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Biology subjects

Lyman, C. A.

Publications and source records attributed to Lyman, C. A..

2 recordsLinked to original sources

Inflammation as a Silent Partner in Opioid Addiction: A Regulatory Logic Model

The brain and body consist of complex networks of interconnected feedback and feed forward loops. Because these networks are capable of supporting multiple homeostatic states, a stressor or combination of stressors may cause the network to become "stuck" in a persistent maladaptive state, for example, chronic pain and the potentiation of opioid dependency. The current research uses automated text mining of over 14,000 publications to assemble a regulatory circuit consisting of 44 immune and neurotransmission mediators linked by 188 documented regulatory interactions. Decisional logic parameters dictating the regulatory dynamics available to each network model were estimated such that predicted behavior would adhere to observed pathologies. Analysis of this psycho-neuroimmune network confirmed that a broad family of behavioral kinetics may be equally capable of supporting dynamically stable conditions of chronic pain, persistent depression and addiction behaviors. Despite differences in the predicted course of onset, these models typically point to characteristic patterns of increased inflammatory activity in the brain for each of these pathologies, specifically increased expression of the protein complex NF-kB and inflammatory signaling proteins IL1-B, IL6, and TNF. Potential treatments targeting both addiction and chronic pain may therefore benefit from the use of anti-inflammatory drugs as pharmacological potentiators of current behavioral interventions. Clinical RelevanceThis work establishes a methodology for understanding both illness-specific and shared mechanisms underlying addiction, chronic pain, and depression, and the corresponding expression profiles of psychoneuroimmune markers that might facilitate screening and treatment design.

systems biology↗

Attractor Landscapes as a Model Selection Criterion in Data Poor Environments

Modeling of systems for which data is limited often leads to underdetermined model identification problems, where multiple candidate models are equally adherent to data. In such situations additional optimality criteria are useful in model selection apart from the conventional minimization of error and model complexity. This work presents the attractor landscape as a domain for novel model selection criteria, where the number and location of attractors impact desirability. A set of candidate models describing immune response dynamics to SARS-CoV infection is used as an example for model selection based on features of the attractor landscape. Using this selection criteria, the initial set of 18 models is ranked and reduced to 7 models that have a composite objective value with a p-value < 0.05. Additionally, the impact of pharmacologically induced remolding of the attractor landscape is presented.

systems biology↗