Cellular and Systems Modeling

Cellular and Systems Modeling is the study of the dynamics of biological and biomedical processes from a whole-system point of view, using engineering tools, genome-scale information, and mathematical and/or computational models.
Cellular and Systems Modeling undertakes the ambitious task of studying the dynamics of biological and biomedical processes from a whole system point of view. The observed systems range over orders of magnitude, from tissue to cells to molecular assemblies! Engineering tools are used along with genome-scale information in mathematical and/or computational models that usually adopt a top-down approach. Modeling diseases, entire ‘virtual’ cells, or subcellular networks of interactions are among typical tasks. Major research topics include the modeling of complex signaling and regulatory networks, transport mechanisms, spatio-temporal evolution of microphysiological events, as well as establishing the links between the development of complex phenotypes and the seemingly unrelated molecular events.

 

Faculty

Department of Computational & Systems Biology, Chair

Research interests: Biomolecular systems dynamics at multiple scales; evolution of proteins’ sequence, structure, dynamics and function; computer-aided drug discovery and polypharmacology; network models for protein-protein interactions, supramolecular machinery and allostery; modeling and simulations of membrane proteins dynamics and mechanisms of interactions.

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Machine Learning Department, Lane Center for Computational Biology, and Biological Sciences

My primary research areas are computational Biology, Bioinformatics and Machine learning. I am heading the Systems Biology Group at the School of Computer Science at CMU. Our group develops computational methods for understanding the interactions, dynamics and conservation of complex biological systems. Our work addresses issues ranging from the experimental design level to the systems biology level. I am also interested in how shared principles between computation and biology can be used to improve our understanding of both fields. We are looking at algorithms used by nature to see if we can obtain new ideas on how to design better algrotihms for distributed computing systems while at the same time infer new insights regarding information processing in biology.
CAREER
OVERTON

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Departments of Computational & Systems Biology, and Biomedical Informatics

Our ultimate goal is to investigate the molecular mechanisms of chronic diseases.    We develop new computational methods to model biological processes and mine high-dimensional, multi-modal biomedical data.    We are very interested in the effect of gene regulatory networks in disease. 

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Department of Computational & Systems Biology

We investigate the molecular and cellular origins of human epithelial malignancies (e.g., breast cancer, Barrett’s) through computational models. We pursue two interrelated approaches:

Computational Pathology and Bioimaging We develop algorithms to analyze intratumor phenotypic heterogeneity from in situ fluorescent imaging of tissue sections or tissue microarrays.
Computational Biophysics We develop models based on anharmonic fluctuations to discern short-lived and rare intermediate conformations that proteins access to fold, bind, and function.

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Department of Bioengineering
Cells and tissues are shaped by mechanical forces early during development to produce the basic body plan and establish functional organs. Our group combines experimental and theoretical approaches to reverse-engineer these mechanical inherently mechanical processes. Our experimental methods include classical embryological, modern cell biological ones combined with new synthetic systems biology methods.

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Department of Computational & Systems Biology

I am interested in developing mathematical models of biological regulatory processes that integrate specific knowledge about protein-protein interactions. My current research includes the development of specific models of signal transduction and the development of new stochastic simulation algorithms that will greatly broaden the scope of models that can be developed. Other research areas include model reduction, parameter estimation and uncertainty analysis, and automated model construction from databases of protein interactions.

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Department of Biomedical Informatics and Intelligent Systems Program

Dr. Ganapathiraju’s primary area of research is in Systems Biology, specifically on protein-protein interaction prediction at the system level. The outcomes of this research will subsequently be applied to translational bioinformatics. A second core area is in Sequence Analysis, for pattern mining in whole-genome and whole-proteome sequences, with application of suffix array data structures for preprocessing the genome sequences.

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BRAINS

 

Department of Biomedical Informatics
Research Interests:

Application of artificial intelligence, machine learning, Bayesian networks, and other computational methods to problems in biology, medicine, and translational research
Modeling of interactome networks and human diseases
Personalized medicine and cancer bioinformatics
Medical decision support systems
Biosurveillance system development
Image processing

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Lane Center for Computational Biology

We are interested in designing graph and optimization algorithms to extract insight from biological data. In particular, we focus on the following classes of problems:

  • Protein interactions and networks: Evolution of interactions; protein function prediction; clustering within networks; protein structure prediction. This work is supported by NSF grant EF-0849899 and by NSF grant CCF-1053918/CCF-1256087 (CAREER award).
  • Genomics & genome assembly: RNA-seq expression quantification; genome assembly; overlapping genes in bacteria; transcription termination in bacteria (See the TransTermHP program for predicting Rho-independent terminators). This work was supported by NSF grant IIS-0812111. (PI: Mihai Pop) and currently by NIH grant 1R21HG006913.
  • Viral evolution: Reassortment in the influenza genome. This work is supported by NIH grant1R21AI085376.
  • Chromatin structure and function: Algorithms for determining the spatial organization of eukaryotic genomes from Chromosome Conformation Capture data.(Previously supported by a UMIACS New Research Frontiers Award.)

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SLOAN
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Department of Computer Science and Lane Center for Computational Biology

My research spans two areas: Personalized Medicine and Computational Structural Biology.

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CAREER

Department of Computational & Systems Biology

Cells process information about their environment using a complex network of molecular circuits. Our research combines principles of systems and synthetic biology to understand how information flows through these circuits and fine-tunes cellular responses.

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Department of Computational & Systems Biology

Cellular behavior emerges from a complex network of chemical interactions, the details of which remain largely unknown. Many pathways within the cell are redundant or interdependent, hindering our ability to experimentally delineate their in vivo activity. Further complicating matters are slight and unmeasurable differences between cells within a population and the interplay between cells and their environment. Thus, seemingly identical cells may respond differently to the same environmental perturbation. This cellular heterogeneity is particularly problematic in cancer, where slight differences determine whether or not a cell will go on to produce a tumor. Our lab has several on-going projects modeling disease emergence and progression from sub-cellular to multi-cellular systems.

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Department of Biomedical Informatics

Dr. Lu’s research focuses on the computational methods for identifying signaling pathways underlying biological processes and diseases as well as statistical methods for acquiring knowledge from biomedical literature. He was trained in Pharmacology and works in the field of bioinformatics after NLM sponsored postdoctoral training in Biomedical Informatics. His research interest concentrates on applying latent variable models to simulate biological signaling system and text mining.

Currently, Dr. Lu is working on developing his research in translational bioinformatics and systems/computational biology and its application to specific domains relevant to human disease. He is pursuing collaboration in the area of natural language processing and text mining with the eventual goal of establishing a Center or Institute in Translational Bioinformatics.

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Lane Center for Computational Biology, Departments of Biological Sciences and Biomedical Engineering, Machine Learning Department, and Center for Bioimage Informatics

    • The primary focus of current work in the lab is on automated interpretation of fluorescence microscope images.

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Department of Pathology

Biomedical research today is conducted largely in the context of an established paradigm that is fundamentally reductionist in nature. Yet, despite the enormous success of this approach, it is increasingly clear that discrete biological function and the astounding complexity of living systems cannot be understood by studying individual molecules. Instead, most biological characteristics arise from complex interactions among the cell’s numerous molecular constituents. Thus, a key challenge for 21st century biology is to understand the structure and the dynamics of the complex intracellular web of interactions among the various types of molecules that contribute to the function and the physical entity of a living cell. Together with our collaborators, our laboratory focuses on the understanding of the system-level organization of cellular metabolism, and how environmental cues are processed through regulatory pathways leading to rearranged metabolic activities. In the long run, we are also interested in applying this knowledge to improved disease diagnosis and treatment.

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Department of Mathematics

Research Interests

  • Pattern formation in networks of coupled neural oscillators
  • Waves and localized activity generated through nonlocal interactions
  • Network dynamics in the basal ganglia, with implications for motor disorders, particularly Parkinson’s disease
  • Mechanisms and implications of synaptic plasticity, particularly spike-timing-dependent plasticity
  • Geometric singular perturbation theory and its relation to oscillations and bifurcations
  • Stability of solutions to parabolic systems of partial differential equations and related equations

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Department of Chemical Engineering and Lane Center for Computational Biology

Professor Sahinidis concentrates on optimization in biology, chemistry, medicine, and engineering.

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Dept. of Physics & Astronomy

Our research aims to understand the mechanisms of collective behavior and variability in bacterial cultures and their effect on the response of bacteria to changes in the environment. The continuous interaction between the environment and living organisms is one of the main effectors of evolution. There are many known strategies of responding to environmental changes, e.g. by changing the swimming pattern or the gene expression profile. And although many strategies are single-cell based, we often see cooperative behavior arising among members of the colony under certain conditions. By studying the changes in the behavior of bacteria as a function of their concentration, we are able to detect some of the collective mechanisms that govern the bacterial behavior and allow them to better endure environmental stress. Environmental changes that interest us are thermal and chemical. We utilize various optical microscopy techniques to observe the swimming pattern of bacteria under different conditions. As for the expression level of proteins, proteins of interest are labeled with fluorescent markers and the expression level is measured using fluorescence microscopy or flow cytometry.

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Departments of Biological Sciences and Computer Science, and Lane Center for Computational Biology

My research interests are in the area of computational molecular biology and the modeling and simulation of biological systems. My group is currently working most actively on three topics: simulation methods for macromolecular assembly systems, with special focus on more realistic models of assembly in cellular environments; methods for analysis of human genetic variation data, most recently focused on phylogenetics and population substructure analysis; and application of phylogenetic methods to study cancer progression.

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CAREER
PECASE

Department of Mathematics

My research interests are in the area of mathematical biology, in particular, construction of mathematical models of biological systems within the framework of theories of continuum mechanics, dynamical systems, and stochastic dynamics.

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McGowan Institute for Regenerative Medicine and Departments of Surgery and Immunology

Our group’s research has been focused first on studying mechanisms of inflammation and second on gaining a systems-level perspective into this central physiological and pathological process.

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Department of Pharmaceutical Sciences

Dr. Xie’s research group focuses on development and application of GPCRs chemical genomics-based drug design and discovery approach for osteoporosis, multiple myeloma and breast cancer research.  His group has established a drug discovery research platform with the integrated 3D pharmacophore database search, in-silico design and in-vitro bioassay validation as well as medicinal chemistry modification syntheses.  This technology was developed through screening and identifying novel CB2 drug-like molecules with new chemical scaffolds and high CB2 specificity (US patents WO 2009058377).  It has also been successfully applied to identify p18-based drugs for hematopoietic stem cell self-renewal, and novel chemical agents for multiple myeloma and osteoporosis as well as other targets.

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Machine Learning Department, Lane Center for Computational Biology, Language Technologies Institute and Computer Science Department

Research synopsis: My principal research interests lie in the development of machine learning and statistical methodology, and large-scale computational system and architecture, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems.

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CAREER
SLOAN

Department of Computational & Systems Biology

The Xing lab is interested in the following fundamental questions. How do thousands of molecules species orchestrate temporally and spatially to determine a cell phenotype? How can one regulate and direct cell phenotype? Specifically, the lab currently focuses on Epithelial-to-Mesenchymal Transition (EMT), characterized by loss of cell-cell adhesion and increased cell motility. EMT plays important roles in embryonic development, tissue regeneration, wound healing and pathological processes such as fibrosis in lung, liver, and kidney, and cancer metastasis. The lab studies the coupled gene expression and epigenetic dynamics of EMT.

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Department of Biomedical Engineering and Lane Center for Computational Biology

My research interests are in computational cell biology, bioimage informatics, molecular cell mechanics, and fluorescence imaging. Current research topics include:

  • Experimental and computational analysis of spatiotemporal regulatory mechanisms of axonal cargo transport
  • Active control of intracellular transport for targeted intracellular delivery
  • Imaging-based experimental and computational analysis of spatial-temporal dynamics of cell signaling and the cytoskeleton
  • Molecular cell mechanics of the cytoskeleton
  • Advanced experimental and computational fluorescence live imaging

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CAREER

Department of Computational & Systems Biology

The Zuckerman group develops and applies computer simulation methods for studying biological systems. A primary focus is the deployment of sampling algorithms based on statistical physics that can be used to study (i) large-scale, potentially allosteric motions in proteins, (ii) signaling processes encoded in interaction networks, (iii) protein binding, and (iv) protein folding. Among the strategies used in the group are approaches that can yield super-linear parallel performance – estimation of observables using N processors that is more than N times faster than an estimate based on a single-processor simulation. Prof. Zuckerman also has a strong interest in biophysics education, which has led to a textbook and a new online book.

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