Group Seminar

2024

  • 12/03, Rawan Elabd, TBD
  • ...
  • 11/19, Insoo Jeon, TBD
  • ... TBD
  • 10/15, Sanjog Kharel, TBD
  • 10/08: Yoonmi Choi, TBD 
  • 10/01: Jackfin KC, Updates on Android app development 
  • 09/09: [Special Seminar] Ms. Miao He from Tokyo Metropolitan University, Syntrophic Association Between Purple Photosynthetic Bacteria and Tetrathionate-Reducing Bacteria
  • 08/16: [Special Seminar] Dr. Mayumi Seto from Nara Women's University, Prediction of microbial redox functions based on thermodynamics and causal inference
  • 08/12-16: Song Lab - Bioinformatics and Modeling Workshop (SL-BMW) led by Hugh McCullough 
  • 07/09: Sanjog Kharel, Modelling microbial dynamics in deep subsurface via their energetics
  • 07/03: Yoonmi Choi, Current Status of Plant Modeling: Approaches and Insights
  • 06/25: Insoo Jeon, Plant-driven assembly of disease-suppressive soil microbiomes
  • 06/18: Steve Zhang and Mahsa Mohammadi, ASM highlights
  • 06/06: Steve Zhang and Mahsa Mohammadi, ASM dry run
  • 05/21: Manokaran Veeramani, Bioavailable OM selection to improve predictions of aerobic respiration in river corridors
  • 05/14: Sanjog Kharel, Yoonmi Choi, and Mahsa Mohammadi, BSEN 951 Term Project Presentations
  • 05/07: Manokaran Veeramani, Computational inference of spatial microbial interactions from microscopic images​
  • 04/30: Steve Zhang, Building Metabolite-Enzyme Networks using edgeR and NetCom: Functional Enrichment in RSAX and WSAX
  • 04/23: Mahsa Mohammadi, The Impact of Red and White Sorghum Arabinoxylans on Human Gut Microbiota Interactions
  • 04/09: Aimee Kessell, Genome-scale network modeling of chitin-degrading microbial communities and their isolates (thesis defense)
  • 03/26: Aimee Kessell, Genome-scale network modeling of chitin-degrading microbial communities and their isolates (presentation to the Soil Microbiome SFA at PNNL, dry run #2)
  • 03/19: Aimee Kessell, Genome-scale network modeling of chitin-degrading microbial communities and their isolates (dry run #1)
  • 02/27: Sanjog Kharel, Chemolithotrophs in deep subsurface
  • 02/13: Hugh McCullough, Redox Homeostasis and Potential Indicators of Oxidative Stress in Omics Data
  • 02/06: Mahsa Mohammadi, Network-based analysis of metagenomics data 
  • 01/30: Steve Zhang, Update: Community Flux Coupling Analysis in Unicyanobacterial Communities 
  • 01/23: Kiera Chan, Novel Eigenmode-Based Feature Engineering for Efficient and Interpretable Image Classification: Application to Handwritten Digits
  • 01/10: Sanjog Kharel, Lambda Model for Microbial Growth (2): Identification of bioavailable OM

2023

  • 12/19: Andrea Mikulasova, Flux Balance Analysis: Everything I didn’t know
  • 11/28: Manokaran Veeramani, Overview of similarity measures/metrics for comparing spatial data
  • 11/21: Hugh McCullough, Clustering of River Samples by Functional Traits identified by DRAM
  • 11/14: Aimee Kessell, Isolate and Community Model Updates – Improvements and Drawbacks
  • 10/31: Mahsa Mohammadi, Combination of kinetic-based modeling and metabolic network modeling
  • 10/26: Steve Zhang, Combining KIDI with SteadyCom in Modeling Unicyanobacterial Communities
  • 10/19: Sanjog Kharel, Lambda model for microbial growth 
  • 10/12: Toko Hisano, How do incomplete denitrifying bacteria survive in nature?
  • 09/26: Hugh McCullough, Intersections of reaction activity from model predictions and omics data
  • 09/19: Aimee Kessell, Predicting flux within a community via SteadyCom
  • 09/12: Manokaran Veeramani, Inference of Spatial Interactions from Long-Term Image Data
  • 09/05: Mahsa Mohammadi, Review of LIMITS algorithm
  • 08/28: Yoan Ghaffar, Residents' impact on the indoor environment; Kiera Chan, Applying Singular Value Decomposition to Extract Shape Features in Handwritten Digits
  • 08/21: Steve Zhang, Modeling of Metabolism and Autotroph-Heterotroph Interactions in UCC-A and UCC-O
  • 08/14: Hugh McCullough, Soil microbiome responses to environmental perturbations 
  • 07/17: Aimee Kessell, Impacts of Bioenergetic Costs of Chitin Decomposition on Degrader Metabolism and Their Interactions with Cheaters
  • 07/10: Firnaaz Ahamed, Estimating microbial kinetics using substrate-explicit thermodynamic modeling (SXTM)
  • 07/03: Huisun Eom, Data-Driven Modeling using SINDy: Application to Biological data and Education-related Data
  • 06/26: Soo Park, Differential response of nitrate-reducing synthetic communities to varying carbon-to-nitrogen ratio
  • 06/05: [Special Seminar] Dr. Sridharakumar Narasimhan at IIT Madras, A Priori Parameter Identifiability - Impact of Measurements in Complex Reaction Networks 
  • 05/29: All, 2023 Summer and Beyond 
  • 05/22: Manokaran Veeramani, Stochastic simulations for modeling chemical reactions (II)
  • 05/15: Ayeon Park, App development for modeling and simulation of interactions between microbial populations
  • 05/08: Manokaran Veeramani, Stochastic simulations for modeling chemical reactions (I)
  • 05/01: Mahsa Mohammadi, Comparative Study of the Effects of Red and White Sorghum Arabinoxylans on Human Gut Microbiota: Data-Driven Approach
  • 04/24: Steve Zhang, Pathway and Flux Coupling Analysis in Unicyanobacterial Communities
  • 04/10: Hugh McCullough, Structure of Fecal Microbial Communities under Varying Carbohydrate Compositions and Antibiotic Perturbation
  • 04/03: Aimee Kessell, Flux Coupling Analysis – Existing algorithms and new strategies
  • 03/27: Naeun Lee, Metabolic Network Modeling to Unravel the Impact of Copper Deficiency and High-fat Diet on Liver Metabolism
  • 03/20: Firnaaz Ahamed, Coupled flux balance analysis and reactive transport modeling using machine learning
  • 02/27: Soo Park, Changes of N cycling synthetic microbial communities under varying C/N ratios
  • 02/13: Manokaran Veeramani, Modeling of context-dependent microbial interactions in biopolymer degradation networks
  • 02/06: Soo Park, Tutorial: Bipartite graphs using NetworkX and Gephi & Mapping genes onto KEGG pathway using KEGG Color Mapper
  • 01/23: Firnaaz Ahamed, What are the major chemical traits that govern the bioavailability of organic matters in river corridors?
  • 01/09:  Kiera Chan, Coupling the Cybernetic Modeling with Flux Balance Analysis for Dynamic Simulation of Overflow Metabolism in Escherichia coli

2022

  • 12/21: Soo Park, C/N ratio of substrate drives the structure of microbial communities in soil
  • 12/12: Mahsa Mohammadi, A brief review of network analysis methods for studying microbial communities  
  • 11/28: Hugh McCullough, Media carbohydrate composition’s potential Influence on variability of community assembly and peptide utilization
  • 11/07: Steve Zhang, Flux coupling analysis: Methods and applications
  • 10/31: Aimee Kessell, Constructing a binary metabolic network model to more accurately represent consortium growth on chitin, and its monomer, NAG
  • 10/03: Naeun Lee, DEG-integrated modeling to increase consistency between gene expression data and flux distribution 
  • 09/19: Meeting with Dr. Shin Haruta from Tokyo Metropolitan University
  • 08/22: Firnaaz Ahamed, Data-driven optimization allows identification of organic matters governing microbial respiration in river corridors
  • 08/08: Hugh McCullough, Community simplification in vitro: enrichment cultures trend from stochastic to deterministic effects
  • [Hereafter, every other Monday]
  • 07/22: Steve Zhang, Microbial Division of Labor in Polysaccharide-Degrading Communities
  • 07/15: Aimee Kessell, Omics inclusion for an improved model of a chitin-degrading binary consortium
  • 07/08: Naeun Lee, Finding optimal solution through mixed integer quadratic programming
  • 07/01: Firnaaz Ahamed, Data-driven identification of organic matters governing microbial respiration in river corridors
  • 06/17: Hugh McCullough, Observations of Nutrient Niches through Inference of Interactions and Gene Enrichment
  • 06/03: Steve Zhang, Overview of Discrete Fungal Growth Model
  • 05/27: Aimee Kessell, Improving metabolic models through metabolomics constraints
  • 5/20: Hugh McCullough, OTU- and Genus-Level Fecal Bacterial Interaction Networks from Enriched Carbohydrate Culture
  • 05/13: Naeun Lee, Quadratic programming enhancing correlation between gene expressions and reactions
  • 05/06: Firnaaz Ahamed, Model-driven identification of OM molecular signatures controlling biogeochemical transformation in river corridors
  • 04/29: Steve Zhang, Predicting the Specific Growth Rate using SINDy and Neural Networks
  • 04/15: Michael Israel, L-HCM
  • 04/08: Aimee Kessell, Metabolite exchange necessary for growth of a binary consortium on chitin
  • 04/01: Hugh McCullough, The influence of carbohydrate diversity on cultured microbial communities
  • 03/04: Naeun Lee, [Lit. review] Integrating multi-omics data & machine learning in the genome-scale metabolic model
  • 02/25: Hugh McCullough, [Lit. review] EMBED: a low dimensional reconstruction of gut microbiome dynamics based on ecological normal modes
  • 02/18: Steve Zhang, [Lit. review] Mycelial response to spatiotemporal nutrient heterogeneity: A velocity-jump mathematical model
  • 02/11: Michael Israel, Cybernetic modeling with AUMIC and HCM
  • 02/04: Aimee Kessell, C. japonicus and E. coli community modeling with an initial storyboard
  • 01/28: Firnaaz Ahamed, Manuscript storyboard: A generalized priming theory based on microbial trait-centric regulatory response to growth limitations
  • 01/21: Naeun Lee, The impact of lipid-associated pathways by Cu deficiency and high-fat diet
  • 01/14: Hugh McCullough, Comprehensive Exam Proposal overview
  • 01/07: Steve Zhang, Tutorial session: Flux balance and variance analysis using CPLEX in Python

2021

  • 12/17: Michael Israel, Cybernetic Modeling Ⅱ   &   Steve Zhang, Flux Balance and Variance Analysis with CPLEX in Python 
  • 12/03: Aimee Kessell, TBA
  • 11/19: Firnaaz Ahamed, Literature review: Can ensemble modeling improve uncertainties in metabolic network reconstruction?
  • 11/12: Naeun Lee, Identification of impacts by high-fat diet and Cu deficiency via FVA and flux sampling
  • 11/05: Hugh McCullough, AIChE Presentation Practice / Microbiome Meta-Ecosystems and Meta-communities
  • 10/29: Michael Israel, Cybernetic Modeling
  • 10/15: Steve Zhang, FBA
  • 10/08: Aimee Kessell, A Deeper Look into Microbial Cross-Feeding to Expanding the Metabolic Niche of Bacteria
  • 10/01: Firnaaz Ahamed, Predicting organic matter thermodynamics and respiration kinetics via elemental composition of metabolomics data
  • 09/24: Naeun Lee, Modeling of Cu deficiency in the liver with a high-fat diet, and expected changes of enzymes
  • 09/17: Hugh McCullough, Cultivating and Identifying Interactions of Adjacent Bacterial Communities
  • 09/10: Aimee Kessell, Metabolic Network Modeling Helps Determine Interactions In A Complex Polysaccharide-Decomposing Microbial Community
  • 09/03: Firnaaz Ahamed, Metabolic network reconstruction using omics-enabled global gapfilling (OMEGGA) approach
  • 08/27: Naeun Lee, Preprocessing for integration of transcriptomic data into the model & Ten rules for organized paper
  • 08/20: Hugh McCullough, Paper Share: Challenges in microbial network construction and analysis / Bio Image analysis with ilastik
  • 08/13: Ab Rauf Shah, Disentangling soil microbiome functions by Interkingdom Modeling
  • 07/30: Aimee Kessell, dry-run MPA presentation 
  • 07/23: Naeun Lee, dry-run MPA presentation
  • 07/09: Firnaaz Ahamed, Comparison between variance-based local sensitivity and density-based global sensitivity analysis
  • 07/02: Steve Zhang, Using SINDy and PAWN to Create Equations Predicting the D-Value and Evaluate Sensitivity of Environmental Factors
  • 06/25: Naeun Lee, Omics and metabolic network integration to predict the impacts of Cu limitation on the liver functions and energy metabolism     
  • 06/11: Hugh McCullough, Considerations for experimental models to improve culture of microbial communities
  • 06/01-02: [Hands-on Training] Dr. Joon-Yong Lee from PNNL, KBase app development using SDK 
  • 05/21: Ab Rauf Shah, Tutorial session (II) on "making gap-free genome-scale models"
  • 05/14: Ab Rauf Shah, Tutorial session (I) on "making gap-free genome-scale models"
  • 05/07: All, Summer Plan Sharing 
  • 04/30: Aimee Kessell, Fungal annotation and modeling 
  • 04/23: Firnaaz Ahamed, Science in PNNL River Corridor SFA
  • 04/16: Ab Rauf Shah, Reconstruction and Analysis of Maize root-specific metabolic models captures the programming of metabolism through multiple root types and developmental stages
  • 04/09: Steve Zhang, Pathogen Inactivation in Meats
  • 04/02: Naeun Lee, Suggestion of the possibility that SSAO and BMP7 may be associated with Cu-deficiency adaptive response
  • 03/26: Puranjit Singh, Understanding multiscale model of fungal growth and its regulation (2)
  • 03/19: Puranjit Singh, Understanding multiscale model of fungal growth and its regulation (1)
  • 03/12: Hugh McCullough, Mucins and their role in spatial heterogeneity of gut microbiota
  • 03/05: Aimee Kessell, Science in PNNL Soil Microbiome SFA  
  • 02/26: Firnaaz Ahamed, Modeling microbial competition potentially explains priming effects in soil ecosystem and hyporheic corridor
  • 02/19: Steve Zhang, Using SINDY and sparse regression to predict D- values and parameters
  • 02/12: Naeun Lee, Copper in Lipid Metabolism and Energy Homeostasis & Analysis Liver Transcriptomics Data in Matlab
  • 02/05: Puranjit Singh, Dynamic Flux Balance Analysis
  • 01/29: Hugh McCullough, Inference of microbial interactions from time series data using LIMITS
  • 01/22: Aimee Kessell, Using HCC to solve MATLAB code & Literature Review on Spatial Modeling Technique
  • 01/15: Firnaaz Ahamed, Modelling Consolidated Bioprocessing of Cellulose via Population Balances Coupled with Cybernetic Models
  • 01/08: Hugh McCullough, Microbial Dark Matter; Puranjit Singh, Dynamic Flux Balance Analysis 

2020

  • 12/18: Hyun-Seob Song, Issues in Inferring Microbial Correlation Networks from Relative Abundances 
  • 12/11: Hugh McCullough, Network Analysis of Fecal Microbial Communities under Differing Carbohydrate Contexts
  • 12/04: Bingjun Zhu, Microbial Models for Food Safety and Security
  • 11/20: Aimee Kessell, Integration of Data-driven and FBA for Modeling Context-dependent Interactions
  • 11/13: Puranjit Singh, Cybernetic Modeling of Microbial Growth Patterns
  • 11/06: Jahangeer, Microbial Models for Food Safety and Security
  • 10/30: Bingjun Zhu/Jahangeer,  Microbial Growth Models
  • 10/23: Hyun-Seob Song, Modeling Microbial Intelligence
  • 10/16: Puranjit Singh, What is Flux Balance Analysis?
  • 10/09: Aimee Kessell, Dynamical Flux Balance Analysis for a Binary Model Consortium
  • 10/02: Hugh McCullough, Alternate Sigma Factors and Life Stage Dependency on Microbial Function and Interactions
  • 09/25: Jahangeer, Metabolomics and Lipidomic Data Integration into Metabolic Network Modeling
  • 09/18: Naeun Lee, Metabolic Modeling of Adipose Cell: The Effect of Copper
  • 09/11: Aimee Kessell, Model Construction for Cellvibrio japonicus and Escherichia coli
  • 09/04: Hugh McCullough, Theoretical Frameworks for Context Dependency
  • 08/31: Aimee Kessell, Metabolic Modeling of Cellvibrio japonicus and Escherichia coli
  • 08/21: Naeun Lee, Diseases Classification through Machine Learning using Human Gut Microbiome Dataset
  • 08/07: Aimee Kessell, Science Plans and Context-Dependent Dynamics
  • 07/17: Aimee Kessell, Network Reduction Methods
  • 07/03: Hugh McCullough, Interaction Network Inference and Experimental Design
  • 06/26: Jahangeer, Soil Microbiome Modeling: Multi Omics approach review