We introduce a novel modeling framework for incorporating fear of infection and frustration with social distancing into disease dynamics. We show that the resulting SEIR behavior-perception model has ...three principal modes of qualitative behavior-no
and
. We also demonstrate that the model can produce transient and sustained waves of infection consistent with secondary outbreaks. We fit the model to cumulative COVID-19 case and mortality data from several regions. Our analysis suggests that regions which experience a significant decline after the first wave of infection, such as Canada and Israel, are more likely to contain secondary waves of infection, whereas regions which only achieve moderate success in mitigating the disease's spread initially, such as the United States, are likely to experience substantial secondary waves or uncontrolled outbreaks.
The long-term behaviors of biochemical systems are often described by their steady states. Deriving these states directly for complex networks arising from real-world applications, however, is often ...challenging. Recent work has consequently focused on network-based approaches. Specifically, biochemical reaction networks are transformed into weakly reversible and deficiency zero generalized networks, which allows the derivation of their analytic steady states. Identifying this transformation, however, can be challenging for large and complex networks. In this paper, we address this difficulty by breaking the complex network into smaller independent subnetworks and then transforming the subnetworks to derive the analytic steady states of each subnetwork. We show that stitching these solutions together leads to the analytic steady states of the original network. To facilitate this process, we develop a user-friendly and publicly available package, COMPILES (COMPutIng anaLytic stEady States). With COMPILES, we can easily test the presence of bistability of a CRISPRi toggle switch model, which was previously investigated via tremendous number of numerical simulations and within a limited range of parameters. Furthermore, COMPILES can be used to identify absolute concentration robustness (ACR), the property of a system that maintains the concentration of particular species at a steady state regardless of any initial concentrations. Specifically, our approach completely identifies all the species with and without ACR in a complex insulin model. Our method provides an effective approach to analyzing and understanding complex biochemical systems.
Many biochemical and industrial applications involve complicated networks of simultaneously occurring chemical reactions. Under the assumption of mass action kinetics, the dynamics of these chemical ...reaction networks are governed by systems of polynomial ordinary differential equations. The steady states of these mass action systems have been analyzed via a variety of techniques, including stoichiometric network analysis, deficiency theory, and algebraic techniques (e.g., Gröbner bases). In this paper, we present a novel method for characterizing the steady states of mass action systems. Our method explicitly links a network’s capacity to permit a particular class of steady states, called toric steady states, to topological properties of a generalized network called a
translated chemical reaction network
. These networks share their reaction vectors with their source network but are permitted to have different complex stoichiometries and different network topologies. We apply the results to examples drawn from the biochemical literature.
A repeat expansion in C9ORF72 causes frontotemporal dementia and amyotrophic lateral sclerosis (c9FTD/ALS). RNA of the expanded repeat (r(GGGGCC)exp) forms nuclear foci or undergoes repeat-associated ...non-ATG (RAN) translation, producing “c9RAN proteins.” Since neutralizing r(GGGGCC)exp could inhibit these potentially toxic events, we sought to identify small-molecule binders of r(GGGGCC)exp. Chemical and enzymatic probing of r(GGGGCC)8 indicate that it adopts a hairpin structure in equilibrium with a quadruplex structure. Using this model, bioactive small molecules targeting r(GGGGCC)exp were designed and found to significantly inhibit RAN translation and foci formation in cultured cells expressing r(GGGGCC)66 and neurons transdifferentiated from fibroblasts of repeat expansion carriers. Finally, we show that poly(GP) c9RAN proteins are specifically detected in c9ALS patient cerebrospinal fluid. Our findings highlight r(GGGGCC)exp-binding small molecules as a possible c9FTD/ALS therapeutic and suggest that c9RAN proteins could potentially serve as a pharmacodynamic biomarker to assess efficacy of therapies that target r(GGGGCC)exp.
•(GGGGCC) RNA forms a hairpin structure in equilibrium with a G-quadruplex structure•Neurons directly converted from C9ORF72+ fibroblasts express c9RAN proteins and foci•Small-molecule binders of (GGGGCC)exp RNA ameliorate c9FTD/ALS-associated defects•c9RAN proteins are detected in c9ALS patient cerebrospinal fluid
A repeat expansion in C9ORF72 causes frontotemporal dementia and amyotrophic lateral sclerosis, presumably via the expression of repeat-containing RNA (r(GGGGCC)exp). Su et al. find that small molecules targeting r(GGGGCC)exp can block c9FTD/ALS-associated defects and reveal a potential biomarker for c9FTD/ALS.
Macrocycles are of increasing interest as chemical probes and drugs for intractable targets like protein-protein interactions, but the determinants of their cell permeability and oral absorption are ...poorly understood. To enable rational design of cell-permeable macrocycles, we generated an extensive data set under consistent experimental conditions for more than 200 non-peptidic, de novo-designed macrocycles from the Broad Institute's diversity-oriented screening collection. This revealed how specific functional groups, substituents and molecular properties impact cell permeability. Analysis of energy-minimized structures for stereo- and regioisomeric sets provided fundamental insight into how dynamic, intramolecular interactions in the 3D conformations of macrocycles may be linked to physicochemical properties and permeability. Combined use of quantitative structure-permeability modeling and the procedure for conformational analysis now, for the first time, provides chemists with a rational approach to design cell-permeable non-peptidic macrocycles with potential for oral absorption.
We present a computational method for performing structural translation, which has been studied recently in the context of analyzing the steady states and dynamical behavior of mass-action systems ...derived from biochemical reaction networks. Our procedure involves solving a binary linear programming problem where the decision variables correspond to interactions between the reactions of the original network. We call the resulting network a reaction-to-reaction graph and formalize how such a construction relates to the original reaction network and the structural translation. We demonstrate the efficacy and efficiency of the algorithm by running it on 508 networks from the European Bioinformatics Institutes’ BioModels database. We also summarize how this work can be incorporated into recently proposed algorithms for establishing mono- and multistationarity in biochemical reaction systems.
Network translation has recently been used to establish steady-state properties of mass action systems by corresponding the given system to a generalized one which is either dynamically or ...steady-state equivalent. In this work, we further use network translation to identify network structures which give rise to the well-studied property of absolute concentration robustness in the corresponding mass action systems. In addition to establishing the capacity for absolute concentration robustness, we show that network translation can often provide a method for deriving the steady-state value of the robust species. We furthermore present a MILP algorithm for the identification of translated chemical reaction networks that improves on previous approaches, allowing for easier application of the theory.
Clinical patterns and the associated optimal management of acquired resistance to PD-(L)1 blockade are poorly understood.
All cases of metastatic lung cancer treated with PD-(L)1 blockade at Memorial ...Sloan Kettering were reviewed. In acquired resistance (complete/partial response per RECIST, followed by progression), clinical patterns were distinguished as oligo (OligoAR ≤ 3 lesions of disease progression) or systemic (sAR). We analyzed the relationships between patient characteristics, burden/location of disease, outcomes, and efficacy of therapeutic interventions.
Of 1,536 patients, 312 (20%) had an initial response and 143 developed AR (9% overall, 46% of responders). OligoAR was the most common pattern (80/143, 56%). Baseline tumor mutational burden, depth of response, and duration of response were significantly increased in oligoAR compared with sAR (P < 0.001, P = 0.03, P = 0.04, respectively), whereas baseline PD-L1 and tumor burden were similar. Post-progression, oligoAR was associated with improved overall survival (median 28 months vs. 10 months, P < 0.001) compared with sAR. Within oligoAR, post-progression survival was greater among patients treated with locally-directed therapy (e.g., radiation, surgery; HR, 0.41; P = 0.039). Fifty-eight percent of patients with oligoAR treated with locally-directed therapy alone are progression-free at last follow-up (median 16 months), including 13 patients who are progression-free more than 2 years after local therapy.
OligoAR is a common and distinct pattern of acquired resistance to PD-(L)1 blockade compared with sAR. OligoAR is associated with improved post-progression survival and some cases can be effectively managed with local therapies with durable benefit.
•Recent results give conditions for extinction events in reaction networks with discrete state spaces.•The conditions consist of generating a series of systems of inequalities then checking their ...feasibility.•We present a computational implementation of these conditions written in Python.•We run the program on 458 models from the BioModels database and present our findings.
Recent work of Johnston et al. has produced sufficient conditions on the structure of a chemical reaction network which guarantee that the corresponding discrete state space system exhibits an extinction event. The conditions consist of a series of systems of equalities and inequalities on the edges of a modified reaction network called a domination-expanded reaction network. In this paper, we present a computational implementation of these conditions written in Python and apply the program on examples drawn from the biochemical literature. We also run the program on 458 models from the European Bioinformatics Institute’s BioModels Database and report our results.
Direct observations during intense warm‐air advection over the East Siberian Sea reveal a period of rapid sea‐ice melt. A semistationary, high‐pressure system north of the Bering Strait forced ...northward advection of warm, moist air from the continent. Air‐mass transformation over melting sea ice formed a strong, surface‐based temperature inversion in which dense fog formed. This induced a positive net longwave radiation at the surface while reducing net solar radiation only marginally; the inversion also resulted in downward turbulent heat flux. The sum of these processes enhanced the surface energy flux by an average of ~15 W m−2 for a week. Satellite images before and after the episode show sea‐ice concentrations decreasing from > 90% to ~50% over a large area affected by the air‐mass transformation. We argue that this rapid melt was triggered by the increased heat flux from the atmosphere due to the warm‐air advection.
Key Points
The importance of both large‐scale dynamics and local feedback for sea‐ice melt
The location of extra melt near the ice edge, due to the air‐mass transformation
The role of clouds, longwave radiation and turbulent heat flux for sea‐ice melt
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