Effective interaction graphs arising from resource limitations in gene networks

Protein production in gene networks relies on the availability of resources necessary for transcription and translation, which are found in cells in limited amounts. As various genes in a network compete for a common pool of resources, a hidden layer of interactions among genes arises. Such interactions are neglected by standard Hill-function-based models. In this work, we develop a model with the same dimension as standard Hill-function-based models to account for the sharing of limited amounts of RNA polymerase and ribosomes in gene networks. We provide effective interaction graphs to capture the hidden interactions and find that the additional interactions can dramatically change network behavior. In particular, we demonstrate that, as a result of resource limitations, a cascade of activators can behave like an effective repressor or a biphasic system, and that a repression cascade can become bistable.United States. Air Force Office of Scientific Research (FA9550-12-1-0129)National Institute of General Medical Sciences (U.S.) (P50 GMO98792

Mitigation of ribosome competition through distributed sRNA feedback (extended version)

This paper is an extended version of a paper of the same title accepted to Proceedings of the 55th IEEE Conference on Decision and Control (2016).A current challenge in the robust engineering of synthetic gene networks is context dependence, the unintended interactions among genes and host factors. Ribosome competition is a specific form of context dependence, where all genes in the network compete for a limited pool of translational resources available for gene expression. Recently, theoretical and experimental studies have shown that ribosome competition creates a hidden layer of interactions among genes, which largely hinders our ability to predict design outcomes. In this work, we establish a control theoretic framework, where these hidden interactions become disturbance signals. We then propose a distributed feedback mechanism to achieve disturbance decoupling in the network. The feedback loop at each node consists of the protein product transcriptionally activating a small RNA (sRNA), which forms a translationally inactive complex with mRNA rapidly. We illustrate that with this feedback mechanism, protein production at each node is only dependent on its own transcription factor inputs, and almost independent of hidden interactions arising from ribosome competition.AFOSR grant FA9550-12-1-0129 and ONR grant N00014131007

Robustness of networked systems to unintended interactions with application to engineered genetic circuits

A networked dynamical system is composed of subsystems interconnected through prescribed interactions. In many engineering applications, however, one subsystem can also affect others through "unintended" interactions that can significantly hamper the intended network's behavior. Although unintended interactions can be modeled as disturbance inputs to the subsystems, these disturbances depend on the network's states. As a consequence, a disturbance attenuation property of each isolated subsystem is, alone, insufficient to ensure that the network behavior is robust to unintended interactions. In this paper, we provide sufficient conditions on subsystem dynamics and interaction maps, such that the network's behavior is robust to unintended interactions. These conditions require that each subsystem attenuates constant external disturbances, is monotone or "near-monotone", the unintended interaction map is monotone, and the prescribed interaction map does not contain feedback loops. We employ this result to guide the design of resource-limited genetic circuits. More generally, our result provide conditions under which robustness of constituent subsystems is sufficient to guarantee robustness of the network to unintended interactions

A Dynamical Model for the Low Efficiency of Induced Pluripotent Stem Cell Reprogramming (Extended Version)

This is an extended version of a paper of the same title accepted to the 2016 American Control Conference (ACC)In the past decade, researchers have been able to obtain pluripotent stem cells directly from an organism’s differentiated cells through a process called cell reprogramming. This opens the way to potentially groundbreaking applications in regenerative and personalized medicine, in which ill patients could use self-derived induced pluripotent stem (iPS) cells where needed. While the process of reprogramming has been shown to be possible, its efficiency remains so low after almost ten years since its conception as to render its applicability limited to laboratory research. In this paper, we study a mathematical model of the core transcriptional circuitry among a set of key transcription factors, which is thought to determine the switch among pluripotent and blue early differentiated cell types. By employing standard tools from dynamical systems theory, we analyze the effects on the system’s dynamics of overexpressing the core factors, which is what is performed during the reprogramming process. We demonstrate that the structure of the system is such that it can render the switch from an initial stable steady state (differentiated cell type) to the desired stable steady state (pluripotent cell type) highly unlikely. This finding provides insights into a possible reason for the low efficiency of current reprogramming approaches. We also suggest a strategy for improving the reprogramming process that employs simultaneous overexpression of one transcription factor along with enhanced degradation of another

A dynamical model for the low efficiency of induced pluripotent stem cell reprogramming

In the past decade, researchers have been able to obtain pluripotent stem cells directly from an organism's differentiated cells through a process called cell reprogramming. This opens the way to potentially groundbreaking applications in regenerative and personalized medicine, in which ill patients could use self-derived induced pluripotent stem (iPS) cells where needed. While the process of reprogramming has been shown to be possible, its efficiency remains so low after almost ten years since its conception as to render its applicability limited to laboratory research. In this paper, we study a mathematical model of the core transcriptional circuitry among a set of key transcription factors, which is thought to determine the switch among pluripotent and early differentiated cell types. By employing standard tools from dynamical systems theory, we analyze the effects on the system's dynamics of overexpressing the core factors, which is what is performed during the reprogramming process. We demonstrate that the structure of the system is such that it can render the switch from an initial stable steady state (differentiated cell type) to the desired stable steady state (pluripotent cell type) highly unlikely. This finding provides insights into a possible reason for the low efficiency of current reprogramming approaches. We also suggest a strategy for improving the reprogramming process that employs simultaneous overexpression of one transcription factor along with enhanced degradation of another.Massachusetts Institute of Technology. Undergraduate Research Opportunities Program (Paul E. Gray Fund)United States. Air Force Office of Scientific Research (BRI Grant FA9550-14-1-0060

A quasi-integral controller for adaptation of genetic modules to variable ribosome demand

The behavior of genetic circuits is often poorly predictable. A gene’s expression level is not only determined by the intended regulators, but also affected by changes in ribosome availability imparted by expression of other genes. Here we design a quasi-integral biomolecular feedback controller that enables the expression level of any gene of interest (GOI) to adapt to changes in available ribosomes. The feedback is implemented through a synthetic small RNA (sRNA) that silences the GOI’s mRNA, and uses orthogonal extracytoplasmic function (ECF) sigma factor to sense the GOI’s translation and to actuate sRNA transcription. Without the controller, the expression level of the GOI is reduced by 50% when a resource competitor is activated. With the controller, by contrast, gene expression level is practically unaffected by the competitor. This feedback controller allows adaptation of genetic modules to variable ribosome demand and thus aids modular construction of complicated circuits

An animal model of SARS produced by infection of Macaca mulatta with SARS coronavirus.

A new SARS animal model was established by inoculating SARS coronavirus (SARS-CoV) into rhesus macaques (Macaca mulatta) through the nasal cavity. Pathological pulmonary changes were successively detected on days 5-60 after virus inoculation. All eight animals showed a transient fever 2-3 days after inoculation. Immunological, molecular biological, and pathological studies support the establishment of this SARS animal model. Firstly, SARS-CoV-specific IgGs were detected in the sera of macaques from 11 to 60 days after inoculation. Secondly, SARS-CoV RNA could be detected in pharyngeal swab samples using nested RT-PCR in all infected animals from 5 days after virus inoculation. Finally, histopathological changes of interstitial pneumonia were found in the lungs during the 60 days after viral inoculation: these changes were less marked at later time points, indicating that an active healing process together with resolution of an acute inflammatory response was taking place in these animals. This animal model should provide insight into the mechanisms of SARS-CoV-related pulmonary disease and greatly facilitate the development of vaccines and therapeutics against SARS

Structure of the human activating natural cytotoxicity receptor NKp30 bound to its tumor cell ligand B7-H6

As revealed by the first crystal structure of a natural cytotoxicity receptor bound to its ligand, NKp30 engages B7-H6 in a manner structurally distinct from that of other CD28 family members

Effective interactions arising from resource limitations in gene transcription networks/

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (pages 77-81).Protein production in gene transcription networks relies on the availability of resources necessary for transcription and translation, such as RNA polymerase (RNAP) and ribosomes, which are found in cells in limited amounts. As various genes in a network compete for a common pool of resources, a hidden layer of interactions among genes arises. Such interactions are not reflected by standard Hill-function-based models and their interaction graphs. Recent experimental results have revealed that resource limitations can affect the behavior of gene networks, and thus impede our efforts to analyze and design gene transcription networks. This thesis mainly addresses two problems: how to model the hidden interactions due to resource limitations, and the potential effects of these hidden interactions. A model is developed to account for the sharing of limited amounts of RNAP and ribosomes in gene networks. The model is based on deterministic reaction rate equations, and can be reduced to have the same dimension as the standard Hill-function-based models. Hidden interactions due to resource limitations are identified using this model, and transformed into simple rules to modify the interaction graph of a network. The model is applied to two common network motifs, the activation and repression cascades, where the hidden interactions dramatically change their behaviors. In particular, it is demonstrated that, as a result of resource limitations, a cascade of activators can behave like an effective repressor or a biphasic system, and that a repression cascade can become bistable. The results presented in this thesis may be helpful to mitigate the undesirable effects arising from resource limitations in the future.by Yili Qian.S.M