Systems Biology Commons^™

From Fair To Cure: Guidelines For Computational Models Of Biological Systems, Herbert M. Sauro, Eran Agmon, Michael L. Blinov, John H. Gennari, Joseph L. Hellerstein, Adel Heydarabadipour, Bartholomew E. Jardine, Elebeoba May, David P. Nickerson, Lucian P. Smith, Gary D. Bader, Frank T. Bergmann, Patrick M. Boyle, Andreas Dräger, James R. Faeder, Song Feng, Juliana Freire, Fabian Fröhlich, James A. Glazier, Thomas E. Gorochowski, Tomas Helikar, Henning Hermjakob, Stefan Hoops, Peter Hunter, Princess I. Imoukhuede, Sarah M. Keating, Matthias König, Reinhard Laubenbacher, Leslie M. Loew, Carlos F. Lopez, William W. Lytton, Rahuman S. Malik-Sheriff, Andrew Mcculloch, Pedro Mendes, Lealem Mulugeta, Chris J. Myers, Jerry G. Myers, Anna Niarakis, David D. Van Niekerk, Brett G. Olivier, Alexander A. Patrie, Ellen M. Quardokus, Nicole Radde, Johann M. Rohwer, Sven Sahle, James C. Schaff, Falk Schreiber, T. J. Sego, Janis Shin, Jacky L. Snoep, Rajanikanth Vadigepalli, H. Steven Wiley, Dagmar Waltemath, Ion I. Moraru

Computational Medicine Center Faculty Papers

Guidelines for managing scientific data have been established under the FAIR principles, requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model …

Ai-Powered Multi-Omics Integration For Predictive Modeling Of Genotype-Environment-Phenotype Relationships, You Wu

Dissertations, Theses, and Capstone Projects

This dissertation presents a series of machine learning frameworks for modeling genotype–environment–phenotype relationships through integrative predictive modeling of multi-omics data. The work addresses three major axes of biological complexity: modeling biological information transmission cross-levels from genes to proteins to phenotypes, predicting molecular features cross-scale from cells to tissues to organisms, and translating phenotypes cross-species from model systems to humans. Each proposed method also tackles key machine learning (ML) challenges in the biomedical domain, including data scarcity, domain shift, out-of-distribution (OOD) generalization, and hierarchical modeling. Specifically, this dissertation introduces five novel deep learning algorithms: MultiDCP predicts drug-induced transcriptomic and viability responses …

Biases And Blind-Spots In Genome-Wide Crispr-Cas9 Knockout Screens, Merve Dede

Dissertations and Theses (Open Access)

Adaptation of the bacterial CRISPR-Cas9 system to mammalian cells revolutionized the field of functional genomics, enabling genome-scale genetic perturbations to study essential genes, whose loss of function results in a severe fitness defect. There are two types of essential genes in a cell. Core essential genes are absolutely required for growth and proliferation in every cell type. On the other hand, context-dependent essential genes become essential in an environmental or genetic context. The concept of context-dependent gene essentiality is particularly important in cancer, since killing cancer cells selectively without harming surrounding healthy tissue remains a major challenge. The toxicity of …

Advancing Human Health In The Decade Ahead: Pregnancy As A Key Window For Discovery: A Burroughs Wellcome Fund Pregnancy Think Tank., Yoel Sadovsky, Sam Mesiano, Graham J Burton, Michelle Lampl, Jeffrey C Murray, Rachel M Freathy, Anita Mahadevan-Jansen, Ashley Moffett, Nathan D Price, Paul H Wise, Derek E Wildman, Ralph Snyderman, Nigel Paneth, John Anthony Capra, Marcelo A Nobrega, Yaacov Barak, Louis J Muglia

Articles, Abstracts, and Reports

Recent revolutionary advances at the intersection of medicine, omics, data sciences, computing, epidemiology, and related technologies inspire us to ponder their impact on health. Their potential impact is particularly germane to the biology of pregnancy and perinatal medicine, where limited improvement in health outcomes for women and children has remained a global challenge. We assembled a group of experts to establish a Pregnancy Think Tank to discuss a broad spectrum of major gestational disorders and adverse pregnancy outcomes that affect maternal-infant lifelong health and should serve as targets for leveraging the many recent advances. This report reflects avenues for future …

Computational Analysis Of Genomic Variants Affecting Predicted Microrna:Target Interactions In Prostate Cancer., Angélica Paola Hernández Pérez

KGI Theses and Dissertations

Prostate cancer (PCa) is the most common cancer of men in the United States and is third only to lung and colon as a cause of cancer death. Clinical behavior of the disease is variable and the combination of prostate-specific antigen (PSA) screening and Gleason score staging are currently the best available molecular and pathology tools to predict outcomes. Cancer biology research establishes microRNAs (miRNAs) as key molecular components in both normal and pathological states. Thus, elucidating miRNAs perturbed by genomic alterations will expand our understanding of the molecular taxonomy of PCa with the aim to complement current practices in …

Systems Biology Approaches To Probe Gene Regulation In Bacteria, Diogo F. Troggian Veiga

Dissertations and Theses (Open Access)

Mechanisms that allow pathogens to colonize the host are not the product of isolated genes, but instead emerge from the concerted operation of regulatory networks. Therefore, identifying components and the systemic behavior of networks is necessary to a better understanding of gene regulation and pathogenesis. To this end, I have developed systems biology approaches to study transcriptional and post-transcriptional gene regulation in bacteria, with an emphasis in the human pathogen Mycobacterium tuberculosis (Mtb).

First, I developed a network response method to identify parts of the Mtb global transcriptional regulatory network utilized by the pathogen to counteract phagosomal stresses …

Modelling Β2ar Regulation, Sharat J. Vayttaden

Dissertations and Theses (Open Access)

The β2 adrenergic receptor (β2AR) regulates smooth muscle relaxation in the vasculature and airways. Long- and Short-acting β-agonists (LABAs/SABAs) are widely used in treatment of chronic obstructive pulmonary disorder (COPD) and asthma. Despite their widespread clinical use we do not understand well the dominant β2AR regulatory pathways that are stimulated during therapy and bring about tachyphylaxis, which is the loss of drug effects. Thus, an understanding of how the β2AR responds to various β-agonists is crucial to their rational use. Towards that end we have developed deterministic models that explore the mechanism of drug- induced β2AR regulation. These mathematical models …