(4ba) Big Data Analytics for Disease Systems Biology

Predictive models of metabolism capture the interplay between complex biological processes that guide therapeutic development for non-infectious diseases such as diabetes, obesity, cancer, and non-alcoholic fatty liver disease, and inform metabolic engineering strategies for de-bottlenecking and optimization of industrial bioprocesses. Construction of quantitative and predictive models for biological networks relies on efficient analysis and mining of large-scale biological data. The paucity of such meta-analysis tools motivates the development of robust and scalable algorithms capable of identifying meaningful cell state markers and bottlenecks limiting the development of efficient therapeutics. The challenge of designing novel supervised learning algorithms based on transcriptomic, proteomic, metabolomic, and fluxomic datasets involves simultaneous parameterization and simulation of biological processes at various time scales. Such detailed models will provide valuable insights into disease pathophysiology to guide the development of effective and efficient therapeutics.

Research Experience

My research career has focused on integrating various types of omics-data with genome-scale models of metabolism in order to characterize the physiological state of organisms and response to genetic and environmental perturbations. My doctoral work at the Maranas Lab (Penn State University) focused on the development of tools and resources for generating genome-scale fluxomic datasets and then using those generated datasets to construct predictive kinetic models of metabolism. My postdoctoral work at the Lewis Lab (UCSD) expands on my doctoral work with the integration of transcriptomics, CRISPR screening data, and process data to extract context-specific models that accurately emulate the cell’s physiological state and simulate a multi-scale process for therapeutic production using CHO cells. This poster will explore the following works.

Doctoral Projects

The overarching goal of my doctoral work was to establish a platform to construct and simulate predictive models of metabolism to inform metabolic engineering strategies. This task was limited by the high computational cost associated with model training as well as a shortage of large-scale datasets to improve the predictive capabilities of the metabolic models, and was addressed using a two-pronged strategy:

1. Development of tools and algorithms for genome-scale ¹³C-Fluxomics: Computational challenges associated with inferring the in vivo fluxome from stable-isotope tracers limited the scope of models used for data analysis. This work expanded the scope of ¹³C metabolic flux analysis to genome-scale models for the first time while providing insights into energy allocation in coli and uncovering a novel bifurcated topology for carbon conservation in Synechocystis. In addition to this, guidelines were proposed for the analysis of stable-isotope labeling data while generating previously unavailable large-scale fluxomic datasets for the construction of kinetic models of metabolism.
2. Accelerated parameterization of near-genome-scale metabolic models: This work involved the development of K-FIT, a robust and scalable platform for constructing predictive models of metabolism for coli using large-scale metabolomic and fluxomic datasets.

Postdoctoral Projects (ongoing work)

1. Identification of biologically relevant models of metabolism using transcriptomics and CRISPR screen data: Although various algorithms exist for model extraction using transcriptomics data, these extracted models fail to emulate gene dispensability effects leading to over- or under-estimation of pathway usage by the organism. CRISPR screens provide an insight into the sensitivity of the cell’s physiological state to genetic perturbations. In conjunction with transcriptomic data, this provides a high-confidence list of inactive biological processes, thereby aiding model simplification prior to integration of datasets described in “doctoral projects”.
2. Development of a multiscale bioreactor model for CHO bioprocessing: This ongoing work involves the construction of a bioreactor model for antibody production using CHO cells that interfaces reactor conditions with cell metabolism using parameterized boundary conditions. The model itself will be applied to process optimization and predictive control.

Select Publications

1. Gopalakrishnan, S., & Maranas, C. D. (2015a). 13C metabolic flux analysis at a genome-scale. Metab Eng, 32, 12-22. doi:10.1016/j.ymben.2015.08.006
2. Gopalakrishnan, S., Pakrasi, H. B., & Maranas, C. D. (2018). Elucidation of photoautotrophic carbon flux topology in Synechocystis PCC 6803 using genome-scale carbon mapping models. Metab Eng, 47, 190-199. doi:10.1016/j.ymben.2018.03.008
3. Gopalakrishnan, S., Dash, S., & Maranas, C. (2020). K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data. Metab Eng. doi:10.1016/j.ymben.2020.03.001

Future Research Plan

Non-alcoholic fatty liver disease (NAFLD) is a spectrum of liver diseases ranging from simple steatosis to steatohepatitis, which eventually progresses into liver cirrhosis and hepatic cell carcinoma. NAFLD is primarily characterized by lipid accumulation within the liver in the absence of alcohol consumption and is known to affect as much as 15% of the global population. Currently, the stage of the disease cannot be accurately determined without a liver biopsy and in the absence of available effective pharmacotherapy, lifestyle and dietary changes are the only available long-term management options. Although the pathogenesis of NAFLD is not fully understood, various studies have identified the diet, gut microbiota, obesity, and genetic predisposition as factors known to increase the risk of developing NAFLD. The availability of accurate predictive models that can navigate the complexities of biological systems can greatly accelerate the discovery of biomarkers and new therapeutics. Motivated by this, I would like to expand on my previous work with multi-omics data integration to construct predictive “whole-cell” models of human hepatic metabolism that informs the discovery of novel therapeutics. This involves a three-phase plan consisting of: (i) Developing large-scale data integration pipelines, (ii) Extracting novel biological insights and characterizing pathogenesis and pathophysiology of NAFLD, and (iii) Identifying treatment strategies that restores the cells to their former healthy state. These aims are of interest to, and within the funding scope of NIH centers including NIC and NIDDK, and potential research program partnerships with pharmaceutical industries.

Teaching Interests

I have served as a Teaching Assistant for the core Chemical Engineering course “Process Heat Transfer” in the Fall semester of 2016. My responsibilities included organizing recitation sessions and grading of homework and exams for a class strength of 135 students. As a future faculty, I am interested in teaching a specialized course in Applied Systems Biology and Metabolic modeling at the graduate level that is tied to my research program, a more generalized course in Mathematical Modeling Techniques at both undergraduate and graduate level, as well as an introductory course in Material and Energy Balances at the undergraduate level.