Unified tumor growth mechanisms from multimodel inference and dataset integration.

Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of cells associated with the SCLC-N or SCLC-A2 subtypes, the transition from the SCLC-A subtype to the SCLC-Y subtype through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor treatment resistance.

Unsupervised logic-based mechanism inference for network-driven biological processes.

Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.

ERK positive feedback regulates a widespread network of tyrosine phosphorylation sites across canonical T cell signaling and actin cytoskeletal proteins in Jurkat T cells.

Competing positive and negative signaling feedback pathways play a critical role in tuning the sensitivity of T cell receptor activation by creating an ultrasensitive, bistable switch to selectively enhance responses to foreign ligands while suppressing signals from self peptides. In response to T cell receptor agonist engagement, ERK is activated to positively regulate T cell receptor signaling through phosphorylation of Ser(59) Lck. To obtain a wide-scale view of the role of ERK in propagating T cell receptor signaling, a quantitative phosphoproteomic analysis of 322 tyrosine phosphorylation sites by mass spectrometry was performed on the human Jurkat T cell line in the presence of U0126, an inhibitor of ERK activation. Relative to controls, U0126-treated cells showed constitutive decreases in phosphorylation through a T cell receptor stimulation time course on tyrosine residues found on upstream signaling proteins (CD3 chains, Lck, ZAP-70), as well as downstream signaling proteins (VAV1, PLCγ1, Itk, NCK1). Additional constitutive decreases in phosphorylation were found on the majority of identified proteins implicated in the regulation of actin cytoskeleton pathway. Although the majority of identified sites on T cell receptor signaling proteins showed decreases in phosphorylation, Tyr(598) of ZAP-70 showed elevated phosphorylation in response to U0126 treatment, suggesting differential regulation of this site via ERK feedback. These findings shed new light on ERK's role in positive feedback in T cell receptor signaling and reveal novel signaling events that are regulated by this kinase, which may fine tune T cell receptor activation.