Lilly Innovation Fellowship Award (LIFA) postdoctoral fellow
Context-specific models of Caenorhabditis elegans:
Genome-scale models (GSMs) of metabolic networks
are providing a new context to analyze different data types. Therefore,
development of novel approaches that integrate a diverse set of “-omics”
data (for e.g. transcriptomic, metabolomic, proteomic, etc.) with the GSMs to
better predict organismal phenotypes under different contexts. These models,
integration of contextual data with the GSMs, are often called context-specific
models (CSMs). Tissue-specific models of eukaryotic organisms are an example of CSMs;
here, the tissue is the context. To this end, I am developing tissue-specific
models of Caenorhabditis elegans (roundworm), a transparent nematod, to study:
(i) fat accumulation and metabolism, (ii) host-microbe interactions, and
(iii) metabolic interactions amongst tissues. The project involves analyses of
large experimental data sets, developing algorithms to understand
“expression-to-activity” processing of metabolic reactions, and generation of
metabolic models of various C. elegans tissues.
Comparative analysis of different contexts The fundamental structure of central carbon metabolism in the metabolic network remains same in different contexts of the cell: glycolysis/gluconeogenesis, citric acid cycle, pentose phosphate pathway, amino acid synthesis and metabolism, nucleotide metabolism, etc. However, the organism differs by small sets of reactions amongst them. These sets change the behavior of these organisms drastically such as to how well they grow, maintain, and divide in the same environmental condition. It even changes the proteomic, transcriptomic, and genomic profiles under different contexts. Known the similarities and differences between the contexts, what other interesting properties exist.
mathematical models of evolution of metabolism: The number of reactions catalyzed by an enzyme within a cell is not necessarily 1. In other words, distribution of number of reactions catalyzed by an enzyme to number of enzymes within a metablic network is not uniform. Why such a relationship between enzymes and reactions catalyzed should exist is a very interesting question? Are there any fitness costs or benefits associated with it? How evolutionary pressures give rise to such distributions? Some of the projects in this study include calculation of distribution of enzyme-reaction relationships; and calculation of epistatic interactions to qualitatively understand the adaptive fitness landscape.
synthetic & systems biology of microbial metabolism: Rates of non-spontaneous reactions within a cell is "somewhat" controlled by the concentration of enzymes. Enzyme concentration within a metabolic pathway cause metabolic bottlenecks, which leads to non-optimal reaction rates and sub-optimal production of metabolites within the same pathway. Why such bottlenecks exist within, say, a linear pathway where a "hypothetical" multi-functional enzyme could catalyze all the reactions in the same pathway? Are there any advantages that are offered by specific enzymes? Answers to these questions could facilitate building synthetic proteins & enzymes which can catalyze reactions more efficietly and increase the yield of commercially valuable products.
Prasad Lab, Colorado State University (2010 - 2016):
Photosynthetic microbes like cyanobacteria and
unicellular algae have been identified as potential sources of biofuels.
Faster growth rates and non-competing interest with the food industry make
them an obvious choice. However, biofuel precursors like fatty acids
produced downstream in the metabolic network. This limits the amount of
low-carbon metabolites available for production of these high-carbon
chemical compounds. Hence, to understand the production/distribution of these
low-carbon metabolites within the metabolic network, requires an
understanding of systems view of photosynthetic microbial metabolism. Some
current projects include development of kinetic model of photosynthesis; and
constraint based metabolic model of Synechocystis sp. PCC6803, a
cyanobacterium, to facilitate prediction of intracellular fluxes within the metabolic
Other than studying photosynthetic microbes, my work in Prasad Lab also included studying (i) mathematical models of evolution of metabolism, and (ii) synthetic and systems biology of mcirobial metabolism. See above for description of my interest in the topic.
Chaplen/Murthy Lab, Oregon State University (2009 - 2010): Photosynthetic microbes such as single cell green algae and cyanobacteria are presently being commercialized as a potential source of lipids and carbohydrates, to produced bio-fuels and bio-products. Genomic and biochemical information have previously been used to create mathematical models of the metabolic network of the green algae, C. reinhardtii. I used a mathematical model to show that different biomass compositions and different nutrient uptake rates can lead to similar growth rate of the green algae, leading us to the conclusion that actual biomass composition of the algae may be changing during its growth.
Fowler Lab, Oregon State University (2009): Z. Mays, also known as Maize, contain DNA sequences called transposons that can change its position within the genome, creating or reversing mutations. Transposons often cause to alter the size of the genome and pose problems in sequencing the genome. My project in this lab focussed on determining differences in the genetic make-up of an individual maize plant by Polymerase Chain Reaction (PCR) and discovering transposon insertion sites using a technique called TAIL-PCR. This project also involved analysis of sequences which elevate the genomic location of mutations caused by a transposon called Activator (Ac). Further, the research involved setting up of plant crosses for which leaves were collected for sampling and hence, involved field work.
A. thaliana, a small flowering plant common in Eurasia, is a popular model organism for plant biology. In this plant, the exocyst is a protein complex which helps in initiation and maintenance of polarized cells. My project involved studying effects of proteins sec8 and exo70A on root growth. The experiments involved media preparation and seed culture for plant growth, strain selection for desired plant mutants, and root length and growth analysis.
Biotech Park, Lucknow, India (2007): This project, first, introduced to me the world of biofuels. Biofuel producing crops include Jatropha curcas and Pongamia pinnata. This project involved production of biofuel using Jatropha via trans-esterification reaction, a process of exchanging the organic (preferably, alkyl) group of an ester with the organic group of an alcohol facilitated by an acid or base catalyst. The research work conducted here involved solvent extraction using hexane/soxhlet apparatus and rotor vaporization. This project was conducted as a part of final undergraduate project in the final year. This also involved visiting essential oil extraction plants in and around the Biotech Park.
Joshi CJ, et al.; StanDep: capturing transcriptomic variability improves context-specific metabolic models. PLoS Computational Biology, 2020
Saba J, et al.; Dietary serine enhances chemotherapeutic toxicity in C. elegans through altering microbiota metabolism. Nature Communications, 2020
Joshi CJ, O'Rourke EJ, and Lewis NE; What are housekeeping genes? (submitted) eLife, 2020
Richelle A et al.; What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data. (submitted) Molecular Systems Biology, 2020
Armingol E et al.; Inferring the spatial code of cell-cell interactions and communication across a whole animal body. (submitted) Nature Communications, 2020
Richelle A, Joshi CJ, and Lewis NE; Assessing key decisions for transcriptomics data integration in biochemical networks. PLoS Computational Biology, 2019
Witting MA, et al.; Modeling meets Metabolomics - The WormJam consensus model as basis for metabolic studies in the model organism Caenorhabditis elegans. Frontiers in Molecular Biosciences, 2018
Hastings J, et al.; WormJam: A consensus C. elegans Metabolic Reconstruction and Metabolomics Community and Workshop Series. Worm, 2017
Joshi CJ, Peebles CAM, and Prasad A; Modeling and analysis of bioproduct formation in Synechocystis sp. PCC6803 using a new genome-scale metabolic metabolic network reconstruction. Algal Research, 2017.
Joshi CJ and Prasad A; Epistatic interactions among metabolic genes depend upon environmental conditions. Molecular BioSystems, 2014
Cyrielle C, Joshi CJ, Lewis NE, Laetitia M, Andersen MR; Adaptation of generic metabolic models to specific cell lines for improved modelling of biopharmaceutical production and prediction of processes. Wiley-Blackwell Biotechnology Series (accepted).
Biomedical Engineering Society (BMES; October 14 - October 17, 2020) - Are housekeeping genes essential?
Q-Bio Summer School (Q-bio; July 8 - July 21, 2015) - Modeling metabolic reconstruction of Synechocystis sp. PCC6803.
American Chemical Society (ACS; March 22 - March 26, 2015) - A genome-scale metabolic reconstruction of Synechocystis sp. PCC6803 taking into account molecular mechanisms under photoautotrophic conditions.
Biophysical Society (BPS; February 2 - February 6, 2013) - Analysis of Metabolic Robustness: E. coli and Synechocystis sp. PCC6803.
American Institute of Chemical Engineers (AIChE; October 28 - November 2, 2012) - Comparison of Network Structures that Confer Resilience Against Genetic Perturbations in Microbial Metabolism.
National Renewable Energy Laboratory (NREL; October 12, 2012) - Using Computational Modeling to Interrogate the Metabolic Robustness of Cyanobacteria. Presented by my adviser, Dr. Ashok Prasad.
Colorado Center for Biorefining and Biofuels Semi-Annual Meeting (C2B2; August 25/26, 2011) - Fluxomics for Rational Design of a H2-producing Cyanobacterial System via Synthetic Biology.
COnstraint Based Reconstruction Analysis Conference (COBRA; October 13 - October 18, 2018) - Fine tuning thresholds to facilitate integration of transcriptomics data.
Metabolic Pathway Analysis (MPA; July 23 - July 28, 2017) - Generating tissue-specific metabolic models.
COnstraint Based Reconstruction Analysis Conference (COBRA Conference; May 20 -May 23, 2014) - Epistatic Interactions Depend on Environmental Effects: an FBA Study.
COnstraint Based Reconstruction Analysis Conference (COBRA Conference; May 20 -May 23, 2014) - Structure and Role of Enzyme-Reaction Association in Microbial Metabolism. Presented by my adviser, Dr. Ashok Prasad.
Colorado Center for Biorefining and Biofuels Semi-Annual Meeting (C2B2; October 17/18, 2013).
Q-bio Conference (August 7 - August 10, 2013) - Comparative Analysis of Metabolic Robustness: E. coli and Synechocystis sp. PCC6803. Presented by my adviser, Dr. Ashok Prasad.
BioPhysical Society (BPS; February 2 - February 6, 2013).
Molecular and Cellular Biophysics Symposium (MCB Symposium; April 19, 2012).
Molecular and Cellular Biophysics Symposium (MCB Symposium; April 8, 2011).
American Society for Agricultural and Biological Engineers (ASABE; June 20 - June 23, 2010) - Modeling Lipid and Carbohydrate accumulation in Green Algae, using Constraint Based Modeling.
Iternational Conference on Bioinformatics (ICB; December 18 - December 20, 2006)
National Workshop on Bio-materials and Bio-sciences (BMBS; October 21, 2005)