Biology subjects

Subbaroyan, A.

Publications and source records attributed to Subbaroyan, A..

6 recordsLinked to original sources

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

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

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

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

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↗

bioRxiv2021

Minimum complexity drives regulatory logic in Boolean models of living systems

The properties of random Boolean networks as models of gene regulation have been investigated extensively by the statistical physics community. In the past two decades, there has been a dramatic increase in the reconstruction and analysis of Boolean models of biological networks. In such models, neither network topology nor Boolean functions (or logical update rules) should be expected to be random. In this contribution, we focus on biologically meaningful types of Boolean functions, and perform a systematic study of their preponderance in gene regulatory networks. By applying the k[P] classification based on number of inputs k and bias P of functions, we find that most Boolean functions astonishingly have odd bias in a reference biological dataset of 2687 functions compiled from published models. Subsequently, we are able to explain this observation along with the enrichment of read-once functions (RoFs) and its subset, nested canalyzing functions (NCFs), in the reference dataset in terms of two complexity measures: Boolean complexity based on string lengths in formal logic which is yet unexplored in the biological context, and the average sensitivity. Minimizing the Boolean complexity naturally sifts out a subset of odd-biased Boolean functions which happen to be the RoFs. Finally, we provide an analytical proof that NCFs minimize not only the Boolean complexity, but also the average sensitivity in their k[P] set.

systems biology↗