Protein nanowires with tunable functionality and programmable self-assembly using sequence-controlled synthesis.

Advances in synthetic biology permit the genetic encoding of synthetic chemistries at monomeric precision, enabling the synthesis of programmable proteins with tunable properties. Bacterial pili serve as an attractive biomaterial for the development of engineered protein materials due to their ability to self-assemble into mechanically robust filaments. However, most biomaterials lack electronic functionality and atomic structures of putative conductive proteins are not known. Here, we engineer high electronic conductivity in pili produced by a genomically-recoded E. coli strain. Incorporation of tryptophan into pili increased conductivity of individual filaments >80-fold. Computationally-guided ordering of the pili into nanostructures increased conductivity 5-fold compared to unordered pili networks. Site-specific conjugation of pili with gold nanoparticles, facilitated by incorporating the nonstandard amino acid propargyloxy-phenylalanine, increased filament conductivity ~170-fold. This work demonstrates the sequence-defined production of highly-conductive protein nanowires and hybrid organic-inorganic biomaterials with genetically-programmable electronic functionalities not accessible in nature or through chemical-based synthesis.

An exact algorithm for finding cancer driver somatic genome alterations: the weighted mutually exclusive maximum set cover problem.

BACKGROUND: The mutual exclusivity of somatic genome alterations (SGAs), such as somatic mutations and copy number alterations, is an important observation of tumors and is widely used to search for cancer signaling pathways or SGAs related to tumor development. However, one problem with current methods that use mutual exclusivity is that they are not signal-based; another problem is that they use heuristic algorithms to handle the NP-hard problems, which cannot guarantee to find the optimal solutions of their models. METHOD: In this study, we propose a novel signal-based method that utilizes the intrinsic relationship between SGAs on signaling pathways and expression changes of downstream genes regulated by pathways to identify cancer signaling pathways using the mutually exclusive property. We also present a relatively efficient exact algorithm that can guarantee to obtain the optimal solution of the new computational model. RESULTS: We have applied our new model and exact algorithm to the breast cancer data. The results reveal that our new approach increases the capability of finding better solutions in the application of cancer research. Our new exact algorithm has a time complexity of [Formula: see text](Note: Following the recent convention, we use a star * to represent that the polynomial part of the time complexity is neglected), which has solved the NP-hard problem of our model efficiently. CONCLUSION: Our new method and algorithm can discover the true causes behind the phenotypes, such as what SGA events lead to abnormality of the cell cycle or make the cell metastasis lose control in tumors; thus, it identifies the target candidates for precision (or target) therapeutics.

How can we improve Science, Technology, Engineering, and Math education to encourage careers in Biomedical and Pathology Informatics?

The Computer Science, Biology, and Biomedical Informatics (CoSBBI) program was initiated in 2011 to expose the critical role of informatics in biomedicine to talented high school students.[1] By involving them in Science, Technology, Engineering, and Math (STEM) training at the high school level and providing mentorship and research opportunities throughout the formative years of their education, CoSBBI creates a research infrastructure designed to develop young informaticians. Our central premise is that the trajectory necessary to be an expert in the emerging fields of biomedical informatics and pathology informatics requires accelerated learning at an early age.In our 4(th) year of CoSBBI as a part of the University of Pittsburgh Cancer Institute (UPCI) Academy (http://www.upci.upmc.edu/summeracademy/), and our 2nd year of CoSBBI as an independent informatics-based academy, we enhanced our classroom curriculum, added hands-on computer science instruction, and expanded research projects to include clinical informatics. We also conducted a qualitative evaluation of the program to identify areas that need improvement in order to achieve our goal of creating a pipeline of exceptionally well-trained applicants for both the disciplines of pathology informatics and biomedical informatics in the era of big data and personalized medicine.