BCB Faculty Seminar - Cademartiri, Friedberg, Wu

Event
Wednesday, September 6, 2017 - 4:10pm
Event Type: 

Three BCB Faculty will present at the BCB Faculty Seminar on Wednesday, Sept. 6 at 4:10 p.m. in 1424 MBB:


Ludivico Cademartiri


Departments: Chemical and Biological Engineering and Materials Science and Engineering


Research Interests: We have recently been awarded a Beckman Young Investigator Award by the Arnold and Mabel Beckman Foundation to explore collective behaviors in plants that might be originating from root-root connections that would enable plants to form communities capable of exchanging effectively small amounts of signaling molecules over large distances. Interestingly, the behavior of this community should then depend on the topology of the network formed by the root systems. Our proposal detailed the construction of experimental setups for the creation of such communities of plants and their investigation.


This study is highly interdisciplinary and would require expertise that extend far beyond pure engineering. We are therefore interested in candidates that would understand biology but also have the mathematical capabilities to handle network theory, without being afraid of the highly disciplined type of experimental work that is required.


Area of Expertise: Materials Science


Contact: lcademar@iastate.edu


Cademartiri Lab


515-294-4549

3109 Gilman Hall



Iddo Friedberg


DepartmentVeterinary Microbiology and Preventive Medicine


I am interested in large scale analyses of proteins, genomes and metagenomes.


Metagenomics is the study of genomic material extracted directly from the environment. New sequencing technologies have enabled the study of whole populations of genomes taken from microbial communities in the field, as opposed to single species clonal cultures in the lab. Metagenomics offers a way to study how genomes evolve to cope with the microbial biotic and abiotic environments. Our lab helped developed a method to study the correlation between the human gut microbiota and gut gene expression. We are applying this method towards studying infant gut development the effect of gut microbes on human health and wellness.


Bacterial Genome Evolution: Gene blocks are a common occurrence in bacteria: these are genes which lie close together on the chromosome, and may participate in a common cellular or biochemical function. Operons are gene blocks whose member genes are co-transcribed. We have developed a new method to describe the evolution of operons and gene blocks in bacteria. We describe a small set of evolutionary events that can take place in gene block evolution, and count these events to create a new type of molecular clock that tells us how fast or how slow certain gene blocks may have evolved. We hope to learn how new funcitons are acquired by ensembles of genes such as these.


Function Prediction


Another interest of mine is the prediction of protein function. Genomics, proteomics and various other ``-omics'' inundate us with sequence and structure information, but the biological functions of those proteins in many cases still eludes us. Computational prediction of protein and gene function is a rapidly growing research field in bioinformatics [4]. I am the co-organizer of the automated computational protein function prediction meetings: AFP. The AFP meetings bring together researchers to discuss various methods for protein function prediction. My personal interest in function prediction lies in predicting function from protein structure [5]. We have recently started work on predicting gene function based on its genomic context in bacteria, using both genomic and metagenomic data towards that end.


Structural Signatures


We are interested in locating ``structural signatures'' that span different protein folds. My working hypothesis is that there are short local structural commonalities between proteins that otherwise share no obvious structure or function. Detecting these commonalities can help us understand protein evolution, folding, and design. [1] ,  [2]


Different Representations of Protein Structures


The computational representation of a protein's 3D structure is a challenging problem because of varying and often conflicting considerations: at first sight it seems that as far as information is concerned, more is better, hence the drive to atomic level description. However, elaboration on the atomic level can be very ``noisy'' and be time and memory intensive. Therefore we often ask what is the minimal information we need to achieve a specific task, without going into the unnecessary detail of representing each and every atom. I am interested in different computational representations of protein structures suitable for different tasks. In one study we have shown that a 1D representation of protein structures can be used for fast database searching and alignments, and still preserve relevant structural information. [3].


Area of Expertise: Bioinformatics, genomics, Critical Assessments


Contact: idoerg@iastate.edu


Lab Website


Google Scholar


ORCID


515-294-5959

2118 Vet Med



Zhijun Wu


Department: Mathematics


Research:

Nonlinear optimization, game theory, numerical linear algebra, linear programming, integer & combinatorial optimization, mathematical biology, protein modeling, modeling of evolution and natural selection


Selected Papers:


  • Wang M, Huang Y, Wu, Z, Simulation of yeast cooperation in 2D, Bull Math Biol 78, 531-555, 2016
  • Huang Y, Wang M, Hao Y, Zhou W, Wu Z, Optimality and stability conditions of symmetric evolutionary games with applications to genetic selection, J Math Biosci & Engineering 12, 503-523, 2015
  • Park J, Jernigan R, Wu Z, Coarse-grained NMR vs refined GNM for protein residue-level fluctuations, Bull Math Biol 75, 124-160, 2013
  • Huang Y, Wu Z, Game dynamic model for yeast development, Bull Math Biol 74, 1469-1484, 2012
  • Sit A, Wu Z, Solving a generalized distance geometry problem for protein structure determination, Bull Math Biol 73, 2809-2836, 2011

Area of Expertise: optimization and game theory; protein modeling; evolutionary dynamics; 


Contact: zhijun@iastate.edu


Zhijun Wu - Mathematic Department's website


515-294-8165

462 Carver Hall

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