Henry Mirsky

Bio: Henry Mirsky is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Systems biology & Circadian rhythm. The author has an hindex of 4, co-authored 7 publications receiving 219 citations.

A model of the cell-autonomous mammalian circadian clock

Abstract: Circadian timekeeping by intracellular molecular clocks is evident widely in prokaryotes and eukaryotes. The clockworks are driven by autoregulatory feedback loops that lead to oscillating levels of components whose maxima are in fixed phase relationships with one another. These phase relationships are the key metric characterizing the operation of the clocks. In this study, we built a mathematical model from the regulatory structure of the intracellular circadian clock in mice and identified its parameters using an iterative evolutionary strategy, with minimum cost achieved through conformance to phase separations seen in cell-autonomous oscillators. The model was evaluated against the experimentally observed cell-autonomous circadian phenotypes of gene knockouts, particularly retention of rhythmicity and changes in expression level of molecular clock components. These tests reveal excellent de novo predictive ability of the model. Furthermore, sensitivity analysis shows that these knockout phenotypes are robust to parameter perturbation.

Automatic Control in Systems Biology

Abstract: The reductionist approaches of molecular and cellular biology have produced revolutionary advances in our understanding of biological function and information processing. The difficulty associated with relating molecular components to their systemic function led to the development of systems biology, a relatively new field that aims to establish a bridge between molecular level information and systems level understanding. The novelty of systems biology lies in the emphasis on analyzing complexity in networked biological systems using integrative rather than reductionist approaches. By its very nature, systems biology is a highly interdisciplinary field that requires the effective collaboration of scientists and engineers with different technical backgrounds, and the interdisciplinary training of students to meet the rapidly evolving needs of academia, industry, and government. This chapter summarizes state-of-the-art developments of automatic control in systems biology with substantial theoretical background and illustrative examples.

Distribution-based sensitivity metric for highly variable biochemical systems

Abstract: Classical sensitivity analysis is routinely used to identify points of fragility or robustness in biochemical networks However, intracellular systems often contain components that number in the thousands to tens or less and consequently motivate a stochastic treatment Although methodologies exist to quantify sensitivities in stochastic models, they differ substantially from those used in deterministic regimes Therefore it is not possible to tell whether observed differences in sensitivity measured in deterministic and stochastic elaborations of the same network are the result of methodology or model form The authors introduce here a distribution-based methodology to measure sensitivity that is equally applicable in both regimes, and demonstrate its use and applicability on a sophisticated mathematical model of the mouse circadian clock that is available in both deterministic and stochastic variants The authors use the method to produce sensitivity measurements on both variants They note that the rank-order sensitivity of the clock to parametric perturbations is extremely well conserved across several orders of magnitude The data show that the clock is fragile to perturbations in parameters common to the cellular machinery ('global' parameters) and robust to perturbations in parameters that are clock-specific ('local' parameters) The sensitivity measure can be used to reduce the model from its original 73 ordinary differential equations (ODEs) to 18 ODEs and to predict the degree to which parametric perturbation can distort the phase response curve of the clock Finally, the method is employed to evaluate the effect of transcriptional and translational noise on clock function [Includes supplementary material]

Systems Analysis for Systems Biology

Abstract: Publisher Summary This chapter focuses on system's analysis tools and its application to the study of biological systems through mathematical modeling. The tools from classical sensitivity analysis are outlined, and described with application to the unraveling of design principles in complex biophysical networks, particularly with regard to robustness. Applications to optimized experimental design, and hypothesis discrimination are also discussed. In the field of systems biology, sensitivity analysis has been used in a number of applications, including optimized design of synthetic circuits, design of experiments for optimal parameter estimation, and robustness analysis to provide insights into design principles. Examples of how these sensitivity analysis implications and extensions have already been applied to biological systems are provided in this chapter. Sensitivity analysis investigates the changes in a system's behavior in response to infinitesimal parametric perturbations. Results from UPR sensitivity comparisons showed the greatest disparity between the BiP-heterologous unfolded protein binding rate sensitivities for the two models. Sensitivity analysis is also used to explore a stochastic, oscillatory system: the mouse circadian rhythm. The fundamental sensitivity analysis concept, or definition, is quite basic: perturbations in model parameters will affect model outputs. However, its implications, extensions, and applications are profound and numerous in the field of systems biology.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Stochastic Gene Expression in a Lentiviral Positive Feedback Loop: HIV-1 Tat Fluctuations Drive Phenotypic Diversity

Abstract: Stochastic gene expression has been implicated in a variety of cellular processes, including cell differentiation and disease. In this issue of Cell, Weinberger et al. (2005) take an integrated computational-experimental approach to study the Tat transactivation feedback loop in HIV-1 and show that fluctuations in a key regulator, Tat, can result in a phenotypic bifurcation. This phenomenon is observed in an isogenic population where individual cells display two distinct expression states corresponding to latent and productive infection by HIV-1. These findings demonstrate the importance of stochastic gene expression in molecular "decision-making."

Control Principles of Complex Networks

Abstract: A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: It requires an accurate map of the network that governs the interactions between the system's components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in nonlinear dynamics and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these here we review recent advances on the controllability and the control of complex networks, exploring the intricate interplay between a system's structure, captured by its network topology, and the dynamical laws that govern the interactions between the components. We match the pertinent mathematical results with empirical findings and applications. We show that uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.

Identification of small molecule activators of cryptochrome.

Abstract: Impairment of the circadian clock has been associated with numerous disorders, including metabolic disease. Although small molecules that modulate clock function might offer therapeutic approaches to such diseases, only a few compounds have been identified that selectively target core clock proteins. From an unbiased cell-based circadian phenotypic screen, we identified KL001, a small molecule that specifically interacts with cryptochrome (CRY). KL001 prevented ubiquitin-dependent degradation of CRY, resulting in lengthening of the circadian period. In combination with mathematical modeling, our studies using KL001 revealed that CRY1 and CRY2 share a similar functional role in the period regulation. Furthermore, KL001-mediated CRY stabilization inhibited glucagon-induced gluconeogenesis in primary hepatocytes. KL001 thus provides a tool to study the regulation of CRY-dependent physiology and aid development of clock-based therapeutics of diabetes.

A mechanism for robust circadian timekeeping via stoichiometric balance

Abstract: Circadian (B24 h) timekeeping is essential for the lives of many organisms. To understand the biochemical mechanisms of this timekeeping, we have developed a detailed mathematical model of the mammalian circadian clock. Our model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. Using this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. Furthermore, we find that an additional slow negative feedback loop preserves this stoichiometric balance and maintains timekeeping with a fixed period. The role of this mechanism in generating robust rhythms is validated by analysis of a simple and general model and a previous model of the Drosophila circadian clock. We propose a double-negative feedback loop design for biological clocks whose period needs to be tightly regulated even with large changes in gene dosage.