(2jf) Computational Tools for the Discovery and Redesign of Natural and Synthetic Biological Systems

Computational modeling has emerged as an indispensable tool for understanding, discovering, and redesigning biological systems. With the ever-accelerating pace of genome sequencing and deep annotation techniques, the development of computational pipelines for the rapid reconstruction of high-quality metabolic networks has received significant attention. These models provide a rich tapestry of computational tools for quantitative systems-level studies of metabolism. The plasticity of living systems inherited through evolution enables biotechnologists to steer metabolism in many different directions ranging from strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

I am a postdoctoral research associate in the Computational Systems Biology Laboratory at the University of Virginia. I am conducting research on the multi-omic-based characterization of clinically relevant isolates of the antibiotic-resistant pathogen Pseudomonas aeruginosa in the infected mucus layers in various parts of the human body. Before that, during my PhD, I established a foundation in metabolic model development and analysis, which is a rapidly advancing field in Systems Biology. With my background in Chemical Engineering, I sought to bring my engineering skills to my studies of living organisms during my MS and PhD. I learned the standard tools for modeling and analyzing microbial and plant systems as well as developed novel algorithms, tools, and protocols for redesigning their metabolism to achieve desired outputs. My current works that contribute to the development of this poster are

1. Multi-scale model of microbial phenotype modulation by mucins;
2. Development of a systems-level metabolic model for analysis of Staphylococcus aureus physiology;
3. Understanding the effects of heat stress on rice seed development using optimization-based analysis of multi-omics data;
4. Exploring the metabolic landscape of pancreatic ductal adenocarcinoma using genome-scale metabolic modeling and analyses;
5. Elucidating the role of viral auxiliary metabolic genes in modulating microbial interactions in the bovine rumen; and
6. Modeling the methane-recycling community metabolism in freshwater lakes.

The goal of my planned research program is the discovery and redesign of natural and synthetic biological systems to improve our understanding and strategize the engineering efforts of these systems for the benefit of mankind. The complex interplay of genes, transcripts, proteins, and metabolic conversions in natural and synthetic biological systems has been the centermost focus of systems biology and computational biology. To this end, federal and other funding agencies have spent substantial resources to further our understanding of these complex biological systems via the integration of concepts and observations across diverse fields, which is the overall theme of my research. I plan to establish a multi-disciplinary and collaborative research group that will address the upcoming challenges in understanding and manipulating biological systems through the development of novel computational and experimental tools, with three primary directions as described below.

Microbial ecosystems dynamics at scale. I will develop efficient mathematical algorithms, software tools, and a comprehensive knowledge base for microbial ecosystems with important roles in biogeochemistry, biotechnology, and health. I will focus on functionally and biotechnologically important microbial ecosystems e.g., human gut, skin, oral and vaginal microbiomes, airborne microbial communities in rural, forest, and urban environments, and biological wastewater treatment facilities. These microbiomes show distinct dynamics based on environmental changes over small and large time and length scales. Therefore, high-resolution spatiotemporal dynamic modeling frameworks are critical in exploring these interactions and assessing how they contribute to the assembly, stability, and resilience of microbial ecosystems.

Harnessing the metabolic and biotechnological potential from nature. Industrial catalysis has several disadvantages e.g., low catalytic efficiency, lack of enantiomeric specificity, need for high temperature, low pH and high pressure, and high degree of pollution. Isolated novel species from environments with high temperatures can tolerate extreme temperature stresses of industrial processes, thus providing additional advantages over mesophilic organisms. Furthermore, enzymes from extremophiles can be genetically and chemically modified to enhance their key properties: stability, substrate specificity and activity. Thus, the search for new biocatalysts and antibiotics based on the microbial biodiversity of extreme environments is a rapidly expanding discipline requiring the development of dedicated enzymatic and structural screening and characterization platforms.

Systems biomedicine: resolving the multifactorial nature of complex diseases. This project aims to integratively infer and quantify the multi-variate complexity of the molecular and cellular processes of multifactorial diseases like obesity, diabetes, and cancer, with the aim of constructing formal disease models. Knowledge about the higher level of biological organization and molecular understanding of pathogenic events is fundamental in resolving the complex nature and devising efficient preventive treatment strategies. In my laboratory, gene regulatory networks and heuristic models of diseases like obesity, diabetes, and cancer will be developed through the integration of multi-omic datasets using novel bioinformatic algorithms. My lab will collaborate with medical systems all over the world to assemble clinical data on disease progression and patient genealogical and behavioral information. I will develop the genetic/regulation network dynamics and construct new computation tools for modeling the different factors and how they interplay together to manifest a specific disease phenotype.

Teaching Interests

My instructional strategies are rooted deeply in research-based pedagogy. I prioritize the course learning goals that align with engineering problems from real-life case studies that allow long-term retention of knowledge. My lesson plans emphasize the application of critical thinking skills to foster deep learning, and the use of collaborative skills to facilitate active learning. My teaching activities will be designed to couple engineering/science fundamentals to real-world problem solving, apply evidence-based instructional and assessment techniques, and augment effective communication with my students on a personal level. I will also embed engaging activities in the instructional process that are designed to help students develop research and writing skills that are readily transferable across disciplines. I will encourage students to engage in open-ended formative and summative evaluations of the course.

Based on my diverse background in chemical and biomolecular engineering training and my long history of Systems Biology research, I can explain concepts and provide the context in a multidisciplinary manner. I am confident in teaching traditional Chemical engineering topics such as Transport Operations, Chemical Kinetics, Process Control, Chemical Process Design and Optimization as well as new and demanding courses in Biomedical Engineering like Bioprocess Design, Instrumental and Control, Optimization in Biological Systems, Microbial Biomedical Engineering, Computation Strain Design etc. These newly designed courses will provide students with a clear understanding of the computational modeling of living systems and a deeper insight into how to write algorithms that could be employed for in silico design and hypothesis generation.

Featured publications:

1. Islam, M. M., Kolling, and J. Papin, “Transcriptomics-guided genome-scale modeling identifies mucin-induced metabolic reprogramming in Pseudomonas aeruginosa clinical isolates”, in preparation.
2. Islam, M., G. Kolling, E. Glass, J. Goldberg, and J. Papin, “Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes”, in review.
3. Islam, M. M., A. Goertzen, P. K. Singh, and R. Saha, “Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling”, iScience, 2022, 25(6). [link]
4. Alqarzaee, A. A., S. S. Chaudhari, M. Islam, V. Kumar, M. C. Zimmerman, R. Saha, K. W. Bayles, D. Frees, and V. C. Thomas, “Staphylococcal ClpXP protease targets the cellular antioxidant system to eliminate fitness-compromised cells in stationary phase”, PNAS, 2021, 118(47). [link]
5. Islam, M. M.*, W. Schroeder*, and R. Saha, “Kinetic Modeling of Metabolism: Present and Future”, Current Opinion in Systems Biology, 2021, 26:72-78. [link]
6. Islam, M. M., K. Sandhu, H. Walia, and R. Saha, “Transcriptomic data-driven discovery of global regulatory features in developing rice seeds under heat stress”, Computational and Structural Biotechnology Journal, 2020, 18:2556-2567. [link]
7. Islam, M. M., Le, T., Daggumati, S. R., and R. Saha, “Investigation of microbial community interactions between lake Washington methanotrophs using genome-scale metabolic modeling”, PeerJ, 2020, 8:e9464. [link]
8. Islam, M. M., C. Thomas, M. Van Beek, J. Ahn, A. A. Alqarzaee, C. Zhou, P. D. Fey, K. W. Bayles, and R. Saha, "An integrated computational and experimental study to elucidate Staphylococcus aureus metabolism", npj Systems Biology and Applications, 2020, 6: 3. [link]
9. Islam, M. M., S. C. Fernando, and R. Saha, “Metabolic Modeling Elucidates Metabolic Transactions in the Rumen Microbiome and the Metabolic Shifts upon virome Interactions”, Frontiers in Microbiology, 2019, 10: 2412. [link]
10. Islam, M. M., A. Al-Siyabi, R. Saha, and T. Obata, “Dissecting Metabolic Flux in C4 plants - Experimental and Theoretical Approaches”, Phytochemistry Reviews, 2018, 17: 1253. [link]
11. Islam, M. M. and R. Saha, “Computational Approaches on Stoichiometric and Kinetic Modeling for Efficient Strain Design”, Methods in Molecular Biology, 2018, (1671): 63-82. [link]
12. Zomorrodi, A.*, M., Islam*, and C. Maranas, “d-OptCom: Dynamic Multi-level and Multi-Objective Metabolic Modeling of Microbial Communities”, ACS Synthetic Biology, 2014, 3 (4). [link]

*Authors with equal contribution