**4. Conclusions**

76 Amyotrophic Lateral Sclerosis

Fig. 7. Percentage of total treatment combinations belonging to each efficacy type.

**Category Avg Stdev Max Count**  Axonal Transport 1.08 0.16 1.74 59 Chemistry 1.02 0.05 1.39 107 Energetics 1.23 0.32 2.44 229 Excitotoxicity 1.09 0.24 2.33 160 Free Radical 1.06 0.14 1.93 311 Genetics 1.01 0.02 1.21 86 Inflammation 1.03 0.07 1.66 195 Necro-Apoptosis 1.10 0.23 2.35 327 Proteomics 1.04 0.15 2.05 154 Systemic 1.05 0.07 1.23 22

**Category Avg Stdev Max Count**  Axonal Transport 1.08 0.17 2.82 17395 Chemistry 1.19 0.26 4.85 23101 Energetics 1.09 0.19 2.82 22332 Excitotoxicity 1.05 0.13 2.13 33020 Free Radical 1.04 0.12 2.01 12929 Genetics 1.05 0.12 3.26 22589 Inflammation 1.09 0.19 2.67 35586 Necro-Apoptosis 1.09 0.20 2.60 22603 Proteomics 1.06 0.14 2.05 11322 Systemic 1.00 0.00 1.00 0

Table 4. Synergistic two-way combination treatment predictions.

Table 5. Synergistic three-way combination treatment predictions.

Tables 4 and 5, show the efficacies of 2-way and 3-way synergistic combination treatments. A linearly additive treatment (combination A&B effect = A effect + B effect) was assigned an efficacy factor of 1.0. Thus, synergistic treatments have efficacy factors >1 and sub-additive treatments have efficacy factors <1. Therefore, categories with higher average and maximum efficacy factors have a tendency to produce greater synergistic effects in combination.

*System dynamics revealed.* Conventional wisdom in ALS research has been that there is a single, specific root cause. However, dynamic meta-analysis predicts that a system-level instability is the actual problem (see oscillations in Figure 4). Lending credence to the predictions of dynamic meta-analysis is the mounting evidence from recent studies that indicates that multiple mutations or underlying mechanisms can result in the symptoms characterized as ALS (Rothstein 2009).

*Novel treatment strategies identified.* In this small scale feasibility study of ALS, dynamic metaanalysis predicts that reducing the overall feedback gain will be more effective than identifying and ameliorating a single "source" or point of initiation (Mitchell and Lee 2010). That is, inducing small changes across multiple categories of mechanisms is more effective than inducing a large change in a single category. Additionally, treatments that target the underyling system dyanmics, such as stabilizing oscillations, could be another potentially effective path.

*Combination treatment prediction enabled.* Another key opportunity afforded by dynamic meta-analysis is an innovative approach to predicting combination treatment effectiveness in a high-throughput manner. The interacting differential equations of dynamic metaanalysis implicitly include all possible treatment interactions. Thus, potential synergistic combinations can be identified before they are explicitly examined experimentally. For example, in spinal cord injury, a sweep of all possible combinations of virtual treatments revealed that none were synergistic (Mitchell and Lee 2008). While disappointing, we were at least able to discover treatments that combined linearly. On the other hand, in our initial evaluation of ALS, a small percentage of treatment combinations show very profound synergism! It appears that in ALS, the broader the treatment the more effective it becomes. (Figure 6 illustrates an example of a 3-way combination that appears to arrest the oscillatory explosion observed in the control case.) Finally, dynamic meta-analysis is not only well suited to identify promising combinations but can be used to prioritize them as well.
