Frontiers in systems biology最新文献

Circular RNAs (circRNAs) have gained prominence as important players in various biological processes such as gastric cancer (GC). Identification of several dysregulated circRNAs may serve as biomarkers for early diagnosis or as novel therapeutic targets. Predictive models can suggest potential new interactions and regulatory roles of circRNAs in GCs. Experimental validations of key interactions are being performed using in vitro models, confirming the significance of identified circRNA networks. The aim of this review is to highlight the important circRNAs associated with GC. On top of that an overview of the mechanistic details of the biogenesis and functionalities of the circRNAs are also presented. Furthermore, the potentialities of the circRNAs in the field of new drug discovery are deciphered.

Introduction: Pediatric Acute Liver Failure (PALF) presents as a rapidly evolving, multifaceted, and devastating clinical syndrome whose precise etiology remains incompletely understood. Consequently, predicting outcomes-whether survival or mortality-and informing liver transplantation decisions in PALF remain challenging. We have previously implicated High-Mobility Group Box 1 (HMGB1) as a central mediator in PALF-associated dynamic inflammation networks that could be recapitulated in acetaminophen (APAP)-treated mouse hepatocytes (HC) in vitro. Here, we hypothesized that Growth/Differentiation Factor-15 (GDF-15) is involved along with HMGB1 in PALF.

Methods: 28 and 23 inflammatory mediators including HMGB1 and GDF15 were measured in serum samples from PALF patients and cell supernatants from wild-type (C57BL/6) mouse hepatocytes (HC) and from cells from HC-specific HMGB1-null mice (HC-HMGB1^-/-) exposed to APAP, respectively. Results were analyzed computationally to define statistically significant and potential causal relationships.

Results: Circulating GDF-15 was elevated significantly (P < 0.05) in PALF non-survivors as compared to survivors, and together with HMGB1 was identified as a central node in dynamic inflammatory networks in both PALF patients and mouse HC. This analysis also pointed to MIG/CXCL9 as a differential node linking HMGB1 and GDF-15 in survivors but not in non-survivors, and, when combined with in vitro studies, suggested that MIG suppresses GDF-15-induced inflammation.

Discussion: This study suggests GDF-15 as a novel PALF outcome biomarker, posits GDF-15 alongside HMGB1 as a central node within the intricate web of systemic inflammation dynamics in PALF, and infers a novel, negative regulatory role for MIG.

Uncertainty is ubiquitous in biological systems. For example, since gene expression is intrinsically governed by noise, nature shows a fascinating degree of variability. If we want to use a model to predict the behaviour of such an intrinsically stochastic system, we have to cope with the fact that the model parameters are never exactly known, but vary according to some distribution. A key question is then to determine how the uncertainties in the parameters affect the model outcome. Knowing the latter uncertainties is crucial when a model is used for, e.g., experimental design, optimisation, or decision-making. To establish how parameter and model prediction uncertainties are related, Monte Carlo approaches could be used. Then, the model is evaluated for a huge number of parameters sets, drawn from the multivariate parameter distribution. However, when model solutions are computationally expensive this approach is intractable. To overcome this problem, so-called spectral expansion (SE) methods have been developed to quantify prediction uncertainty within a probabilistic framework. Such SE methods have a basis in, e.g., computational mathematics, engineering, physics, and fluid dynamics, and, to a lesser extent, systems biology. The computational costs of SE schemes mainly stem from the calculation of the expansion coefficients. Furthermore, SE effectively leads to a surrogate model which captures the dependence of the model on the uncertainty parameters, but is much simpler to execute compared to the original model. In this paper, we present an innovative scheme for the calculation of the expansion coefficients. It guarantees that the model has to be evaluated only a restricted number of times. Especially for models of high complexity this may be a huge computational advantage. By applying the scheme to a variety of examples we show its power, especially in challenging situations where solutions slowly converge due to high computational costs, bifurcations, and discontinuities.

In the context of in silico clinical trials, mechanistic computer models for pathophysiology and pharmacology (here Quantitative Systems Pharmacology models, QSP) can greatly support the decision making for drug candidates and elucidate the (potential) response of patients to existing and novel treatments. These models are built on disease mechanisms and then parametrized using (clinical study) data. Clinical variability among patients is represented by alternative model parameterizations, called virtual patients. Despite the complexity of disease modeling itself, using individual patient data to build these virtual patients is particularly challenging given the high-dimensional, potentially sparse and noisy clinical trial data. In this work, we investigate the applicability of simulation-based inference (SBI), an advanced probabilistic machine learning approach, for virtual patient generation from individual patient data and we develop and evaluate the concept of nearest patient fits (SBI NPF), which further enhances the fitting performance. At the example of rheumatoid arthritis where prediction of treatment response is notoriously difficult, our experiments demonstrate that the SBI approaches can capture large inter-patient variability in clinical data and can compete with standard fitting methods in the field. Moreover, since SBI learns a probability distribution over the virtual patient parametrization, it naturally provides the probability for alternative parametrizations. The learned distributions allow us to generate highly probable alternative virtual patient populations for rheumatoid arthritis, which could potentially enhance the assessment of drug candidates if used for in silico trials.

Diabetes mellitus affects roughly one in ten people globally and is the world's ninth leading cause of death. However, a significant portion of chronic complications that contribute to mortality can be prevented with proper treatment and medication. Glucagon-like peptide 1 receptor agonists, such as Exendin-4, are one of the leading classes of Type 2 diabetes treatments but are prohibitively expensive. In this study, experimental models for recombinant Exendin-4 protein production were designed in both Escherichia coli and Saccharomyces cerevisiae. Protein expression in the chromosomally integrated S. cerevisiae strain was observed at the expected size of Exendin-4 and confirmed by immunoassay. This provides a foundation for the use of this Generally Regarded as Safe organism as an affordable treatment for Type 2 diabetes that can be propagated, prepared, and distributed locally.

Chimeric antigen receptor T (CAR T) cell therapy has shown remarkable success in treating various leukemias and lymphomas. Cellular kinetic (CK) and pharmacodynamic (PD) behavior of CAR T cell therapy is distinct from other therapies due to its living nature. CAR T CK is typically characterized by an exponential expansion driven by target binding, fast initial decline (contraction), and slow long-term decline (persistence). Due to the dependence of CK on target binding, CK and PD of CAR T therapies are inherently and bidirectionally linked. In this work, we develop a semi-mechanistic model of CAR T CK/PD, incorporating molecular-scale binding, T cell dynamics with multiple phenotypes, and tumor growth and killing. We calibrate this model to published CK and PD data for a CD19-targeting CAR T cell therapy. Using sensitivity analysis, we explore variability in response due to patient- and drug-specific properties. We further explore the impact of tumor characteristics on CAR T-cell expansion and efficacy through individual- and population-level parameter scans.

Polyhydroxyalkanoates (PHA) are popular biopolymers due to their potential use as biodegradable thermoplastics. In this study, three aerobic sequencing batch reactors were operated identically except for their temperatures, which were set at 15 °C, 35 °C, and 48 °C. The reactors were subjected to a feast-famine feeding regime, where carbon sources are supplied intermittently, to enrich PHA-accumulating microbial consortia. The biomass was sampled for 16S rRNA gene amplicon sequencing of both DNA (during the enrichment phase) and cDNA (during the enrichment and accumulation phases). All temperatures yielded highly enriched PHA-accumulating consortia. Thermophilic communities were significantly less diverse than those at low or mesophilic temperatures. In particular, Thauera was highly adaptable, abundant, and active at all temperatures. Low temperatures resulted in reduced PHA production rates and yields. Analysis of the microbial community revealed a collapse of community diversity during low-temperature PHA accumulation, suggesting that the substrate dosing strategy was unsuccessful at low temperatures. This points to future possibilities for optimizing low-temperature PHA accumulation.

Background: The regulatory mechanisms guiding CD4 T cell differentiation are complex and are further influenced by intrinsic cell variability along with that of microenvironmental cues, such as cytokine and nutrient availability.

Objective: This study aims to expand our understanding of CD4 T cell differentiation by examining the influence of intrinsic noise on cell fate.

Methodology: A model based on a complex regulatory network of early signaling events involved in CD4 T cell activation and differentiation was described in terms of a set of stochastic differential equation to assess the effect of noise intensity on differentiation efficiency to the Th1, Th2, Th17, Treg, and ${\text{T}}_{FH}$ effector phenotypes under defined cytokine and nutrient conditions.

Results: The increase of noise intensity decreases differentiation efficiencies. In a microenvironment of Th1-inducing cytokines and optimal nutrient conditions, noise levels of 3 $\%$ , 5 $\%$ and 10 $\%$ render Th1 differentiation efficiencies of 0.87, 0.76 and 0.62, respectively, underscoring the sensitivity of the network to random variations. Further increments of noise reveal that the network is relatively stable until noise levels of 20 $\%$ , where the resulting cell phenotypes becomes heterogeneous. Notably, Treg differentiation showed the highest robustness to noise perturbations. A combined Th1-Th2 cytokine environment with optimal nutrient levels induces a dominant Th1 phenotype; however, removal of glutamine shifts the balance towards the Th2 phenotype at all noise levels, with an efficiency similar to that obtained under Th2-only cytokine conditions. Similarly, combinations of Th1/Treg and Treg/Th17-inducing cytokines along with the removal of either tryptophan or oxygen shift the dominant Th1 and Treg phenotypes towards Treg and Th17 respectively. Model results are consistent with differentiation efficiency patterns obtained under well-controlled experimental settings reported in the literature.

Conclusion: The stochastic CD4 T cell mathematical model presented here demonstrates a noise-dependent modulation of T cell differentiation induced by cytokines and nutrient availability. Modeling results can be explained by the network topology, which assures that the system will arrive at stable states of cell functionality despite variable levels of biological intrinsic noise. Moreover, the model provides insights into the robustness of the T cell differentiation process.

Both machine learning and mechanistic modelling approaches have been used independently with great success in systems biology. Machine learning excels in deriving statistical relationships and quantitative prediction from data, while mechanistic modelling is a powerful approach to capture knowledge and infer causal mechanisms underpinning biological phenomena. Importantly, the strengths of one are the weaknesses of the other, which suggests that substantial gains can be made by combining machine learning with mechanistic modelling, a field referred to as Scientific Machine Learning (SciML). In this review we discuss recent advances in combining these two approaches for systems biology, and point out future avenues for its application in the biological sciences.