Natural Polymers

Computations and modeling have emerged as indispensable tools that drive the process of understanding, discovery and redesign of biological systems. With the ever-accelerating pace of genome sequencing and annotation information generation, the development of computational pipelines for the rapid reconstruction of high-quality metabolic networks has received significant attention. Nowadays, we use computations to reconstruct models of metabolism that account for stoichiometry, regulation, kinetics and increasingly every macromolecular species present. These models provide a rich tapestry for computational tools to quantitatively assess the metabolic phenotypes for various systems-level studies and to develop engineering interventions at the DNA, RNA or enzymatic level by careful tuning in the biophysical modeling frameworks. This plasticity of living systems inherited through evolution enables biotechnologists to steer metabolism to 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.

Stoichiometric and kinetic models are increasingly becoming available for a variety of organisms including Microbes, Plants, and Microbial communities. A growing number of computational strain design procedures relying upon mathematical optimization frameworks have emerged benefiting from the rapid advancements in the reconstruction of genome‐scale metabolic models, thus addressing the challenge of identifying and quantifying these interventions and minimizing the counteractions of the organisms in response to them. These large models together with constraint-based methods represent a key foundational advance in Systems Biology and metabolic engineering and are crucial for sustainable development in food, pharmaceuticals and bioproduction of the future.

During my PhD, I established a foundation in metabolic model development and analysis, which is the rapid advancing field in Systems Biology. With my background in Chemical Engineering during my post-baccalaureate studies, I sought to bring engineering skills to my studies of living organisms under the guidance of Professor Costas Maranas during my MS and Professor Rajib Saha during my PhD. I have learnt 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. Development and analysis of a systems-level pan-genome metabolic model for analysis of Staphylococcus aureus physiology;
2. Understanding the effects of heat stress on rice seed development using optimization-based analysis of transcriptomic data;
3. Elucidating the role of viral auxiliary metabolic genes in modulating microbial interactions in bovine rumen; and
4. Modeling the methane-recycling community metabolism in freshwater lakes.

My proposed research platform will focus on in silico genome-scale metabolic modeling algorithms, along with the incorporation of multi-level omics information to provide a diverse array of toolboxes for new discovery in the metabolism of living organisms and metabolic reprogramming for desired results. Specifically, the reconstruction and analysis of genome-scale metabolic models of microbes, plants and microbial ecosystems, bioinformatics analysis and integration of multi-omics data will be my priority research areas. I will start my future research career with the following investigations:

1. Identify the adaptive regulatory mechanisms in antibiotic resistant microbes and predict novel therapeutic targets against their pathogenicity.
2. Develop in silico models of whole plant metabolism integrated with global regulatory information elucidated from multi-omic analyses to understand and combat abiotic stress response.
3. Identify the key metabolic interaction dynamics within natural and synthetic microbiomes causal to the dominance of resistant, predatory, or beneficial microbes.

Teaching interests

My overarching goal in teaching is to develop a student-centered environment, where students can actively participate in the learning process. My specific goals for student learning are to develop problem solving skills, analytic skills, and, ultimately, the ability to think holistically in order to synthesize a creative solution and/or an insightful advice, and to deliver them successfully. In a Chemical Engineering major, these skills are immensely useful for the students to succeed in their future academic and professional endeavor. I encourage student-faculty contact, cooperation among students, active learning, and emphasizes time on task while respecting diverse talents and ways of learning.

Based on my diverse background in chemical and biomolecular engineering training and my long history of Systems Biology research, I am able to explain concepts and provide context in a multidisciplinary manner to retain the interest of a diverse audience. I am confident in my teaching capability of both undergraduate and graduate courses in traditional chemical engineering topics such as Transport Operations, Chemical Kinetics, Process Control, Chemical Process Design and Optimization as well as new and demanding courses like Bioprocess Design, Instrumental and Control, Optimization in Biological Systems, 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. I believe these courses will prepare future generation of successful engineers, scientists and systems and synthetic biologists by providing them the foundational knowledge and introducing them to current state-of-the-art algorithms and toolboxes for discovery and redesign of biological systems.

Featured publications

1. Islam, M. M., J. K. Sandhu, H. Walia, and R. Saha, “Transcriptomic data-driven discovery of global regulatory features in developing rice seeds under heat stress”, in review.
2. M., Islam, Le, T., Daggumati, S. R., and R. Saha, “Investigation of microbial community interactions between lake Washington methanotrophs using genome-scale metabolic modeling”, (accepted) PeerJ, 2020.
3. Islam, M. M., V. 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.
4. 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.
5. 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.
6. 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
7. 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)

*Authors with equal contribution