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Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present an elegant modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding entirely novel strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally-determined from experiments, augmented with dynamical network computations involving end point objective functions, mutual information, change point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method (CIM), utilizing the strategies described above. The CIM is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this state-of-the-art framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights.STATEMENT OF SIGNIFICANCECellular processes like cell cycle are overly complex, involving multiple players interacting at multiple levels, and explicit modeling of such systems is challenging. The availability of longitudinal RNA measurements provides an opportunity to “reverse-engineer” for novel regulatory models. We develop a novel framework, inspired using goal-oriented cybernetic model, to implicitly model transcriptional regulation by constraining the system using inferred temporal goals. A preliminary causal network based on information-theory is used as a starting point, and our framework is used to distill the network to temporally-based networks containing essential molecular players. The strength of this approach is its ability to dynamically model the RNA temporal measurements. The approach developed paves the way for inferring regulatory processes in many complex cellular processes.

Vincristine is a core chemotherapeutic drug administered to pediatric acute lymphoblastic leukemia patients. Despite its efficacy in treating leukemia, it can lead to severe peripheral neuropathy in a sub-group of the patients. Peripheral neuropathy is a debilitating and painful side-effect that can severely impact an individual’s quality of life. Currently, there are no established predictors of peripheral neuropathy incidence during the early stage of chemotherapeutic treatment. As a result, patients who are not susceptible to peripheral neuropathy may receive sub-therapeutic treatment due to an empirical upper cap on the dose, while others may experience severe neuropathy at the same dose. Contrary to previous genomics based approaches, we employed a metabolomics approach to identify small sets of metabolites that can be used to predict a patient’s susceptibility to peripheral neuropathy at different time points during the treatment. Using those identified metabolites, we developed a novel strategy to predict peripheral neuropathy and subsequently adjust the vincristine dose accordingly. In accordance with this novel strategy, we created a free user-friendly tool, VIPNp, for physicians to easily implement our prediction strategy. Our results showed that focusing on metabolites, which encompasses both genotypic and phenotypic variations, can enable early prediction of peripheral neuropathy in pediatric leukemia patients.

In the bacteria species Enterococcus faecalis, transfer of plasmid pCF10 by conjugation between donor cells that possess sets of genes conferring drug resistance and recipient cells leads to the spread of this resistance in a population. A complex signaling molecule network is responsible for communication between recipient and donor cells. Up to this point, the nature of these signaling molecules has been modeled deterministically from the point of view of an “average” cell. While this view has been cured slightly by stochastic treatments, a model is needed that better accounts for the individual nature of each cell, each cell’s interactions with other cells, and each cell’s distinct effect on the population’s environment. To this end, a population balance model has been formulated that considers these features. Of interest is the distribution of protein PrgB, a membrane-bound protein that indicates the degree to which conjugation has been initiated. As a result of the interactions between cells and the signaling molecules, two steady state levels of protein concentration exist, one for an “off” state of conjugation and one for an “on” state. While the single-cell approach predicts a bimodal distribution of protein resulting from this bistability, the population balance approach demonstrates that a bistable system such as that of plasmid pCF10 can give rise to a unimodal stationary distribution. While there is still much work to be done on understanding conjugation in Enterococcus faecalis, this study has delineated two important concepts associated with this complex phenomenon (i.e. bistability versus bimodality).

Vincristine is a vinca alkaloid, a commonly used chemotherapy drug for treating leukemia, lymphoma, multiple myeloma and some pediatric cancers. Its major dose-limiting side effect is peripheral neuropathy. The current dosing of “standard-dose-for-all” ignores the genetic and phenotypic variations among different patients, and causes severe neuropathy in some patients while ineffectively treats the others. In the present study, we aim to discover novel biomarkers involved in vincristine-induced neuropathy and identify patients with varied metabolic characteristics. Thus treatment can be tailored accordingly to improve outcomes of vincristine treatment. Pre-dose and post-dose serum samples were collected from two groups of patients (low and high toxicity groups) at the beginning of treatment and at the end of treatments. Liquid chromatography–mass spectrometry (LC-MS) was used to identify and quantify metabolites in the samples. Metabolomics data analysis tools were utilized to analyze the raw spectrum obtained from LC-MS. From statistical analysis and modeling, we identified 27 compounds that showed a difference in intensity between low toxicity and high toxicity patients at the beginning of the treatment. Further verification against database and validation are needed to confirm the biomarkers to be able to be useful in clinics. . Successful validation of the biomarkers will enable the clinicians to treat the patients according to their characteristics which will ultimately improve the survival and quality-of-life of cancer patients.