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Understanding the dynamical hallmarks of network motifs is one of the fundamental aspects of systems biology. Positive feedback loops constituting one or two nodes - self-activation, toggle switch, and double activation loops - are the commonly observed motifs in regulatory networks underlying cell-fate decision systems. Their individual dynamics are well studied; they are capable of exhibiting bistability. However, studies across various biological systems suggest that such positive feedback loops are interconnected with one another, and design principles of coupled bistable motifs remain unclear. What happens to the bistability or multistability traits and the phenotypic space (collection of phenotypes exhibited by a system) due to the couplings? In this study, we explore a set of such interactions using discrete and continuous simulation methods. Our results suggest that the most frequent states in coupled networks follow the 'rules' within a motif (double activation, toggle switch) and those across the two motifs in terms of how the two motifs have been coupled. Moreover, 'hybrid' states can be observed, too, where one of the above-mentioned 'rules' can be compromised, leading to a more diverse phenotypic repertoire. Furthermore, adding direct and indirect selfactivations to these coupled networks can increase the frequency of multistability. Thus, our observations revealed specific dynamical traits exhibited by various coupled bistable motifs.

Integrative modeling of macromolecular structures usually results in an ensemble of models that satisfy the input information. The model precision, or variability among these models is estimated globally, i.e., a single precision value is reported for the model. However, it would be useful to identify regions of high and low precision. For instance, low-precision regions can suggest where the next experiments could be performed and high-precision regions can be used for further analysis, e.g., suggesting mutations. We develop PrISM (Precision for Integrative Structural Models), using autoencoders, a type of unsupervised deep neural network, to efficiently and accurately annotate precision for integrative models. The method is benchmarked and tested on five examples of binary protein complexes and five examples of large protein assemblies. The annotated precision is shown to be consistent with localization density maps, while providing more fine-grained information. Finally, the generated networks are also interpreted by gradient-based attention analysis. Significance Statement Validation of integrative models and data is an open research challenge. This is timely due to the new worldwide Protein Data Bank archive for integrative structures (http://pdb-dev.wwpdb.org). Currently, a single precision value is reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of information available for different regions. We develop a method using unsupervised deep learning to efficiently and accurately annotate precision for regions of an integrative model. This will ultimately improve the quality and utility of deposited structures.

Elucidating the design principles of regulatory networks driving cellular decision-making has fundamental implications in mapping and eventually controlling cell-fate decisions. Despite being complex, these regulatory networks often only give rise to a few phenotypes. Previously, we identified two “teams” of nodes in a small cell lung cancer regulatory network that constrained the phenotypic repertoire and aligned strongly with the dominant phenotypes obtained from network simulations (Chauhan et al., 2021). However, it remained elusive whether these “teams” exist in other networks, and how do they shape the phenotypic landscape. Here, we demonstrate that five different networks of varying sizes governing epithelial-mesenchymal plasticity comprised of two “teams” of players – one comprised of canonical drivers of epithelial phenotype and the other containing the mesenchymal inducers. These “teams” are specific to the topology of these regulatory networks and orchestrate a bimodal phenotypic landscape with the epithelial and mesenchymal phenotypes being more frequent and dynamically robust to perturbations, relative to the intermediary/hybrid epithelial/ mesenchymal ones. Our analysis reveals that network topology alone can contain information about corresponding phenotypic distributions, thus obviating the need to simulate them. We propose “teams” of nodes as a network design principle that can drive cell-fate canalization in diverse decision-making processes.

Low-Density polyethylene is subject to biodegradation using a fungal consortium comprising of Aspergillus niger, Aspergillus flavus and Aspergillus oryzae under laboratory conditions. The extent of biodegradation has been compared with the use of potato dextrose broth and czapek dox broth media and also in the presence and absence of Tween 80 additive. Biodegradation was performed replacing the sucrose in czapek dox broth with shredded Low-Density polyethylene as well. The biodegradation was carried out for a period of 55 days. The degree of biodegradation has been analyzed using the loss of weight, FT-IR, and SEM analysis. A maximum weight loss of 26.15% was obtained by using potato dextrose broth over a period of 55 days., Biodegradation, Polyethylene, Fungi, Aspergillus niger, Aspergillus oryzae, Aspergillus flavus, LDPE, SEM, FT-IR

This chapter will review all the applications artificial intelligence plays a role in while dealing with the environmental science domain. Artificial intelligence has continued to ease the lives of people worldwide, by reducing the time and increasing the accuracy of all human-based research in every domain. Environmental science as a domain is important at this point so that we can accurately analyse and correct the detrimental effects that humans have caused the planet for self-development and improving comfort. This chapter will describe the role, applications, and future developmental role that artificial intelligence can play to clean up the environment, starting from transport to ocean clean-up. As artificial intelligence continues to grow, it should be adopted to improve its ability to preserve and improve environmental conditions.

One of the most widely used deep learning architectures for image classification, Convolutional Neural Networks (CNNs) are used in a diverse range of research areas. Over the past five years, CNNs have been extended for use in disease classification and diagnosis based on body imaging data. In this paper, we propose one such CNN model to diagnose Alzheimer's Disease using raw, volumetric Magnetic Resonance Imaging (MRI) scans. The MRI dataset used contains 857 scans (302 AD and 555 Normal Control) in total and was procured from the ADNI study. The performance of the proposed residual CNN was compared with 3-D ResNet-18 with and without an attention mechanism. Finally, 3-D Gradient-weighted Class Activation Mapping was used to evaluate how effective the models were in recognizing brain regions pertaining to AD. The best performing model obtained an accuracy of 91%.