Abstracts
Rudiyanto Gunawan
A Systems Perspective on Biology: From Networks to Function
The field of systems biology has matured and is now widely used to address research questions across a spectrum of biological scales, from molecular networks to ecosystems. Systems biology is an integrative approach that aims to understand biological systems by assembling their parts into a cohesive whole. In this approach, function is viewed as an emergent property arising from the network of interactions among these parts. Network modeling is fundamental to systems biology, providing a mathematical framework for investigating how biological components and their interactions give rise to emergent functions that are greater than the sum of their parts. Moreover, network models enable us to predict how biological systems respond to various perturbations, such as genetic mutations, environmental changes, or pharmaceutical interventions. This predictive capacity is important not only for advancing our understanding of biology but also for the development of novel therapeutic strategies. In this talk, I will present examples of biological network modeling and analysis, including studies drawn from my own research. Using these examples, I will highlight key challenges in applying network modeling within systems biology and discuss the possible solutions.
Gaudenz Danuser
Imaging the Cell Morphological Control of Oncogenic Signals
Cell morphology is used in research and clinical practice as a signature of cell behavior. Many studies have linked cell morphotype to function, and for almost two centuries pathologists have exploited morphology as biomarkers for the diagnosis and stratification of disease. The tight association between a cell morphotype and a cell state has gained even more significance with the growing number of machine learning applications that derive from morphology predictions not only of behavior but of genetic and molecular cell states. Regardless of whether the connection between morphology and cell state is analyzed by human or machine, these analyses place the morphotype implicitly or explicitly downstream of cell state regulation, i.e., ‘form follows function’. However, our recent work has provided evidence that cell morphology is part of the regulator, i.e., ‘function follows form’. We have identified mechanisms by which cell shape dictates signaling and metabolism. Especially in the context of cancer we find that specific morphogenic programs amplify the oncogenic penetrance of genetic and molecular aberrations and/or confer adaptive drug resistance. These discoveries have been enabled by innovation in microscopy and computer vision to quantify with high resolution the interplay between signaling and morphogenesis in experimental systems that do not artificially constrain morphogenesis.
Leonidas Bleris
Engineering (in) Human Cells Using Genome Editing
Our research integrates systems biology and genome editing, spanning three key areas: exploring fundamental biological mechanisms, developing innovative technologies, and investigating complex genetic disorders. We study the stochastic nature of eukaryotic protein synthesis, pioneer genetic Physical Unclonable Functions (PUFs) using CRISPR, and advance our understanding of solitary fibrous tumors (SFT) caused by NAB2-STAT6 gene fusion. This multifaceted approach allows us to gain insights into basic cellular processes, create novel biotechnological tools, and bridge the gap between fundamental research and clinical applications.
Walter Voit
Advancing Systems-Level Understanding of Biomedical Phenomena through Educational Gaming
In an increasingly digital age, workforce development requires innovative approaches that engage young learners in ways that traditional educational models often fail to achieve. Polycraft World, a popular modification (mod) for the widely acclaimed video game Minecraft, presents a unique opportunity to gamify a student’s educational experience, bridging the gap between entertainment and rigorous scientific education. Developed at The University of Texas at Dallas, Polycraft World is not just a gaming platform but a comprehensive educational tool designed to make complex scientific concepts accessible and engaging for students. By integrating core subjects like AP Biology and AP Chemistry into game mechanics, the goal is to foster a generation of students who are not only prepared for advanced studies in the biomedical sciences but also deeply enthusiastic about these subjects.
Polycraft World allows players to explore, experiment, and create within a virtual space that mirrors certain aspects of the real world. Players gather resources, synthesize polymers, and engage in chemical processes that simulate real-world reactions. This dynamic approach enables students to learn essential chemistry concepts—such as molecular bonding, reaction mechanisms, and polymerization—through direct interaction and experimentation. The objective is to extend these mechanics to cover systems in biology, medicine and pharmacology, providing players with tools and challenges that involve biological processes, drug interactions, and even cellular mechanisms in a simplified yet accurate way to enhance the workforce development pipeline in this industry. Polycraft World offers a scalable solution for integrating systems pharmacology into secondary education. Educators can customize gameplay to align with specific learning objectives, tailoring experiences to meet the needs of diverse student groups.
In this presentation, I will discuss the design of Polycraft World, focusing on potential applications in teaching systems pharmacology. I will also examine case studies and initial feedback from students who have used the mod in educational settings, highlighting the impact on learning outcomes and engagement. By sharing our insights and results, we hope to inspire other educators and industry professionals to consider the use of gamified learning tools in workforce development, ultimately helping to build a skilled and motivated generation of scientists and healthcare professionals.
Sepideh Dolatshahi
Systems Biology in Immunology – Rational Design of Vaccines and Immunotherapies
Nearly all physiological systems in the body interact with the immune system and engineering solutions hinge upon understanding this complex system. Systems biology and quantitative approaches are uniquely positioned to study normal and pathological functions of the immune system across time and space. Here we present two case studies featuring a combination of multiplexed experimental measurements with computational modeling (mechanistic, statistical, and machine-learning) to inform vaccines and immunotherapies. The first study is focused on the immunity at the neonatal-maternal interface, where we developed a mechanistic kinetic dynamic model of placental antibody transfer. This model was used to identify the placental Fc receptor (FcR) called FcγRIIb and expressed by endothelial cells as a limiting factor in receptor-mediated transfer. This model was also used as an in silico prenatal vaccine testbed and unveiled precision prenatal immunization opportunities that account for a patient’s anticipated gestational length, placental size, and FcR expression by modulating vaccine timing, dosage, and adjuvant. For example, we found that the optimal maternal vaccination time fell during the second trimester, which is earlier than the window recommended by the Center for Disease Control and Prevention. In a second study, featuring a different flavor of systems biology methods, we used data-driven statistical learning approaches to uncover the immune interactions in the tumor microenvironment (TME). The success of molecular and cellular immunotherapies is dependent on their ability to disrupt or leverage tumor-immune and immune-immune interactions in the TME. Tumor cells evade immune control through various immunomodulatory mechanisms. We leveraged the spatial heterogeneity of the tumor microenvironment at single cell resolution to identify and validate these immunomodulatory cell-cell interactions linked with outcome in non-small cell lung cancer. These interactions can be targeted toward novel combination therapies to circumvent the immunosuppression.
Milo Lin
A Circuit Representation to Simplify Molecular Systems
Biological science has used circuits as an analogy to describe intracellular systems, but hasn’t benefited from the laws of electrical circuits because this analogy had been metaphorical and not mathematically rigorous. Recently, we found a mapping from nonequilibrium systems such as protein signaling networks, to electronic circuits obeying Ohm’s law. This mapping enables systematic coarse graining and design of advanced circuits from the modular composition of simple circuits, allowing simple laws to emerge from seemingly intractable biomolecular complexity. This mapping also allows applying theorems from circuit theory to non-equilibrium statistical mechanics, from which they take on new meaning. Examples include: (1) deriving the maximum information gain in a biomolecular reaction, and (2) deriving a general relation for the efficiency of chemical engines. We hope to leverage this approach to predict, manipulate, and design biomolecular systems at a scale that is on par with what we can do for electronic systems.
Tian Hong
Systems Approaches for Understanding Cellular Phenotypic Transitions
The dynamics of many biological processes, such as cancer progression, regeneration and immune responses, often involves phenotypic plasticity of cells driven by complex gene regulatory networks. In this talk, I will discuss our recent work on cell fate decisions and transitions in the contexts of development and cancer with combinations of bioinformatics and mathematical modeling. The discussion will include examples of gene networks for divergent transcriptional programs for epithelial-mesenchymal transition and an elementary biochemical reaction network that enables bistability, oscillation and the regeneration of heterogenous expression patterns in cell populations. Finally, I will introduce our ongoing work on inferring the roles of intercellular communication in the establishment of heterogeneity.
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Speakers
Leonidas Bleris is a TI Distinguished Bioengineering Faculty Fellow and the Associate Head in the Bioengineering Department of the University of Texas at Dallas. Before joining UTD, Bleris was a Postdoctoral Fellow at the FAS Center for Systems Biology at Harvard University. Bleris earned a Ph.D. in Electrical Engineering from Lehigh University in 2006. He received a Diploma in Electrical and Computer Engineering in 2000 from Aristotle University of Thessaloniki, Greece. Bleris was awarded the Christine Mirzayan Science and Technology Policy Graduate Fellowship from the National Academy of Science (NAS) and served with the Board of Mathematical Sciences and their Applications. During 2008-2018 was an Independent Expert with the European Commission under the “Science, Economy and Society” directorate. Bleris received the 2014 Junior Faculty Research Award from the Erik Jonsson School of Engineering and Computer Science. His research has focused on systems biology, mammalian synthetic biology and genome editing, and has been supported from the National Institutes of Health (NIH) and the National Science Foundation (NSF) including the NSF CAREER award.
Gaudenz Danuser is the founding chair of the Lyda Hill Department of Bioinformatics at the University of Texas Southwestern Medical Center (UTSW). He is also the Director the Cecil H. and Ida Green Center for Systems Biology, he holds the Patrick E. Haggerty Distinguished Chair in Basic Biomedical Science and is a Scholar of the Cancer Prevention and Research Institute of Texas (CPRIT). Before moving to UTSW, Danuser led research laboratories at ETH Zurich, at The Scripps Research Institute, and at Harvard Medical School. His lab’s research is currently focused on understanding the roles shape regulation play in metastatic cell proliferation, survival and therapy resistance. To address these questions the lab develops innovative quantitative imaging methods to experimentally probe these processes and uses machine learning and tools from financial mathematics to compile the data in mechanistic models. Danuser is a devoted teacher in areas of computational cell biology and AI both at the institutional and international level.
Sepideh Dolatshahi is an Assistant Professor of Biomedical Engineering at the University of Virginia (UVA), a core member of UVA Cancer Center and the Carter Immunology Center. Her Systems Immunology lab combines multiplex experimental measurements with computational methods to solve problems in the context of cancer, infectious diseases, and maternal-neonatal immunology. Before joining UVA, she was a postdoctoral researcher in the Department of Biological Engineering at MIT. Prior to that, she received her Ph.D. in 2015 at the Georgia Institute of Technology. Her work has been recognized by several national awards such as the American Association of Immunologists (AAI) Chambers-Thermo Fisher Scientific Memorial Award in cancer research, Jeffress Trust award in Interdisciplinary Research and the Hypothesis Fund. She was selected by the UVA BME students as the most inclusive professor of the year in 2023.
Rudiyanto Gunawan is an Associate Professor in the Department of Chemical and Biological Engineering at the University at Buffalo, State University of New York (SUNY). His research expertise lies in computational systems biology and bioinformatics. He leads a research group that develops and applies innovative methods for extracting mechanistic and actionable insights from biological data, utilizing rigorous mathematical foundations, systems modeling and analysis, machine learning, and optimization algorithms. He has co-authored over 90 peer-reviewed articles in prominent journals spanning systems biology, bioinformatics, biomedicine, bioprocess engineering, and biogerontology.
Tian Hong is a computational biologist interested in using mathematical and computational approaches to understand complex biological systems such as cancer cell plasticity. He is currently an Associate Professor at the Department of Biological Sciences, The University of Texas at Dallas. Before moving to The University of Texas at Dallas, he was an Associate Professor at the University of Tennessee, Knoxville. He obtained his Ph.D. in Genetics, Bioinformatics and Computational Biology from Virginia Tech and completed his postdoctoral training in the Department of Mathematics at University of California, Irvine. His current research focuses on modeling and single-cell analysis for cell fate transitions, including the epithelial-mesenchymal transition (EMT), a reversible process that is critical for development and cancer progression. By combining data-driven and theory-guided approaches, he developed models for explaining EMT’s complex features such as multiple intermediate states, partial reversibility and divergence. These interdisciplinary approaches will be useful for understanding cellular functions of gene regulatory networks in a wide range of physiological and pathological contexts. His research is funded by NIH and NSF.
Milo Lin is an Associate Professor in the Green Center for Systems Biology in the Lyda Hill Department of Bioinformatics at UTSW. He holds secondary appointments in the Department of Biophysics and Center for Alzheimer’s and Neurodegenerative Diseases. A physicist by training, he is interested in using methods of statistical physics and machine learning to derive testable principles for the behavior of complex biological systems.
Walter Everett Voit received a B.S. in Computer Science in 2005 and a Masters in Artificial Intelligence from UT Dallas in 2006. He subsequently pursued a PhD at Georgia Tech, where he was named a Presidential Scholar and selected to the prestigious TI:GER program, a partnership with the College of Management and Emory Law School. He received his doctorate degree in Materials Science and Engineering in 2009. He is now an associate professor at UTD and an entrepreneur who explores the thermomechanics of multi-phase, high-performance polymers, flexible bioelectronics, next generation neural interfaces, 3-D printing, degradable polymers, photo polymerization-induced phase separation, thiol-click chemistries and the effects of ionizing radiation on polymers. He has authored more than 150 journal articles and book chapters. Voit is currently CEO of Pedegree Studios, Inc., a startup company whose mission to “Deliver Personalized Learning” maps into a critical need for society as outlined by the National Academy of Engineers. Pedegree embeds complex life-like problems of varying difficulty into gameplay to stimulate long-term knowledge and skills mastery within a fun, social, reinforcing environment. Voit was cofounder and Chief Executive Officer of Adaptive3D, which pursued next-generation acoustics and additive manufacturing based on specialty polymers and thiol chemistries. He furthermore co-founded Pascalor, Qualia, Inc. and Polycraft World in 2015, Skin Aware and BackStop Neural in 2016, Qualia Labs, Qualia Oto, Capavax and Adaptive Holdings in 2017, as well as Regulife Medical and Inspire Bioelectronics in 2020. Voit was elected to the National Academy of Inventors in 2023 and was named Tech Titans inventor of the year across DFW in 2023 for his patent portfolio, which contains more than 90 patents and provisional patents in the US and across the world and was successfully commercialized into products used today in shoes, outdoor sports equipment, furniture and HVAC systems and is being further developed into applications for automotive, aerospace and medical device companies.
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