(2hh) Big Data Analytics for Disease Systems Biology and Bioprocess Engineering

A mechanistic understanding of cellular states and function is critical to the effective treatment of noncommunicable diseases and design of efficient production platforms for biopharmaceutical applications. Treatment regimens for noncommunicable diseases primarily aim to manage clinical symptoms and improve quality of life but frequently do not address the actual cause of the disease. On the bioprocessing front, high-producing cell lines are selected using a manual and time-intensive screening process to screen a large library of transfected host cell lines, which remains the primary bottleneck in the design of an efficient production process. The common factor in both challenges is the lack of a mechanistic understanding of eukaryotic (commonly CHO and human tissues) cellular function and resource allocation. My research interest lies in leveraging large-scale omics data and systems biology tools to characterize the biological mechanisms underpinning cellular phenotypic shifts, which are, in essence, the cell’s response to a changing condition. The two main challenges are (i) inferring statistically significant biological interactions from omics data, and (ii) deploying the model in a predictive setting by parameterization and/or interfacing them with machine-learning models, both of which require the development of novel, fast and efficient supervised learning algorithms based on transcriptomic, proteomic, metabolomic, and fluxomic datasets. Hybrid models combining the structural details with parameterized machine-learning models are a powerful tool to accelerate build-design-test cycles for strain engineering and media optimization in bioprocessing applications. On the biomedical side, these aid in the identification of biomarkers, discovery of novel therapeutics, and improve the quality of life of patients suffering from non-communicable diseases.

Research Experience

My research career has focused on integrating various types of omics-data with genome-scale models of metabolism with the goal of mechanistically predicting cellular responses to genetic and environmental perturbations. My doctoral work at the Maranas Lab (Penn State University) focused on developing 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 by integrating 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.

Postdoctoral Projects (ongoing work)

1. Characterizing the cellular machinery involved in manufacturing viral vectors for gene therapy: The high transduction efficiency of viral vectors make them attractive tools for gene therapy. Unlike antibodies, viral vectors are difficult to manufacture. In this work, we leverage multi-omics datasets from a panel of cell lines to identify the cellular state and metabolic shifts shifts occurring in a typical adeno-associated virus (AAV) production process, reconstruct the AAV synthesis and assembly pathways, and devise strategies to safely suppress the cellular immune response pathways to enable the safe production of AAVs.
2. Biological characterization of producer CHO cell lines: Understanding changes in cell state and metabolism in response to reactor conditions is critical to optimizing and controlling a bioprocess. In this work, we compare and contrast the metabolic and transcriptomic characteristics of selected drug-producing clones with their parent pool cell lines to elucidate key differences in gene expression and pathway usage that contribute to increased antibody production in producer clones, and identify intracellular metabolic bottlenecks that can be engineered to improve the productivity of high-producing clones.
3. Development of a multiscale bioreactor model for CHO bioprocessing: This work involves the construction of a hybrid bioreactor model for antibody production using CHO cells that interfaces reactor conditions with cell metabolism using a parameterized machine-learning model. The model itself will be applied to process optimization and predictive control.

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.

Select Publications

1. Gopalakrishnan, S., et al. (2022). Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. Metab Eng, 75, 181-191. doi: 1016/j.ymben.2022.12.003
2. Gopalakrishnan, S., et al. Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks. (Under Review)
3. Gopalakrishnan, S., et al. COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling (In preparation)
4. 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
5. 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
6. 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

My research program will focus on reconstructing, parameterizing, and simulating biological networks (metabolic, transcriptional regulation, and signal transduction) to address challenges in three major areas: cell line engineering for bioprocessing, treating and managing dysglycemia, and modeling host-virus interactions. For cell line engineering, we will initially focus on E. coli, which is the preferred platform for producing non-glycosylated biologics. The key challenge is to “guide” the cells to alternate metabolic state that favor the production of our desired product over the cell’s natural objective of biomass maximization. Being a well-studied model organism with available genome-scale models, large-scale kinetic models and a large library of multi-omics datasets makes E. coli the perfect candidate organism for the development of large-scale modeling frameworks. Motivated by this, I will expand on my previous work with multi-omics data integration (K-FIT algorithm) and incorporate intracellular signaling, gene regulation, and the protein secretory pathway to construct a detailed mechanistic model for a producer E. coli. This involves a four-phase plan consisting of: (i) Integrating the protein secretory pathway with the current genome-scale model for E. coli, (ii) Reconstructing and overlaying the signaling and gene regulatory networks with the genome-scale metabolic model for E. coli, (iii) Identifying appropriate environmental triggers to induce state sift from growth to production in a bioreactor, and (iv) Identifying genome-editing targets to design specialized strains of E. coli with high volumetric productivity. For the dysglycemia research, we will leverage multi-omics data along with clinical data to (i) uncover the mechanisms leading up to insulin resistance in major tissues, (ii) quantify the role of hormonal control and its impact on blood glucose management in diabetics, (iii) characterize the interplay between insulin resistance, metabolic syndrome, and polycystic ovary syndrome, and (iv) identify the molecular mechanisms by which genetic markers increase the risk of developing type 1 and type 2 diabetes. The overarching goal of the host-virus interaction avenue of research is to characterize the molecular mechanisms using which the infecting virus hijacks the host cell machinery for self-replication. Reconstructing these pathways will (i) inform strategies to improve the volumetric productivity of viral vectors for gene therapy, and (ii) reveal novel therapeutic targets to suppress pathogenic viral replication and contain outbreaks.

Teaching Interests

I 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 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.