Obtaining and estimating kinetic parameters from the literature

Kinetic modeling of leucine-mediated signaling and protein metabolism in human skeletal muscle

Skeletal muscle protein levels are governed by the relative rates of muscle protein synthesis (MPS) and breakdown (MPB). The mechanisms controlling these rates are complex, and their integrated behaviors are challenging to study through experiments alone. The purpose of this study was to develop and analyze a kinetic model of leucine-mediated mTOR signaling and protein metabolism in the skeletal muscle of young adults. Our model amalgamates published cellular-level models of the IRS1-PI3K-Akt-mTORC1 signaling system and of skeletal-muscle leucine kinetics with physiological-level models of leucine digestion and transport and insulin dynamics. The model satisfactorily predicts experimental data from diverse leucine feeding protocols. Model analysis revealed that total levels of p70S6K are a primary determinant of MPS, insulin signaling substantially affects muscle net protein balance via its effects on MPB, and p70S6K-mediated feedback of mTORC1 signaling reduces MPS in a dose-dependent manner.

System and network biology-based computational approaches for drug repositioning

Recent advances in computational biology have not only fastened the drug discovery process but have also proven to be a powerful tool for the search of existing molecules of therapeutic value for drug repurposing. The system biology-based drug repurposing approaches shorten the time and reduced the cost of the whole process when compared to de novo drug discovery. In the present pandemic situation, these computational approaches have emerged as a boon to tackle the COVID-19 associated morbidities and mortalities. In this chapter, we present the overview of system biology-based network system approaches which can be exploited for the drug repurposing of disease. Besides, we have included information on relevant repurposed drugs which are currently used for the treatment of COVID-19.

Systems biology of cellular membranes: a convergence with biophysics

Systems biology and systems medicine have played an important role in the last two decades in shaping our understanding of biological processes. While systems biology is synonymous with network maps and '-omics' approaches, it is not often associated with mechanical processes. Here, we make the case for considering the mechanical and geometrical aspects of biological membranes as a key step in pushing the frontiers of systems biology of cellular membranes forward. We begin by introducing the basic components of cellular membranes, and highlight their dynamical aspects. We then survey the functions of the plasma membrane and the endomembrane system in signaling, and discuss the role and origin of membrane curvature in these diverse cellular processes. We further give an overview of the experimental and modeling approaches to study membrane phenomena. We close with a perspective on the converging futures of systems biology and membrane biophysics, invoking the need to include physical variables such as location and geometry in the study of cellular membranes. WIREs Syst Biol Med 2017, 9:e1386. doi: 10.1002/wsbm.1386 For further resources related to this article, please visit the WIREs website.

Good practices for building dynamical models in systems biology

Dynamic models can offer deep understanding of information processing mechanisms in physiology, cell signaling, and biological regulation when they are appropriately detailed. Here, we describe some of the key aspects of the model-building process, including proper parameterization and error analysis, as well as common mistakes, such as model-tweaking and oversimplification, which can decrease the value of the models.

Transcription control pathways decode patterned synaptic inputs into diverse mRNA expression profiles

Synaptic plasticity requires transcription and translation to establish long-term changes that form the basis for long term memory. Diverse stimuli, such as synaptic activity and growth factors, trigger synthesis of mRNA to regulate changes at the synapse. The palette of possible mRNAs is vast, and a key question is how the cell selects which mRNAs to synthesize. To address this molecular decision-making, we have developed a biochemically detailed model of synaptic-activity triggered mRNA synthesis. We find that there are distinct time-courses and amplitudes of different branches of the mRNA regulatory signaling pathways, which carry out pattern-selective combinatorial decoding of stimulus patterns into distinct mRNA subtypes. Distinct, simultaneously arriving input patterns that impinge on the transcriptional control network interact nonlinearly to generate novel mRNA combinations. Our model combines major regulatory pathways and their interactions connecting synaptic input to mRNA synthesis. We parameterized and validated the model by incorporating data from multiple published experiments. The model replicates outcomes of knockout experiments. We suggest that the pattern-selectivity mechanisms analyzed in this model may act in many cell types to confer the capability to decode temporal patterns into combinatorial mRNA expression.

Advanced systems biology methods in drug discovery and translational biomedicine

Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.