× Openings at all levels are available. Please contact Costas D. Maranas at costas@psu.edu for additional details.

International Genetically Engineered Machines logo.

Maranas lab from Chemical Engineering in collaboration with Wash U's iGEM (International Genetically Engineered Machines) team is recruiting self-motivated undergraduate students from all backgrounds to participate in the research this summer! iGEM is the world's premier synthetic biology competition and involves designing and engineering microbes to solve real world problems. Participating in iGEM is a fantastic way to learn about all of the different sides of research, from the actual research to administrative aspects that go on behind the scenes.

"$3.9 million NSF research grant seeks to reduce crops' fertilizer dependence"

Previous projects have involved engineering a nitrogen-fixing strain of E. coli and engineering the production of the spice saffron in E. coli and cyanobacteria. This year, the iGEM team will have the opportunity to interact directly with the lab's of Professors Maranas (from Penn State), Pakrasi, Zhang, and Moon (from Wash U) in designing and executing a new project.

maranas.che.psu.edu
pages.wustl.edu/photo.synth.bio
zhang.eece.wustl.edu
moon.eece.wustl.edu

The competition will require a commitment of a few hours a week in the Spring semester, as well as a few hours a week in the Fall semester to prepare for the competition. Team members are compensated with a stipend for the summer, and the project wraps up with the regional and international competitions in the fall.

There is no previous research experience required, just an interest in synthetic biology and a desire to learn. If you are interested, you can learn more about iGEM below, and contact Professor Costas Maranas at costas[at]psu.edu for more information regarding the application.


AIChE logo.

Undergraduate AIChE projects

Project 1. Computational protein design

Proteins are one of the most versatile modular assembling systems in nature. Experimentally, more than 0.1 million protein structures have been identified and reported in a public database. Such an enormous variety of structures depends on the sequence of amino acids along the peptide chain of each protein. The main objective of protein design is understanding how these structural and functional properties of a protein can be encoded in this sequence. Computational protein design involves predicting existing amino acids which need to be replaced with new amino acids that (a) accomplishes a desired biochemical objective, and (b) retains its structural integrity. The computational workflow for this project will involve using mixed-integer linear optimization program to predict which amino acids need to be replaced with what new ones. Three dimensional computational models of these proteins will be used to compute the interatomic forces at play in these systems that dictate whether the new protein is able to accomplish the biochemical objective. Projects include, switching specificity of substrates of enzymes, altering pore size of channel proteins, and design antibody proteins to bind to a disease causing antigen protein. For example, an enzyme redesign work will involve starting with a natural enzyme and make changes to its substrate binding pocket such that it can now catalyze a substrate we care about, using the original reaction mechanism. This work will involve coding in Python (C or C++) and requires basic biochemistry knowledge.

Project 2. Constructing a synthetic consortium between algae and bacteria

Bacteria such as Escherichia coli feed on externally available sugars and biochemicals for their growth. But the algae can perform photosynthesis and therefore can synthesize sugars and other biochemicals from cheap and renewable raw materials such as CO2, sunlight and water. Previous studies have shown that it is possible to establish a microbial consortium where algae synthesize the sugars and other biochemicals from CO2 and bacteria consumes these sugars for its growth. Since bacteria can be easily engineered for the production of chemicals of commercial interest, such a consortium offers a potential bioprocess scheme where chemicals can be produced directly from CO2. This project will use mathematical models of metabolism and microbial communities to: 1) study and understand the metabolic exchanges that can happen between the members of the synthetic consortia 2) Identify the set of biochemicals that are needed to be exchanged between the members in order to improve the stability of the consortium 3) identify genetic engineering strategies that will enhance the stability of the consortia. This project would train the student in: 1) modeling the microbial metabolism using genome-scale metabolic models for the purpose of bioprocess applications 2) modeling the metabolism of microbial communities for the purpose of pharmaceutical and biomedical applications.

Project 3. Annotation and standardization of genome-scale metabolic models

Genome-scale metabolic models (GEMs) are used to catalogue the metabolic capability of organisms in a computations-ready format. These models can be applied to study an expanding and increasingly complex range of problems, including finding new synthesis routes of desired products. One challenge associated with genome-scale metabolic models is ensuring that the models are standardized and fully annotated. This challenge is compounded by the evolving landscape of databases and standards. The student will examine GEMs previously published by the Maranas group and subject them to a suite of tests that scrutinize standardization, consistency and annotations. The student will update the models as needed to confirm passing of the test suite. The student will learn and use a distributed version-control system for tracking changes. Based on student’s progress and interests, the project can continue to support ongoing efforts of new GEMs being developed by the Maranas group. This work will provide a basic understanding of metabolic processes and modeling, and computational tools and software used in their analysis. A basic understanding of biochemistry and Python code-writing is helpful but not required.

Project 4. Annotate carbon mappings used in metabolic flux analysis

Metabolic flux analysis (MFA) makes use of atom mappings of reactions to elucidate usage of metabolic networks. The analysis requires atom mapping models that are accurate. One challenge involves the ease of use of these mapping models, especially when drawing on existing models for use in the construction of models for new organisms. The student will annotate the carbon atoms used in the existing genome-scale carbon mapping models developed by the Maranas group. These annotations provide the basis for standardization of the mappings. Based on student’s interests, the next stage would be developing visualization tools for atom mappings and MFA, standardizing numbering schemes and annotations, constructing large-/genome-scale mapping models or, alternatively, learning computational approaches to solving MFA problems. For the initial project, a basic understanding of biochemistry is required and Python code-writing is helpful but is not required.

Project 5. Genome-scale metabolic model reconstruction for Hansenula polymorpha DL-1

Hansenula polymorpha (or Ogataea parapolymorpha) DL-1 is a methylotrophic yeast known for its thermo-tolerance and has been employed for recombinant protein production and ethanol production in high temperature. It is also used as the model organism for studying methanol metabolism and peroxisome function. To assist further study of the organism, a genome-scale metabolic model can be reconstructed to capture its metabolism and allow computational study on the organism's capability. In this project, the student is expected to search and query biochemical information available for the organism from various sources, including databases and literature. In the second stage, simulation case studies will be designed to validate the model and gain insights on the observed organism's metabolic capabilities. A basic understanding of cellular metabolism is required for this project. MATLAB or Python coding skill will be required for the second stage of the project.

Project 6. Metabolic pathway design for small molecule drugs

Biocatalysis is a promising platform for producing the chemicals our society depends on. Many molecules that can be synthesizes via bioprocesses, however, are either not produced naturally, or produced at very low titer by organisms that are expensive to grow (such as plants). If we can design bacteria to produce commodity molecules such as small molecule drugs or their chemical precursors from inexpensive feedstocks, we can reduce the production cost of those chemicals and, subsequently, their burden to the consumer. Some researchers have identified a set of small molecule drugs and potential design strategies for synthesizing them from E. coli native metabolites. Their list of candidate molecules was larger, however, than the set that they could identify pathways for. Using the pathway design tools developed in Maranas lab, the student will design novel pathways for some of the target drug molecules, identify suitable host organisms for their production, and design genetic intervention strategies that will maximize production of the desired compounds using the host organism’s genome-scale metabolic model. A basic understanding of biochemistry is required and Python code-writing is helpful but is not required.

Project 7. Deciphering the genetic basis of disease progression

Understanding the causes and effects of variations in the genome sequence of an organism have been the focus of a variety of studies, ranging from improving agricultural yield to understanding the adaptive processes conferring pathogenicity in disease-causing microbes. These variations usually occur as point mutations (such as an ‘A’ → ‘T’) in the genome sequence called single-nucleotide polymorphisms (SNPs). In this project, we will focus on a newly-developed tool ‘SNPeffect’ that identifies the role of a SNP by constructing “scenarios” for its effects while integrating all systemic observations (such as metabolite and enzyme levels) using a metabolic network. A metabolic network is a mechanistic description of the gamut of biochemical conversions occurring inside an organism, that work together to sustain life. Using this framework, the relationship that SNPs in disease-causing microorganisms (as well as the human host) have with clinical outcome will be investigated. Using systems such as drug-resistant Mycobacterium tuberculosis and gastric cancer-causing Helicobacter pylori, we will explore medically and biologically relevant SNPs in microbes, and their effects on the human host. This would entail 1) identifying functional SNPs that confer increased disease progression, 2) investigating their impact(s) on the host metabolism, and 3) identifying design strategies aimed at increasing health that leverage the host genetic repertoire. This project combines skills such as numerical optimization, big data processing, and bioinformatics, and a working knowledge of any coding language (such as MATLAB or Python) is preferred.