Jewel Plummer Cobb: A trailblazing life of impact.

Stacey Finley commemorates the life and work of biologist Jewel Plummer Cobb, whose outstanding contributions to cancer research, education and advocacy had far-reaching impact.

Integrating mechanism-based T cell phenotypes into a model of tumor-immune cell interactions.

Interactions between cancer cells and immune cells in the tumor microenvironment influence tumor growth and can contribute to the response to cancer immunotherapies. It is difficult to gain mechanistic insights into the effects of cell-cell interactions in tumors using a purely experimental approach. However, computational modeling enables quantitative investigation of the tumor microenvironment, and agent-based modeling, in particular, provides relevant biological insights into the spatial and temporal evolution of tumors.

Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer.

Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism.

Modeling the heterogeneous apoptotic response of caspase-mediated signaling in tumor cells.

Resisting apoptosis is a hallmark of cancer. For this reason, it may be possible to force cancer cells to die by targeting components along the apoptotic signaling pathway. However, apoptosis signaling is challenging to understand due to dynamic and complex behaviors of ligands, receptors, and intracellular signaling components in response to cancer therapy.

Flux sampling in genome-scale metabolic modeling of microbial communities.

Background: Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions and identify strategies to optimize the community. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model; however, the fluxes predicted by FBA depend on a user-defined cellular objective.

Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer.

Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment (TME), which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the TME. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival.

A data-driven Boolean model explains memory subsets and evolution in CD8+ T cell exhaustion.

T cells play a key role in a variety of immune responses, including infection and cancer. Upon stimulation, naïve CD8+ T cells proliferate and differentiate into a variety of memory and effector cell types; however, failure to clear antigens causes prolonged stimulation of CD8+ T cells, ultimately leading to T cell exhaustion (TCE). The functional and phenotypic changes that occur during CD8+ T cell differentiation are well characterized, but the underlying gene expression state changes are not completely understood.

A systems and computational biology perspective on advancing CAR therapy.

In the recent decades, chimeric antigen receptor (CAR) therapy signaled a new revolutionary approach to cancer treatment. This method seeks to engineer immune cells expressing an artificially designed receptor, which would endue those cells with the ability to recognize and eliminate tumor cells. While some CAR therapies received FDA approval and others are subject to clinical trials, many aspects of their workings remain elusive.

Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities.

Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model.

Calibrating agent-based models to tumor images using representation learning.

Agent-based models (ABMs) have enabled great advances in the study of tumor development and therapeutic response, allowing researchers to explore the spatiotemporal evolution of the tumor and its microenvironment. However, these models face serious drawbacks in the realm of parameterization - ABM parameters are typically set individually based on various data and literature sources, rather than through a rigorous parameter estimation approach. While ABMs can be fit to simple time-course data (such as tumor volume), that type of data loses the spatial information that is a defining feature of ABMs.

Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer.

1Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment, which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the tumor microenvironment. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival.

Proof-of-concept for incorporating mechanistic insights from multi-omics analyses of polymyxin B in combination with chloramphenicol against Klebsiella pneumoniae.

Carbapenemase-resistant Klebsiella pneumoniae (KP) resistant to multiple antibiotic classes necessitates optimized combination therapy. Our objective is to build a workflow leveraging omics and bacterial count data to identify antibiotic mechanisms that can be used to design and optimize combination regimens. For pharmacodynamic (PD) analysis, previously published static time-kill studies (J Antimicrob Chemother 70, 2015, 2589) were used with polymyxin B (PMB) and chloramphenicol (CHL) mono and combination therapy against three KP clinical isolates over 24 h.

Systematic Bayesian posterior analysis guided by Kullback-Leibler divergence facilitates hypothesis formation.

Bayesian inference produces a posterior distribution for the parameters of a mathematical model that can be used to guide the formation of hypotheses; specifically, the posterior may be searched for evidence of alternative model hypotheses, which serves as a starting point for hypothesis formation and model refinement. Previous approaches to search for this evidence are largely qualitative and unsystematic; further, demonstrations of these approaches typically stop at hypothesis formation, leaving the questions they raise unanswered. Here, we introduce a Kullback-Leibler (KL) divergence-based ranking to expedite Bayesian hypothesis formation and investigate the hypotheses it generates, ultimately generating novel, biologically significant insights.

Kinetic and data-driven modeling of pancreatic β-cell central carbon metabolism and insulin secretion.

Pancreatic β-cells respond to increased extracellular glucose levels by initiating a metabolic shift. That change in metabolism is part of the process of glucose-stimulated insulin secretion and is of particular interest in the context of diabetes. However, we do not fully understand how the coordinated changes in metabolic pathways and metabolite products influence insulin secretion.

Modelling predicts differences in chimeric antigen receptor T-cell signalling due to biological variability.

In recent decades, chimeric antigen receptors (CARs) have been successfully used to generate engineered T cells capable of recognizing and eliminating cancer cells. The structure of CARs typically includes costimulatory domains, which enhance the T-cell response upon antigen encounter. However, it is not fully known how those co-stimulatory domains influence cell activation in the presence of biological variability.

Computational analysis of 4-1BB-induced NFκB signaling suggests improvements to CAR cell design.

Background: Chimeric antigen receptor (CAR)-expressing cells are a powerful modality of adoptive cell therapy against cancer. The potency of signaling events initiated upon antigen binding depends on the costimulatory domain within the structure of the CAR. One such costimulatory domain is 4-1BB, which affects cellular response via the NFκB pathway.

Phosphatases are predicted to govern prolactin-mediated JAK-STAT signaling in pancreatic beta cells.

Patients with diabetes are unable to produce a sufficient amount of insulin to properly regulate their blood glucose levels. One potential method of treating diabetes is to increase the number of insulin-secreting beta cells in the pancreas to enhance insulin secretion. It is known that during pregnancy, pancreatic beta cells proliferate in response to the pregnancy hormone, prolactin (PRL).

Exogenous Lactogenic Signaling Stimulates Beta Cell Replication In Vivo and In Vitro.

As patients recently diagnosed with T1D and patients with T2D have residual beta cell mass, there is considerable effort in beta cell biology to understand the mechanisms that drive beta cell regeneration as a potential cellular therapy for expanding patients' residual beta cell population. Both mouse and human studies have established that beta cell mass expansion occurs rapidly during pregnancy. To investigate the mechanisms of beta cell mass expansion during pregnancy, we developed a novel in vivo and in vitro models of pseudopregnancy.

Mechanistic characterization of endothelial sprouting mediated by pro-angiogenic signaling.

Objective: We aim to quantitatively characterize the crosstalk between VEGF- and FGF-mediated angiogenic signaling and endothelial sprouting, to gain mechanistic insights and identify novel therapeutic strategies.

Methods: We constructed an experimentally validated hybrid agent-based mathematical model that characterizes endothelial sprouting driven by FGF- and VEGF-mediated signaling. We predicted the total sprout length, number of sprouts, and average length by the mono- and co-stimulation of FGF and VEGF.

Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through integration of constraint-based models and LC-MS metabolomics.

Colorectal cancer (CRC) is a major cause of morbidity and mortality in the United States. Tumor-stromal metabolic crosstalk in the tumor microenvironment promotes CRC development and progression, but exactly how stromal cells, in particular cancer-associated fibroblasts (CAFs), affect the metabolism of tumor cells remains unknown. Here we take a data-driven approach to investigate the metabolic interactions between CRC cells and CAFs, integrating constraint-based modeling and metabolomic profiling.

A field guide to cultivating computational biology.

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology.

Dynamic Regulation of JAK-STAT Signaling Through the Prolactin Receptor Predicted by Computational Modeling.

Introduction: The expansion of insulin-producing beta cells during pregnancy is critical to maintain glucose homeostasis in the face of increasing insulin resistance. Prolactin receptor (PRLR) signaling is one of the primary mediators of beta cell expansion during pregnancy, and loss of PRLR signaling results in reduced beta cell mass and gestational diabetes. Harnessing the proliferative potential of prolactin signaling to expand beta cell mass outside of the context of pregnancy requires quantitative understanding of the signaling at the molecular level.

Fund Black scientists.

Our nationwide network of BME women faculty collectively argue that racial funding disparity by the National Institutes of Health (NIH) remains the most insidious barrier to success of Black faculty in our profession. We thus refocus attention on this critical barrier and suggest solutions on how it can be dismantled.