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Results for “systems biology”

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bioRxiv2020

Integrated information structure collapses with anesthetic loss of conscious arousal in Drosophila melanogaster

The physical basis of consciousness remains one of the most elusive concepts in current science. One influential conjecture is that consciousness is to do with some form of causality, measurable through information. The integrated information theory of consciousness (IIT) proposes that conscious experience, filled with rich and specific content, corresponds directly to a hierarchically organised, irreducible pattern of causal interactions; i.e. an integrated informational structure among elements of a system. Here, we tested this conjecture in a simple biological system (fruit flies), estimating the information structure of the system during wakefulness and general anesthesia. Consistent with this conjecture, we found that integrated interactions among populations of neurons during wakefulness collapsed to isolated clusters of interactions during anesthesia. We used classification analysis to quantify the accuracy of discrimination between wakeful and anesthetised states, and found that informational structures inferred conscious states with greater accuracy than a scalar summary of the structure, a measure which is generally championed as the main measure of IIT. In stark contrast to a view which assumes feedforward architecture for insect brains, especially fly visual systems, we found rich information structures, which cannot arise from purely feedforward systems, occurred across the fly brain. Further, these information structures collapsed uniformly across the brain during anesthesia. Our results speak to the potential utility of the novel concept of an "informational structure" as a measure for level of consciousness, above and beyond simple scalar values. Author summaryThe physical basis of consciousness remains elusive. Efforts to measure consciousness have generally been restricted to simple, scalar quantities which summarise the complexity of a system, inspired by integrated information theory, which links a multi-dimensional, informational structure to the contents of experience in a system. Due to the complexity of the definition of the structure, assessment of its utility as a measure of conscious arousal in a system has largely been ignored. In this manuscript we evaluate the utility of such an information structure in measuring the level of arousal in the fruit fly. Our results indicate that this structure can be more informative about the level of arousal in a system than even the single-value summary proposed by the theory itself. These results may push consciousness research towards the notion of multi-dimensional informational structures, instead of traditional scalar summaries.

neuroscience↗

bioRxiv2021

SYS-Mut: Decoding the Functional Significance of Rare Somatic Mutations in Cancer

Current tailored-therapy efforts in cancer are largely focused on a small number of highly recurrently-mutated driver genes but therapeutic targeting of these oncogenes remains challenging. On the other hand, the vast number of genes mutated infrequently across cancers have received less attention, in part, due to a lack of understanding of their biologic significance. Here we present SYS-Mut, a systems biology platform that can robustly infer the biologic consequences of somatic mutations by integrating routine multi-omic profiles in primary tumors. We established the accuracy of SYS-Mut by recapitulating the functional impact of known driver genes in PanCancer datasets. Subsequent application of SYS-Mut on low-frequency gene mutations in Head and Neck Cancers (HNSC), followed by molecular and pharmacogenetic validation, revealed the lipidogenic network as a novel therapeutic vulnerability in aggressive HNSC. SYS-Mut is thus a robust scalable framework that enables discovery of new targetable avenues in cancer.

cancer biology↗

bioRxiv2022

Building insightful, memory-enriched models to capture long-time biochemical processes from short-time simulations

The ability to predict and understand the complex molecular motions occurring over diverse timescales ranging from picoseconds to seconds and even hours occurring in biological systems remains one of the largest challenges to chemical theory. Markov State Models (MSMs), which provide a memoryless description of the transitions between different states of a biochemical system, have provided numerous important physically transparent insights into biological function. However, constructing these models often necessitates performing extremely long molecular simulations to converge the rates. Here we show that by incorporating memory via the time-convolutionless generalized master equation (TCL-GME) one can build a theoretically transparent and physically intuitive memory-enriched model of biochemical processes with up to a three orders of magnitude reduction in the simulation data required while also providing a higher temporal resolution. We derive the conditions under which the TCL-GME provides a more efficient means to capture slow dynamics than MSMs and rigorously prove when the two provide equally valid and efficient descriptions of the slow configurational dynamics. We further introduce a simple averaging procedure that enables our TCL-GME approach to quickly converge and accurately predict long-time dynamics even when parameterized with noisy reference data arising from short trajectories. We illustrate the advantages of the TCL-GME using alanine dipeptide, the human argonaute complex, and FiP35 WW domain.

biophysics↗

bioRxiv2023

Hierarchical Gaussian process models explore the dark meltome of thermal proteome profiling experiments.

Thermal proteome profiling (TPP) is a proteome wide technology that enables unbiased detection of protein drug interactions as well as changes in post-translational state of proteins between different biological conditions. Statistical analysis of temperature range TPP (TPP-TR) datasets relies on comparing protein melting curves, describing the amount of non-denatured proteins as a function of temperature, between different conditions (e.g. presence or absence of a drug). However, state-of-the-art models are restricted to sigmoidal melting behaviours while unconventional melting curves, representing up to 50% of TPP-TR datasets, have recently been shown to carry important biological information. We present a novel statistical framework, based on hierarchical Gaussian process models and named GPMelt, to make TPP-TR datasets analysis unbiased with respect to the melting profiles of proteins. GPMelt scales to multiple conditions, and extension of the model to deeper hierarchies (i.e. with additional sub-levels) allows to deal with complex TPP-TR protocols. Collectively, our statistical framework extends the analysis of TPP-TR datasets for both protein and peptide level melting curves, offering access to thousands of previously excluded melting curves and thus substantially increasing the coverage and the ability of TPP to uncover new biology. Author summaryProteins interactions with other proteins, nucleic acids or metabolites, are key to all biological processes. Being able to detect these interactions is essential to understand biological systems. Thermal proteome profiling is a proteome-wide biological assay able to capture these interactions. It consists in analysing the effect of heat treatment on proteins. Indeed, proteins, under physiological conditions, are folded. This folding gets disrupted as the temperature increases. The way this unfolding happens, called the melting profile of the protein, informs on the interactions of proteins. For example, the interaction of a protein with another protein can increase (thermally stabilise) or decrease (thermally destabilise) the temperature at which this protein starts unfolding. In this work, we present a new statistical method, named GPMelt, to analyse these melting profiles. Notably, GPMelt allows to analyse any melting profiles, independently of their shapes. The proposed improvements over previously published methods allow to investigate more robustly the melting profiles of more proteins, hence increasing the ability of thermal proteome profiling assays to discover new protein interactions. We anticipate that these advancements will aid in unravelling complex biological phenomena.

bioinformatics↗

bioRxiv2024

Uncovering Latent Biological Function Associations through Gene Set Embeddings

The complexity of biological systems has increasingly been unraveled through computational methods, with biological network analysis now focusing on the construction and exploration of well-defined interaction networks. Traditional graph-theoretical approaches have been instrumental in mapping key biological processes using high-confidence interaction data. However, these methods often struggle with incomplete or/and heterogeneous datasets. In this study, we extend beyond conventional bipartite models by integrating attribute-driven knowledge from the Molecular Signatures Database (MSigDB) using the node2vec algorithm. Our approach explores unsupervised biological relationships and uncovers potential associations between genes and biological terms through network connectivity analysis. By embedding both human and mouse data into a shared vector space, we validate our findings cross-species, further strengthening the robustness of our method. This integrative framework reveals both expected and novel biological insights, offering a comprehensive perspective that complements traditional biological network analysis and paves the way for deeper understanding of complex biological processes and diseases.

bioinformatics↗

bioRxiv2025

bHLH35 mediates specificity in plant responses to multiple stress conditions

How biological systems respond to stress is a fundamental question in biology, primarily addressed using the reductionist approach of applying one stress condition at a time. In nature, however, organisms experience a multitude of stresses, simultaneously or sequentially, questioning the validity of the reductionist approach for predicting plant responses to stress under natural conditions. Here, we reveal that in the flowering plant Arabidopsis thaliana, the transcriptional regulator bHLH35 is required for plant survival under a specific set of stress conditions that includes a combination of salinity, excess light, and heat, occurring simultaneously (but not for each of these stresses applied individually or in any other combination). Under these conditions, bHLH35 interacts with NAC069 and binds the promoter of LBD31, also specifically required for survival under the 3-stress combination. Our findings uncover a high degree of specificity in the response of organisms to stress, a specificity that would not have been revealed using the reductionist approach, and one that should be taken into consideration when developing agronomically important crops with heightened resilience to climate change.

plant biology↗

bioRxiv2025

Ecological inheritance facilitates the coexistence of environmental helpers and free-riders

Variation in social traits and behaviours is widespread in nature and can be maintained by selection. Many social traits influence fitness indirectly by modifying shared environments that are transmitted across generations, a process known as ecological inheritance. Here, we investigate how variation in environmentally mediated social behaviour, such as helping to improve shared resources, can emerge and persist in spatially subdivided populations where locally modified environments are transmitted across generations. Using mathematical and computational modelling, we show that ecological inheritance, when combined with limited dispersal, readily leads to the stable coexistence of two types with opposite environmental legacies: helpers, who improve the local environment for the future at a personal cost, and environmental free-riders, who benefit without contributing to the detriment of future generations. In turn, this polymorphism generates lasting spatial heterogeneity in environmental quality and, consequently, in survival and reproduction--particularly under isolation-by-distance, creating stable clusters of high- and low-quality habitats across an otherwise homogeneous landscape. These findings reveal how ecological inheritance and spatial structure interact to stabilise polymorphism, potentially driving long-term behavioural, ecological, and fitness variation across diverse biological systems.

evolutionary biology↗

bioRxiv2018

In Silico Processing of the Complete CRISPR-Cas Spacer Space for Identification of PAM Sequences

Despite extensive exploration of the diversity of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) systems, biological applications have been mostly confined to Class 2 systems, specifically the Cas9 and Cas12 (formerly Cpf1) single effector proteins. A key limitation of exploring and utilizing other CRISPR-Cas systems with unique functionalities, particularly Class I types and their multi-protein effector complex, is the knowledge of the systems protospacer adjacent motif (PAM) sequence identity. In this work, we developed a systematic pipeline, named CASPERpam, that enables us to comprehensively assess the PAM sequences of all the available CRISPR-Cas systems in the NCBI database of bacterial genomes. The CASPERpam analysis revealed that within the 30,389 assemblies previously screen for CRISPR arrays, there exists 26,364 spacers that match somewhere in the viral, bacterial, and plasmid databases of NCBI, using the constraints of 95% sequence identity and 95% sequence coverage for blast hits. When grouping these results by species, we were able to identify putative PAM sequences for 1,049 among 1,493 unique species. The remaining species either have insufficient data or an undetermined result from the analysis. Finally, we were able to infer certain design principles that are relevant for understanding PAM diversity and a baseline for further experimental studies including PAM assays. We envision CASPERpam is a useful bioinformatic tool for understanding and harnessing the diversity of CRISPR systems.

bioinformatics↗

bioRxiv2026

Design and Evolution of an Orthogonal HaloTag for Multiplexed Labeling in Cells

The self-labeling protein HaloTag is used to install a wide variety of functional small molecules in cells and living organisms with exquisite specificity with respect to cell type and subcellular localization. HaloTag is a core part of many biotechnology-based tools for sensing, tracking, and manipulating biological systems with a high degree of spatial and temporal control. Due to the limitations of fluorescent proteins and other self-labeling proteins, most of these tools have historically been restricted to a single channel. In this work, we used structure-guided rational design and directed evolution to produce an orthogonal HaloTag protein called OrthoTag which reacts selectively with a modified chloroalkane substrate. OrthoTag retains many of HaloTags superior properties, and reaction rate measurements show OrthoTag and its substrate have 60-fold mutual orthogonality to HaloTag. We demonstrate the application of OrthoTag for multiplexed labeling experiments in mammalian cells with minimal optimization. Going forward, OrthoTag can be directly incorporated into any HaloTag-based system to allow simultaneous measurement or manipulation of two biological targets or processes. The availability of multiple high-performance self-labeling proteins will enable the continued development of new multiplexed biotechnology methods.

biochemistry↗

bioRxiv2023

Development of cell-free transcription-translation systems in three soil Pseudomonads

In vitro transcription-translation (TX-TL) can enable faster engineering of biological systems. This speed-up can be significant, especially in difficult-to-transform chassis. This work shows the successful development of TX-TL systems using three soil-derived wild-type Pseudomonads known to promote plant growth: Pseudomonas synxantha, Pseudomonas chlororaphis, and Pseudomonas aureofaciens. One, P. synxantha, was further characterized. A lysate test of P. synxantha showed a maximum protein yield of 2.5 M at 125 proteins per DNA template and a maximum protein synthesis rate of 20 nM/min. A set of different constitutive promoters driving mNeonGreen expression were tested in TX-TL and integrated into the genome, showing similar normalized strengths for in vivo and in vitro fluorescence. This correspondence between the TX-TL derived promoter strength and the in vivo promoter strength indicates these lysate-based cell-free systems can be used to characterize and engineer biological parts without genome integration, enabling a faster designbuild-test cycle.

synthetic biology↗

bioRxiv2021

Rapid profiling of protein complex re-organization in perturbed systems

Protein complexes constitute the primary functional modules of cellular activity. To respond to perturbations, complexes undergo changes in their abundance, subunit composition or state of modification. Understanding the function of biological systems requires global strategies to capture this contextual state information on protein complexes and interaction networks. Methods based on co-fractionation paired with mass spectrometry have demonstrated the capability for deep biological insight but the scope of studies using this approach has been limited by the large measurement time per biological sample and challenges with data analysis. As such, there has been little uptake of this strategy beyond a few expert labs into the broader life science community despite rich biological information content. We present a rapid integrated experimental and computational workflow to assess the re-organization of protein complexes across multiple cellular states. It enables complex experimental designs requiring increased sample/condition numbers. The workflow combines short gradient chromatography and DIA/SWATH mass spectrometry with a data analysis toolset to quantify changes in complex organization. We applied the workflow to study the global protein complex rearrangements of THP-1 cells undergoing monocyte to macrophage differentiation and a subsequent stimulation of macrophage cells with lipopolysaccharide. We observed massive proteome organization in functions related to signaling, cell adhesion, and extracellular matrix during differentiation, and less pronounced changes in processes related to innate immune response induced by the macrophage stimulation. We therefore establish our integrated differential pipeline for rapid and state-specific profiling of protein complex organization with broad utility in complex experimental designs.

systems biology↗

bioRxiv2020

A novel copper-sensing two-component system for activating Dsb genes in bacteria

Copper is an essential element for biological systems but becomes toxic when present in excess. In Pseudomonas aeruginosa, an important human pathogen, the resistance to copper requires the induction of dsbDEG operon encoding proteins involved in disulfide-bond formation (Dsb). However, it is unknown how the copper stress induces the transcription of the operon. Here, we report that the exogenous copper induces the transcription of the dsbDEG operon through a new copper-sensing two-component system named DsbRS. The dsbRS is divergently transcribed from the dsbDEG operon, and the response regulator DsbR binds to the intergenic region between the operons. In the absence of copper, the sensor kinase DsbS acts as a phosphatase toward DsbR and thus blocks the transcription of the operons. However, in the presence of copper, the metal ion directly binds to the sensor domain of DsbS, for which the Cys82 residue plays a critical role. The copper-binding appears to inhibit the phosphatase activity of DsbS, leading to activation of DsbR. The copper resistance of the dsbRS knock-out mutant was restored by ectopic expression of the dsbDEG operon, confirming the critical role of the operon in the resistance to copper. Strikingly, cognates of dsbRS-dsbDEG pair are widely distributed across eubacteria. Also, a DsbR-binding site, which contains the consensus sequence 5-TAA-N7-TTAAT-3, is detected in the promoter region of dsbDEG homologs in those species. Thus, regulation of Dsb genes by DsbRS represents a novel mechanism by which bacterial cells cope with copper stress. ImportanceCopper is an essential redox active cofactor that becomes highly cytotoxic when present in excess. Therefore, in order to evade copper toxicity, bacteria must perceive copper stress and tightly regulate genes expression. In the present study, we identify a new copper-sensing two-component system (designated DsbRS) in Pseudomonas aeruginosa, an important human pathogen. We provide multiple lines of evidence that upon copper binding to the periplasmic domain of DsbS, its phosphatase activity is blocked, and the phosphorylated DsbR directly activates the transcription of a number of copper-induced genes including those involved in protein disulfide-bond formation (Dsb). This study suggests that regulation of Dsb genes by DsbRS may be an underappreciated regulatory mechanism by which bacteria sense and respond to copper.

microbiology↗

bioRxiv2026

MondrianMap: Navigating Gene Set Hierarchies with Multi-Resolution Enrichment Maps

Gene Ontology encodes genes as a hierarchy, yet every enrichment visualization flattens it into a ranked list, discarding the ability to view the same process at different levels of abstraction. We present MondrianMap, a free interactive web application (https://mondrianmap.smartdrugdiscovery.org/) that organizes enrichment results into 13 semantically principled layers derived from the GOALS framework and renders them as color-encoded rectangular maps where block area reflects significance, color encodes effect direction, and spatial proximity preserves semantic relations, all within an interactive interface. Three case studies across the NIH Common Fund Data Ecosystem demonstrate that visualization facilitates recognition of patterns that are difficult to discern in flat outputs: (1) LINCS CRISPR perturbations reveal that TP53 and KRAS knockouts produce opposite color maps at a single semantic layer, the same immune recruitment processes suppressed by TP53 loss are activated by KRAS disruption; (2) GTEx aging signatures expose the inflammaging paradox as an immediate visual phenomenon, identical antimicrobial defense programs appear uniformly upregulated in aging blood yet uniformly downregulated in aging liver at matched semantic resolution; and (3) MoTrPAC exercise data capture temporal dynamics as color transitions where brown adipose tissue undergoes a threshold switch from two enriched terms to thirty-four at a single molecular layer, while cardiac tissue reverses from uniformly activated to uniformly suppressed glycolytic metabolism as adaptation progresses. MondrianMap facilitates hierarchical visual reasoning, complementing statistical enrichment reporting for biological discovery. HIGHLIGHTSO_LIMondrianMap provides multi-resolution enrichment visualization for Gene Ontology C_LIO_LILayer-specific views reveal directional oppositions difficult to discern in flat term lists C_LIO_LINavigating layers organizes one enrichment into a multi-scale biological narrative C_LIO_LIDemonstrated on cancer, aging, and exercise data across three CFDE programs C_LI IN BRIEFMondrianMap is a web application that transforms gene set enrichment results into layered visualizations encoding regulation, significance, and semantic hierarchy. Across cancer, aging, and exercise datasets, viewing enrichment at defined semantic layers exposes directional inversions, temporal switches, and multi-scale biological narratives that are difficult to extract from conventional flat enrichment outputs. THE BIGGER PICTUREWhen researchers measure gene expression changes in disease, aging, or drug response, they rely on enrichment analysis to translate thousands of molecular measurements into interpretable biological themes. The standard output is a ranked list of processes sorted by statistical significance. This format served the field well when studies examined a single condition; however, modern genomics routinely compares dozens of tissues, timepoints, and perturbations, each generating hundreds of enriched terms. The critical limitation is not statistical power; however, interpretive structure: a flat list cannot show whether two conditions activate the same biological process in opposite directions, whether a process visible at one level of abstraction disappears or transforms at another, or how a tissues functional response evolves across time. These are precisely the questions that define contemporary systems biology. Does a tumor suppressor gene silence the same immune program that an oncogene activates? Does aging drive the same defense pathway upward in the blood and downward in the liver? Does an exercise response flip from activation to suppression as the tissue adapts? Answering these questions requires a visualization framework that preserves hierarchy, encodes direction, and enables comparison at matched levels of biological resolution. MondrianMap provides this framework by organizing Gene Ontology terms into quantitatively defined semantic layers and rendering enrichment as color-encoded rectangular maps navigable from molecular mechanism to system-level theme. The result is a tool that extends enrichment analysis from a reporting step into an interactive framework for biological reasoning, hypothesis generation, and cross-dataset discovery.

Systems Biology↗

bioRxiv2021

A Driven Disordered Systems Approach to Biological Evolution in Changing Environments

Biological evolution of a population is governed by the fitness landscape, which is a map from genotype to fitness. However, a fitness landscape depends on the organisms environment, and evolution in changing environments is still poorly understood. We study a particular model of antibiotic resistance evolution in bacteria where the antibiotic concentration is an environmental parameter and the fitness landscapes incorporate tradeoffs between adaptation to low and high antibiotic concentration. With evolutionary dynamics that follow fitness gradients, the evolution of the system under slowly changing antibiotic concentration resembles the athermal dynamics of disordered physical systems under quasistatic external drives. Specifically, our model can be described as a system with interacting hysteretic elements, and it exhibits effects such as hysteresis loops and memory formation under antibiotic concentration cycling. Using methods familiar from studies in this field, we derive a number of analytical and numerical results. Our approach provides a general framework for studying motifs of evolutionary dynamics in biological systems in a changing environment.

evolutionary biology↗

bioRxiv2025

Atomic resolution ensembles of intrinsically disordered and multi-domain proteins with Alphafold

Intrinsically disordered proteins are ubiquitous in biological systems and play essential roles in a wide range of biological processes and diseases. Despite recent advances in high-resolution structural biology techniques and breakthroughs in deep learning-based protein structure prediction, accurately determining structural ensembles of IDPs at atomic resolution remains a major challenge. Here we introduce bAIes, a Bayesian framework that integrates AlphaFold2 predictions with physico-chemical molecular mechanics force fields to generate accurate atomic-resolution ensembles of IDPs. We show that bAIes produces structural ensembles that match a wide range of high- and low-resolution experimental data across diverse systems, achieving accuracy comparable to atomistic molecular dynamics simulations but at a fraction of their computational cost. Furthermore, bAIes outperforms state-of-the-art IDP models based on coarse-grained potentials as well as deep-learning approaches. Our findings pave the way for integrating structural information from modern deep-learning approaches with molecular simulations, advancing ensemble-based understanding of disordered proteins.

bioinformatics↗

bioRxiv2025

A High-Sensitivity Stopped-Flow EPR System to Monitor Millisecond Conformational Kinetics in Spin-Labeled Proteins

Electron paramagnetic resonance (EPR) spectroscopy is a powerful tool for studying biological systems, with applications in drug discovery, protein dynamics, membrane biology, and enzyme mechanisms. However, sample volume requirements and sensitivity limitations have historically constrained time-resolved measurements of protein dynamics using stopped-flow (SF) EPR spectroscopy. To address these challenges, we developed a high-sensitivity SF EPR system featuring a custom dielectric resonator, an optimized low-volume sample tube geometry design, and the SF mixer assembly integrated into the resonator housing. This system significantly reduces sample requirements for the investigation of protein conformational dynamics on the millisecond timescale. We demonstrate its capabilities through two applications: the analysis of T4 lysozyme unfolding kinetics, which revealed site-specific variations in the folding pathway, and the measurement of ligand-induced conformational changes in the {beta}2 adrenergic receptor, a challenging membrane-protein system. This advancement broadens the applicability of SF EPR to complex, biomedically relevant proteins, facilitating studies of protein-protein and protein-ligand interactions in diverse biological processes.

biophysics↗

bioRxiv2021

A Methodology for Vertical Translation Between Molecular and Organismal Level in Biological Feedback Loops

Feedback loops are among the primary network motifs in living organisms, ensuring survival via homeostatic control of key metabolites and physical properties. However, from a scientific perspective, their characterization is unsatisfactory since the usual modelling methodology is incompatible with the physiological and biochemical basis of metabolic networks. Therefore, any "vertical translation", i.e. the study of the correspondence between molecular and organismal levels of causality, is difficult and in most cases impossible. As a viable solution, we demonstrate an alternative modelling platform for biological feedback loops that is based on key biochemical principles, including mass action law, enzyme kinetics, binding of mediators to transporters and receptors, and basic pharmacological properties. Subsequently, we show how this framework can be used for translating from molecular to systems-level behaviour. Basic elements of the proposed modelling platform include Michaelis-Menten kinetics defining nonlinear dependence of the output y(t) on an input signal x(t) with the Hill-Langmuir equation y(t) = G * x(t)n / (D + x(t)n), non-competitive inhibition for linking stimulatory and inhibitory inputs with y(t) = G + x1(t) / ((D + x1(t) * (1 + x2(t) / KI)) and processing structures for distribution and elimination. Depending on the structure of the feedback loop, its equifinal (steady-state) behaviour can be solved in form of polynomials, with a quadratic equation for the simplest case with one feedback loop and a Hill exponent of 1, and higher-grade polynomials for additional feedback loops and/or integer Hill exponents > 1. As a companion to the analytical solution, a flexible class library (CyberUnits) facilitates computer simulations for studying the transitional behaviour of the feedback loop. Unlike other modelling strategies in biocybernetics and systems biology, this platform allows for straightforward translation from the statistical properties of single molecules on a "microscopic" level to the behaviour of the whole feedback loop on an organismal "macroscopic" level. An example is the Michaelis constant D, which is equivalent to (k-1 + k2) / k1, where k1, k-1 and k2 denote the rate constants for the association and dissociation of the enzyme-substrate or receptor-hormone complex, respectively. From the perspective of a single molecule the rate constants represent the probability (per unit time) that the corresponding reaction will happen in the subsequent time interval. Therefore 1/k represents the mean lifetime of the complex. Very similar considerations apply to the other described constants of the feedback loop. In summary, this modelling technique renders the translation from a molecular level to a systems perspective possible. In addition to providing new insights into the physiology of biological feedback loops, it may be a valuable tool for multiple disciplines of biomedical research, including drug design, molecular genetics and investigations on the effects of endocrine disruptors.

systems biology↗

bioRxiv2023

Turing pattern prediction in three-dimensional domains: the role of initial conditions and growth

Reaction-diffusion systems have been widely used to model pattern formation in biological systems. However, the emergence of Turing patterns in three-dimensional (3D) domains remains relatively unexplored. A few studies on this topic have shown that extending pattern formation from 2D to 3D is not straightforward. Linear stability analysis, which is commonly used to associate admissible wave modes with predicted patterns in 1D and 2D, has yet to be applied in 3D. We have used this approach, together with finite element modelling of a Turing system with Schnakenberg kinetics, to investigate the effects of initial conditions and growing domains on the competition between admissible modes in 3D Turing pattern emergence. Our results reveal that non-random initial conditions on the activator play a stronger role than those of the inhibitor. We also observe a path dependency of the evolving pattern within a growing domain. Our findings shed new light on the mechanisms ensuring reliable pattern formation in 3D domains and have important implications for the development of more robust models of morphogen patterning in developmental processes.

developmental biology↗