Data help generate knowledge, and more data mean more opportunities to generate knowledge. Advancements in science and technology lead to giant leaps in developing automated and sophisticated high-throughput data generation techniques. All these data are hiding crucial information in plain sight, and they can only be unraveled by asking the right questions. Humans are smart and intelligent creatures; we do have certain limitations when it comes to exploring massive amounts of data. Computers, on the contrary, are relentless. What if we can train these machines to become sensible and intelligent like humans and explore the data? This aspiration of scientists paved the path to the creation of computational tools and AI, integrating human excellence with the untiring computer power to explore the data in possible perspectives. In this chapter we will explore many such approaches and success stories in different domains of synthetic biology that helped further health care.

Ancestral genomes are essential for studying the diversification of life from the last universal common ancestor to modern organisms. Methods have been proposed to infer ancestral gene order, but they lack scalability, limiting the depth to which gene neighbourhood evolution can be traced back. Here we introduce edgeHOG, a tool designed for accurate ancestral gene order inference with linear time complexity. We validated edgeHOG on various benchmarks and applied it to the entire OMA orthology database, encompassing 2,845 extant genomes across all domains of life. We reconstructed ancestral gene order for 1,133 ancestral genomes, including ancestral contigs for the last common ancestor of eukaryotes, dating back around 1.8 billion years, and observed significant functional association among neighbouring genes. EdgeHOG also dates gene adjacencies, allowing the detection of both conserved gene clusters and chromosomal rearrangements.

The impact of qDTY12.1 in maintaining yield under drought has not been consistent across genetic backgrounds. We hypothesized that synergism or antagonism with additive-effect peripheral genes across the background genome either enhances or undermines its full potential. By modeling the transcriptional networks across sibling qDTY12.1-introgression lines with contrasting yield under drought (LPB = low-yield penalty; HPB = high-yield penalty), the qDTY12.1-encoded DECUSSATE gene (OsDEC) was revealed as the core of a synergy with other genes in the genetic background. OsDEC is expressed in flag leaves and induced by progressive drought at booting stage in LPB but not in HPB. The unique OsDEC signature in LPB is coordinated with 35 upstream and downstream peripheral genes involved in floral development through the cytokinin signaling pathway. Results support the differential network rewiring effects through genetic coupling–uncoupling between qDTY12.1 and other upstream and downstream peripheral genes across the distinct genetic backgrounds of LPB and HPB. The functional DEC-network in LPB defines a mechanism for early flowering as a means for avoiding the drought-induced depletion of photosynthate needed for reproductive growth. Its impact is likely through the timely establishment of stronger source-sink dynamics that sustains a robust reproductive transition under drought.

Because of the continuous rise of foodborne illnesses caused by the consumption of raw fruits and vegetables, effective post-harvest anti-microbial strategies are necessary. The aim of this study was to evaluate the anti-microbial efficacy of ozone (O3) against two common causes of fresh produce contamination, the Gram-negative Escherichia coli O157:H7 and Gram-positive Listeria monocytogenes, and to relate its effects to potential mechanisms of xenobiosis by transcriptional network modeling. The study on non-host tomato environment correlated the dose × time aspects of xenobiosis by examining the correlation between bacterial survival in terms of log-reduction and defense responses at the level of gene expression. In E. coli, low (1 μg O3/g of fruit) and moderate (2 μg O3/g of fruit) doses caused insignificant reduction in survival, while high dose (3 μg/g of fruit) caused significant reduction in survival in a time-dependent manner. In L. monocytogenes, moderate dose caused significant reduction even with short-duration exposure. Distinct responses to O3 xenobiosis between E. coli and L. monocytogenes are likely related to differences in membrane and cytoplasmic structure and components. Transcriptome profiling by RNA-Seq showed that primary defenses in E. coli were attenuated after exposure to a low dose, while the responses at moderate dose were characterized by massive upregulation of pathogenesis and stress-related genes, which implied the activation of defense responses. More genes were downregulated during the first hour at high dose, with a large number of such genes getting significantly upregulated after 2 hr and 3 hr. This trend suggests that prolonged exposure led to potential adaptation. In contrast, massive downregulation of genes was observed in L. monocytogenes regardless of dose and exposure duration, implying a mechanism of defense distinct from that of E. coli. The nature of bacterial responses revealed by this study should guide the selection of xenobiotic agents for eliminating bacterial contamination on fresh produce without overlooking the potential risks of adaptation.

Escherichia coli serotype O157:H7 is one of the major agents of pathogen outbreaks associated with fresh fruits and vegetables. Gaseous chlorine dioxide (ClO2) has been reported to be an effective intervention to eliminate bacterial contamination on fresh produce. Although remarkable positive effects of low doses of ClO2 have been reported, the genetic regulatory machinery coordinating the mechanisms of xenobiotic effects and the potential bacterial adaptation remained unclear. This study examined the temporal transcriptome profiles of E. coli O157:H7 during exposure to different doses of ClO2 in order to elucidate the genetic mechanisms underlying bacterial survival under such harsh conditions. Dosages of 1 μg, 5 μg, and 10 μg ClO2 per gram of tomato fruits cause different effects with dose-by-time dynamics. The first hour of exposure to 1 μg and 5 μg ClO2 caused only partial killing with significant growth reduction starting at the second hour, and without further significant reduction at the third hour. However, 10 μg ClO2 exposure led to massive bacterial cell death at 1 h with further increase in cell death at 2 and 3 h. The first hour exposure to 1 μg ClO2 caused activation of primary defense and survival mechanisms. However, the defense response was attenuated during the second and third hours. Upon treatment with 5 μg ClO2, the transcriptional networks showed massive downregulation of pathogenesis and stress response genes at the first hour of exposure, with decreasing number of differentially expressed genes at the second and third hours. In contrast, more genes were further downregulated with exposure to 10 μg ClO2 at the first hour, with the number of both upregulated and downregulated genes significantly decreasing at the second hour. A total of 810 genes were uniquely upregulated at the third hour at 10 μg ClO2, suggesting that the potency of xenobiotic effects had led to potential adaptation. This study provides important knowledge on the possible selection of target molecules for eliminating bacterial contamination on fresh produce without overlooking potential risks of adaptation.

We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein-RNA interfaces to probe the binding hot spots at protein-RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multiinterface residues participating simultaneously in protein-protein and protein-RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues.We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental δ δG values in a particular class, and provides a stepping-stone towards the engineering of protein-RNA recognition sites with desired affinity.

We present an algorithm 'Layersr' to peel the atoms of proteins as layers. Using Layers we show an efficient way to transform protein structures into 2D pattern, named residue transition pattern (RTP), which is independent of molecular orientations. RTP explains the folding patterns of proteins and hence identification of similarity between proteins is simple and reliable using RTP than with the standard sequence or structure based methods. Moreover, Layers generates a fine-tunable coarse model for the molecular surface by using non-random sampling. The coarse model can be used for shape comparison, protein recognition and ligand design. Additionally, Layers can be used to develop biased initial configuration of molecules for protein folding simulations. We have developed a random forest classifier to predict the RTP of a given polypeptide sequence. Layers is a standalone application; however, it can be merged with other applications to reduce the computational load when working with large datasets of protein structures. Layers is available freely at http://www.csb.iitkgp.ernet.in/applications/mol-layers/main.