Network motifs: theory and experimental approaches

Development and validation of a transcription factor regulatory network-based signature for individualized prognostic risk in lung adenocarcinoma

Despite significant progress in diagnostic and therapeutic modalities, lung adenocarcinoma (LUAD) still exhibits a high recurrence risk and a low 5-year survival rate. Reliable prognostic signatures are imperative for risk stratification in LUAD patients. This study encompassed 2740 patients from 23 LUAD cohorts, including one single-cell RNA sequencing (scRNA-seq) dataset, five bulk RNA-seq datasets, and 17 microarray datasets. Using scRNA-seq dataset, we defined a group of epithelial-specific transcription factors significantly over-represented in the epithelial-to-mesenchymal transition (EMT) gene set (enrichment ratio [ER] = 5.80, Fisher's exact test p < .001), and the corresponding target genes were significantly enriched in the cancer driver gene set (ER = 2.74, p < .001), indicating of their crucial roles in the EMT process and tumor progression. We constructed a single-cell gene pairs (scGPS) signature, composed of 3521 gene pairs derived from the epithelial cell-specific transcription factor regulatory network, to predict overall survival (OS) of LUAD. High-risk patients identified by scGPS in the discovery cohort exhibited significantly worse OS compared to low-risk patients (Hazard ratio [HR] = 1.78, 95% CI: 1.29-2.46, log-rank p = 1.80 × 10-4). The scGPS outperformed other established gene signatures and demonstrated robust prognostic stratification across various independent datasets, including microarray data and even early-stage LUAD patients. It remained an independent prognostic factor after adjusting for clinical and pathologic factors. In addition, combining scGPS with tumor stage further enhanced prognostic accuracy compared to using stage alone. The scGPS signature offers individualized prognosis estimations, showing significant potential for practical application in clinical settings.

Computational modeling of plant root development: the art and the science

Plant root development, like any developmental process, arises from the interplay between processes like gene expression, cell-cell signaling, cell growth and division, and tissue mechanics, which unfold over a wide range of temporal and spatial scales. Computational models are uniquely suited to integrate these different processes and spatio-temporal scales to investigate how their interplay determines developmental outcomes and have become part of mainstream plant developmental research. Still, for non-modeling experts, it often remains unclear how models are built, why a particular modeling approach was chosen, and how to interpret and value model outcomes. This review attempts to explain the science behind the art of model building, illustrating the simplifications that are often made to keep models simple to understand and when these are and are not justified. Similarly, it discusses when it is safe to ignore certain processes like growth or tissue mechanics and when it is not. Additionally, this review discusses a range of major breakthrough modeling articles. Their approaches are linked to classical concepts and models in developmental biology like the French flag positional information gradient of Lewis Wolpert and the repetitive patterning mechanism proposed by Turing, in addition to highlighting the lessons they taught us on plant root development.

Adenosine-Triggered Dynamic and Transient Aptamer-Based Networks Integrated in Liposome Protocell Assemblies

The development of transient dissipative nucleic-acid-based reaction circuits and constitutional dynamic networks attracts growing interest as a means of emulating native dynamic reaction circuits. Recent efforts applying enzymes, DNAzymes, or light as catalysts controlling the transient, dissipative functions of DNA networks and circuits were reported. Moreover, the integration of the dynamic networks in protocell assemblies and the identification of potential applications are challenging objectives. Here, we introduce the adenosine (AD) aptamer subunit complex coupled with adenosine deaminase (ADA) as a versatile recognition/catalytic framework for driving transient allosterically AD-stabilized DNAzyme circuits or dissipative AD-stabilized constitutional dynamic networks. In addition, the AD/ADA-driven transient frameworks are integrated into liposome assemblies as protocell models. Functionalized liposomes carrying allosterically ATP-stabilized DNAzymes cleaving EGR-1 mRNA are fused with MCF-7 breast cancer cells, demonstrating effective gene therapy and selective apoptosis of cancer cells.

Dissecting the role of bHLH transcription factors in the potato spindle tuber viroid (PSTVd)-tomato pathosystem using network approaches

Viroids, minimalist plant pathogens, pose significant threats to crops by causing severe diseases. Transcriptome profiling technologies have significantly advanced the analysis of viroid-infected host plants, providing critical insights into gene regulation by these pathogens. Despite these advancements, the presence of numerous genes of unknown function continues to limit a complete understanding of the transcriptome data. Co-expression analysis addresses this issue by clustering genes into modules based on global gene expression levels, with genes in the same cluster likely participating in the same biological pathways. In a previous study, we emphasized the importance of basic helix-loop-helix (bHLH) proteins in transcriptional reprogramming in tomato host in response to different potato spindle tuber viroid (PSTVd) strains. In the current research, we delve into tissue-specific gene modules, particularly in root and leaf tissues, governed by bHLH transcription factors (TFs) during PSTVd infections. Utilizing public datasets that span Control (C), mock-inoculated, PSTVd-mild (M), and PSTVd-severe (S23) strains in time-course infections, we uncovered differentially expressed gene modules. These modules were functionally characterized to identify essential hub genes, notably highlighting the regulatory coordination of bHLH TFs, depicted through the significant bifan motif found in these interactions. Expanding on these findings, we explored bipartite networks, discerning both common and unique bHLH TF regulatory roles. Our findings reveal that bHLH TFs play pivotal roles in regulating processes such as energy metabolism and facilitating rapid membrane repair in infected roots. In leaves, changes in the external layers affected photosynthesis, linking bHLH TFs to distinct metabolic functions. Through this holistic approach, we deepen our understanding of viroid-host interactions and the intricate regulatory mechanisms underpinning them.

Single-cell multiomics reveals a gene regulatory circuit driving leukemia cell differentiation

Cancer differentiation therapy aims to induce the maturation of neoplastic cells, but the mechanisms regulating cell fate decisions in oncogenic contexts remain unclear. In this study, we integrated single-cell chromatin accessibility and single-cell transcriptome analyses to explore the regulatory trajectories of a classical PML/RARα+ acute promyeloid leukemia (APL) cell line (NB4) post treatment by all-trans-retinoid acid (ATRA). Our findings indicated that ATRA activated specific PML/RARα-target enhancers to trigger a regulatory circuit composed of a positive feedforward gene regulatory circuit involving two transcription factors, SPI1 and CEBPE. This regulatory circuit was both necessary and sufficient to drive NB4 cells through an intermediate cell fate decision point to initiate terminal granulopoiesis. Moreover, ectopic expression of SPI1 and CEBPE promoted granulocytic differentiation in non-APL leukemia cell lines HL60 and K562. Our study sheds mechanistic insights into the differentiation trajectories induced by ATRA and illustrates a gene regulatory circuit that could be widely applied to promote differentiation of leukemia cells.

RSK2 and its binding partners: an emerging signaling node in cancers

The growth factor-mediated mitogen-activated protein kinase (MAPK) signaling pathways in cancer development have become increasingly important in the discovery of therapeutic agents for the treatment of cancer. RSK2 has been historically overlooked in studies regarding its involvement in physiology and signaling pathways related to human diseases, except for Coffin-Lowry syndrome, because it is located downstream of ERKs. For the last 25 years, the authors' laboratory has made groundbreaking discoveries regarding the role of RSK2, especially by elucidating its binding partners, signaling network, and crosstalk. RSK2 is an important emerging target for developing anticancer drugs. Nevertheless, further studies on the detailed mechanism and signaling network are necessary to avoid the unexpected effects of RSK2 inhibitors. This paper describes a new paradigm of RSK2, where it works as a signaling node to modulate diverse cellular processes, including cell proliferation and transformation, cell cycle regulation, chromatin remodeling, and immune response and inflammation regulation.

Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity

Chimeric antigen receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies, but poor specificity has limited its applicability to solid tumors. By contrast, natural T cells harboring T cell receptors (TCRs) can discriminate between neoantigen-expressing cancer cells and self-antigen-expressing healthy tissues but have limited potency against tumors. We used a high-throughput platform to systematically evaluate the impact of co-expressing a TCR and CAR on the same CAR T cell. While strong TCR-antigen interactions enhanced CAR activation, weak TCR-antigen interactions actively antagonized their activation. Mathematical modeling captured this TCR-CAR crosstalk in CAR T cells, allowing us to engineer dual TCR/CAR T cells targeting neoantigens (HHATL8F/p53R175H) and human epithelial growth factor receptor 2 (HER2) ligands, respectively. These T cells exhibited superior anti-cancer activity and minimal toxicity against healthy tissue compared with conventional CAR T cells in a humanized solid tumor mouse model. Harnessing pre-existing inhibitory crosstalk between receptors, therefore, paves the way for the design of more precise cancer immunotherapies.

Engineering coupled consortia-based biosensors for diagnostic

Synthetic multicellular systems have great potential for performing complex tasks, including multi-signal detection and computation through cell-to-cell communication. However, engineering these systems is challenging, requiring precise control over the cell concentrations of distinct members and coordination of their activity. Here, we develop a bacterial consortia-based biosensor for Heme and Lactate, wherein members are coupled through a global shared quorum-sensing signal that simultaneously controls the activity of the diverse biosensing strains. The multicellular system incorporates a gene circuit that computes the minimum between each biosensor's activity and the shared signal. We evaluate three consortia configurations: one where the shared signal is externally supplied, another directly produced via an inducible gene circuit, and a third generated through an incoherent feedforward loop (IFFL) gene circuit. Among these configurations, the IFFL system, which maintains the shared signal at low and stable levels over an extended period, demonstrates improved performance and robustness against perturbations in cell populations. Finally, we examine these coupled consortia to monitor Lactate and Heme in humanized fecal samples for diagnostics.

Bioinspired Nucleic Acid-Based Bandpass Filters and Their Concentration-Adaptive Functions

Natural signaling networks can act as bandpass filters to interpret external stimuli within defined concentration ranges for differential cellular activities. Replicating such a bandpass filtering mechanism by synthetic networks poses a significant challenge. Herein, we introduce a modular design of nucleic acid-based multilayer threshold-gated incoherent feedforward networks as multiband bandpass filters to produce mutually exclusive responses within defined input concentration ranges. In these networks, nucleic acids demonstrate triple functionality by acting as threshold-gated entities to discern input concentration levels, serving as network nodes to assemble incoherent feedforward loops for nonlinear signal processing, and functioning as signal transduction units for coupling downstream functional modules. These modular networks enable the fine-tuning of filtering performance in terms of band position, bandwidth, cascades, and responses. A mathematical simulation model allows us to predict the filtering behaviors under various conditions. Also, the networks are integrated with upstream signal conversion modules to process concentration information on molecules beyond nucleic acids, such as adenosine and its derivatives. Furthermore, connections to downstream functional modules allow the system to regulate various processes in a concentration bandpass manner, realizing concentration-adaptive DNAzyme biocatalysis, tristate logic operations, RNA transcription, and DNA condensate formation. These findings underscore the potential of enzyme-free DNA reaction networks in complex signal processing and lay a solid foundation for developing chemical and material systems with highly adaptive and autonomous behaviors.

Temperature signals drive grass secondary cell wall thickening

In grasses, stem elongation is driven by intercalary meristems at node-internode junctions, where cells divide, elongate, and in some cell types secondary wall maturation. Cellulose is the predominant polymer in plant cells and the most abundant biopolymer on Earth. It is synthesized at the plasma membrane by multi-protein complexes that include CELLULOSE SYNTHASE A (CESA) proteins. To investigate the spatiotemporal regulation of cellulose deposition during development, we developed a CESA8 luciferase gene expression reporter system in Brachypodium distachyon . High bioluminescence was observed in stem nodes, a specific region of elongating internodes, and the inflorescence, indicating sites of active secondary wall deposition. Within internodes, luminescence followed a distinct pattern, with a "dark zone" directly above the node with minimal signal, followed by a "bright zone" approximately 5 mm above the node where bioluminescence peaked. Histological, biophysical, and transcript analysis confirmed that luminescence intensity correlates with thickened secondary cell walls, increased cellulose crystallinity, and elevated CESA8 transcript levels. Time-lapse imaging revealed that CESA8 expression follows a robust diurnal rhythm governed by thermocycles alone, with peak expression occurring in the early morning. Temperature pulse experiments revealed an immediate but transient response of CESA8 to temperature shifts, which we modeled as an incoherent feed-forward loop. Finally, we found a strong correlation between CESA8 expression and stem elongation, highlighting the role of secondary cell wall thickening in supporting upright growth. These findings provide new insights into the regulation of secondary wall formation and its integration with environmental cues, advancing our understanding of grass stem development.

SIGNIFICANCE

Understanding how grasses build strong stems is essential for improving biomass production and crop resilience. In grasses, stem elongation and secondary cell wall thickening occur in distinct zones, yet the precise timing and regulation of this process remain unclear. To investigate this phenomenon, we developed a real-time imaging system to track the expression of CESA8 , a key gene involved in cellulose synthesis. Our findings reveal that secondary wall thickening follows a daily rhythm controlled by temperature rather than light. These insights provide a foundation for optimizing plant architecture in bioenergy crops, improving their efficiency and sustainability.

An algorithm for the transformation of the Petri net models of biological signaling networks into influence graphs

A common depiction for biological signaling networks is the influence graph in which the activation and inhibition effects between molecular species are shown with vertices and arcs connecting them. Another formalism for reaction-based models is the Petri nets which has a graphical representation and a mathematical notation that enables structural analysis and quantitative simulation. In this paper, we present an algorithm based on Petri nets topological features for the transformation of the computational model of a biological signaling network into an annotated influence graph. We also show the transformation of the Petri nets model of the beta-adrenergic receptor activating the PKA-MAPK signaling network into its representation as an influence graph.

Architecture of genome-wide transcriptional regulatory network reveals dynamic functions and evolutionary trajectories in Pseudomonas syringae

The model Gram-negative plant pathogen Pseudomonas syringae utilises hundreds of transcription factors (TFs) to regulate its functional processes, including virulence and metabolic pathways that control its ability to infect host plants. Although the molecular mechanisms of regulators have been studied for decades, a comprehensive understanding of genome-wide TFs in Psph 1448A remains limited. Here, we investigated the binding characteristics of 170 of 301 annotated TFs through chromatin immunoprecipitation sequencing (ChIP-seq). Fifty-four TFs, 62 TFs, and 147 TFs were identified in top-level, middle-level, and bottom-level, reflecting multiple higher-order network structures and direction of information flow. More than 40,000 TF pairs were classified into 13 three-node submodules which revealed the regulatory diversity of TFs in Psph 1448A regulatory network. We found that bottom-level TFs performed high co-associated scores to their target genes. Functional categories of TFs at three levels encompassed various regulatory pathways. Three and 25 master TFs were identified to involve in virulence and metabolic regulation, respectively. Evolutionary analysis and topological modularity network revealed functional variability and various conservation of TFs in P. syringae (Psph 1448A, Pst DC3000, Pss B728a, and Psa C48). Overall, our findings demonstrated a global transcriptional regulatory network of genome-wide TFs in Psph 1448A. This knowledge can advance the development of effective treatment and prevention strategies for related infectious diseases.

From genes to patterns: a framework for modeling the emergence of embryonic development from transcriptional regulation

Understanding embryonic patterning, the process by which groups of cells are partitioned into distinct identities defined by gene expression, is a central challenge in developmental biology. This complex phenomenon is driven by precise spatial and temporal regulation of gene expression across many cells, resulting in the emergence of highly organized tissue structures. While similar emergent behavior is well understood in other fields, such as statistical mechanics, the regulation of gene expression in development remains less clear, particularly regarding how molecular-level gene interactions lead to the large-scale patterns observed in embryos. In this study, we present a modeling framework that bridges the gap between molecular gene regulation and tissue-level embryonic patterning. Beginning with basic chemical reaction models of transcription at the single-gene level, we progress to model gene regulatory networks (GRNs) that mediate specific cellular functions. We then introduce phenomenological models of pattern formation, including the French Flag and Temporal Patterning/Speed Regulation models, and integrate them with molecular/GRN realizations. To facilitate understanding and application of our models, we accompany our mathematical framework with computer simulations, providing intuitive and simple code for each model. A key feature of our framework is the explicit articulation of underlying assumptions at each level of the model, from transcriptional regulation to tissue patterning. By making these assumptions clear, we provide a foundation for future experimental and theoretical work to critically examine and challenge them, thereby improving the accuracy and relevance of gene regulatory models in developmental biology. As a case study, we explore how different strategies for integrating enhancer activity affect the robustness and evolvability of GRNs that govern embryonic pattern formation. Our simulations suggest that a two-step regulation strategy, enhancer activation followed by competitive integration at the promoter, ensures more standardized integration of new enhancers into developmental GRNs, highlighting the adaptability of eukaryotic transcription. These findings shed new light on the transcriptional mechanisms underlying embryonic patterning, while the overall modeling framework serves as a foundation for future experimental and theoretical investigations.

Regulatory variation controlling architectural pleiotropy in maize

An early event in plant organogenesis is establishment of a boundary between the stem cell containing meristem and differentiating lateral organ. In maize (Zea mays), evidence suggests a common gene network functions at boundaries of distinct organs and contributes to pleiotropy between leaf angle and tassel branch number, two agronomic traits. To uncover regulatory variation at the nexus of these two traits, we use regulatory network topologies derived from specific developmental contexts to guide multivariate genome-wide association analyses. In addition to defining network plasticity around core pleiotropic loci, we identify new transcription factors that contribute to phenotypic variation in canopy architecture, and structural variation that contributes to cis-regulatory control of pleiotropy between tassel branching and leaf angle across maize diversity. Results demonstrate the power of informing statistical genetics with context-specific developmental networks to pinpoint pleiotropic loci and their cis-regulatory components, which can be used to fine-tune plant architecture for crop improvement.

Regulatory roles of the AraC family transcription factor yeaM in the virulence and biofilm formation of Salmonella Typhimurium

Salmonella Typhimurium (S. typhimurium) is a significant zoonotic pathogen responsible for gastroenteritis and severe systemic infections in various hosts. The AraC family transcription factors are key gene expression regulators in prokaryotes, essential for bacterial adaptation to the environment and virulence. Despite their importance, the role of yeaM, a member of this family in S. typhimurium, remains unexplored. To elucidate yeaM regulatory function in virulence and biofilm formation, we engineered mutant and complementary strains of the yeaM gene using homologous recombination. We assessed their capabilities in biofilm formation under different conditions, macrophage adherence and invasion, and virulence in mice. Additionally, we identified potential target genes regulated by yeaM through transcriptome sequencing and confirmed these findings using an electrophoretic mobility shift assay (EMSA) and a dual-luciferase reporter assay. Our results demonstrate that, compared to the parental strain SL1344 and the complemented strain CΔyeaM, the ΔyeaM strain exhibited significantly enhanced biofilm formation, increased invasion of mouse intestinal epithelial cells, enhanced intracellular proliferation within macrophages, and elevated induction of macrophage apoptosis. Furthermore, the ΔyeaM deletion strain displayed significantly increased virulence in mice and enhanced proliferation in milk. Transcriptome analysis revealed that S. typhimurium pathogenicity island 4 (SPI4) genes (siiA, siiB, siiC, siiD, siiF, and siiE) were significantly upregulated following the deletion of the yeaM gene. EMSA and dual-luciferase reporter assays further showed that the yeaM protein can bind to the promoter of the siiA gene and suppress its expression, thereby modulating the biofilm formation and virulence of S. typhimurium.

Counterfactual learning for higher-order relation prediction in heterogeneous information networks

Heterogeneous Information Networks (HINs) play a crucial role in modeling complex social systems, where predicting missing links/relations is a significant task. Existing methods primarily focus on pairwise relations, but real-world scenarios often involve multi-entity interactions. For example, in academic collaboration networks, an interaction occurs between a paper, a conference, and multiple authors. These higher-order relations are prevalent but have been underexplored. Moreover, existing methods often neglect the causal relationship between the global graph structure and the state of relations, limiting their ability to capture the fundamental factors driving relation prediction. In this paper, we propose HINCHOR, an end-to-end model for higher-order relation prediction in HINs. HINCHOR introduces a higher-order structure encoder to capture multi-entity proximity information. Then, it focuses on a counterfactual question: "If the global graph structure were different, would the higher-order relation change?" By presenting a counterfactual data augmentation module, HINCHOR utilizes global structure information to generate counterfactual relations. Through counterfactual learning, HINCHOR estimates causal effects while predicting higher-order relations. The experimental results on four constructed benchmark datasets show that HINCHOR outperforms existing state-of-the-art methods.

Mechanochemical bistability of intestinal organoids enables robust morphogenesis

Reproducible pattern and form generation during embryogenesis is poorly understood. Intestinal organoid morphogenesis involves a number of mechanochemical regulators such as cell-type-specific cytoskeletal forces and osmotically driven lumen volume changes. It is unclear how these forces are coordinated in time and space to ensure robust morphogenesis. Here we show how mechanosensitive feedback on cytoskeletal tension gives rise to morphological bistability in a minimal model of organoid morphogenesis. In the model, lumen volume changes can impact the epithelial shape via both direct mechanical and indirect mechanosensitive mechanisms. We find that both bulged and budded crypt states are possible and dependent on the history of volume changes. We test key modelling assumptions via biophysical and pharmacological experiments to demonstrate how bistability can explain experimental observations, such as the importance of the timing of lumen shrinkage and robustness of the final morphogenetic state to mechanical perturbations. This suggests that bistability arising from feedback between cellular tensions and fluid pressure could be a general mechanism that coordinates multicellular shape changes in developing systems.

Antagonizing cis-regulatory elements of a conserved flowering gene mediate developmental robustness

Developmental transitions require precise temporal and spatial control of gene expression. In plants, such regulation is critical for flower formation, which involves the progressive maturation of stem cell populations within shoot meristems to floral meristems, followed by rapid sequential differentiation into floral organs. Across plant taxa, these transitions are orchestrated by the F-box transcriptional cofactor gene UNUSUAL FLORAL ORGANS (UFO). The conserved and pleiotropic functions of UFO offer a useful framework for investigating how evolutionary processes have shaped the intricate cis-regulation of key developmental genes. By pinpointing a conserved promoter sequence in an accessible chromatin region of the tomato ortholog of UFO, we engineered in vivo a series of cis-regulatory alleles that caused both loss- and gain-of-function floral defects. These mutant phenotypes were linked to disruptions in predicted transcription factor binding sites for known transcriptional activators and repressors. Allelic combinations revealed dosage-dependent interactions between opposing alleles, influencing the penetrance and expressivity of gain-of-function phenotypes. These phenotypic differences support that robustness in tomato flower development requires precise temporal control of UFO expression dosage. Bridging our analysis to Arabidopsis, we found that although homologous sequences to the tomato regulatory region are dispersed within the UFO promoter, they maintain similar control over floral development. However, phenotypes from disrupting these sequences differ due to the differing expression patterns of UFO. Our study underscores the complex cis-regulatory control of dynamic developmental genes and demonstrates that critical short stretches of regulatory sequences that recruit both activating and repressing machinery are conserved to maintain developmental robustness.

Investigating Gene Expression Noise Reduction by MicroRNAs and MiRISC Reinforcement by Self-Feedback Regulation of mRNA Degradation

MicroRNA (miRNA) induced silencing complex (miRISC) is the targeting apparatus and arguably rate-limiting step of the miRNA-mediated regulatory subsystem - the major noise reducing though metabolically wasteful mechanism. Recently, we reported that miRISC channels miRNA-mediated regulatory activity back onto their own mRNAs to form negative self-feedback loops, a noise-reduction technique in engineering and synthetic/systems biology. Here, we describe mathematical modeling that predicts mRNA expression noise to correlate negatively with degradation rate (K deg ) and noise reduction by self-feedback control ofK deg . We also calculatedK deg and expression noise of mRNAs detected in a cutting-edge total-RNA single-cell RNA-seq (scRNA-seq) dataset. As predicted, miRNA-targeted mRNAs exhibited higherK deg values in conjunction with lower inter-cell expression noise. Moreover, as predicted by our self-feedback loop model, miRISC mRNAs (AGO1/2/3 and TNRC6A/B/C) exhibited further reduced expression noise. In short, mathematical-modeling and total-RNA scRNA-seq data-analysis shed insight into operational trade-off between noise reduction and metabolic/energetic expenditure in producing miRNA-targeted mRNAs destined for enhanced degradation and translational inhibition, as well as negative self-feedback loop reinforcement of miRISC - the core of miRNA-mediated noise-reduction subsystem. To our knowledge, this is the first report of concurrent mRNA degradation and expression noise analyses and of noise reduction by self-feedback control of mRNA degradation.

Interpretable AI for inference of causal molecular relationships from omics data

The discovery of molecular relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise in this context but lack causal interpretation. Here, we show that a popular feature attribution model, under certain assumptions, estimates an average of a causal quantity reflecting the direct influence of one variable on another. We leverage this insight to propose a precise definition of a gene regulatory relationship and implement a new tool, CIMLA (Counterfactual Inference by Machine Learning and Attribution Models), to identify differences in gene regulatory networks between biological conditions, a problem that has received great attention in recent years. Using extensive benchmarking on simulated data, we show that CIMLA is more robust to confounding variables and is more accurate than leading methods. Last, we use CIMLA to analyze a previously published single-cell RNA sequencing dataset from subjects with and without Alzheimer's disease (AD), discovering several potential regulators of AD.

Temporal dose inversion properties of adaptive biomolecular circuits

Cells have the capacity to encode and decode information in the temporal features of molecular signals. Many pathways, for example, generate either sustained or pulsatile responses depending on the context, and such diverse temporal behaviors have a profound impact on cell fate. Here we focus on how molecular pathways can convert the temporal features of dynamic signals, in particular how they can convert transient signals into persistent downstream events and vice versa. We describe this type of behavior as temporal dose inversion, and we demonstrate that it can be achieved through adaptive molecular circuits. We consider motifs known as incoherent feedforward loop (IFFL) and negative feedback loop (NFL), and identify parametric conditions that enable temporal dose inversion. We next consider more complex versions of these circuits that could be realized using enzymatic signaling and gene regulatory networks, finding that both circuits can exhibit temporal dose inversion. Finally, we consider a generalized IFFL topology, and we find that both the time delay in the inhibition pathway and the relative signal intensities of the activation and inhibition signals are key determinants for temporal dose inversion. Our investigation expands the potential use of adaptive circuits as signal processing units and contributes to our understanding of the role of adaptive circuits in nature.

The Genomic Code: the genome instantiates a generative model of the organism

How does the genome encode the form of the organism? What is the nature of this genomic code? Inspired by recent work in machine learning and neuroscience, we propose that the genome encodes a generative model of the organism. In this scheme, by analogy with variational autoencoders (VAEs), the genome comprises a connectionist network, embodying a compressed space of 'latent variables', with weights that get encoded by the learning algorithm of evolution and decoded through the processes of development. The generative model analogy accounts for the complex, distributed genetic architecture of most traits and the emergent robustness and evolvability of developmental processes, while also offering a conception that lends itself to formalization.

Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning

Motivation

Single-cell RNA sequencing (scRNA-seq) data offers unprecedented opportunities to infer gene regulatory networks (GRNs) at a fine-grained resolution, shedding light on cellular phenotypes at the molecular level. However, the high sparsity, noise, and dropout events inherent in scRNA-seq data pose significant challenges for accurate and reliable GRN inference. The rapid growth in experimentally validated transcription factor-DNA binding data (e.g., ChIP-seq) has enabled supervised machine learning methods, which rely on known gene regulatory interactions to learn patterns, and achieve high accuracy in GRN inference by framing it as a gene regulatory link prediction task. This study addresses the gene regulatory link prediction problem by learning informative vectorized representations at the gene level to predict missing regulatory interactions. However, a higher performance of supervised learning methods requires a large amount of known TF-DNA binding data, which is often experimentally expensive and therefore limited in amount. Advances in large-scale pre-training and transfer learning provide a transformative opportunity to address this challenge. In this study, we leverage large-scale pre-trained models, trained on extensive scRNA-seq datasets and known as single-cell foundation models (scFMs). These models are combined with joint graph-based learning to establish a robust foundation for gene regulatory link prediction.

Results

We propose scRegNet, a novel and effective framework that leverages scFMs with joint graph-based learning for gene regulatory link prediction. scRegNet achieves state-of-the-art results in comparison with nine baseline methods on seven scRNA-seq benchmark datasets. In addition, scRegNet is more robust than the baseline methods on noisy training data.

Availability

The source code is available at https://github.com/sindhura-cs/scRegNet.

Randomization based evaluation of distinct topological and cancer expression characteristics of mutually acting gene pairs

Small scale molecular network patterns and motifs are crucial for systems level understanding of cellular information transduction. Using randomizations, we statistically explored, previously overlooked basic patterns of mutually acting pairs, i.e. mutually positive (PP) or negative (NN) and positive-negative (PN) pairs, in two comprehensive and distinct large-scale molecular networks from literature; the human protein signaling network (PSN) and the human gene regulatory network (GRN). Only the positive and negative signs of all interacting pairs were randomized, while the gene pairs and the number of positive and negative signs in the original network were kept constant. While the numbers of NN and PN pairs were significantly higher, the number of PP pairs was significantly lower than randomly expected values. Genes participating in mutual pairs were more connected than other genes. NN genes were more connected than PP and PN in GRN for all types of degree values, including in, out, positive or negative connections, but less connected for in-degree and more connected for out-degree values in PSN. They also had significantly high number of intersections with each other and PN pairs than randomly expected values, indicating potential cooperative mechanisms. The three mutual interaction designs we examined had distinct RNA and protein expression correlation characteristics. NN protein pairs were uniquely over-represented across normal tissue samples, whose negative correlations were lost across cancer tissue samples. PP and PN pairs showed non-random positive RNA or protein expression correlation across normal or cancer tissue samples. Moreover, we developed an online tool, i.e. MGPNet, for further user specific analysis of mutual gene pairs. We identified SNCA with significantly enriched negatively correlated NN pairs. Unique non-random characteristics of mutual gene pairs identified in two different comprehensive molecular networks could provide valuable information for a better comparative understanding of molecular design principles between normal and cancer states. Insight Box/Paragraph Statement: This study provides a systems-level perspective on cellular information transduction by analyzing mutually acting pairs of genes. By examining mutually positive (PP), mutually negative (NN), and positive-negative (PN) pairs in the human protein signaling network (PSN) and the human gene regulatory network (GRN), we uncover significant variations in their connectivity and expression correlation. Our findings highlight the unique features of NN pairs across normal and cancer tissues and offer insights into molecular design principles. The development of the MGPNet tool further enhances user-specific analyses, enabling a deeper understanding of gene pair mechanisms and their potential cooperative roles in cellular processes.

Epigenetic Mechanisms in the Transcriptional Regulation of Circadian Rhythm in Mammals

Almost all organisms, from the simplest bacteria to advanced mammals, havea near 24 h circadian rhythm. Circadian rhythms are highly conserved across different life forms and are regulated by circadian genes as well as by related transcription factors. Transcription factors are fundamental to circadian rhythms, influencing gene expression, behavior in plants and animals, and human diseases. This review examines the foundational research on transcriptional regulation of circadian rhythms, emphasizing histone modifications, chromatin remodeling, and Pol II pausing control. These studies have enhanced our understanding of transcriptional regulation within biological circadian rhythms and the importance of circadian biology in human health. Finally, we summarize the progress and challenges in these three areas of regulation to move the field forward.

Chemical waves in reaction-diffusion networks of small organic molecules

Chemical waves represent one of the fundamental behaviors that emerge in nonlinear, out-of-equilibrium chemical systems. They also play a central role in regulating behaviors and development of biological organisms. Nevertheless, understanding their properties and achieving their rational synthesis remains challenging. In this work, we obtained traveling chemical waves using synthetic organic molecules. To accomplish this, we ran a thiol-based reaction network in an unstirred flow reactor. Our observations revealed single or multiple waves moving in either the same or opposite directions, a behavior controlled by the geometry of our reactor. A numerical model can fully reproduce this behavior using the proposed reaction network. To better understand the formation of waves, we varied the diffusion coefficient of the fast inhibitor component of the reaction network by attaching polyethylene glycol tails with different lengths to maleimide and studied how these changes affect the properties of the waves and conditions for their sustained production. These studies point towards the importance of the molecular titration network motif in controlling the production of chemical waves in this system. Furthermore, we used machine learning (ML) tools to identify phase boundaries for classes of dynamic behaviors of this system, thus demonstrating the applicability of ML tools for the study of experimental nonlinear reaction-diffusion systems.

AdaMotif: Graph Simplification via Adaptive Motif Design

With the increase of graph size, it becomes difficult or even impossible to visualize graph structures clearly within the limited screen space. Consequently, it is crucial to design effective visual representations for large graphs. In this paper, we propose AdaMotif, a novel approach that can capture the essential structure patterns of large graphs and effectively reveal the overall structures via adaptive motif designs. Specifically, our approach involves partitioning a given large graph into multiple subgraphs, then clustering similar subgraphs and extracting similar structural information within each cluster. Subsequently, adaptive motifs representing each cluster are generated and utilized to replace the corresponding subgraphs, leading to a simplified visualization. Our approach aims to preserve as much information as possible from the subgraphs while simplifying the graph efficiently. Notably, our approach successfully visualizes crucial community information within a large graph. We conduct case studies and a user study using real-world graphs to validate the effectiveness of our proposed approach. The results demonstrate the capability of our approach in simplifying graphs while retaining important structural and community information.

Regulatory Effects of Cooperativity and Signal Profile on Adaptive Dynamics in Incoherent Feedforward Loop Networks

Cellular adaptation to external signals is essential for biological functions, and it is an important field of interest in systems biology. This study examines the impact of cooperativity on the adaptation response of the Incoherent Feedforward Loop (IFFL) network motif to various signal profiles. Through comprehensive simulations, we studied how the IFFL motif responds to constant and pulse-type signals under varying levels of cooperativity. The results of our study demonstrate that positive cooperativity generally enhances the system's ability to adapt to different signal profiles. Nevertheless, given specific signal profiles, higher levels of cooperativity may decrease the system's adaptability. On the other hand, the adaptive response breaks down for negative cooperativity. For constant signals, increased positive cooperativity leads to a response with higher amplitude, and it accelerates the response time but delays the return time required to settle back down to the pre-stimulus state. Upon signal cessation, high positive cooperativity not only slows the system's response and return times but, in some cases, can lead to a complete temporary halt in response. For the pulse-like signal, cooperativity increases the maximum amplitude of the oscillatory response. These insights highlight the delicate balance between cooperativity and signal profile in cellular adaptation mechanisms involving the IFFL network motif.

Feed-forward loop improves the transient dynamics of an antithetic biological controller

Integral controller is widely used in industry for its capability of endowing perfect adaptation to disturbances. To harness such capability for precise gene expression regulation, synthetic biologists have endeavoured in building biomolecular (quasi-)integral controllers, such as the antithetic integral controller. Despite demonstrated successes, challenges remain with designing the controller for improved transient dynamics and adaptation. Here, we explore and investigate the design principles of alternative RNA-based biological controllers, by modifying an antithetic integral controller with prevalently found natural feed-forward loops (FFL), to improve its transient dynamics and adaptation performance. With model-based analysis, we demonstrate that while the base antithetic controller shows excellent responsiveness and adaptation to system disturbances, incorporating the type-1 incoherent FFL into the base antithetic controller could attenuate the transient dynamics caused by changes in the stimuli, especially in mitigating the undesired overshoot in the output gene expression. Further analysis on the kinetic parameters reveals similar findings to previous studies that the degradation and transcription rates of the circuit RNA species would dominate in shaping the performance of the controllers.

A Computational Protocol for the Knowledge-Based Assessment and Capture of Pathologies

We propose that one of the main hurdles in delivering comprehensively informed care results from the challenges surrounding the extraction, representation, and retention of prior clinical experience and basic medical knowledge, as well as its translation into time- and context-informed actionable interventions. While emerging applications in artificial intelligence-based techniques, for example, large language models, offer impressive pattern association capabilities, they often fall short in producing human-readable explanations crucial to their integration into clinical care. Moreover, they require large well-defined and well-integrated data sets that typically conflict with the availability of such data in all but a few areas of medicine, for example, medical imaging and neuroimaging, noninvasive monitoring of bio-electrical activity, etc. In this chapter, we argue that approximate reasoning rooted in the knowledge that is explainable to the human clinician may offer attractive avenues for the introduction of such knowledge in a systematic way that supports formal retention, sharing, and reuse of new clinical and basic medical experience. We outline a conceptual protocol that targets the use of sparse and disparate data of different types and from different sources, seamlessly drawing on our collective experience and that of others. We illustrate the utility of such an integrative approach by applying the latter to the assessment and reconciliation of data from different experimental models, human and animal, in the example use case of a complex health condition.

Modeling ncRNA-Mediated Circuits in Cell Fate Decision: From Systems Biology to Synthetic Biology

Noncoding RNAs (ncRNAs) play critical roles in essential cell fate decisions. However, the exact molecular mechanisms underlying ncRNA-mediated bistable switches remain elusive and controversial. In recent years, systematic mathematical and quantitative experimental analyses have made significant contributions to elucidating the molecular mechanisms of controlling ncRNA-mediated cell fate decision processes. In this chapter, we review and summarize the general framework of mathematical modeling of ncRNA in a pedagogical way and the application of this general framework to real biological processes. We discuss the emerging properties resulting from the reciprocal regulation between mRNA, miRNA, and competing endogenous mRNA (ceRNA). We also explore the efforts within the synthetic biology approach to understand the fundamental design principles underlying cell fate decisions. Both the positive feedback loops between ncRNAs and transcription factors and the emerging properties from the miRNA-mRNA reciprocal regulation enable bistable switches to direct cell fate decisions.

Central control of dynamic gene circuits governs T cell rest and activation

The ability of cells to maintain distinct identities and respond to transient environmental signals requires tightly controlled regulation of gene networks1-3. These dynamic regulatory circuits that respond to extracellular cues in primary human cells remain poorly defined. The need for context-dependent regulation is prominent in T cells, where distinct lineages must respond to diverse signals to mount effective immune responses and maintain homeostasis4-8. Here we performed CRISPR screens in multiple primary human CD4+ T cell contexts to identify regulators that control expression of IL-2Rα, a canonical marker of T cell activation transiently expressed by pro-inflammatory effector T cells and constitutively expressed by anti-inflammatory regulatory T cells where it is required for fitness9-11. Approximately 90% of identified regulators of IL-2Rα had effects that varied across cell types and/or stimulation states, including a subset that even had opposite effects across conditions. Using single-cell transcriptomics after pooled perturbation of context-specific screen hits, we characterized specific factors as regulators of overall rest or activation and constructed state-specific regulatory networks. MED12 - a component of the Mediator complex - serves as a dynamic orchestrator of key regulators, controlling expression of distinct sets of regulators in different T cell contexts. Immunoprecipitation-mass spectrometry revealed that MED12 interacts with the histone methylating COMPASS complex. MED12 was required for histone methylation and expression of genes encoding key context-specific regulators, including the rest maintenance factor KLF2 and the versatile regulator MYC. CRISPR ablation of MED12 blunted the cell-state transitions between rest and activation and protected from activation-induced cell death. Overall, this work leverages CRISPR screens performed across conditions to define dynamic gene circuits required to establish resting and activated T cell states.

Mechanistic Modeling the Role of MicroRNAs and Transcription Factors in Disease Progression

In this chapter, we illustrate the utilization of network analysis and mechanistic modeling, two potent branches of systems biology, to simplify the representation of intricate biological processes such as cell signaling, gene regulation, and metabolic pathways. Specifically, we demonstrate the application of a well-established method to generate a microRNA-transcription factor-gene regulatory feed-forward loop network extracted from the GEO dataset GSE163877. Furthermore, we outline a method for constructing a deterministic model using the LSODA method based on the sub-network. This model furnishes insights into the roles of crucial differentially expressed microRNAs and transcription factors in gene expression associated with Alzheimer's disease progression. Our analysis of the model reveals elevated kinetics of synthesis for EGR1, miR-6891, miR-4786, and LTBP1. The model suggests the linear upregulation of miR-8080, miR-3921, HSPB6, and downregulation MX2 gene. The rest of the miRNA, TFs, and genes shows a momentary variation in expression and if the system is undisturbed, they attain equilibrium. Thus, we elucidate how mechanistic modeling, along with perturbation studies and network analysis of expression data, can yield diverse insights into the trajectory of disease progression.

Transcriptional regulators ensuring specific gene expression and decision-making at high TGFβ doses

TGFβ-signaling regulates cancer progression by controlling cell division, migration, and death. These outcomes are mediated by gene expression changes, but the mechanisms of decision-making toward specific fates remain unclear. Here, we combine SMAD transcription factor imaging, genome-wide RNA sequencing, and morphological assays to quantitatively link signaling, gene expression, and fate decisions in mammary epithelial cells. Fitting genome-wide kinetic models to our time-resolved data, we find that most of the TGFβ target genes can be explained as direct targets of SMAD transcription factors, whereas the remainder show signs of complex regulation, involving delayed regulation and strong amplification at high TGFβ doses. Knockdown experiments followed by global RNA sequencing revealed transcription factors interacting with SMADs in feedforward loops to control delayed and dose-discriminating target genes, thereby reinforcing the specific epithelial-to-mesenchymal transition at high TGFβ doses. We identified early repressors, preventing premature activation, and a late activator, boosting gene expression responses for a sufficiently strong TGFβ stimulus. Taken together, we present a global view of TGFβ-dependent gene regulation and describe specificity mechanisms reinforcing cellular decision-making.

Assembly Graph as the Rosetta Stone of Ecological Assembly

Ecological assembly-the process of ecological community formation through species introductions-has recently seen exciting theoretical advancements across dynamical, informational, and probabilistic approaches. However, these theories often remain inaccessible to non-theoreticians, and they lack a unifying lens. Here, I introduce the assembly graph as an integrative tool to connect these emerging theories. The assembly graph visually represents assembly dynamics, where nodes symbolise species combinations and edges represent transitions driven by species introductions. Through the lens of assembly graphs, I review how ecological processes reduce uncertainty in random species arrivals (informational approach), identify graphical properties that guarantee species coexistence and examine how the class of dynamical models constrain the topology of assembly graphs (dynamical approach), and quantify transition probabilities with incomplete information (probabilistic approach). To facilitate empirical testing, I also review methods to decompose complex assembly graphs into smaller, measurable components, as well as computational tools for deriving empirical assembly graphs. In sum, this math-light review of theoretical progress aims to catalyse empirical research towards a predictive understanding of ecological assembly.

Chromatin-based memory as a self-stabilizing influence on cell identity

Cell types are traditionally thought to be specified and stabilized by gene regulatory networks. Here, we explore how chromatin memory contributes to the specification and stabilization of cell states. Through pervasive, local, feedback loops, chromatin memory enables cell states that were initially unstable to become stable. Deeper appreciation of this self-stabilizing role for chromatin broadens our perspective of Waddington's epigenetic landscape from a static surface with islands of stability shaped by evolution, to a plasticine surface molded by experience. With implications for the evolution of cell types, stabilization of resistant states in cancer, and the widespread plasticity of complex life.

QOMIC: quantum optimization for motif identification

Motivation

Network motif identification (MI) problem aims to find topological patterns in biological networks. Identifying disjoint motifs is a computationally challenging problem using classical computers. Quantum computers enable solving high complexity problems which do not scale using classical computers. In this article, we develop the first quantum solution, called QOMIC (Quantum Optimization for Motif IdentifiCation), to the MI problem. QOMIC transforms the MI problem using a integer model, which serves as the foundation to develop our quantum solution. We develop and implement the quantum circuit to find motif locations in the given network using this model.

Results

Our experiments demonstrate that QOMIC outperforms the existing solutions developed for the classical computer, in term of motif counts. We also observe that QOMIC can efficiently find motifs in human regulatory networks associated with five neurodegenerative diseases: Alzheimer's, Parkinson's, Huntington's, Amyotrophic Lateral Sclerosis, and Motor Neurone Disease.

Availability and implementation

Our implementation can be found in https://github.com/ngominhhoang/Quantum-Motif-Identification.git.

Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?

During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are the major barriers currently met by biomedical investigators? It is suggested that during the last 2 decades there was a shift towards a growing need to elucidate complex systems, and that this was not sufficiently fulfilled by previously successful methods such as theoretical modeling or computer simulation (ii) What is the potential of AI to meet the aforementioned need? it is suggested that recent AI methods are well-suited to perform classification and prediction tasks on multivariate systems, and possibly help in data interpretation, provided their efficiency is properly validated. (iii) Recent representative results obtained with machine learning suggest that AI efficiency may be comparable to that displayed by human operators. It is concluded that AI should durably play an important role in biomedical practice. Also, as already suggested in other scientific domains such as physics, combining AI with conventional methods might generate further progress and new applications, involving heuristic and data interpretation.

Tumor Biomechanics-Inspired Future Medicine

Malignant tumors pose a significant global health challenge, severely threatening human health. Statistics from the World Health Organization indicate that, in 2022, there were nearly 20 million new cancer cases and 9.7 million cancer-related deaths. Therefore, it is urgently necessary to study the pathogenesis of cancer and explore effective diagnostic and treatment strategies. In recent years, research has highlighted the importance of mechanical cues in tumors, which have become a new hallmark of cancer and a key factor in regulating tumor behavior. This suggests that studying the mechanical properties of tumors may open potential new avenues for understanding the pathogenesis, diagnosis, and therapeutic intervention of cancer. This review summarizes the mechanical characteristics of tumors and the development of tumor diagnostics and treatments targeting specific mechanical factors. Finally, we propose new ideas and insights for the application of mechanomedicine in cancer diagnosis and treatment in the future.

Emergence of temporal noise hierarchy in co-regulated genes of multi-output feed-forward loop

Natural variations in gene expression, called noise, are fundamental to biological systems. The expression noise can be beneficial or detrimental to cellular functions. While the impact of noise on individual genes is well-established, our understanding of how noise behaves when multiple genes are co-expressed by shared regulatory elements within transcription networks remains elusive. This lack of understanding extends to how the architecture and regulatory features of these networks influence noise. To address this gap, we study the multi-output feed-forward loop motif. The motif is prevalent in bacteria and yeast and influences co-expression of multiple genes by shared transcription factors (TFs). Focusing on a two-output variant of the motif, the present study explores the interplay between its architecture, co-expression (symmetric and asymmetric) patterns of the two genes, and the associated noise dynamics. We employ a stochastic modeling approach to investigate how the binding affinities of the TFs influence symmetric and asymmetric expression patterns and the resulting noise dynamics in the co-expressed genes. This knowledge could guide the development of strategies for manipulating gene expression patterns through targeted modulation of TF binding affinities.

Advantage looping: Gene regulatory circuits between microRNAs and their target transcription factors in plants

The 20 to 24 nucleotide microRNAs (miRNAs) and their target transcription factors (TF) have emerged as key regulators of diverse processes in plants, including organ development and environmental resilience. In several instances, the mature miRNAs degrade the TF-encoding transcripts, while their protein products in turn bind to the promoters of the respective miRNA-encoding genes and regulate their expression, thus forming feedback loops (FBLs) or feedforward loops (FFLs). Computational analysis suggested that such miRNA-TF loops are recurrent motifs in gene regulatory networks (GRNs) in plants as well as animals. In recent years, modeling and experimental studies have suggested that plant miRNA-TF loops in GRNs play critical roles in driving organ development and abiotic stress responses. Here, we discuss the miRNA-TF FBLs and FFLs that have been identified and studied in plants over the past decade. We then provide some insights into the possible roles of such motifs within GRNs. Lastly, we provide perspectives on future directions for dissecting the functions of miRNA-centric GRNs in plants.

Stable developmental patterns of gene expression without morphogen gradients

Gene expression patterns in developing organisms are established by groups of cross-regulating target genes that are driven by morphogen gradients. As development progresses, morphogen activity is reduced, leaving the emergent pattern without stabilizing positional cues and at risk of rapid deterioration due to the inherently noisy biochemical processes at the cellular level. But remarkably, gene expression patterns remain spatially stable and reproducible over long developmental time spans in many biological systems. Here we combine spatial-stochastic simulations with an enhanced sampling method (Non-Stationary Forward Flux Sampling) and a recently developed stability theory to address how spatiotemporal integrity of a gene expression pattern is maintained in developing tissue lacking morphogen gradients. Using a minimal embryo model consisting of spatially coupled biochemical reactor volumes, we study a prototypical stripe pattern in which weak cross-repression between nearest neighbor expression domains alternates with strong repression between next-nearest neighbor domains, inspired by the gap gene system in the Drosophila embryo. We find that tuning of the weak repressive interactions to an optimal level can prolong stability of the expression patterns by orders of magnitude, enabling stable patterns over developmentally relevant times in the absence of morphogen gradients. The optimal parameter regime found in simulations of the embryo model closely agrees with the predictions of our coarse-grained stability theory. To elucidate the origin of stability, we analyze a reduced phase space defined by two measures of pattern asymmetry. We find that in the optimal regime, intact patterns are protected via restoring forces that counteract random perturbations and give rise to a metastable basin. Together, our results demonstrate that metastable attractors can emerge as a property of stochastic gene expression patterns even without system-wide positional cues, provided that the gene regulatory interactions shaping the pattern are optimally tuned.

A regulatory module mediating temperature control of cell-cell communication facilitates tree bud dormancy release

The control of cell-cell communication via plasmodesmata (PD) plays a key role in plant development. In tree buds, low-temperature conditions (LT) induce a switch in plasmodesmata from a closed to an open state, which restores cell-to-cell communication in the shoot apex and releases dormancy. Using genetic and cell-biological approaches, we have identified a previously uncharacterized transcription factor, Low-temperature-Induced MADS-box 1 (LIM1), as an LT-induced, direct upstream activator of the gibberellic acid (GA) pathway. The LIM1-GA module mediates low temperature-induced plasmodesmata opening, by negatively regulating callose accumulation to promote dormancy release. LIM1 also activates expression of FT1 (FLOWERING LOCUS T), another LT-induced factor, with LIM1-FT1 forming a coherent feedforward loop converging on low-temperature regulation of gibberellin signaling in dormancy release. Mathematical modeling and experimental validation suggest that negative feedback regulation of LIM1 by gibberellin could play a crucial role in maintaining the robust temporal regulation of bud responses to low temperature. These results reveal genetic factors linking temperature control of cell-cell communication with regulation of seasonally-aligned growth crucial for adaptation of trees.

Getting it right: suppression and leveraging of noise in robust decision-making

Noise is a ubiquitous feature for all organisms growing in nature. Noise (defined here as stochastic variation) in the availability of nutrients, water and light profoundly impacts their growth and development. Not only is noise present as an external factor but cellular processes themselves are noisy. Therefore, it is remarkable that organisms can display robust control of growth and development despite noise. To survive, various mechanisms to suppress noise have evolved. However, it is also becoming apparent that noise is not just a nuisance that organisms must suppress but can be beneficial as low noise can facilitate the response of an organism to a sub-threshold input signal in a stochastic resonance mechanism. This review discusses mechanisms capable of noise suppression or noise leveraging that might play a significant role in robust temporal regulation of an organism's response to their noisy environment.

The master regulator OxyR orchestrates bacterial oxidative stress response genes in space and time

Bacteria employ diverse gene regulatory networks to survive stress, but deciphering the underlying logic of these complex networks has proved challenging. Here, we use time-resolved single-cell imaging to explore the functioning of the E. coli regulatory response to oxidative stress. We observe diverse gene expression dynamics within the network. However, by controlling for stress-induced growth-rate changes, we show that these patterns involve just three classes of regulation: downregulated genes, upregulated pulsatile genes, and gradually upregulated genes. The two upregulated classes are distinguished by differences in the binding of the transcription factor, OxyR, and appear to play distinct roles during stress protection. Pulsatile genes activate transiently in a few cells for initial protection of a group of cells, whereas gradually upregulated genes induce evenly, generating a lasting protection involving many cells. Our study shows how bacterial populations use simple regulatory principles to coordinate stress responses in space and time. A record of this paper's transparent peer review process is included in the supplemental information.

Deconstructing inflammatory memory across tissue set points using cell circuit motifs

Tissue ecosystems are cellular communities that maintain set points through a network of intercellular interactions. We position health and chronic inflammatory disease as alternative stable set points that are (1) robust to perturbation and (2) capable of adaptation and memory. Inflammatory memory, which is the storage of prior experience to durably influence future responsiveness, is central to how tissue ecosystems may be pushed past tipping points that stabilize disease over health. Here, we develop a reductionist framework of circuit motifs that recur in tissue set points. In type 2 immunity, we distinctly find the emergence of 2-cell positive feedback motifs. In contrast, directional motif relays and 3-cell networks feature more prominently in type 1 and 17 responses. We propose that these differences guide the ecologic networks established after surpassing tipping points and associate closely with therapeutic responsiveness. We highlight opportunities to improve our current knowledge of how circuit motifs interact when building toward tissue-level networks across adaptation and memory. By developing new tools for circuit motif nomination and applying them to temporal profiling of tissue ecosystems, we hope to dissect the stability of the chronic inflammatory set point and open therapeutic avenues for rewriting memory to restore health.

GnuR Represses the Expression of Glucose and Gluconate Catabolism in Pseudomonas putida KT2440

In Pseudomonas putida KT2440, a prime chassis for biotechnology, the clustered distribution of glucose catabolism genes and four related transcription factors (TFs) may facilitate the tight regulation of glucose catabolism. However, the genes under the direct control of these TFs remain unidentified, leaving their regulatory roles elusive. Furthermore, the carbon source gluconate was metabolised similarly to glucose in KT2440, but the responses of these catabolic and TF genes to gluconate were unclear. Here, these mysteries were unravelled through multi-omics analysis integrated with physiological studies. First, we found that the expression of these catabolic and TF genes were significantly induced by both glucose and gluconate in KT2440. The independent responses of these genes to glucose and gluconate were differentiated in the gcd deletion mutant. We then defined the regulon of GnuR, one of the four related TFs, and discovered that GnuR directly repressed the expression of catabolic genes involved in the Entner-Doudoroff and the peripheral glucose and gluconate metabolism pathways. These results were further confirmed by physiological studies. Finally, a regulatory mode of an incoherent feedforward loop involving GnuR is proposed.

How synchronized human networks escape local minima

Finding the global minimum in complex networks while avoiding local minima is challenging in many types of networks. In human networks and communities, adapting and finding new stable states amid changing conditions due to conflicts, climate changes, or disasters, is crucial. We studied the dynamics of complex networks of violin players and observed that such human networks have different methods to avoid local minima than other non-human networks. Humans can change the coupling strength between them or change their tempo. This leads to different dynamics than other networks and makes human networks more robust and better resilient against perturbations. We observed high-order vortex states, oscillation death, and amplitude death, due to the unique dynamics of the network. This research may have implications in politics, economics, pandemic control, decision-making, and predicting the dynamics of networks with artificial intelligence.

Reconstructing signaling history of single cells with imaging-based molecular recording

The intensity and duration of biological signals encode information that allows a few pathways to regulate a wide array of cellular behaviors. Despite the central importance of signaling in biomedical research, our ability to quantify it in individual cells over time remains limited. Here, we introduce INSCRIBE, an approach for reconstructing signaling history in single cells using endpoint fluorescence images. By regulating a CRISPR base editor, INSCRIBE generates mutations in genomic target sequences, at a rate proportional to signaling activity. The number of edits is then recovered through a novel ratiometric readout strategy, from images of two fluorescence channels. We engineered human cell lines for recording WNT and BMP pathway activity, and demonstrated that INSCRIBE faithfully recovers both the intensity and duration of signaling. Further, we used INSCRIBE to study the variability of cellular response to WNT and BMP stimulation, and test whether the magnitude of response is a stable, heritable trait. We found a persistent memory in the BMP pathway. Progeny of cells with higher BMP response levels are likely to respond more strongly to a second BMP stimulation, up to 3 weeks later. Together, our results establish a scalable platform for genetic recording and in situ readout of signaling history in single cells, advancing quantitative analysis of cell-cell communication during development and disease.

MIR164B ensures robust Arabidopsis leaf development by compensating for compromised POLYCOMB REPRESSIVE COMPLEX2 function

Robustness is pervasive throughout biological systems, enabling them to maintain persistent outputs despite perturbations in their components. Here, we reveal a mechanism contributing to leaf morphology robustness in the face of genetic perturbations. In Arabidopsis (Arabidopsis thaliana), leaf shape is established during early development through the quantitative action of the CUP-SHAPED COTYLEDON2 (CUC2) protein, whose encoding gene is negatively regulated by the co-expressed MICRORNA164A (MIR164A) gene. Compromised epigenetic regulation due to defective Polycomb Repressive Complex 2 (PRC2) function results in the transcriptional derepression of CUC2 but has no impact on CUC2 protein dynamics or early morphogenesis. We solve this apparent paradox by showing that compromised PRC2 function simultaneously derepresses the expression of another member of the MIR164 gene family, MIR164B. This mechanism dampens CUC2 protein levels, thereby compensating for compromised PRC2 function and canalizing early leaf morphogenesis. Furthermore, we show that this compensation mechanism is active under different environmental conditions. Our findings shed light on how the interplay between different steps of gene expression regulation can contribute to developmental robustness.