Biosystems

Exploring the emergence of spatio-temporal patterns due to nonlinearities in gene expression is a relatively new development. In this work, we explore the effect of resource constraint on gene regulatory motif from both equilibrium and spatio-temporal standpoint, taking into consideration the degradation class of resource, protease. We have demonstrated that protease-tagged degradation can cause an emergent bistability to form in the system in a steady-state scenario. Instead of a graded linear response in protein synthesis, two Saddle-node bifurcations caused by protease competition provide a switch-like response with hysteresis, where two drastically differing protein concentrations can coexist. We next turn our attention to spatio-temporal analysis: we extend our study for a two-dimensional sheet of cells with diffusible protein molecules and report the stationary patterns.To investigate the reasons behind these non-homogeneous stationary patterns, we investigate the traveling wave solution and observe that a stationary pattern is formed by the traveling wave solution. Considering that proteases play a major role in the regulation and expression of genes in a variety of diseased scenarios, the repercussions of this spatial patterning caused by protease competition can be extensive in gene regulatory systems.

Histidine kinases (HKs) are part of many signaling pathways, by being implicated in two components systems (TCS). Using autophosphorylation and phosphotransfer to a response regulators (RR), they enable organisms to adapt to their environment. Most HKs are transmembrane proteins with a sensing domain outside of the cell and two catalytic domains called HisKA and HATPase. HATPase is required for interaction with the ATP and HisKA contains the phosphorylated histidine residue. HKs are involved in various environmental adaptation mechanisms, like light sensing or biochemical changes. Studying their diversity is therefore important to better understand how cells interacts with their environment. There exist incomplete HKs (iHKs) lacking either the HisKA or HATPase domain. Some iHKs with an HATPase domain possess a section of their sequence where an HisKA domain could be expected. These iHKs may contain "true" HKs, with unknown HisKA domain, that could fill gaps in various signaling pathways. In this study we analyzed 869 964 sequences of iHKs having an HATPase domain but lacking an HisKA domain. We identified 18 HisKA-like profiles and did multiple meta-studies to assessed their HisKA-like characteristics. We found that their 3D structures matched the structure of known HisKA domains. We saw that the genomic context of the genes associated to these profiles contained genes implicated in signal transduction pathways. We cross-validated some of our profiles with curated annotations, as well as with a "negative dataset" made of non-HK proteins. We believe that our work could help improve the annotation of regulation pathways in prokaryotes.

The budding yeast S. cerevisiae, a foundational model for cell cycle studies, exhibits a complex phase organisation (G1, S, G2/M) governed by checkpoints ensuring faithful cellular inheritance. However, the existence of a distinct G2 phase in yeast remains debated, with some advocating for a prometaphase instead. To address this issue, we developed a discrete-event, qualitative, and possibilistic model, the first one to our knowledge, to integrate organelle-level components (replication forks, sister chromatids, mitotic spindle, bud) while remaining parsimonious. Unlike molecular-centred or overly complex whole-cell models, this approach bridges broad systemic and finer mechanistic scales. Our results demonstrate that the model faithfully recapitulates cell cycle progression and supports the dispensable G2 phase. This possibilistic model inspired from recent applications in ecology advocates in favor of the necessity of prometaphase. This study thus provides a unifying and flexible framework to resolve long-standing ambiguities in yeast cell dynamics, while avoiding the pitfalls of excessive complexity or reductionism.

Circadian rhythms are self-sustained biological oscillations that coordinate diverse physiological processes in plants, including growth, metabolism, and environmental responses. These rhythms arise from an interconnected transcriptional translational feedback network that integrates multiple entrainment cues such as light and temperature. The plant circadian clock is organized around key regulatory loops involving CCA1, LHY, PRRs, TOC1, ELF4, LUX, and other transcriptional regulators, whose coordinated interactions ensure precise and robust oscillations. In this study, we developed an ordinary differential equation based mathematical model, building upon a previous framework to incorporate additional regulatory modules and transcriptional controls that better reflect experimentally observed behaviour. To elucidate the regulatory organization of this model, we performed a multi-layered computational analysis combining four complementary approaches: (i) period sensitivity analysis to quantify how parameter perturbations influence the systems timing, (ii) phase portrait analysis to visualize dynamic interactions among key components, (iii) knockout analysis to identify parameters essential for sustained rhythmicity, and (iv) network impact analysis using composite weighted network indices to evaluate hierarchical control across the network. Together, these analyses reveal that transcriptional repression, protein degradation, and light-regulated synthesis form the dominant control mechanisms within the circadian system. The results highlight a hierarchical and robust network structure centred on the CCA1/LHY and PRRs feedback loop, with redundant modules ensuring stability under perturbations. Thus, this model provides an improved, biologically consistent framework for dissecting the dynamic architecture of the plant circadian clock and guiding future experimental validation.

The size structure of phytoplankton communities plays a key role in the fate of carbon fixed by photosynthesis. Whether phytoplankton cells sink, enter the microbial loop, or are consumed by larger organisms is generally determined by their size. Grazing has been advanced as a factor determining size structure, but sources of mortality other than grazing, such as viruses also are recognized to be important. Based on the observation that cell size and genome size are related in phytoplankton, we hypothesize that viruses can also play a role in shaping the size structure of the phytoplankton community. Because cell size is related to genome size, we suggest that phytoplankton species with larger genomes will have a more developed immune system to defend against viral infection. As a first step to test this hypothesis, we screened the published transcriptomes of 125 phytoplankton species for expressed viral and immune-response related genes. We found a significant negative correlation between host-cell size and viral-gene diversity, and a positive correlation between host-cell size and the number of immune-response related genes. Our hypothesis supported by preliminary findings opens new pathways to explore whether we should consider viruses as an additional evolutionary driver for larger phytoplankton size, along with grazing and nutrients.

Phase synchronized population dynamics of various species constituting a complex ecosystem elevates the risk of their extinction due to both environmental stochasticity and simulateneous low density fluctuations. Therefore, an extremely vital approach to measure the extinction risk of an ecosystem as a whole is to quantify the phase synchrony among the species populations co-habiting and interacting with each other in an ecosystem. Generally, in models describing population dynamics of ecosystems, both trophic and non-trophic inter-species interactions are modelled as interactions between two species. This approach contradicts the fact with such a large number of species living in close proximity, more than two species must partake in the same interaction influencing the population dynamics of each other. To address this, higher-order interactions need to be incorporated in the models describing population dynamics of an ecosystem. Consequently, their effect on phase synchronization of populations also need to be investigated. In this study, we model a species-rich ecosystem as a complex phase oscillator network and examine the phase dynamics of the total population. Each node of this network represents a constituent species, modelled as a Sakugachi-Kuramoto phase oscillator coupled non-linearly to the other nodes through both first-order and higher-order inter-species interactions. These interactions can be both mutualistic (positive) and antagonistic (negative) in nature. Along with the higher-order interactions, we also incorporate inherent asymmetry among the nodes to account for habitat heterogeneity. Further, we investigate the effects of both higher-order coupling and asymmetry on the phase synchronization of the total population. Our findings demonstrate that higher-order interactions above a threshold amplitude enforces a transition from synchronous to asynchronous dynamics of the ecosystem. Further, we find that increase in the size and diversity of the ecosystem leads to an increase in the threshold value of higher order coupling required to reach asynchronous dynamics. We also demonstrate that this higher-order induced asynchrony is further promoted by high asymmetry among the individual nodes. Importantly, negative inter-species interactions, if existing to a high degree also induce asynchrony in the system. Moreover, the size of the network also plays a role in deciding the threshold value of higher order coupling required to induce asynchrony.

Transfer RNAs (tRNAs) are known for delivering amino acids to the growing polypeptide chain during translation. They can also influence gene expression, especially in times of nutrient starvation, through differential tRNA expression and modification. tRNAs have a highly consistent cloverleaf structure, but relatively few known regulatory elements govern this conserved structure despite the 20 different standard isotypes. This study examines gene enrichment patterns near tRNA in 1154 fungal genomes. Genes enriched in proteasome regulation, ion transport, and rRNA were found to be significantly closer to tRNAs than other pathways. These results were consistent across KEGG over-representation analysis (ORA), KEGG Gene Set Enrichment Analysis (GSEA), and Gene Ontology (GO) analysis. Proteasome, ion transport, and RNA are all important aspects of protein production and regulation, suggesting that genes required for the synthesis and quality control of proteins, including tRNAs, are located near each other. Protein regulation is an energetically expensive process, and local co-regulation could increase efficiency and stress impacts on proteins.

Intron length is a fascinating example of form without function. The vast majority of intronic space within genomes remains without a provided utility. It often fascinates us to find introns performing any function at all, establishing an attention bias against the vast lacking of utility of the remaining intergenic space. In an attempt to better understand the greater breadth of intronic length, I investigate here what I term The Kinetic Intron Hypothesis. This hypothesis investigates hypothetical dynamics of intron RNA synthesis and degradation. It explores how NTPs stored within intron RNA might function in mitosis and NTP resource management. Preliminary testing of the hypothesis leads to trends that warrant further exploration and validation by the scientific community. SignificanceCurrently no widely acknowledged model exists to characterize the length of introns within genes, yet intron length is massively abundant in eukaryotic genomes. Here I present an attempt to model the length of introns. In doing so, I explore novel hypothesized intron dynamics, presenting preliminary data for previously uncharacterized intron characteristics. The new data and model have the protentional to unveil new avenues of utility for introns at the intracellular level.

Imagine you walk in a plane. You move by making a step of a certain length per time interval in a chosen direction. Repeating this process by randomly sampling step length and turning angle defines a two-dimensional random walk in what we call comoving frame coordinates. This is precisely how Ross and Pearson proposed to model the movements of organisms more than a century ago. Decades later their concept was generalised by including persistence leading to a correlated random walk, which became a popular model in Movement Ecology. In contrast, Langevin equations describing cell migration and used in active matter theory are typically formulated by position and velocity in a fixed Cartesian frame. In this article, we explore the transformation of stochastic Langevin dynamics from the Cartesian into the comoving frame. We show that the Ornstein-Uhlenbeck process for the Cartesian velocity of a walker can be transformed exactly into a stochastic process that is defined self-consistently in the comoving frame, thereby profoundly generalising correlated random walk models. This approach yields a general conceptual framework how to transform stochastic processes from the Cartesian into the comoving frame. Our theory paves the way to derive, invent and explore novel stochastic processes in the comoving frame for modelling the movements of organisms. It can also be applied to design novel stochastic dynamics for autonomously moving robots and drones.

While gene expression in bacteria has been shown to be affected by near-zero or extremely high gravity, a mechanism has not been established to date. In larger organisms, gravity-sensing mechanisms usually rely on a dense body applying directional pressure which can be detected by the cell. Herein we demonstrate a means of observing the effect of gravity on cyanobacteria by differential expression of native pigments in response to both gravity and light. We observe that in the cyanobacterium Synechococcus sp. PCC 7002, the distribution of pigmentation within the cell, and across cell colonies, is regulated by combined directional sensing of incoming light, adhesion to a surface via extracellular matrix, and applied external force, including the normal force of gravity applied to the cell. Cells grown on a substrate orient their thylakoids on the cell faces proximal and distal to the substrate and locate both chlorophyll and phycobilins in both of these membrane regions; phycobilins are primarily targeted to the membrane region nearest to the light source, while chlorophyll is preferentially expressed in the region opposite the overall external force applied to the cell. The mechanism for distribution of pigments appears to be regulated by presence of polyphosphate bodies within the cell, and removal of polyphosphate negates the cells ability to sense external forces. Furthermore, colonial morphology is affected by application of force, with cells responding to the secretions of other cells along a gradient along the expected response to shading. These results represent a critical step toward understanding basal phototrophic regulatory mechanisms of light use and demonstrate the first known intracellular directional gravity response mechanism in a prokaryote. Statement of SignificanceTo date, no directionally sensitive gravity response mechanism has been observed in any prokaryote. We demonstrate the first evidence of a directional response to external force in a cyanobacterium. This pigment distribution force-directed response interacts with the conventional response to directional light. Furthermore, the cells appear to be able to respond to the presence of other cells above them via intercellular signaling which is not simply due to shading by light.

Biological organisation is inherently multi-level: molecular processes, membrane dynamics, cellular geometry and tissue context reciprocally constrain one another, often through boundary-mediated feedback. A recurring theme in theoretical biology is that such organisation is not well captured by models that assume a fixed repertoire of variables and a pre-given state space: what counts as a relevant state description can depend on organisational context and history. The principle of biological relativity further sharpens the same challenge from a different angle, emphasising that no level is causally privileged and that cross-level feedback can close into circular causality. These lines of work motivates for a structural multi-level semantics for modeling the biological pathways. We introduce a constraint-based semantic framework that distinguishes an evolving organisational scaffold--the admissible multi-level patterns and interfaces--from the pathways that traverse and coordinate them. This separation yields mathematical, loop-level diagnostics for boundary-driven circular causality: it identifies when organisational trajectories induce persistent reparameterisations of local state descriptions, and it classifies cyclic regimes into reversible loops, stable history-dependent loops, and unique (rare) organisational reconfigurations. The framework is accompanied by a systematic crosswalk to mainstream causal, dynamical and computational approaches, clarifying what is gained when interfaces and local-global consistency are treated as semantic, rather than purely parametric, structure. We demonstrate the approach on a canonical excitable-cell exemplar by modelling a single Hodgkin spike as a cross-level interface loop coupling membrane, molecular and cellular constraints. Without re-deriving Hodgkin-Huxley kinetics, the resulting diagnostics provide an explicit semantics for boundary-mediated feedback and spike-induced history dependence, including when cyclic activity imprints persistent changes in effective excitability. Together, the case study and comparisons position constraint semantics as a practical mathematical layer for multi-level biological organisation: compatible with existing mechanistic models, yet designed to expose circular causal closure and organisation-dependent state descriptions that standard formalisms typically leave implicit. AMS subject classifications92C30, 92C46, 92B05, 55U10, 55R10

Small interfering RNAs (siRNAs) are largely modified with chemical molecules to enhance their properties for use in molecular biology research and therapeutic applications. Toxicity effects may arise due to these chemical moieties as well as sequence based off-targets at cellular level. Enormous resources are required to experimentally design and test the toxicity of these chemical modifications and their combinations on siRNAs. To address this problem, we developed TOXsiRNA web server to computationally predict the toxicity of chemically modified siRNAs and their off-targets. We selected 2749 siRNAs with different permutations and combinations of 21 different chemical modifications engineered on them. Next, we used Support Vector Machine (SVM), Linear Regression (LR), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) machine learning applications to develop models. Best performance was displayed by mononucleotide composition-based model developed with SVM, offering Pearson Correlation Coefficient (PCC) of 0.91 and 0.92 on training testing and independent validations respectively. Other sequence features like dinucleotide composition binary pattern and their combinations were also tested. Finally, three models of chemically modified siRNAs were implemented on the web server. Other algorithms that include predicting normal as well as chemically modified siRNA knockdown efficacy, off target etc. are also integrated. The resource is hosted online for scientific use freely at url: http://bioinfo.imtech.res.in/manojk/toxsirna.

Signal detection theory posits that subjects in two-stimulus, two-choice discrimination tasks decide by comparing random samples of an evidence variable to a static decision criterion. While the core assumptions of the theory have received ample experimental support, it has become evident that the decision criterion is not static but subject to trial-by-trial fluctuations and can be influenced by experimental manipulations. The mechanisms governing the trial-by-trial criterion changes are however not well understood. Here, we report results from five experiments in which we subjected rats to a two-stimulus, two-choice auditory discrimination task. In the first three experiments, we investigated the effects of stimulus presentation ratios and reward ratios and provide clear evidence that the effects of changing reward ratios are more pronounced than those of stimulus presentation ratios. A model-based analysis revealed that this effect was due to more than tenfold higher learning rates when reward ratios were manipulated. In two separate experiments, we investigated the effect of reward density (i.e., global reward rate) on criterion learning but failed to find consistent effects. A systematic comparison of three different trial-by-trial criterion learning models based on detection theory, the matching law, and reinforcement learning showed that no model was able to capture the differential effects of stimulus presentation and reward ratios. We conclude that subjects explicitly represent either prior stimulus probabilities or entire stimulus distributions, and accordingly future models need to represent these factors as well.

Humans order numbers in space from left to right, with smaller quantities represented preferentially in the left hemispace and larger ones in the right hemispace. The direction of this mental number line (MNL), or more generally of number-space associations (NSA), is influenced by cultural habits such as reading and writing direction. However, a growing body of evidence from pre-verbal infants and non-human animals suggests that number-space mappings may also have biological foundations. In non-human primates, evidence for a directional MNL remains mixed, partly due to small sample sizes and methodological heterogeneity. Here, we tested samples of rhesus (Macaca mulatta) and crab-eating macaques (Macaca fascicularis) across two experiments using spontaneous food-related tasks. In Experiment 1, monkeys chose between identical food quantities (1x1 to 24x24) presented on the left and right. No systematic spatial choice bias emerged as a function of numerical magnitude, and hand use did not differ across exact numerical pairs, although exploratory analyses revealed magnitude-related modulations of manual responses. In Experiment 2, monkeys were habituated to small (4x4) or large (16x16) quantities and subsequently tested with the alternative quantity. Result showed significantly more leftward choices following numerical decreases (16[->]4) and more rightward choices following numerical increases (4[->]16), indicating that relative numerical context, rather than absolute magnitude, elicited directional spatial biases. These findings suggest that in macaques, number-space associations emerge most robustly in comparative contexts involving expectancy violations of magnitude.

Long non-coding RNAs (lncRNAs) account for a major proportion of the transcriptional output in complex organismal genomes. Their emergence as auxiliary regulators of gene expression as well as their roles in metastasis and cancer progression has put them in the limelight. LncRNAs perform multitudes of functions and often moonlight as regulators, scaffolds and guides. Most lncRNAs are cell and tissue specific and can act as markers for diseases as well as targets for therapeutic interventions. LncRNAs are also known to make use of higher order structures such as G-quadruplexes (G4) to facilitate complex functions and interactions. THAP9-antisense1 (AS1) is a lncRNA coding gene (recently annotated by Ensembl) that codes for 12 lncRNA transcripts and has been implicated in many disease pathologies like gastric cancer, spontaneous neutrophil apoptosis, hepatocellular carcinoma, and the progression of oesophageal cancer. It is the antisense gene pair of the THAP9 gene ( a transposase derived gene) with which it shares a promoter. THAP9-AS1 has been reported to be dysregulated during stress and several cancers. However, the exact role of the lncRNA is not well understood. Bioinformatics driven strategies are used to identify putative quadruplex forming sequences (PQSs) within the lncRNA THAP9-AS1. The identified PQSs are further validated using biophysical, spectroscopic and molecular biology driven techniques. The importance of each G-tract in the formation of a particular RNA G-quadruplex (rG4) is studied via the investigation of several deletion mutants. The findings demonstrate the rG4 forming potential of the identified PQSs within THAP9-AS1.

A common assumption in molecular evolution is the fixed selection nature of a mutation, for instance, a neutral mutation is selectively neutral for all individuals who carry the mutation, and so forth a deleterious or beneficial mutation. Our recent work challenged this presumption, postulating that individuals with a specific mutation exhibit a fluctuation in fitness, short for FSI (fluctuating selection among individuals). Moreover, an intriguing phenomenon called selection-duality emerges, that is, a slightly beneficial mutation could be a negative selection (the substitution rate less than the mutation rate). It appears that selection-duality is bounded: the low-bound is the generic neutrality where the mutation is neutral by the means of fitness on average, while the up-bound is the substitution neutrality where the substitution rate equals to the mutation rate. In this paper, we conducted a thorough theoretical analysis to evaluate how many generations needed for a selection-duality mutation to be fixed in a finite population. A striking finding is that the mean fixation time of a selection-duality mutant, including the generic neutrality and the substitution neutrality, is approximately identical, which is considerably shorter than the case of strict neutrality without FSI. One may further envisage that the fast-fixation nature of selection-duality mutations could result in a considerable genetic reduction at linked sites.

BackgroundNicotiana benthamiana has been found to exhibit grafting capability with phylogenetically distant plant species by accomplishing cell-cell adhesion as the first step in graft establishment. Morphological and physiological studies combined with time-course transcriptome analysis revealed that this grafting triggered various biological processes. Thus, further elucidation of accumulated datasets is required to describe the processes during grafting. ResultsIn this study, we performed a Bayesian network analysis to identify crucial gene modules in the transcriptome of Nicotiana benthamiana interfamily grafting. Our bioinformatics analyses of the transcriptome included threshold-based clustering, functional annotation, Bayesian network analysis, module analysis, and hub gene identification. We defined six distinct temporal gene expression patterns in the transcriptome data. Gene ontology enrichment was performed for each expression pattern, and results were summarized as Gene ontology supercluster treemaps. Bayesian gene networks were constructed using the SiGN-BN HC + BS program along with 120 N. benthamiana transcriptome data covering the whole life cycle. Gene modules were identified using two module detection algorithms: Molecular Complex Detection (MCODE) and GLay. Gene modules were characterized by identifying Gene Ontologies and hub genes as the most interconnected nodes in the gene network using the Cytohubba plugin. ConclusionThis study provides further knowledge and enhances our understanding of the molecular mechanisms underlying interfamily grafting.

MicroRNAs (miRNAs) play essential roles in the posttranscriptional regulation of gene expression in organisms. In the process of synthesizing mature miRNAs from miRNA precursors, the miRNA precursors are cleaved via Dicer at their loop structure, after which the miRNA precursors become mature and regulate transcription. However, the consequences of altering the loop sequence are not fully understood. The silkworm Bombyx mori is a lepidopteran insect with many genetic strains. We identified a mutant of the miRNA miR-3260 whose the part of the loop structure was lacking in a silkworm strain with translucent larval skin. Here, we aimed to analyze the role of wild-type miR-3260 and the influence of the mutation of the loop structure in B. mori. First, we identified the genomic region responsible for the translucent larval skin phenotype and determined that the mutated miR-3260 nucleotide sequences. Then, we predicted the binding partners of wild-type miR-3260 using the RNA hybrid tool and found two juvenile hormone (JH)-related genes as targets of wild-type miR-3260. Next, we assessed the relationships between miR-3260 and JH and found that miR-3260 was highly expressed in the Corpora allata and its expression responded to JH treatment. Meanwhile, miR-3260 mimic and inhibitor did not induce the typical phenotypes associated with JH in B. mori. Then, we compared the dicing products from wild-type and mutant miR-3260 precursors and observed that neither form underwent Dicer-mediated cleavage when the loop structure was altered. These results suggest that loop mutations in the miR-3260 precursor may not influence dicing activity, consistent with the lack of observable phenotypic effects.

A morphospace is an abstract space of theoretically possible biological traits, shapes, or property values. It is interesting to explore which parts of a morphospace life occupies, as compared to those parts which could be occupied, but are not. Comparing random and natural non-coding (nc) RNA secondary structures is an established approach to studying morphospace occupation for RNA structures. Most earlier studies have focused on the minimum free energy (MFE) structure, while relatively few have looked at the Boltzmann distribution, describing the ensemble of energetically suboptimal RNA folds. These suboptimal structures may have important roles and functions, and hence should be examined carefully. Here we compare random and natural ncRNA in terms of their Boltzmann distributions, finding that natural RNA tend to have very similar profiles to random RNA, with the main difference being that natural RNA are slightly more energetically stable, except for very short sequences (20 to 30 nucleotides) which tend to be slightly less stable. We infer that natural ncRNA occupy similar parts of the morphospace that random RNA do, indicating that the biophysics of the genotype-phenotype map largely determines the ensemble properties of ncRNA.

BackgroundEpithelial-mesenchymal transition (EMT) and its reverse process Mesenchymal-Epithelial Transition (MET) are crucial during metastasis and therapy resistance. While the dynamics and master regulators of EMT are well-studied, the transcription factors that can prevent EMT or promote MET are relatively less understood. ResultsHere, by integrating bulk and spatial transcriptomic data analysis from cell lines and patient samples, with mechanism-based dynamical modelling, we identify IRF6 as a factor that strongly associates with an epithelial phenotype and is often inhibited during EMT. In vitro experiments in multiple cancer cell lines demonstrate the progression to a mesenchymal phenotype upon IRF6 knock-down, suggesting a role as an inhibitor of EMT. Finally, we observe that IRF6 expression levels correlates with worse patient survival in a subset of solid tumour types. ConclusionOur integrated computational-experimental systems-level analysis suggests that IRF6 is frequently downregulated during EMT and can also prevent the progression towards a complete EMT, underscoring its role as an MET stabilizing factor.