Department of Biological Systems Engineering

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https://hdl.handle.net/10919/24227

Biological Systems Engineering (BSE) is the engineering discipline that applies concepts of biology, chemistry and physics, along with engineering science and design principles, to solve problems in biological systems. Our faculty and students work in a broad range of biological systems, from natural systems, such as watersheds with a focus on water resources, to built systems, such as bioreactors and bioprocessing facilities. We work from the nanoscale to the macroscale. We seek to improve animal, human, and environmental health through development and design of healthy food products, vaccines, bioenergy, biomaterials, and water quality management practices. We convert biological resources, such as switchgrass, plant proteins, and animal manure, into value-added products, such as biopharmaceuticals, biofuels, and biomaterials, in a sustainable manner.

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13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production
Guo, Weihua; Sheng, Jiayuan; Feng, Xueyang (MDPI, 2015-12-25)
Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many ¹³C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms
Accelerating structure-function mapping using the ViVa webtool to mine natural variation
Hamm, Morgan; Moss, Britney; Leydon, Alexander; Gala, Hardik; Lanctot, Amy; Ramos, Román; Klaeser, Hannah; Lemmex, Andrew; Zahler, Mollye; Nemhauser, Jennifer L.; Wright, R. Clay (Wiley, 2018-12-05)
Thousands of sequenced genomes are now publicly available capturing a significant amount of natural variation within plant species; yet, much of this data remains inaccessible to researchers without significant bioinformatics experience. Here, we present a webtool called ViVa (Visualizing Variation) which aims to empower any researcher to take advantage of the amazing genetic resource collected in the Arabidopsis thaliana 1001 Genomes Project (http://1001genomes.org). ViVa facilitates data mining on the gene, gene family or gene network level. To test the utility and accessibility of ViVa, we assembled a team with a range of expertise within biology and bioinformatics to analyze the natural variation within the well-studied nuclear auxin signaling pathway. Our analysis has provided further confirmation of existing knowledge and has also helped generate new hypotheses regarding this well studied pathway. These results highlight how natural variation could be used to generate and test hypotheses about less studied gene families and networks, especially when paired with biochemical and genetic characterization. ViVa is also readily extensible to databases of interspecific genetic variation in plants as well as other organisms, such as the 3,000 Rice Genomes Project (http://snp-seek.irri.org/) and human genetic variation (https://www.ncbi.nlm.nih.gov/clinvar/).
ACWA: An AI-driven Cyber-Physical Testbed for Intelligent Water Systems
Batarseh, Feras; Kulkarni, Ajay; Sreng, Chhayly; Lin, Justice; Maksud, Siam (2023-10-05)
This manuscript presents a novel state-of-the-art cyber-physical water testbed, namely: The AI and Cyber for Water and Agriculture testbed (ACWA). ACWA is motivated by the need to advance water resources’ management using AI and Cybersecurity experimentation. The main goal of ACWA is to address pressing challenges in the water and agricultural domains by utilising cutting-edge AI and data-driven technologies. These challenges include Cyberbiosecurity, resources’ management, access to water, sustainability, and data-driven decision-making, among others. To address such issues, ACWA consists of multiple topologies, sensors, computational nodes, pumps, tanks, smart water devices, as well as databases and AI models that control the system. Moreover, we present ACWA simulator, which is a software-based water digital twin. The simulator runs on fluid and constituent transport principles that produce theoretical time series of a water distribution system. This creates a good validation point for comparing the theoretical approach with real-life results via the physical ACWA testbed. ACWA data are available to AI and water domain researchers and are hosted in an online public repository. In this paper, the system is introduced in detail and compared with existing water testbeds; additionally, example use-cases are described along with novel outcomes such as datasets, software, and AI-related scenarios.
Advancements in the development of HIF-1α-activated protein switches for use in enzyme prodrug therapy
Wright, R. Clay; Khakhar, Arjun; Eshleman, James R.; Ostermeier, Marc (2014-01)
While gene-directed enzyme prodrug therapy has shown potential as a cancer therapeutic in animal and clinical trials, concerns over the efficacy, selectivity, and safety of gene delivery vehicles have restricted its advance. In an attempt to relieve some of the demands on targeted gene delivery vehicles and achieve the full potential of enzyme prodrug therapy, cancer-targeted activity can be engineered into the enzyme itself. We previously engineered a switchable prodrug-activating enzyme that selectively kills human cancer cells accumulating the cancer marker hypoxia-inducible factor-1α (HIF-1α). This HIF-1α-activated protein switch (Haps59) is designed to increase its ability to convert the prodrug 5-fluorocytosine into the chemotherapeutic 5-fluorouracil in a HIF-1α-dependent manner. However, in cancer cell lines expressing Haps59 the 5FC sensitivity difference between the presence and absence of HIF-1α was not as large as desired. In this work, we aimed to improve the cancer specificity of this switch via a directed evolution approach utilizing random mutagenesis, linker mutagenesis, and random insertion and circular permutation. We identified improved HIF-1α-activated protein switches that confer E. coli with modest increases in HIF-1α-dependent 5FC toxicity. Additionally, the current bottleneck in the development of improved HIF-1α-activated protein switches is screening switch candidates in mammalian cells. To accommodate higher throughput and reduce experimental variability, we explored the use of Flp recombinase-mediated isogenic integration in 293 cells. These experiments raised the possibility that Haps59 can be activated by other interactors of the CH1 domain, and experiments in E. coli indicated that CITED2 can also activate Haps59. Although many CH1 binding partners are also oncogenes, CH1's promiscuous binding and subsequent off-target activation of Haps59 needs to be examined under normal physiological conditions to identify off-target activators. With aberrant activating molecules identified, further directed evolution can be performed to improve the cancer specificity of HIF-1α-activated protein switches.
Advances in Biochemical Engineering-Biotechnology
Zhang, Y. H. Percival; Rollin, Joseph A.; Ye, Xinhao; Del Campo, Julia S. Martin; Adams, Michael W. W. (Springer, 2014-07-15)
In vitro hydrogen generation represents a clear opportunity for novel bioreactor and system design. Hydrogen, already a globally important commodity chemical, has the potential to become the dominant transportation fuel of the future. Technologies such as in vitro synthetic pathway biotransformation (SyPaB)—the use of more than 10 purified enzymes to catalyze unnatural catabolic pathways—enable the storage of hydrogen in the form of carbohydrates. Biohydrogen production from local carbohydrate resources offers a solution to the most pressing challenges to vehicular and bioenergy uses: small-size distributed production, minimization of CO2 emissions, and potential low cost, driven by high yield and volumetric productivity. In this study, we introduce a novel bioreactor that provides the oxygen-free gas phase necessary for enzymatic hydrogen generation while regulating temperature and reactor volume. A variety of techniques are currently used for laboratory detection of biohydrogen, but the most information is provided by a continuous low-cost hydrogen sensor. Most such systems currently use electrolysis for calibration; here an alternative method, flow calibration, is introduced. This system is further demonstrated here with the conversion of glucose to hydrogen at a high rate, and the production of hydrogen from glucose 6-phosphate at a greatly increased reaction rate, 157 mmol/L/h at 60 [degrees] C.
Advances in Watershed Management: Modeling, Monitoring, and Assessment
Benham, Brian L.; Yagow, Eugene R.; Chaubey, I.; Douglas-Mankin, K. R. (American Society of Agricultural and Biological Engineers, 2011)
This article introduces a special collection of nine articles that address a wide range of topics all related to improving the application of watershed management planning. The articles are grouped into two broadly defined categories.. modeling applications, and monitoring and assessment. The modeling application articles focus on one of two widely used watershed-scale water quality modeling packages: HSPF or SWAT The HSPF article assesses the model's robustness when applied to watersheds across a range of topographic settings and climatic conditions. In the SWAT-related articles, researchers used the model to inform watershed management efforts in a variety of ways, including subwatershed prioritization in the context of achieving broader watershed management goals, examining the utility of applying SWAT in a watershed receiving groundwater inputs from outside the topographic watershed boundaries, and estimating the uncertainty and risk associated with meeting TMDL target loads. The monitoring and assessment articles cover such diverse topics as an examination of how best management practice effectiveness is assessed, examination of estimated nutrient loads to a reservoir where a nutrient TMDL has been developed, examination of the sources of fecal indicator bacteria in an urban watershed, and detailed accounting of issues related to flow measurements in small watersheds. The articles in this collection contribute to the body of literature that seeks to inform and advance sound watershed management planning and execution.
AgroSeek: a system for computational analysis of environmental metagenomic data and associated metadata
Liang, Xiao; Akers, Kyle; Keenum, Ishi M.; Wind, Lauren L.; Gupta, Suraj; Chen, Chaoqi; Aldaihani, Reem; Pruden, Amy; Zhang, Liqing; Knowlton, Katharine F.; Xia, Kang; Heath, Lenwood S. (2021-03-10)
Background Metagenomics is gaining attention as a powerful tool for identifying how agricultural management practices influence human and animal health, especially in terms of potential to contribute to the spread of antibiotic resistance. However, the ability to compare the distribution and prevalence of antibiotic resistance genes (ARGs) across multiple studies and environments is currently impossible without a complete re-analysis of published datasets. This challenge must be addressed for metagenomics to realize its potential for helping guide effective policy and practice measures relevant to agricultural ecosystems, for example, identifying critical control points for mitigating the spread of antibiotic resistance. Results Here we introduce AgroSeek, a centralized web-based system that provides computational tools for analysis and comparison of metagenomic data sets tailored specifically to researchers and other users in the agricultural sector interested in tracking and mitigating the spread of ARGs. AgroSeek draws from rich, user-provided metagenomic data and metadata to facilitate analysis, comparison, and prediction in a user-friendly fashion. Further, AgroSeek draws from publicly-contributed data sets to provide a point of comparison and context for data analysis. To incorporate metadata into our analysis and comparison procedures, we provide flexible metadata templates, including user-customized metadata attributes to facilitate data sharing, while maintaining the metadata in a comparable fashion for the broader user community and to support large-scale comparative and predictive analysis. Conclusion AgroSeek provides an easy-to-use tool for environmental metagenomic analysis and comparison, based on both gene annotations and associated metadata, with this initial demonstration focusing on control of antibiotic resistance in agricultural ecosystems. Agroseek creates a space for metagenomic data sharing and collaboration to assist policy makers, stakeholders, and the public in decision-making. AgroSeek is publicly-available at https://agroseek.cs.vt.edu/ .
Ajna: A Wearable Shared Perception System for Extreme Sensemaking
Wilchek, Matthew; Luther, Kurt; Batarseh, Feras A. (ACM, 2024)
This paper introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of depth sensors and cameras. These limitations confine object detection to close proximity and hinder perception beyond immediate surroundings, through obstructions, or across different structural levels, impacting collaborative use. It harnesses the Inertial Measurement Unit (IMU) in AR devices to measure users? relative distances from a set physical point, enabling object detection sharing among multiple users across obstacles like walls and over distances. We tested Ajna's effectiveness in a controlled study with 15 participants simulating emergency situations in a multi-story building. We found that Ajna improved object detection, location awareness, and situational awareness, and reduced search times by 15%. Ajna's performance in simulated environments highlights the potential of artificial intelligence (AI) to enhance sensemaking in critical situations, offering insights for law enforcement, search and rescue, and infrastructure management.
Alterations in the molecular composition of COVID-19 patient urine, detected using Raman spectroscopic/computational analysis
Robertson, John L.; Senger, Ryan S.; Talty, Janine; Du, Pang; Sayed-Issa, Amr; Avellar, Maggie L.; Ngo, Lacy T.; Gomez de la Espriella, Mariana; Fazili, Tasaduq N.; Jackson-Akers, Jasmine Y.; Guruli, Georgi; Orlando, Giuseppe (PLOS, 2022-07-01)
We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine ‘molecular fingerprint’, representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016–2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9–1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR) <90 mL/min/1.73m2 (range 59–84 mL/min/1.73m2). We could not determine if any of these patients had antecedent clinical pathology abnormalities. Our technology (Raman Chemometric Urinalysis—Rametrix®) had an overall prediction accuracy of 97.6% for detecting complex, multimolecular fingerprints in urine associated with COVID-19 disease. The sensitivity of this model for detecting COVID-19 was 90.9%. The specificity was 98.8%, the positive predictive value was 93.0%, and the negative predictive value was 98.4%. In assessing severity, the method showed to be accurate in identifying symptoms as mild, moderate, or severe (random chance = 33%) based on the urine multimolecular fingerprint. Finally, a fingerprint of ‘Long COVID-19’ symptoms (defined as lasting longer than 30 days) was located in urine. Our methods were able to locate the presence of this fingerprint with 70.0% sensitivity and 98.7% specificity in leave-one-out cross-validation analysis. Further validation testing will include sampling more patients, examining correlations of disease severity and/or duration, and employing metabolomic analysis (Gas Chromatography–Mass Spectrometry [GC-MS], High Performance Liquid Chromatography [HPLC]) to identify individual components contributing to COVID-19 molecular fingerprints.
Analysis of crab meat volatiles as possible spoilage indicators for blue crab (Callinectes sapidus) meat by gas chromatography-mass spectrometry
Sarnoski, Paul J.; O'Keefe, Sean F.; Jahncke, Michael L.; Mallikarjunan, Parameswarakumar; Flick, George J. Jr. (Elsevier, 2010-10-01)
Traditionally crab meat spoilage has been evaluated using sensory panels. A method was developed using solid-phase microextraction–gas chromatography–mass spectrometry (SPME–GC–MS) to examine the aroma profile of blue crab (Callinectes sapidus) for chemical indicators of spoilage. The chemicals found to correlate best with spoilage were trimethylamine (TMA), ammonia, and indole over a period of 7 days. In addition, chemicals previously not identified in the aroma profile of blue crab were tentatively detected. Scan mode of the mass spectrometer was used to qualitatively determine compounds extracted from the volatile profile of spoiling blue crab by the SPME fiber. Selected ion monitoring (SIM) mode of the mass spectrometer improved resolution, identified compounds at low concentrations, and allowed spoilage related compounds to be detected in one chromatographic run without sample heating. TMA increased linearly. A significant difference in TMA concentrations were found for day 0 and day 4 samples. Indole concentrations corresponded well with sensory and microbial evaluations, in early, mid, and highly spoiled crab meat samples.
Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India
Loukika, Kotapati Narayana; Keesara, Venkata Reddy; Sridhar, Venkataramana (MDPI, 2021-12-13)
The growing human population accelerates alterations in land use and land cover (LULC) over time, putting tremendous strain on natural resources. Monitoring and assessing LULC change over large areas is critical in a variety of fields, including natural resource management and climate change research. LULC change has emerged as a critical concern for policymakers and environmentalists. As the need for the reliable estimation of LULC maps from remote sensing data grows, it is critical to comprehend how different machine learning classifiers perform. The primary goal of the present study was to classify LULC on the Google Earth Engine platform using three different machine learning algorithms—namely, support vector machine (SVM), random forest (RF), and classification and regression trees (CART)—and to compare their performance using accuracy assessments. The LULC of the study area was classified via supervised classification. For improved classification accuracy, NDVI (normalized difference vegetation index) and NDWI (normalized difference water index) indices were also derived and included. For the years 2016, 2018, and 2020, multitemporal Sentinel-2 and Landsat-8 data with spatial resolutions of 10 m and 30 m were used for the LULC classification. ‘Water bodies’, ‘forest’, ‘barren land’, ‘vegetation’, and ‘built-up’ were the major land use classes. The average overall accuracy of SVM, RF, and CART classifiers for Landsat-8 images was 90.88%, 94.85%, and 82.88%, respectively, and 93.8%, 95.8%, and 86.4% for Sentinel-2 images. These results indicate that RF classifiers outperform both SVM and CART classifiers in terms of accuracy.
Analysis of the causes of extreme precipitation in major cities of Peninsular India using remotely sensed data
Kotrike, Tharani; Keesara, Venkata Reddy; Sridhar, Venkataramana (Elsevier, 2024-01)
Analyzing multiple-source water usage patterns and affordability in rural central Appalachia
Dudzinski, Emerald; Ellis, Kimberly P.; Krometis, Leigh-Anne H.; Albi, Kate; Cohen, Alasdair (2024-07-18)
Nearly 500,000 American households lack complete plumbing, and more than 21 million Americans are reliant on public drinking water systems with at least one annual health-based drinking water violation. Rural, low-income, and minority communities are significantly more likely to be burdened with unavailable or unsafe in-home drinking water. Lack of access and distrust of the perceived quality of municipally supplied water are leading an increasing number of Americans to rely instead on less regulated, more expensive, and potentially environmentally detrimental water sources, such as roadside springs and bottled water. Previous research studies have stressed the importance of considering the economic burden of all water related expenditures including financial and non-financial water related costs; however, past examinations of water costs have primarily focused on municipal water supplies. We propose an economic model to consider the full economic burden associated with multiple-source water use by incorporating both direct costs (e.g., utility bills, well maintenance, bottled water purchase, payments for water hauling/delivery) and indirect water-related expenditures (e.g., transportation costs to gather water, productivity lost due to time spent collecting). Using data gathered from household surveys along with the economic model, this study estimates the economic burden from two case studies in rural Central Appalachia with persistent water quality concerns: (1) McDowell County, WV (n=15) and (2) Letcher and Harlan Counties, KY (n=9). All surveyed households (n=24) rely on multiple-source water to meet their needs, frequently citing their perception of unsafe in-home tap water. Bottled water was the most common choice for drinking water in both settings (92%, n=24), though roadside spring use was also prevalent in McDowell County, WV (53%, n=15). The results show that multiple-water source use is associated with a large economic burden. Households reliant primarily on bottled water as their drinking water source spent 12.3% (McDowell County, WV) and 5.6% (Letcher and Harlan Counties, KY) of their respective county’s median household income (MHI) on water related expenditures. Households reliant primarily on roadside springs as their drinking water source spent 11.8% (McDowell County, WV) of MHI on water related expenditures. Hence, the vast majority of participating households (92%, n=24) spend above the US water affordability threshold of 2% MHI. The application of this economic model highlights major water affordability concerns in water insecure Appalachian communities and provides a foundation for future studies and enhancements.
Annual Report of AE Extension Project 10, with Project Statements
Seitz, Charles E. (1924)
Annual Report for Project 10 detailing Virginia Cooperative Extension activities in 1924, with project statements.
Annual Report of Agricultural Engineering Department, Extension Division
Seitz, Charles E. (1920)
Summary of extension projects coordinated through the Agricultural Engineering Department for 1919-20.
Annual Report of Agricultural Engineering Department, Extension Division
(1921)
Summary of extension projects coordinated through the Agricultural Engineering Department for 1920-21.
Annual Report of Agricultural Engineering Extension Project 10
Seitz, Charles E. (1924)
Annual Report for Project 10 detailing Virginia Cooperative Extension activities in 1924.
Annual Report of Agricultural Engineering Extension Project 10, (copy 2)
Seitz, Charles E. (1923)
Annual Report for Project 10 detailing Virginia Cooperative Extension activities in 1923.
Annual Report of Agricultural Engineering Extension Project 10, with photos
Seitz, Charles E. (1923)
Annual Report for Project 10 detailing Virginia Cooperative Extension activities in 1923.
Annual Report of Agricultural Engineering Resident Instruction
Seitz, Charles E. (1930)
Summary of activities related to instruction provided through the Agricultural Engineering Department.

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