling networks from mass cytometry data.

Mass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. However, computational analysis is required to reconstruct such networks with a mechanistic model. We propose our Mass cytometry Signaling Network Analysis Code (McSNAC), a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data. McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption often breaks down as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of protein species involved in signaling. We carry out a series of in silico experiments here to show (1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; (2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured. These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.

Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods.

In a healthy state, pain plays an important role in natural biofeedback loops and helps to detect and prevent potentially harmful stimuli and situations. However, pain can become chronic and as such a pathological condition, losing its informative and adaptive function. Efficient pain treatment remains a largely unmet clinical need. One promising route to improve the characterization of pain, and with that the potential for more effective pain therapies, is the integration of different data modalities through cutting edge computational methods. Using these methods, multiscale, complex, and network models of pain signaling can be created and utilized for the benefit of patients. Such models require collaborative work of experts from different research domains such as medicine, biology, physiology, psychology as well as mathematics and data science. Efficient work of collaborative teams requires developing of a common language and common level of understanding as a prerequisite. One of ways to meet this need is to provide easy to comprehend overviews of certain topics within the pain research domain. Here, we propose such an overview on the topic of pain assessment in humans for computational researchers. Quantifications related to pain are necessary for building computational models. However, as defined by the International Association of the Study of Pain (IASP), pain is a sensory and emotional experience and thus, it cannot be measured and quantified objectively. This results in a need for clear distinctions between nociception, pain and correlates of pain. Therefore, here we review methods to assess pain as a percept and nociception as a biological basis for this percept in humans, with the goal of creating a roadmap of modelling options.

Computational Random Mutagenesis to Investigate RAS Mutant Signaling.

This chapter describes how mathematical models can be used to investigate the possible range of behaviors for mutant forms of a protein. A mathematical model of the RAS signaling network that has previously been developed and applied to specific RAS mutants will be adapted for the process of computational random mutagenesis. By using this model to computationally investigate the range of RAS signaling outputs that would be anticipated over a wide range of the relevant parameter space, one can gain intuition about the types of behaviors that would be demonstrated by biological RAS mutants.

Insulin Signaling Pathway Model in Adipocyte Cells.

Worldwide, type 2 diabetes mellitus (T2DM) is one of the most pervasive and fast-growing disorders, bringing long-term adverse effects. T2DM arises from pancreatic β-cells deficiency to produce enough insulin or when the body cannot effectively use the insulin produced by such cells. Accordingly, early diagnosis will decrease the long-term effects and high-healthcare costs of diabetes. The objective is developing an integrated mathematical model of the insulin signaling network based on Brännmark's model, which can simulate the signaling events more comprehensively with the added key components. In this study, a thorough mathematical model of the insulin signaling network was developed by expanding the previously validated model and incorporating the glycogen synthesis module. Parameters (69 parameters) of the integrated model were evaluated by a genetic algorithm by fitting the model predictions to eighty percent of experimental data from the literature. Twenty percent of the experimental data were used to evaluate the final optimized model. The time-response curves indicate that the GS phosphorylation reaches its maximum in response to 10-7 M insulin after 4 min, while the maximum phosphorylated GSK3 is attained within ~50 min. The dose-response curves for the GSP and GSK3 of the insulin signaling intermediaries in response to the increased concentration of insulin, after 10 min, in the input from 0-100 nM exhibits a decreasing trend, whereas an increasing trend was observed for the GS and GSK3P. The GSK and GS phosphorylation sensitivity was enhanced by increasing the initial insulin concentration level from 0.001 to 100 nM. However, the sensitivity of GSK3 to insulin concentration changes (from 0.001 to 100 nM) was 3-fold higher than GS sensitivity. Considerably, the trends of all signaling components simulated by the expanded model shows high compatibility with experimental data (R2 ≥ 0.9), which approves the accuracy of the proposed model. The proposed mathematical model can be used in many biological systems and combined with the whole-body model of the blood glucose regulation system for a better understanding of the causes and potential treatment of type 2 diabetes. Although, this model is not a complete description of insulin signaling, yet it can make profound contributions to improvements regarding other important components and signaling branches such as epidermal growth factor (EGF) signaling, as well as signaling in other cell types in the model structure of future works.

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network.

Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule- based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and dynamics, acquired through model simulations. In line with previous reports, we confirm that the Kappa model recapitulates the general dynamics of its ODE counterpart with minor differences. These occur when molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one. As reported for other molecular systems, we find that, also for the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, easing model reuse. In parallel with these analyses, we report a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of more complex dynamic models to study this important molecular system.

Integration of Transcriptome and Metabolome Analyses Reveals the Mechanistic Basis for Cadmium Accumulation in Maize.

Cadmium (Cd) pollution in soil has become a major environmental issue worldwide. However, the underlying molecular mechanism of low grain-Cd accumulation (GCA) in maize is still largely unknown. Herein, we report the mechanistic basis for low GCA in maize by a multiomics approach. The low GCA genotype L63 showed normal vacuolar formation and a lower capacity of xylem loading of Cd than the high-accumulator L42 under Cd stress. Transcriptomic sequencing identified 84 low-GCA-associated genes which are mainly involved in the S-adenosylmethionine (SAM) cycle, metal transport, and vacuolar sequestration. A metabolome analysis revealed that L63 plants had a more active SAM cycle and a greater capacity for terpenoid synthesis and phenylalanine metabolism than L42. Combining the analysis of transcriptome and metabolome characterized several genes as key genes involved in the determination of Cd accumulation. Our study identifies a mechanistic basis for low Cd accumulation in maize grains and provides candidate genes for genetic improvement of crops.

Signaling network model of cardiomyocyte morphological changes in familial cardiomyopathy

Familial cardiomyopathy is a precursor of heart failure and sudden cardiac death. Over the past several decades, researchers have discovered numerous gene mutations primarily in sarcomeric and cytoskeletal proteins causing two different disease phenotypes: hypertrophic (HCM) and dilated (DCM) cardiomyopathies. However, molecular mechanisms linking genotype to phenotype remain unclear. Here, we employ a systems approach by integrating experimental findings from preclinical studies (e.g., murine data) into a cohesive signaling network to scrutinize genotype to phenotype mechanisms. We developed an HCM/DCM signaling network model utilizing a logic-based differential equations approach and evaluated model performance in predicting experimental data from four contexts (HCM, DCM, pressure overload, and volume overload). The model has an overall prediction accuracy of 83.8%, with higher accuracy in the HCM context (90%) than DCM (75%). Global sensitivity analysis identifies key signaling reactions, with calcium-mediated myofilament force development and calcium-calmodulin kinase signaling ranking the highest. A structural revision analysis indicates potential missing interactions that primarily control calcium regulatory proteins, increasing model prediction accuracy. Combination pharmacotherapy analysis suggests that downregulation of signaling components such as calcium, titin and its associated proteins, growth factor receptors, ERK1/2, and PI3K-AKT could inhibit myocyte growth in HCM. In experiments with patient-specific iPSC-derived cardiomyocytes (MLP-W4R;MYH7-R723C iPSC-CMs), combined inhibition of ERK1/2 and PI3K-AKT rescued the HCM phenotype, as predicted by the model. In DCM, PI3K-AKT-NFAT downregulation combined with upregulation of Ras/ERK1/2 or titin or Gq protein could ameliorate cardiomyocyte morphology. The model results suggest that HCM mutations that increase active force through elevated calcium sensitivity could increase ERK activity and decrease eccentricity through parallel growth factors, Gq-mediated, and titin pathways. Moreover, the model simulated the influence of existing medications on cardiac growth in HCM and DCM contexts. This HCM/DCM signaling model demonstrates utility in investigating genotype to phenotype mechanisms in familial cardiomyopathy.

An Evolutionary Paradigm Favoring Cross Talk between Bacterial Two-Component Signaling Systems.

The prevalent paradigm governing bacterial two-component signaling systems (TCSs) is specificity, wherein the histidine kinase (HK) of a TCS exclusively activates its cognate response regulator (RR). Cross talk, where HKs activate noncognate RRs, is considered evolutionarily disadvantageous because it can compromise adaptive responses by leaking signals. Yet cross talk is observed in several bacteria. Here, to resolve this paradox, we propose an alternative paradigm where cross talk can be advantageous. We envisioned programmed environments, wherein signals appear in predefined sequences. In such environments, cross talk that primes bacteria to upcoming signals may improve adaptive responses and confer evolutionary benefits. To test this hypothesis, we employed mathematical modeling of TCS signaling networks and stochastic evolutionary dynamics simulations. We considered the comprehensive set of bacterial phenotypes, comprising thousands of distinct cross talk patterns competing in varied signaling environments. Our simulations predicted that in programmed environments phenotypes with cross talk facilitating priming would outcompete phenotypes without cross talk. In environments where signals appear randomly, bacteria without cross talk would dominate, explaining the specificity widely seen. Additionally, a testable prediction was that the phenotypes selected in programmed environments would display one-way cross talk, ensuring priming to future signals. Interestingly, the cross talk networks we deduced from available data on TCSs of Mycobacterium tuberculosis all displayed one-way cross talk, which was consistent with our predictions. Our study thus identifies potential evolutionary underpinnings of cross talk in bacterial TCSs, suggests a reconciliation of specificity and cross talk, makes testable predictions of the nature of cross talk patterns selected, and has implications for understanding bacterial adaptation and the response to interventions. IMPORTANCE Bacteria use two-component signaling systems (TCSs) to sense and respond to environmental changes. The prevalent paradigm governing TCSs is specificity, where signal flow through TCSs is insulated; leakage to other TCSs is considered evolutionarily disadvantageous. Yet cross talk between TCSs is observed in many bacteria. Here, we present a potential resolution of this paradox. We envision programmed environments, wherein stimuli appear in predefined sequences. Cross talk that primes bacteria to upcoming stimuli could then confer evolutionary benefits. We demonstrate this benefit using mathematical modeling and evolutionary simulations. Interestingly, we found signatures of predicted cross talk patterns in Mycobacterium tuberculosis. Furthermore, specificity was selected in environments where stimuli occurred randomly, thus reconciling specificity and cross talk. Implications follow for understanding bacterial evolution and for interventions.

Biological function and molecular mechanism of the transcription factor GLKs in plants: a review

GLKs (GOLDEN 2-LIKEs) are a group of plant-specific transcription factors regulating the chloroplast biogenesis, differentiation and function maintains by triggering the expression of the photosynthesis-associated nuclear genes (PhANGs). The GLKs also play important roles in nutrient's accumulation in fruits, leaf senescence, immunity and abiotic stress response. The expression of GLK genes were affected by multiple hormones or environmental factors. Therefore, GLKs were considered as the key nodes of regulatory network in plant cells, and potential candidates to improve the photosynthetic capacity of crops. Since numerous researches of GLKs have been reported in plants, the biological function, molecular mechanism of GLKs genes and its applications in breeding were summarized and a GLK-mediated signaling network model was developed. This review may facilitate future research and application of GLKs.

Influence maximization in Boolean networks

The optimization problem aiming at the identification of minimal sets of nodes able to drive the dynamics of Boolean networks toward desired long-term behaviors is central for some applications, as for example the detection of key therapeutic targets to control pathways in models of biological signaling and regulatory networks. Here, we develop a method to solve such an optimization problem taking inspiration from the well-studied problem of influence maximization for spreading processes in social networks. We validate the method on small gene regulatory networks whose dynamical landscapes are known by means of brute-force analysis. We then systematically study a large collection of gene regulatory networks. We find that for about 65% of the analyzed networks, the minimal driver sets contain less than 20% of their nodes.

Multiscale model of heart growth during pregnancy: integrating mechanical and hormonal signaling.

Pregnancy stands at the interface of mechanics and biology. The growing fetus continuously loads the maternal organs as circulating hormone levels surge, leading to significant changes in mechanical and hormonal cues during pregnancy. In response, maternal soft tissues undergo remarkable growth and remodeling to support the mother and baby for a healthy pregnancy. We focus on the maternal left ventricle, which increases its cardiac output and mass during pregnancy. This study develops a multiscale cardiac growth model for pregnancy to understand how mechanical and hormonal cues interact to drive this growth process. We coupled a cell signaling network model that predicts cell-level hypertrophy in response to hormones and stretch to a compartmental model of the rat heart and circulation that predicts organ-level growth in response to hemodynamic changes. We calibrated this multiscale model to data from experimental volume overload and hormonal infusions of angiotensin 2 (AngII), estrogen (E2), and progesterone (P4). We then validated the model's ability to capture interactions between inputs by comparing model predictions against published observations for the combinations of VO + E2 and AngII + E2. Finally, we simulated pregnancy-induced changes in hormones and hemodynamics to predict heart growth during pregnancy. Our model produced growth consistent with experimental data. Overall, our analysis suggests that the rise in P4 during the first half of gestation is an important contributor to heart growth during pregnancy. We conclude with suggestions for future experimental studies that will provide a better understanding of how hormonal and mechanical cues interact to drive pregnancy-induced heart growth.