Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation

What information-processing strategies and general principles are sufficient to enable self-organized morphogenesis in embryogenesis and regeneration? We designed and analyzed a minimal model of self-scaling axial patterning consisting of a cellular network that develops activity patterns within implicitly set bounds. The properties of the cells are determined by internal ‘genetic’ networks with an architecture shared across all cells. We used machine-learning to identify models that enable this virtual mini-embryo to pattern a typical axial gradient while simultaneously sensing the set boundaries within which to develop it from homogeneous conditions—a setting that captures the essence of early embryogenesis. Interestingly, the model revealed several features (such as planar polarity and regenerative re-scaling capacity) for which it was not directly selected, showing how these common biological design principles can emerge as a consequence of simple patterning modes. A novel “causal network” analysis of the best model furthermore revealed that the originally symmetric model dynamically integrates into intercellular causal networks characterized by broken-symmetry, long-range influence and modularity, offering an interpretable macroscale-circuit-based explanation for phenotypic patterning. This work shows how computation could occur in biological development and how machine learning approaches can generate hypotheses and deepen our understanding of how featureless tissues might develop sophisticated patterns—an essential step towards predictive control of morphogenesis in regenerative medicine or synthetic bioengineering contexts. The tools developed here also have the potential to benefit machine learning via new forms of backpropagation and by leveraging the novel distributed self-representation mechanisms to improve robustness and generalization.

Why enterprise resource planning initiatives do succeed in the long run: A case-based causal network

Despite remarkable academic efforts, why Enterprise Resource Planning (ERP) post-implementation success occurs still remains elusive. A reason for this shortage may be the insufficient addressing of an ERP-specific interior boundary condition, i.e., the multi-stakeholder perspective, in explaining this phenomenon. This issue may entail a gap between how ERP success is supposed to occur and how ERP success may actually occur, leading to theoretical inconsistency when investigating its causal roots. Through a case-based, inductive approach, this manuscript presents an ERP success causal network that embeds the overlooked boundary condition and offers a theoretical explanation of why the most relevant observed causal relationships may occur. The results provide a deeper understanding of the ERP success causal mechanisms and informative managerial suggestions to steer ERP initiatives towards long-haul success.

Bayesian Causal Network Modeling Suggests Adolescent Cannabis Use Promotes Accelerated Prefrontal Cortical Thinning

While there is substantial evidence that cannabis use is associated with differences in brain structure and function, most of this evidence is correlational in nature. This is particularly true regarding the association of adolescent cannabis use on human brain development, which cannot be tested in an experimental approach. Bayesian causal network (BCN) modeling attempts to identify probable causal associations in correlational data by using the conditional probabilities among a set of interrelated variables to estimate directional associations between those variables. The current report builds on a recent analysis conducted by Albaugh et al. (2021) that found an association between neurodevelopment and cannabis use in the IMAGEN study of adolescent brain development. Here, we employ BCN modeling on the same sample to provide evidence that the associations found previously are driven by cannabis use affecting neurodevelopment and not, for example, by a pre-existing neurodevelopmental trajectory that also promotes cannabis use. Structural MRI was acquired at ages 14 and 19, from which average cortical thickness was derived for a region of interest in the dorsal prefrontal cortex identified by Albaugh et al. as differing in adolescents who initiated cannabis use between ages 14 and 19. Adolescents were all cannabis naïve at age 14 and 46% had used cannabis at least once by age 19. We tested multiple learning algorithms with a variety of different parameters to build BCNs that would describe the relationship between cortical thickness and cannabis use. All BCN models strongly suggested a directional relationship from cannabis use between the ages of 14 and 19 to accelerated cortical thinning during that same period. Acknowledging that BCN modeling cannot prove a causal relationship between adolescent cannabis use and accelerated cortical thinning, these results are consistent with a body of preclinical and human research suggesting that adolescent cannabis use adversely affects brain development.