Biology subjects

Samal, A.

Publications and source records attributed to Samal, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

UVREK: Development and analysis of an expression profile knowledgebase of biomolecules induced by ultraviolet radiation exposure

Humans encounter diverse environmental factors which can have impact on their health. One such environmental factor is the ultraviolet (UV) radiation, which is part of the physical component of the exposome. UV radiation is the leading cause of skin cancer and is a significant global health concern. A large body of published research has been conducted to uncover the mechanisms underlying the adverse outcomes of UV radiation exposure on living beings. These studies involve identifying the biomolecules induced upon UV radiation exposure. A few previous efforts have attempted to compile this information in the form of a database, but such earlier efforts have certain limitations. To fill this gap, we present a structured database named UVREK, containing manually curated data on biomolecules induced by UV radiation exposure from published literature. UVREK has compiled information on 985 genes, 470 proteins, 54 metabolites and 77 miRNAs along with their metadata. Thereafter, an enrichment analysis performed on the human gene set of UVREK database showed the importance of transcription related processes in UV related response, and enrichment of pathways involved in cancer and aging. While significantly contributing towards characterizing the physical component of the exposome, we expect the compiled data in UVREK will serve as a valuable resource for development of better UV protection mechanisms such as UV sensors and sunscreens. Noteworthy, UVREK is the only resource to date, compiling varied types of biomolecular responses to UV radiation with corresponding metadata. UVREK is openly accessible at: https://cb.imsc.res.in/uvrek/.

bioinformatics↗

bioRxiv2024

Leveraging integrative toxicogenomic approach towards development of stressor-centric adverse outcome pathway networks for plastic additives

Plastics are widespread pollutants found in atmospheric, terrestrial and aquatic ecosystems due to their extensive usage and environmental persistence. Plastic additives, that are utilized to achieve specific functionality in plastics, leach into the environment upon plastic degradation and pose considerable risk to ecological and human health. Limited knowledge concerning the presence of plastic additives throughout the plastic life cycle has hindered their effective regulation, thereby posing risks to product safety. In this study, we leveraged the adverse outcome pathway (AOP) framework to understand the mechanisms underlying plastic additives-induced toxicities. We first identified an exhaustive list of 6470 plastic additives from chemicals documented to be found in plastics. Next, we leveraged heterogenous toxicogenomics and biological endpoints data from five exposome-relevant resources, and identified associations between 1287 plastic additives and 322 complete and high quality AOPs within AOP-Wiki. Based on these plastic additive-AOP associations, we constructed a stressor-centric AOP network, wherein the stressors are categorized into 10 priority use sectors and AOPs are linked to 27 disease categories. We visualized the plastic additives-AOP network for each of the 1287 plastic additives and made them available in a dedicated website: https://cb.imsc.res.in/saopadditives/. Finally, we showed the utility of the constructed plastic additives-AOP network by identifying 28 highly relevant AOPs associated with benzo[a]pyrene, and thereafter, explored the associated toxicity pathways leading to respiratory and gastrointestinal system diseases in humans and developmental disorders in aquatic species. Overall, the constructed plastic additives-AOP network will enable regulatory risk assessment of plastic additives, thereby contributing towards a toxic-free circular economy for plastics.

pharmacology and toxicology↗

bioRxiv2023

An integrative data-centric approach to derivation and characterization of an adverse outcome pathway network for cadmium-induced toxicity

Cadmium is a prominent toxic heavy metal that contaminates both terrestrial and aquatic environments. Owing to its high biological half-life and low excretion rates, cadmium causes a variety of adverse biological outcomes. Adverse outcome pathway (AOP) networks were envisioned to systematically capture toxicological information to enable risk assessment and chemical regulation. Here, we leveraged AOP-Wiki and integrated heterogeneous data from four other exposome-relevant resources to build the first AOP network relevant for inorganic cadmium-induced toxicity. From AOP-Wiki, we filtered 309 high confidence AOPs, identified 312 key events (KEs) associated with inorganic cadmium, and thereafter, curated 30 cadmium relevant AOPs (cadmium-AOPs), using a data-centric approach. By constructing the undirected AOP network, we identified a large connected component of 18 cadmium-AOPs. Further, we analyzed the directed network of 59 KEs and 82 key event relationships (KERs) in the largest component using graph-theoretic approaches. Subsequently, we mined published literature using artificial intelligence-based tools to provide auxiliary evidence of cadmium association for all KEs in the largest component. Finally, we performed case studies to verify the rationality of cadmium-induced toxicity in humans and aquatic species. Overall, cadmium-AOP network constructed in this study will aid ongoing research in systems toxicology and chemical exposome.

pharmacology and toxicology↗

bioRxiv2023

ViCEKb: Vitiligo-linked Chemical Exposome Knowledgebase

Vitiligo is a complex disease wherein the environmental factors, in conjuction with the underlying genetic predispositions, trigger the autoimmune destruction of melanocytes, ultimately leading to depigmented patches on the skin. Apart from being susceptible to other autoimmune disorders, the affected patients may face social stigmatization leading to a decreased quality in life. While genetic factors have been extensively studied, the knowledge on environmental triggers remains sparse and less understood. Therefore, a comprehensive understanding of the environemental triggers will not only explain the complex etiology of the disease, it will also enable the prioritization of potential toxic chemicals in human chemcical exposome. Towards this, we present the first comprehensive knowledgbase of vitiligo triggering chemicals compiled from published literature namely, Vitiligo-linked Chemical Exposome Knowledgbase (ViCEKb). ViCEKb involved an extensive and systematic manual effort in curation of published literature and subsequent compilation of 113 unique chemical triggers of vitiligo. ViCEKb standardises various chemical information, and categorizes the chemicals based on their evidences and sources of exposure. Importantly, ViCEKb contains a wide range of metrics necessary for different toxicological evaluations. Moreover, our extensive cheminformatics-based analysis of the ViCEKb chemical space highlighted its diversity and uniqueness in comparison to other skin specific chemical regulatory lists. We also observed that ViCEKb chemicals are present in various consumer products and are not regulated. Additionally, a transcriptomics based analysis of ViCEKb chemical perturbations in skin cell samples highlighted the commonality in their linked biological processes. Overall, we present the first comprehensive effort in compilation and exploration of various chemical triggers of vitiligo. We believe such a resource will enable in deciphering the complex etiology of vitiligo and aid in the characterization of human chemical exposome. ViCEKb is freely available for academic research at: https://cb.imsc.res.in/vicekb.

bioinformatics↗

bioRxiv2023

Cheminformatics analysis of multi-target structure-activity landscape of environmental chemicals binding to human endocrine receptors

In human exposome, environmental chemicals can target, and disrupt different endocrine axes, ultimately leading to several endocrine disorders. Such chemicals, termed endocrine disrupting chemicals (EDCs), can promiscuously bind to different endocrine receptors and lead to varying biological endpoints. Thus, understanding the complexity in molecule-receptor binding of environmental chemicals can aid in the development of robust toxicity predictors. Towards this, the ToxCast project has generated the largest resource on the chemical-receptor activity data for environmental chemicals that were screened across various endocrine receptors. However, the heterogeneity in the multi-target structure-activity landscape of such chemicals is not yet explored. In this study, we systematically curated the chemicals targeting 8 human endocrine receptors, their activity values and biological endpoints from the ToxCast chemical library. We employed dual-activity difference and triple-activity difference maps to identify single-, dual-, and triple-target cliffs across different target combinations. We annotated the identified activity cliffs through matched molecular pair (MMP) based approach, and observed that a small fraction of activity cliffs form MMPs. Further, we structurally classified the activity cliffs and observed that R-group cliffs form the highest fraction among the cliffs identified in various target combinations. Finally, we leveraged the mechanism of action (MOA) annotations to analyze structure-mechanism relationships, identified strong MOA-cliffs and weak MOA-cliffs, for each of the 8 endocrine receptors. Overall, insights from this first study analyzing the structure-activity landscape of environmental chemicals targeting multiple human endocrine receptors, will likely contribute towards the development of better toxicity prediction models for characterizing the human chemical exposome.

pharmacology and toxicology↗

bioRxiv2023

Preponderance of generalized chain functions in reconstructed Boolean models of biological networks

Boolean networks (BNs) have been extensively used to model the dynamics of gene regulatory networks (GRNs) that underlie cellular decisions. The dynamics of BNs depend on the network architecture and regulatory logic rules (or Boolean functions (BFs)) associated with nodes, both of which have been shown to be far from random in large-scale studies of reconstructed Boolean models. At the level of the BFs, nested canalyzing functions (NCFs) have been shown to be strongly enriched in such GRN models. The central question we address here is whether that enrichment is due to certain sub-types of NCFs. To answer this, we build on one sub-type of NCFs, the chain functions (or chain-0 functions) proposed by Gat-Viks and Shamir. First, we propose 2 other sub-types of NCFs, namely, the class of chain-1 functions which is the dual of the class of chain-0 functions, and generalized chain functions, the union of the chain-0 and chain-1 types. Next, we find that the fraction of NCFs that are chain-0 functions decays exponentially with the number of inputs, and exhibits a fractal-like behaviour as a function of the bias for a fixed number of inputs. Moreover, we explain several of these observations analytically. Then, by analyzing 5990 BFs extracted from a large dataset of reconstructed Boolean models, and 2 other datasets, we find that generalized chain functions are significantly enriched within the NCFs. Lastly, we illustrate the severe restriction imposed by generalized chain functions compared to NCFs for 3 biological models and perform model selection on them using known relative stability constraints.

systems biology↗

bioRxiv2023

Machine learning models for prediction of xenobiotic chemicals with high propensity to transfer into human milk

Breast milk serves as a vital source of essential nutrients for infants. However, human milk contamination via transfer of environmental chemicals from maternal exposome is a significant concern for infant health. Machine learning based predictive toxicology models can be valuable in predicting chemicals with high propensity to transfer into human milk. To this end, we build such classification- and regression-based models by employing multiple machine learning algorithms and leveraging the largest curated dataset to date of 375 chemicals with known Milk to Plasma concentration (M/P) ratios. Our Support Vector Machine (SVM) based classifier outperforms other models in terms of different performance metrics, when evaluated on both (internal) test data and external test dataset. Specifically, the SVM based classifier on (internal) test data achieved a classification accuracy of 77.33%, specificity of 84%, sensitivity of 64%, and F-score of 65.31%. When evaluated on an external test dataset, our SVM based classifier is found to be generalizable with sensitivity of 77.78%. While we were able to build highly predictive classification models, our best regression models for predicting the M/P ratio of chemicals could achieve only moderate R2 values on the (internal) test data. As noted in earlier literature, our study also highlights the challenges in developing accurate regression models for predicting the M/P ratio of xenobiotic chemicals. We have made our complete workflow, train and test datasets, and computer codes for the classification and regression models publicly available via a dedicated GitHub repository. Overall, this study attests the immense potential of predictive computational toxicology models in characterizing the myriad chemicals in the human exposome.

pharmacology and toxicology↗

bioRxiv2023

Biologically meaningful regulatory logic enhances the convergence rate in Boolean networks and bushiness of their state transition graph

Boolean network (BN) models of gene regulatory networks (GRNs) have gained widespread traction as they can easily recapitulate cellular phenotypes via their attractor states. The overall dynamics of such models are embodied in the systems state transition graph (STG) which is highly informative. Indeed, even if two BN models have the same network structure and recover the same attractors, their STGs can be drastically different depending on the type of regulatory logic rules or Boolean functions (BFs) employed. A key objective of the present work is to systematically delineate the effects of different classes of regulatory logic rules on the structural features of the STG of reconstructed Boolean GRNs, while keeping BN structure and biological attractors fixed. Furthermore, we ask how such global features might be driven by characteristics of the underlying BFs. For that, we draw from ideas and concepts proposed in cellular automata for both the structural features and their associated proxies. We use the network of 10 reconstructed Boolean GRNs to generate ensembles that differ in the type of logic used while keeping their structure fixed and recovering their biological attractors, and compute quantities associated with the structural features of the STG: bushiness and convergence, that are based on the number of garden-of-Eden (GoE) states and transient times to reach attractor states when originating at them. We find that ensembles employing biologically meaningful BFs have higher bushiness and convergence than those employing random ones. Computing these global measures gets expensive with larger network sizes, stressing the need for more feasible proxies. We thus adapt Wuensches Z-parameter to BFs in BNs and provide 4 natural variants, which along with the network sensitivity, comprise our descriptors of local dynamics. One variant of the network Z-parameter as well as the network sensitivity correlate particularly very well with the bushiness, serving as a good proxy for the same. Finally, we provide an excellent proxy for the convergence based on computing transient lengths originating at random states rather than GoE states.

systems biology↗

bioRxiv2023

Analysis of structure-activity and structure-mechanism relationships among thyroid stimulating hormone receptor binding chemicals by leveraging ToxCast library

Thyroid stimulating hormone receptor (TSHR) is an integral part of the hypothalamic-pituitary-thyroid axis. Notably, dysregulation in TSHR activation in humans can lead to adverse effects such as Graves disease, hypothyroidism and Hashimotos disease. Moreover, animal studies have shown that binding of endocrine disrupting chemicals (EDCs) with TSHR can lead to developmental toxicity. Several such chemicals have also been screened for their adverse physiological effects in human cell lines through various high-throughput assays under the ToxCast project. The vast resource of data generated through ToxCast has enabled the development of different toxicity predictors, but they can be limited in their predictive ability due to the heterogeneity in structure-activity relationships among chemicals. In an attempt to explore this heterogeneity, we systematically investigated structure-activity and structure-mechanism relationships among the TSHR binding chemicals from ToxCast. By employing structure-activity similarity (SAS) map, we identified 79 activity cliffs among 509 chemicals in the TSHR agonist dataset and 69 activity cliffs among 650 chemicals in the TSHR antagonist dataset. Further, by using the matched molecular pair (MMP) approach, we find that the resultant activity cliffs (MMP-cliffs) are a subset of activity cliffs identified via the SAS map approach. Moreover, by leveraging ToxCast mechanism of action (MOA) annotations for chemicals common to both TSHR agonist and antagonist datasets, we identified 3 chemical pairs as Strong MOA-cliffs and 19 chemical pairs as Weak MOA-cliffs. In conclusion, the insights from this systematic analysis of the structure-activity as well as the structure-mechanism relationships of TSHR binding chemicals are likely to inform ongoing efforts towards development of better predictive toxicity models for characterizing the chemical exposome.

pharmacology and toxicology↗

bioRxiv2023

T9GPred: A Comprehensive Computational Tool for the Prediction of Type 9 Secretion System, Gliding Motility and the Associated Secreted Proteins

Type 9 secretion system (T9SS) is one of the least characterized secretion systems exclusively found in the Bacteroidetes phylum which comprise various environmental and economically relevant bacteria. While T9SS plays a central role in bacterial movement termed gliding motility, survival and pathogenicity, there is an unmet need for a comprehensive tool that predicts T9SS, gliding motility and proteins secreted via T9SS. In this study, we develop such a computational tool, Type 9 secretion system and Gliding motility Prediction (T9GPred). To build this tool, we manually curated published experimental evidence and identified mandatory components for T9SS and gliding motility prediction. We also compiled experimentally characterized proteins secreted via T9SS and determined the presence of three unique types of C-terminal domain signals, and these insights were leveraged to predict proteins secreted via T9SS. Notably, using recently published experimental evidence, we show that T9GPred has high predictive power. Thus, we used T9GPred to predict the presence of T9SS, gliding motility and associated secreted proteins across 693 completely sequenced Bacteroidetes strains. T9GPred predicted 402 strains to have T9SS, of which 327 strains are also predicted to exhibit gliding motility. Further, T9GPred also predicted putative secreted proteins for the 402 strains. In a nutshell, T9GPred is a novel computational tool for systems-level prediction of T9SS and streamlining future experimentation. The source code of the computational tool is available in our GitHub repository: https://github.com/asamallab/T9GPred. The tool and its predicted results are compiled in a web server available at: https://cb.imsc.res.in/t9gpred/.

bioinformatics↗

bioRxiv2023

Trial Averaging for Deep EEG Classification

Many signals, particularly of biological origin, suffer from a signal-to-noise ratio sufficiently low that it can be difficult to classify individual examples reliably, even with relatively sophisticated machine-learning techniques such as deep learning. In some cases, the noise can be high enough that it is even difficult to achieve convergence during training. We considered this problem for one data type that often suffers from such difficulties, namely electroencephalography (EEG) data from cognitive neuroscience studies in humans. One solution to increase signal-to-noise is, of course, to perform averaging among trials, which has been employed before in other studies of human neuroscience but not, to our knowledge, investigated rigorously, particularly not in deep learning. Here, we parametrically studied the effects of different amounts of trial averaging during training and/or testing in a human EEG dataset, and compared the results to that of a related algorithm, Mixup. Broadly, we found that even a small amount of averaging could significantly improve classification, particularly when both training and testing data were subjected to averaging. Simple averaging clearly outperformed Mixup, although the benefits of averaging differed across classification categories. Overall, our results confirm the value of averaging during training and testing when single-trial classification is not strictly necessary for the application in question. HighlightsO_LIAveraging trials can dramatically improve performance in classification of EEG data C_LIO_LIThe benefits can be seen when averaging on both training and test datasets C_LIO_LISimple trial averaging outperformed a popular related algorithm, Mixup C_LIO_LIHowever, effects of averaging differed across different stimulus categories C_LI

neuroscience↗

bioRxiv2023

Leveraging Developmental Landscapes for Model Selection in Boolean Gene Regulatory Networks

Boolean models are a well-established framework to model developmental gene regulatory networks (DGRN) for acquisition of cellular identity. During the reconstruction of Boolean DGRNs, even if the network structure is given, there is generally a very large number of combinations of Boolean functions (BFs) that will reproduce the different cell fates (biological attractors). Here we leverage the developmental landscape to enable model selection on such ensembles using the relative stability of the attractors. First we show that 5 previously proposed measures of relative stability are strongly correlated and we stress the usefulness of the one that captures best the cell state transitions via the mean first passage time (MFPT) as it also allows the construction of a cellular lineage tree. A property of great computational convenience is the relative insensitivity of the different measures to changes in noise intensities. That allows us to use stochastic approaches to estimate the MFPT and thus to scale up the computations to large networks. Given this methodology, we study the landscape of 3 Boolean models of Arabidopsis thaliana root development and find that the latest one (a 2020 model) does not respect the biologically expected hierarchy of cell states based on their relative stabilities. Therefore we developed an iterative greedy algorithm that searches for models which satisfy the expected hierarchy of cell states. By applying our algorithm to the 2020 model, we find many Boolean models that do satisfy the expected hierarchy. Our methodology thus provides new tools that can enable reconstruction of more realistic and accurate Boolean models of DGRNs.

systems biology↗

bioRxiv2022

EPEK: creation and analysis of an Ectopic Pregnancy Expression Knowledgebase

Ectopic pregnancy (EP) is one of the leading causes of maternal mortality, where the fertilized embryo grows outside of the uterus. Recent experiments on mice have uncovered the importance of genetic factors in the transport of embryos inside the uterus. In the past, efforts have been made to identify possible gene or protein markers in EP in humans through multiple expression studies. Although there exist comprehensive gene resources for other maternal health disorders, there is no specific resource that compiles the genes associated with EP from such expression studies. Here, we address that knowledge gap by creating a computational resource, Ectopic Pregnancy Expression Knowledgebase (EPEK), that involves manual compilation and curation of expression profiles of EP in humans from published articles. In EPEK, we compiled information on 314 differentially expressed genes, 17 metabolites, and 3 SNPs associated with EP. Computational analyses on the gene set from EPEK showed the implication of cellular signaling processes in EP. We also identified possible exosome markers that could be clinically relevant in the diagnosis of EP. In a nutshell, EPEK is the first and only dedicated resource on the expression profile of EP in humans. EPEK is accessible at https://cb.imsc.res.in/epek.

bioinformatics↗

bioRxiv2022

Discrete Ricci curvatures capture age-related changes in human brain functional connectivity networks

Geometry-inspired notions of discrete Ricci curvature have been successfully used as markers of disrupted brain connectivity in neuropsychiatric disorders, but their ability to characterize age-related changes in functional connectivity is unexplored. Here, we apply Forman-Ricci curvature and Ollivier-Ricci curvature to compare functional connectivity networks of healthy young and older subjects from the Max Planck Institute Leipzig Study for Mind-Body-Emotion Interactions (MPI-LEMON) dataset (N = 225). We found that both Forman-Ricci curvature and Ollivier-Ricci curvature can capture whole-brain and region-level age-related differences in functional connectivity. Meta-analysis decoding demonstrated that those brain regions with age-related curvature differences were associated with cognitive domains known to manifest age-related changes - movement, affective processing and somatosensory processing. Moreover, the curvature values of some brain regions showing age-related differences exhibited correlations with behavioral scores of affective processing. Finally, we found an overlap between brain regions showing age-related curvature differences and those brain regions whose non-invasive stimulation resulted in improved movement performance in older adults. These results suggest that both Forman-Ricci curvature and Ollivier-Ricci curvature correctly identify brain regions that are known to be functionally or clinically relevant. Our results add to a growing body of evidence demonstrating the sensitivity of discrete Ricci curvature measures to changes in the organisation of functional connectivity networks, both in health and disease.

neuroscience↗

bioRxiv2022

Identification of activity cliffs in structure-activity landscape of androgen receptor binding chemicals

Androgen mimicking environmental chemicals can bind to Androgen receptor (AR) and can cause severe effects on the reproductive health of males. Predicting such endocrine disrupting chemicals (EDCs) in the human exposome is vital for improving current chemical regulations. To this end, QSAR models have been developed to predict androgen binders. However, a continuous structure-activity relationship (SAR) wherein chemicals with similar structure have similar activity does not always hold. Activity landscape analysis can help map the structure-activity landscape and identify unique features such as activity cliffs. Here we performed a systematic investigation of the chemical diversity along with the global and local structure-activity landscape of a curated list of 144 AR binding chemicals. Specifically, we clustered the AR binding chemicals and visualized the associated chemical space. Thereafter, consensus diversity plot was used to assess the global diversity of the chemical space. Subsequently, the structure-activity landscape was investigated using SAS maps which capture the activity difference and structural similarity among the AR binders. This analysis led to a subset of 41 AR binding chemicals forming 86 activity cliffs, of which 14 are activity cliff generators. Additionally, SALI scores were computed for all pairs of AR binding chemicals and the SALI heatmap was also used to evaluate the activity cliffs identified using SAS map. Finally, we provide a classification of the 86 activity cliffs into six categories using structural information of chemicals at different levels. Overall, this investigation reveals the heterogeneous nature of the structure-activity landscape of AR binding chemicals and provides insights which will be crucial in preventing false prediction of chemicals as androgen binders and developing predictive computational toxicity models in the future.

pharmacology and toxicology↗

bioRxiv2022

Scaffold and structural diversity of the secondary metabolite space of medicinal fungi

Medicinal fungi including mushrooms have well documented therapeutic uses. The MeFSAT database provides a curated library of more than 1800 secondary metabolites produced by medicinal fungi for potential use in high throughput screening (HTS) studies. In this study, we perform a cheminformatics based investigation of the scaffold and structural diversity of the secondary metabolite space of medicinal fungi, and moreover, perform a detailed comparison with approved drugs, other natural product libraries and semi-synthetic libraries. We find that the secondary metabolite space of MeFSAT has similar or higher scaffold diversity in comparison to other natural product libraries analysed here. Notably, 94% of the scaffolds in the secondary metabolite space of MeFSAT are not present in the approved drugs. Further, we find that the secondary metabolites of medicinal fungi, on the one hand are structurally far from the approved drugs, while on the other hand are close in terms of molecular properties to approved drugs. Lastly, chemical space visualization using dimensionality reduction methods showed that the secondary metabolite space has minimal overlap with the approved drug space. In a nutshell, our results underscore that the secondary metabolite space of medicinal fungi is a valuable resource for identifying potential lead molecules for natural product based drug discovery.

pharmacology and toxicology↗

bioRxiv2022

IMPPAT 2.0: an enhanced and expanded phytochemical atlas of Indian medicinal plants

Compilation, curation, digitization and exploration of the phytochemical space of Indian medicinal plants can expedite ongoing efforts toward natural product and traditional knowledge based drug discovery. To this end, we present IMPPAT 2.0, an enhanced and expanded database, compiling manually curated information on 4010 Indian medicinal plants, 17967 phytochemicals, 1095 therapeutic uses and 1133 traditional Indian medicinal formulations. Notably, IMPPAT 2.0 compiles associations at the level of plant parts, and provides a FAIR compliant non-redundant in silico stereo-aware library of 17967 phytochemicals from Indian medicinal plants. The phytochemical library has been annotated with several useful properties to enable easier exploration of the chemical space. We also filtered a subset of 1335 drug-like phytochemicals of which majority have no similarity to existing approved drugs. Using cheminformatics, we have characterized the molecular complexity and molecular scaffold based structural diversity of the phytochemical space of Indian medicinal plants, and performed a comparative analysis with other chemical libraries. Altogether, IMPPAT is the largest phytochemical atlas of Indian medicinal plants which is accessible at: https://cb.imsc.res.in/imppat/.

pharmacology and toxicology↗

bioRxiv2022

Composition structures and biologically meaningful logics: plausibility and relevance in bipartite models of gene regulation

Boolean network models have widely been used to study the dynamics of gene regulatory networks. However, such models are coarse-grained to an extent that they abstract away molecular specificities of gene regulation. In contrast, bipartite Boolean network models of gene regulation explicitly distinguish genes from transcription factors (TFs). In such models, multiple TFs may simultaneously contribute to the regulation of a gene by forming heteromeric complexes. The formation of heteromeric complexes gives rise to composition structures in the corresponding bipartite network. Remarkably, composition structures can severely restrict the number of Boolean functions (BFs) that can be assigned to a gene. The introduction of bipartite Boolean network models is relatively recent, and so far an empirical investigation of their biological plausibility is lacking. Here, we estimate the prevalence of composition structures arising through heteromeric complexes in Homo sapiens. Moreover, we present an additional mechanism by which composition structures arise as a result of multiple TFs binding to the cis-regulatory regions of a gene and we provide empirical support for this mechanism. Next, we compare the restriction in BFs imposed by composition structures and by biologically meaningful properties. We find that two types of minimally complex BFs, namely nested canalyzing functions (NCFs) and read-once functions (RoFs), are more restrictive than composition structures. Finally, using a compiled dataset of 2687 BFs from published models, we find that composition structures are highly enriched in real biological networks, but that this enrichment is most likely driven by NCFs and RoFs.

systems biology↗