Some Investigations Concerning the CTMC and the ODE Model Derived From Bio-PEPA

Engineering new metabolic pathways in isolated cells for the degradation of guanidinoacetic acid and simultaneous production of creatine

Here we report, for the first time, the engineering of human red blood cells (RBCs) with an entire metabolic pathway as a potential strategy to treat patients with guanidinoacetate methyltransferase (GAMT) deficiency, capable of reducing the high toxic levels of guanidinoacetate acid (GAA) and restoring proper creatine levels in blood and tissues. We first produced a recombinant form of native human GAMT without any tags to encapsulate into RBCs. Due to the poor solubility and stability features of the recombinant enzyme, both bioinformatics studies and extensive optimization work were performed to select a mutant GAMT enzyme, where only four critical residues were replaced, as a lead candidate. However, GAMT-loaded RBCs were ineffective in GAA consumption and creatine production because of the limiting intra-erythrocytic S-adenosyl methionine (SAM) content unable to support GAMT activity. Therefore, a recombinant form of human methionine adenosyl transferase (MAT) was developed. RBCs co-entrapped with both GAMT and MAT enzymes performed, in vitro, as a competent cellular bioreactor to remove GAA and produce creatine, fueled by physiological concentrations of methionine and the ATP generated by glycolysis. Our results highlight that metabolic engineering of RBCs is possible and represents proof of concept for the design of novel therapeutic approaches.

A hybrid Factored Frontier algorithm for Dynamic Bayesian Networks with a biopathways application

Dynamic Bayesian Networks (DBNs) can serve as succinct probabilistic dynamic models of biochemical networks. To analyze these models, one must compute the probability distribution over system states at a given time point. Doing this exactly is infeasible for large models; hence one must use approximate algorithms. The Factored Frontier algorithm (FF) is one such algorithm. However FF as well as the earlier Boyen-Koller (BK) algorithm can incur large errors. To address this, we present a new approximate algorithm called the Hybrid Factored Frontier (HFF) algorithm. At each time slice, in addition to maintaining probability distributions over local states-as FF does-HFF explicitly maintains the probabilities of a number of global states called spikes. When the number of spikes is 0, we get FF and with all global states as spikes, we get the exact inference algorithm. We show that by increasing the number of spikes one can reduce errors while the additional computational effort required is only quadratic in the number of spikes. We validated the performance of HFF on large DBN models of biopathways. Each pathway has more than 30 species and the corresponding DBN has more than 3,000 nodes. Comparisons with FF and BK show that HFF is a useful and powerful approximate inferencing algorithm for DBNs.