정하웅 교수
Hawoong Jeong
KAIST 물리학과 · 물리·천문학
연구실 소개
정하웅 교수의 연구실은 복잡계 네트워크의 구조적 특성과 동적 진화를 수학적·물리적 모델링을 바탕으로 탐구합니다. 특히 생물학적 네트워크(대사 네트워크, 단백질 상호작용 네트워크), 사회적 네트워크, 세포 내 분자 네트워크의 스케일링 법칙과 상호작용 메커니즘을 분석하며, 실험 데이터와 시뮬레이션을 융합한 다학제적 접근을 펼칩니다. 이는 생물학적 기능의 이해와 약물 타겟 예측, 도시 계획의 최적화 등 실제 문제 해결에도 응용됩니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15A key ingredient of current models proposed to capture the topological evolution of complex networks is the hypothesis that highly connected nodes increase their connectivity faster than their less connected peers, a phenomenon called preferential attachment. Measurements on four networks, namely the science citation network, Internet, actor collaboration and science coauthorship network indicate that the rate at which nodes acquire links depends on the node's degree, offering direct quantitativ
In a cell or microorganism the processes that generate mass, energy, information transfer, and cell fate specification are seamlessly integrated through a complex network of various cellular constituents and reactions 1 .However, despite the key role these networks play in sustaining various cellular functions, their large-scale structure is essentially unknown.Here we present the first systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three
When a new facility like a grocery store, a school, or a fire station is planned, its location should ideally be determined by the necessities of people who live nearby. Empirically, it has been found that there exists a positive correlation between facility and population densities. In the present work, we investigate the ideal relation between the population and the facility densities within the framework of an economic mechanism governing microdynamics. In previous studies based on the global
A major goal of pharmaceutical bioinformatics is to develop computational tools for systematic in silico molecular target identification. Here we demonstrate that in the yeast Saccharomyces cerevisiae the phenotypic effect of single gene deletions simultaneously correlates with fluctuations in mRNA expression profiles, the functional categorization of the gene products, and their connectivity in the yeasts protein-protein interaction network. Building on these quantitative correlations, we devel
DNA compaction in a bacterial cell is in part carried out by entropic (depletion) forces induced by "free" proteins or crowding particles in the cytoplasm. Indeed, recent in vitro experiments highlight these effects by showing that they alone can condense the E. coli chromosome to its in vivo size. Using molecular dynamics simulations and a theoretical approach, we study how a flexible chain molecule can be compacted by crowding particles with variable sizes in a (cell-like) cylindrical space. O
Social network analysis has long been an untiring topic of sociology. However, until the era of information technology, the availability of data, mainly collected by the traditional method of personal survey, was highly limited and prevented large-scale analysis. Recently, the exploding amount of automatically generated data has completely changed the pattern of research. For instance, the enormous amount of data from so-called high-throughput biological experiments has introduced a systematic o
We introduce a self-organized surface growth model in $2+1$ dimensions with an anisotropic avalanche process, which is expected to be in the universality class of the anisotropic quenched Kardar-Parisi-Zhang (KPZ) equation with alternative signs of the nonlinear KPZ terms. It turns out that the surface height correlation function in each direction scales distinctively. The anisotropic behavior is attributed to the asymmetric behavior of the quenched KPZ equation in $1+1$ dimensions with respect
Rich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by scientists with human ingenuity. In this study, we propose AgentNet, a model-free data-driven framework consisting of deep neural networks to reveal and analyze the hidden interactions in complex systems from observed data alone. AgentNet utilizes a graph attent
Glycosylation is a highly complex process to produce a diverse repertoire of cellular glycans that are attached to proteins and lipids. Glycans are involved in fundamental biological processes, including protein folding and clearance, cell proliferation and apoptosis, development, immune responses, and pathogenesis. One of the major types of glycans, N-linked glycans, is formed by sequential attachments of monosaccharides to proteins by a limited number of enzymes. Many of these enzymes can acce
Painting is an art form that has long functioned as a major channel for the creative expression and communication of humans, its evolution taking place under an interplay with the science, technology, and social environments of the times. Therefore, understanding the process based on comprehensive data could shed light on how humans acted and manifested creatively under changing conditions. Yet, there exist few systematic frameworks that characterize the process for painting, which would require
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