Time varying causal network reconstruction of a mouse cell cycle

Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach

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 a modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding rational 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 endpoint 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, based on the strategies described above. The cybernetic-inspired method 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 innovative framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights.

Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach

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 SIGNIFICANCE

Cellular 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.

Perspectives on transcriptomics in animal physiology studies

Reductionist approaches in physiology and biochemistry are essential for understanding how animals cope and adapt to their environments. Transcriptomics is no longer restricted to a select few, and accessibility and affordability continue to facilitate its rapid growth as a science. More than 6000 publications (a conservative estimate) over the past decade quantify the response of the transcriptome to a wide breadth of questions in animal physiology. Transcriptomes have been quantified under conditions of hypoxia, climate change, salinity, drought, environmental pollution, and ultraviolet radiation among others; these studies have greatly improved understanding of the molecular machinery required for organismal adaptation. These "snapshots in time" however are never complete as the transcriptome is exquisitely sensitive to an individual's current physiologic state. Animal physiologists new to the field must recognize limitations of transcriptome technologies and consider experimental designs that strengthen physiologic interpretation. Current estimates suggest that a sample size of 6 or more are required for RNA-seq experiments in order to capture the majority of differentially expressed genes confidently. "Outside-the-box" approaches for statistical analyses of data derived from RNA-seq should be explored, as studies continue to point out that high false discoveries rates are pervasive with RNA-seq studies, reminiscent of the early days of microarrays. Incorporating biological variability, rather than reducing it (i.e. pooling strategies), into experimental designs is essential. Moreover, real-time PCR must not be viewed as a "validation step" to justify low samples sizes, but rather an orthogonal method to strengthen biological interpretation. The use of proper experimental controls in transcriptomics studies (i.e. spike-in controls and technical replication) are recommended and there is a pressing need for inter-laboratory tests (round robin experiments) to quantify repeatability and to identify sources of transcriptome variation within the context of animal physiology. Testing the reproducibility of transcriptome experiments in light of physiology in non-model organisms would be a significant contribution to the community. Single cell transcriptomics and multiplexing barcoding strategies such as decode-seq are poised to further advance the reductionist view of animal physiology; researchers are encouraged to consult literature herein and elsewhere for guidance on best practices and limitations of transcriptome technologies when studying the physiology of animals.