Protein dynamics analysis identifies candidate cancer driver genes and mutations in TCGA data

From Cognitive Agents to Cognitive Systems: Theoretical, Methodological, and Empirical Developments of van Gelder's (1998) "Dynamical Hypothesis"

Over two decades have passed since the publication of van Gelder's (1998) "dynamical hypothesis." In that paper, van Gelder proposed that cognitive agents were not digital computers-per the representational computational approach-but dynamical systems. The evolution of the dynamical hypothesis was driven by parallel advances in three areas. Theoretically, a deeper understanding of genetics, biology, neuroscience, and cognitive science inspired questions about how systems within each domain dynamically interact and extend their effects across spatiotemporal scales. Methodologically, more sophisticated and domain-general tools allowed researchers to discover, model, and quantify system dynamics, structure, and patterns across multiple scales to generate a more comprehensive system-level understanding of behaviors. Empirically, we can analyze a system's behavior while preserving its natural dynamics, revealing evidence that the reductionist approach leads to an incomplete understanding of the components and the overall system. Researchers have traditionally reduced a complex system into its component processes and assumed that the parts can be recombined to explain the whole. These three advances fundamentally altered our understanding of a "cognitive agent:" How their behaviors are driven by long-range coordination across multiple processes, how the interdependent and nested structure of interacting variables produces behaviors that are greater than the sum of its parts, and how environmental constraints shape adaptive yet stable behavioral patterns.

Cell phenotypes can be predicted from propensities of protein conformations

Proteins exist as dynamic conformational ensembles. Here we suggest that the propensities of the conformations can be predictors of cell function. The conformational states that the molecules preferentially visit can be viewed as phenotypic determinants, and their mutations work by altering the relative propensities, thus the cell phenotype. Our examples include (i) inactive state variants harboring cancer driver mutations that present active state-like conformational features, as in K-Ras4BG12V compared to other K-Ras4BG12X mutations; (ii) mutants of the same protein presenting vastly different phenotypic and clinical profiles: cancer and neurodevelopmental disorders; (iii) alterations in the occupancies of the conformational (sub)states influencing enzyme reactivity. Thus, protein conformational propensities can determine cell fate. They can also suggest the allosteric drugs efficiency.

Cancer-keeper genes as therapeutic targets

Finding cancer-driver genes has been a central theme of cancer research. We took a different perspective; instead of considering normal cells, we focused on cancerous cells and genes that maintained abnormal cell growth, which we named cancer-keeper genes (CKGs). Intervening CKGs may rectify aberrant cell growth, making them potential cancer therapeutic targets. We introduced control-hub genes and developed an efficient algorithm by extending network controllability theory. Control hub are essential for maintaining cancerous states and thus can be taken as CKGs. We applied our CKG-based approach to bladder cancer (BLCA). All genes on the cell-cycle and p53 pathways in BLCA were identified as CKGs, showing their importance in cancer. We discovered that sensitive CKGs - genes easily altered by structural perturbation - were particularly suitable therapeutic targets. Experiments on cell lines and a mouse model confirmed that six sensitive CKGs effectively suppressed cancer cell growth, demonstrating the immense therapeutic potential of CKGs.