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Developmental systems biology flourishing on new technologies

Journal of Genetics and Genomics 2008年第10期35卷 577-584页

作者： Jing-Dong J. Han Yi Liu Huiling Xue Kai Xia Hong Yu Shanshan Zhu Zhang Chen Wei Zhang Zheng Huang Chunyu Jin Bo Xian Jing Li Lei Hou Yixing Han Chaoqun Niu Timothy C. Alcon Chinese Academy of Sciences Key Laboratory of Molecular Developmental Biology Center for Molecular Systems Biology Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China Key Laboratory of Machine Perception （Ministry of Education） Peking University Beijing 100871 China Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA 50011-1040 USA

Organism development is a systems level process. It has benefited greatly from the recent technological advances in the field of systems biology. DNA microarray, phenome, interactome and transcriptome mapping, the new generation of deep sequencing technologies, and faster and better computational and modeling approaches have opened new frontiers for both systems biologists and developmental biologists to reexamine the old developmental biology questions, such as pattern formation, and to tackle new problems, such as stem cell reprogramming. As showcased in the International Developmental systems biology Symposium organized by Chinese Academy of Sciences, developmental systems biology is flourishing in many perspectives, from the evolution of developmental systems, to the underlying genetic and molecular pathways and networks, to the genomic, epigenomic and noncoding levels, to the computational analysis and modeling. We believe that the field will continue to reap rewards into the future with these new approaches.

关键词： developmental systems biology new technologies symposium

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IDH1 fine-tunes cap-dependent translation initiation

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Journal of Molecular Cell biology 2019年第10期11卷 816-828页

作者： Lichao Liu JYuyang Lu Fajin Li Xudong Xing Tong Li Xuerui Yang Xiaohua Shen Tsinghua-Peking Center for Life Sciences School of Medicine and School of Life SciencesTsinghua UniversityBeijing 100084China MOE Key Laboratory of Bioinformatics Center for Synthetic Systems BiologySchool of Life SciencesTsinghua UniversityBeijing 100084China Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program School of Life SciencesTsinghua UniversityBeijing 100084China

The metabolic enzyme isocitrate dehydrogenase 1(IDH1)catalyzes the oxidative decarboxylation of isocitrate to a-ketoglutarate(a-KG).Its mutation often leads to aberrant gene expression in ***1 was reported to bind thousands of RNA transcripts in a sequence-dependent manner;yet,the functional significance of this RNA-binding activity remains ***,we report that IDH1 promotes mRNA translation via direct associations with polysome mRNA and translation *** proteomic analysis in embryonic stem cells(ESCs)revealed strikingenrichmentof ribosomal proteins and translation regulators in IDH1-bound protein *** performed ribosomal profiling and analyzed mRNA transcripts that are associated with actively translating ***,knockout of IDH1 in ESCs led to significant downregulation of polysome-bound mRNA in IDH1 targets and subtle upregulation of ribosome densities at the start codon,indicating inefficient translation initiation upon loss of *** IDH1 to a luciferase mRNA via the MS2-MBP system promotes luciferase translation,independently of the catalytic activity of ***,IDH1 fails to enhance luciferase translation driven by an internal ribosome entry ***,these results reveal an unforeseen role of IDH1 in fine-tuning cap-dependent translation via the initiation step.

关键词： IDH1 proteomic interactome translation regulation RNA-binding protein

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Lateral transfer of mRNA and protein by migrasomes modifies the recipient cells

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细胞研究（英文版） 2021年第2期31卷 237-240页

作者： Mingli Zhu Qin Zou Rongyao Huang Ying Li Xudong Xing Jianhuo Fang Liang Ma Lifei Li Xuerui Yang Li Yu State Key Laboratory of Membrane Biology Tsinghua University-Peking University Joint Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life ScienceTsinghua University Beijing 100084 China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science Tsinghua University Beijing 100084 China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science Tsinghua University Beijing 100084 China MOE Key Laboratory of Bioinformatics Tsinghua University Center for Synthetic & Systems Biology Tsinghua University School of Life Science Beijing 100084China State Key Laboratory of Membrane Biology Tsinghua University-Peking University Joint Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life ScienceTsinghua University Beijing 100084 China MOE Key Laboratory of Bioinformatics Tsinghua University Center for Synthetic & Systems Biology Tsinghua University School of Life Science Beijing 100084China

Dear Editor,Migrasomes are recently discovered vesicular organelles which form on the retraction fibers(RFs)of migrating cells.1 Once detached from cells,migrasomes can rupture and release their luminal contents1 in a process named ***,migrasomes have been shown to play an important role in zebrafish organ morphogenesis by releasing chemokine signals to defined regions of the embryo.2 Thus,migracytosis is considered as a major mechanism for migrasomes to carry out their *** has been frequently observed that intact migrasomes can be engulfed by surrounding cells,1 and this has been proposed as a potential mechanism for lateral transfer of cellular contents between ***,it is not clear whether the lateral transfer of cellular contents by migrasomes has functional consequences for the recipient cells.

关键词： Extracellular signalling molecules Organelles

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BioBankWarden: A web-based system to support translational cancer research by managing clinical and biomaterial data

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COMPUTERS IN biology AND MEDICINE 2017年 84卷 254-261页

作者： Ferretti, Yuri Brandao Miyoshi, Newton Shydeo Silva, Wilson Araujo, Jr. Felipe, Joaquim Cezar [a] Inter-institutional Post-graduation Program on Bioinformatics University of São Paulo Brazil[b] Department of Computing and Mathematics Faculty of Philosophy Sciences and Languages of Ribeirão Preto University of São Paulo Brazil[c] Center for Integrative Systems Biology - CISBi NAP/USP University of São Paulo Brazil[d] Department of Genetics School of Medicine of Ribeirão Preto University of São Paulo Brazil

Background: Researchers of translational medicine face numerous challenges in attempting to bring research results to the bedside. This field of research covers a wide range of resources, including blood and tissue samples, which are processed for isolation of RNA and DNA to study cancer omics data (genomics, proteomics and metabolomics). Clinical information about patients' habits, family history, physical examinations, remissions, etc., is also important to underpin studies aimed at identifying patterns that lead to the development of cancer and to its successful treatment. Purpose: Development of a web-based computer system-BioBankWarden-to manage, consolidate and integrate these diversified data, enabling cancer research groups to retrieve and analyze clinical and biomolecular data within an integrative environment. The system has a three-tier architecture comprising database, logic and user-interface layers. Results: The system's integrated database and user-friendly interface allow for the control of patient records, biomaterial storage, research groups, research projects, users and biomaterial exchange. Conclusions: BioBankWarden can be used to store and retrieve specific information from different clinical fields linked to biomaterials collected from patients, providing the functionalities required to support translational research in the field of cancer. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Translational medicine Biological databases Oncogenomics Biomaterial management Clinical data management

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Modularity detection in protein-protein interaction networks

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BMC Research Notes 2011年第1期4卷 1-6页

作者： Narayanan, Tejaswini Gersten, Merril Subramaniam, Shankar Grama, Ananth Department of Electrical and Computer Engineering University of California San Diego United States Graduate Program in Bioinformatics and Systems Biology University of California San Diego United States Department of Bioengineering University of California San Diego United States Department of Computer Science Purdue University West Lafayette IN United States

Background: Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. Results: We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, *** and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. Conclusions: We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the applic

关键词： Biological Network Jaccard Index Input Network High Betweenness Jaccard Similarity Coefficient

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Mapping dihydropteroate synthase evolvability through identification of a novel evolutionarily critical substructure

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International journal of biological macromolecules 2025年 311卷 143325页

作者： Dwipanjan Sanyal A Shivram Deeptanshu Pandey Suharto Banerjee Vladimir N Uversky Danny Muzata Aneesh Chivukula Ravi Jasuja Krishnananda Chattopadhyay Sourav Chowdhury Structural Biology and Bioinformatics Division CSIR-Indian Institute of Chemical Biology Kolkata India. Department of Computer Science and Information Systems Birla Institute of Technology and Science-Pilani Hyderabad India. Department of Biological Sciences Birla Institute of Technology and Science-Pilani Hyderabad India. Max Delbrück Center for Molecular Medicine Berlin Germany USF Health Byrd Alzheimer's Research Institute Morsani College of Medicine University of South Florida Tampa FL 33612 USA. Research Program in Men's Health: Aging and Metabolism Brigham and Women's Hospital Harvard Medical School Boston MA USA. Structural Biology and Bioinformatics Division CSIR-Indian Institute of Chemical Biology Kolkata India. Electronic address: krish@iicb.res.in. Department of Biological Sciences Birla Institute of Technology and Science-Pilani Hyderabad India. Electronic address: sourav.chowdhury@hyderabad.bits-pilani.ac.in.

Protein evolution shapes pathogen adaptation-landscape, particularly in developing drug resistance. The rapid evolution of target proteins under antibiotic pressure leads to escape mutations, resulting in antibiotic resistance. A deep understanding of the evolutionary dynamics of antibiotic target proteins presents a plausible intervention strategy for disrupting the resistance trajectory. Mutations in Dihydropteroate synthase (DHPS), an essential folate pathway protein and sulfonamide antibiotic target, reduce antibiotic binding leading to anti-folate resistance. Deploying statistical analyses on the DHPS sequence-space and integrating deep mutational analysis with structure-based network-topology models, we identified critical DHPS subsequences. Our frustration landscape analysis suggests how conformational and mutational changes redistribute energy within DHPS substructures. We present an epistasis-based fitness prediction model that simulates DHPS adaptive walks, identifying residue positions that shape evolutionary constraints. Our optimality analysis revealed a substructure central to DHPS evolvability, and we assessed its druggability. Combining evolution and structure, this integrated framework identifies a DHPS substructure with significant evolutionary and structural impact. Targeting this region may constrain DHPS evolvability and slow resistance emergence, offering new directions for antibiotic development.

关键词： Dihydropteroate synthase (DHPS) Drug resistance Escape mutations Evolvability Frustration Network-topology model Protein evolution

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Physiologically-based pharmacokinetic (PBPK) model of TiO2 nanoparticles' bio-distribution in rat tissues

Physiologically-based pharmacokinetic (PBPK) model of TiO2 n...

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Nanotechnology 2014: MEMS, Fluidics, Bio systems, Medical, Computational and Photonics - 2014 NSTI Nanotechnology Conference and Expo, NSTI-Nanotech 2014

作者： Laomettachit, T. Liangruksa, M. Bioinformatics and Systems Biology Program School of Bioresources and Technology King Mongkut's University of Technology Thonburi Bangkok 10150 Thailand National Science and Technology Development Agency 111 Thailand Science Park Phahonyothin Road Khlong Nueng Khlong Luang Pathum Thani 12120 Thailand

ISBN: (纸本)9781482258271

The emerging of nanotechnology has increasingly gained expansions and applications in various materials science research and development. However, the exposure to nanoparticles and engineered nanomaterials can lead to adverse biological effects because the small sizes of nanoparticles can enter the human body and deposit in the organs or translocate from the intake area to the secondary organs and can cause inflammation. One of the most used nanoparticles is TiO2, which is commonly found in skin care and household products. It is still unclear how TiO2 nanoparticles are remained in human bodies after exposing. In the present study, we develop a physiologically-based pharmacokinetic (PBPK) model to predict the bio-distribution of TiO2 concentrations in rat tissues. The model is validated with an existing in-vivo study in rats. We also extend our PBPK model to predict cell death caused by TiO2 nanoparticles in the rat liver using a dose-response model. The dose-response model accounts for the interplay between the cellular accumulation of TiO2 due to cell's particle uptake and the dilution of TiO 2 due to cell division. Our developing framework, which can be scaled-up to understand the effects in human system, has a potential to provide the health risk data and to help regulate the human exposure to TiO2 nanoparticles.

关键词： Health risks

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Strategies for pathway-based analysis of genome-wide SNP data

Strategies for pathway-based analysis of genome-wide SNP dat...

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Defense Science Research Conference and Expo (DSR)

作者： P. Patumcharoenpol J. Chan S. Prom-On A. Meechai Bioinformatics and Systems Biology Program School of Information Technology Computer Engineering Department Biological Engineering Program King Mongkut's University of Technology Thonburi Bangkok Thailand

Numerous genome-wide association studies (GWAS) have been performed in order to determine the susceptible loci of diseases. However, most diseases still cannot be explained by a single locus. Pathway-based methods address this problem by combining those small effects into groups of related genes or pathways. However, because pathway-based approaches consist of multiple steps, there is much room for improvement. This paper presents an improved method for translating list of SNPs from GWAS into list of genes. We incrementally adjusted and compared different modifications applied to Parkinson's disease (PD) genome-wide SNP data and then used GSEA-P to analyze the resulting list of genes. After careful adjustments, we are able to identify more relevant pathways that were not found previously.

关键词： bioinformatics Genomics Cancer Parkinson's disease Databases

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Molecular Characterization of Leading Edge Protrusions in the Absence of Arp2/3 Complex

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Microscopy and Microanalysis 2015年第S3期21卷 1283-1284页

作者： Karen L Anderson Christopher Page Praveen Suraneni Rong Li Niels Volkmann Dorit Hanein Bioinformatics and Systems Biology Program Sanford-Burnham Medical Research Institute La Jolla CA 92037 Stowers Institute for Medical Research Kansas City MO 64110

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Improving protein-RNA interface prediction by combining sequence homology based method with a naive bayes classifier: Preliminary results 11

Improving protein-RNA interface prediction by combining sequ...

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2011 ACM Conference on bioinformatics, Computational biology and Biomedicine, ACM-BCB 2011

作者： Xue, Li C. Walia, Rasna EL-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States Department of Computer Science Iowa State University Ames IA 50011 United States Department of Systems and Computer Engineering Al-Azhar University Cairo Egypt

ISBN: (纸本)9781450307963

Protein-RNA interactions play important roles in cellular processes like protein synthesis, RNA processing, and gene expression regulation. Reliable identification of the interfaces involved in RNA-protein interactions is essential for comprehending the mechanisms and the functional implications of these interactions and provides a valuable guide for rational drug discovery and design. Because the determination of 3D structures of protein-RNA complexes has various technical limitations and is typically costly, reliable in silico interface prediction methods that require only the sequence information are urgently needed. We present HomPRIP, a homologous sequence based method for predicting protein-RNA interfaces, based on our conservation analysis of protein-RNA interfaces. We test Hom-PRIP on a benchmark dataset of 199 proteins and compare it with the state-of-the-art protein-RNA interface prediction methods. Our results show that HomPRIP can reliably identify protein-RNA interface residues in 71% of test proteins with at least one putative sequence homolog passing the similarity thresholds of HomPRIP. Moreover, to facilitate predictions for proteins with no identified homologs, we develop HomPRIP-NB, a method combining the HomPRIP predictor and a Naive Bayes (NB) classifier trained using evolutionary information derived from alignments against the NCBI nr database. Our results suggest that HomPRIP-NB significantly outperforms the state-of-the-art machine learning methods for predicting protein-RNA interface residues. Copyright © 2011 ACM.

关键词： Proteins

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