diff --git a/dynamo/tools/dynamics.py b/dynamo/tools/dynamics.py
index 6b0a46913..9a1952e10 100755
--- a/dynamo/tools/dynamics.py
+++ b/dynamo/tools/dynamics.py
@@ -716,6 +716,7 @@ def dynamics(
                 kin_param_pre,
                 valid_bools_,
                 ind_for_proteins,
+                cur_cells_bools,
             )
 
         elif assumption_mRNA.lower() == "kinetic":
diff --git a/dynamo/tools/utils.py b/dynamo/tools/utils.py
index 6640c3bdd..f39ec0265 100755
--- a/dynamo/tools/utils.py
+++ b/dynamo/tools/utils.py
@@ -1552,11 +1552,12 @@ def set_param_ss(
     kin_param_pre,
     valid_ind,
     ind_for_proteins,
+    cur_cells_bools,
 ):
     params_df = pd.DataFrame(index=adata.var.index)
     if experiment_type == "mix_std_stm":
         if alpha is not None:
-            if cur_grp == _group[0]:
+            if kin_param_pre + "alpha" not in adata.varm:
                 adata.varm[kin_param_pre + "alpha"] = np.zeros((adata.shape[1], alpha[1].shape[1]))
             adata.varm[kin_param_pre + "alpha"][valid_ind, :] = alpha[1]
             (
@@ -1581,13 +1582,13 @@ def set_param_ss(
     else:
         if alpha is not None:
             if len(alpha.shape) > 1:  # for each cell
-                if cur_grp == _group[0]:
+                if kin_param_pre + "alpha" not in adata.varm:
                     adata.varm[kin_param_pre + "alpha"] = (
                         sp.csr_matrix(np.zeros(adata.shape[::-1]))
                         if sp.issparse(alpha)
                         else np.zeros(adata.shape[::-1])
                     )  #
-                adata.varm[kin_param_pre + "alpha"][valid_ind, :] = alpha  #
+                adata.varm[kin_param_pre + "alpha"][valid_ind, :][:, cur_cells_bools] = alpha  #
                 params_df.loc[valid_ind, kin_param_pre + "alpha"] = alpha.mean(1)
             elif len(alpha.shape) == 1:
                 if cur_grp == _group[0]:
@@ -1753,10 +1754,10 @@ def set_param_kinetic(
             np.where(cur_cells_bools)[0][:, np.newaxis],
             np.where(valid_ind)[0],
         )
-        if cur_grp == _group[0]:
-            adata.layers["cell_wise_alpha"] = sp.csr_matrix((adata.shape), dtype=np.float64)
+        if kin_param_pre + "cell_wise_alpha" not in adata.layers:
+            adata.layers[kin_param_pre + "cell_wise_alpha"] = sp.csr_matrix((adata.shape), dtype=np.float64)
         alpha = alpha.T.tocsr() if sp.issparse(alpha) else sp.csr_matrix(alpha, dtype=np.float64).T
-        adata.layers["cell_wise_alpha"][cur_cells_ind, valid_ind_] = alpha
+        adata.layers[kin_param_pre + "cell_wise_alpha"][cur_cells_ind, valid_ind_] = alpha
     else:
         params_df.loc[valid_ind, kin_param_pre + "alpha"] = alpha
     params_df.loc[valid_ind, kin_param_pre + "a"] = a
@@ -1818,9 +1819,9 @@ def get_vel_params(
     for param in params:
         if param == "alpha":
             if not skip_cell_wise:
-                if "cell_wise_alpha" in adata.layers.keys():
-                    target_params.append(adata[:, genes].layers["cell_wise_alpha"])
-                elif "alpha" in adata.varm.keys():
+                if kin_param_pre + "cell_wise_alpha" in adata.layers.keys():
+                    target_params.append(adata[:, genes].layers[kin_param_pre + "cell_wise_alpha"])
+                elif kin_param_pre + "alpha" in adata.varm.keys():
                     target_params.append(adata[:, genes].varm[kin_param_pre + "alpha"])
                 else:
                     target_params.append(df.loc[genes, kin_param_pre + "alpha"].values)