Biomedical subjects

John Jeremy Rice

Publications and source records attributed to John Jeremy Rice.

3 recordsLinked to original sources

PubMed2004

Reconstructing biological networks using conditional correlation analysis.

MOTIVATION: One of the present challenges in biological research is the organization of the data originating from high-throughput technologies. One way in which this information can be organized is in the form of networks of influences, physical or statistical, between cellular components. We propose an experimental method for probing biological networks, analyzing the resulting data and reconstructing the network architecture. METHODS: We use networks of known topology consisting of nodes (genes), directed edges (gene-gene interactions) and a dynamics for the genes' mRNA concentrations in terms of the gene-gene interactions. We proposed a network reconstruction algorithm based on the conditional correlation of the mRNA equilibrium concentration between two genes given that one of them was knocked down. Using simulated gene expression data on networks of known connectivity, we investigated how the reconstruction error is affected by noise, network topology, size, sparseness and dynamic parameters. RESULTS: Errors arise from correlation between nodes connected through intermediate nodes (false positives) and when the correlation between two directly connected nodes is obscured by noise, non-linearity or multiple inputs to the target node (false negatives). Two critical components of the method are as follows: (1) the choice of an optimal correlation threshold for predicting connections and (2) the reduction of errors arising from indirect connections (for which a novel algorithm is proposed). With these improvements, we can reconstruct networks with the topology of the transcriptional regulatory network in Escherichia coli with a reasonably low error rate.

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PubMed2003

Ising model of cardiac thin filament activation with nearest-neighbor cooperative interactions.

We have developed a model of cardiac thin filament activation using an Ising model approach from equilibrium statistical physics. This model explicitly represents nearest-neighbor interactions between 26 troponin/tropomyosin units along a one-dimensional array that represents the cardiac thin filament. With transition rates chosen to match experimental data, the results show that the resulting force-pCa (F-pCa) relations are similar to Hill functions with asymmetries, as seen in experimental data. Specifically, Hill plots showing (log(F/(1-F)) vs. log [Ca]) reveal a steeper slope below the half activation point (Ca(50)) compared with above. Parameter variation studies show interplay of parameters that affect the apparent cooperativity and asymmetry in the F-pCa relations. The model also predicts that Ca binding is uncooperative for low [Ca], becomes steeper near Ca(50), and becomes uncooperative again at higher [Ca]. The steepness near Ca(50) mirrors the steep F-pCa as a result of thermodynamic considerations. The model also predicts that the correlation between troponin/tropomyosin units along the one-dimensional array quickly decays at high and low [Ca], but near Ca(50), high correlation occurs across the whole array. This work provides a simple model that can account for the steepness and shape of F-pCa relations that other models fail to reproduce.

Actin Cytoskeleton↗

PubMedDate not supplied

Approaches to modeling crossbridges and calcium-dependent activation in cardiac muscle.

While the primary function of the heart is a pump, ironically, the development of myofilament models that predict developed force have generally lagged behind the modeling of the electrophysiological and Ca2+-handling aspects of heart cells. A major impediment is that the basic events in force generating actin-myosin interactions are still not well understood and quantified despite advanced techniques that can probe molecular levels events and identify numerous energetic states. As a result, the modeler must decide how to best abstract the many identified states into useful models with an essential tradeoff in the level of complexity. Namely, complex models map more directly to biophysical states but experimental data does not yet exist to well constrain the rate constants and parameters. In contrast, parameters can be better constrained in simpler, lumped models, but the simplicity may preclude versatility and extensibility to other applications. Other controversies exist as to why the activation of the actin-myosin is so steeply dependent on activator Ca2+. More specifically steady-state force-[Ca2+] (F-Ca) relationships are similar to Hill functions, presumably as the result of cooperative interactions between neighboring crossbridges and/or regulatory proteins. We postulate that mathematical models must contain explicit representation of nearest-neighbor cooperative interactions to reproduce F-Ca relationships similar to experimental measures, whereas spatially compressing, mean-field approximation used in most models cannot. Finally, a related controversy is why F-Ca relationships show increased Ca2+ sensitivity as sarcomere length (SL) increases. We propose a model that suggests that the length-dependent effects can result from an interaction of explicit nearest-neighbor cooperative mechanisms and the number of recruitable crossbridges as a function of SL.

Actin Cytoskeleton↗